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Validation of the pancreatitis activity scoring system in a large prospective cohort
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Validation of the pancreatitis activity scoring system in a large prospective cohort

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Content 1

Title: Validation of the Pancreatitis Activity Scoring
System in a Large Prospective  Cohort

Candidate: James Buxbaum MD

Thesis Advisor: Christianne Lane PhD

Conferring Major/Program: Clinical, Biomedical, and
Translational Research/Preventive Medicine

Degree being conferred: Master of Science

UNIVERSITY OF SOUTHERN CALIFORNIA

Degree conferral date (anticipated): May 12, 2018
























2
Table of Contents:                                                                             PAGE
Title Page------------------------------------------------------------------------------------------------------------1
Table of Contents-------------------------------------------------------------------------------------------------2
Body Text  
Introduction-----------------------------------------------------------------------------------------------3-4
Methods---------------------------------------------------------------------------------------------------4-10
Summary Results------------------------------------------------------------------------------------10-20
Discussion---------------------------------------------------------------------------------------------20-24

APPENDICES: DESCRIPTION OF STATISTICAL APPROACH, CODE AND OUTPUT  
App1: Distribution of Admission Pass Scores------------------------------------------------------25-32
App2 : Determination of  PASS Cutoff to Predict Severe Pancreatitis----------------------33-35

App3: Multivariate Model for PASS and Severe Pancreatitis-----------------------------------36-45
App4: PASS at Subsequent Inpatient Time Points and Severe Pancreatitis--------------46-49
App5: PASS and SIRS Development, ICU Admission, and Local Complications-------50-51

App6: PASS and Hospitalization Length and Time to Tolerance of Oral Nutrition-----52-60  

App7: Comparison of PASS with Established Predictive Scoring Systems-------------61-65

App8: Discharge PASS Score and Early Readmission <30 Days----------------------------66-74

App9: Sensitivity Analyses for Follow up <30 Days and Zero Scores---------------------75-78

References------------------------------------------------------------------------------------------------------79-80





3
INTRODUCTION:  
Acute pancreatitis presents several challenges for clinicians and investigators alike; the
foremost includes the variability in patient presentation and disease course. While patients with
acute pancreatitis may initially appear to have mild disease, they may rapidly develop critical
illness.  Alternatively, even patients with what is considered mild disease may experience wide
variation in their disease course ranging from full recovery within a few days to prolonge d
illness with protracted hospitalization secondary to pain and inability to tolerate resumption of
oral intake.  A major limitation to developing improved management strategies for patients with
acute pancreatitis has been the lack of a widely accepted method to measure and monitor
disease activity.

Interventional studies have targeted patients with predicted severe pancreatitis.(1-4) However,
predicted severe pancreatitis has protean definitions ranging from various APACHE scores to C
reactive protein levels to clinical findings such as abnormal chest roentgenography.
Unfortunately, heterogeneity of inclusion criterion across studies makes it challenging to identify
which groups of patients benefit from specific therapy. Additionally, while objective acute
pancreatitis outcomes such as death are fortunately rare this necessitates the use of surrogate
measures such as clinical improvement or changes in cytokine levels as study endpoints.(1, 5)
The use of scoring systems that predict severity of disease have predominated in the acute
pancreatitis field with 9 such scoring system developed.(6)  However, there has been a
limitation in quantitative scoring systems that encompass the overall physiologic status of the
patient for studies. For example there is only one scoring system to predict readmission and no
algorithm to gauge disease status over the course of an admission and subsequent post
discharge period.(7)

4
The study of other disease states including inflammatory bowel disease has benefited from the
development of quantitative scoring systems such as the Crohns Disease Activity Index (CDAI)
that can be used to monitor the disease activity during its course.(8-9) To address this need, a
group of international experts recently developed the acute Pancreatitis Activity Scoring System
(PASS) which was designed to provide an objective tool for measurement of disease activity in
patients with acute pancreatitis.(10)  This was developed by literature review to identify potential
important variables and then voting on the variables by an international group of experts using a
modified Delphi process.

A key step in validating any new disease assessment tool is to evaluate the relationship
between the scoring system and clinical outcomes.  Outcomes with high face validity in acute
pancreatitis include the development of transient or persistent organ failure (moderately severe
and severe pancreatitis) as well as local complication such as pseudocysts and necrosis.(6, 11)
In addition, early readmission (<30 days) following discharge is an important benchmark for high
quality and affordable care.(12-13)  

Our aim was to assess the relationship between the PASS score and these important clinical
outcomes in a large cohort of patients with acute pancreatitis.  In addition, we sought to identify
specific thresholds in the PASS score at admission and at discharge to provide a framework for
applying the instrument in clinical practice as well as research settings.

METHODS:
PASS Instrument
The PASS score was developed by systematic literature review to identify potential parameters
followed by parameter selection by a group of international experts utilizing a modified Delphi
process as previously reported.(10)  This involved voting on a number of clinical domains (i.e.
5
nutrition, inflammatory markers) by an expert panel; candidate markers were not derived by
gauging their performance in a patient cohort. The PASS system applies a quantitative weight to
five clinically important parameters (Figure 1) which are summed to calculate the total score.
One hundred points are scored for each dysfunctional organ system, 40 points are given if the
patient is not tolerating a solid diet, and 25 points are added for each SIRS criterion which is
fulfilled. Additionally, pain scores are multiplied by 5 and the intravenous morphine equivalent
dose is similarly multiplied by 5 and these scores are added to the points for organ failure, diet
tolerance, and SIRS criterion to generate the overall PASS.  PASS may be calculated at
admission, sequentially during the pancreatitis admission (typically q 12 hours), and at time of
discharge.

Figure 1: Components of PASS Score

Sample
Institutional Review Board approval from the University of Southern California Health Sciences
IRB was obtained for the prospective cohort. We evaluated all patients who presented to the
Los Angeles County Hospital with acute pancreatitis between March 2015 and March 2017. The
6
Los Angeles County Hospital is the largest acute care public hospital in Western United States.
Its mission is not to function as a tertiary referral center but to provide nearly all inpatient care
for a cohort of 1 million disadvantaged patients in central and eastern Los Angeles County. In
comparison to the United States population our population has lower socioeconomic status, has
higher proportion of Hispanic, Asian, and African Americans, and is younger.  

Our study team was alerted regarding potential patients with pancreatitis via a pager system
activated in the emergency department and an electronic notification from the clinical laboratory
reporting the medical record number of patients with elevated lipase.  The diagnosis of acute
pancreatitis was confirmed by two of three criteria: lipase >3 times the upper limit of normal;
characteristic epigastric pain; or cross sectional imaging consistent with acute pancreatitis.
Patients who were transferred from another hospital, left against medical advice, or had clinical
or radiographic evidence of chronic pancreatitis were excluded. While the feasibility of
determining PASS at the Los Angeles County Hospital/University of Southern California was
described by Wu et al, this initial publication did not report the clinical outcomes from our
center.(10) Thus, this represents the initial assessment of the association of PASS with clinical
outcomes in this cohort and the first attempt to validate PASS using a wide range of pancreatitis
outcomes including intensive care unit admission, time to tolerance of oral nutrition, and 30 day
readmission in any cohort.

Disease Parameters
We defined the patient’s first hospitalization between March 2015 and March 2017 as the index
hospitalization and subsequent representations between March 2015 and July 2017 as either
readmission or presentation to the emergency department. During the index hospitalization we
recorded 83 clinically relevant variables including pancreatitis etiology, comorbidities,
pancreatitis admission prior to March 2015, detailed alcohol and smoking history, outpatient
7
medication use, Charlson score, body mass index, vital signs, visual analogue pain score (0-10),
and biochemical parameters including BUN, creatinine, and hematocrit.  

Over the course of the admission we scored whether patients presented with or developed
systemic inflammatory response syndrome (SIRS) following admission, SIRS was defined as
two of four criteria: heart rate >90 beats/minute; respiration>20/min or P aCO2<32mmHg;
temperature <36 or >38
o
C; or WBC<4000 or >12000/mm
3
.(14) The use of antibiotics, initiation
of total parenteral nutrition, and development of local complications was recorded, the latter was
defined as pseudocysts, necrotic collections, or walled off pancreatic necrosis.(15) We scored
the episode as mild, moderately severe and severe according to the revised Atlanta
classification.(15) Development of organ failure was defined as modified Marshall organ failure
score >2.(16) We also captured admission to the intensive care unit (ICU), length of
hospitalization, and time to initiation of oral diet. Given clinical benefit, cholecystectomy is
performed on the same admission for those hospitalized with gallstone pancreatitis at our center
and was also recorded.(17)  

This prospectively ascertained data set was used by two reviewers who calculated the PASS
score at time of admission, discharge, and every 12 hours during the hospitalization.  
Information on laboratories, narcotic administration, and pain scores needed to calculate PASS
score not available in the prospective data set was subsequently identified from the medical
record.

Data Analysis
We performed two sets of analyses to evaluate the relationship between the PASS score and
inpatient as well as post-discharge outcomes in patients with acute pancreatitis.

8
1. PASS score and in-hospital outcomes
We assessed for the relationship between admission PASS score and severity of acute
pancreatitis.  Disease severity was characterized as mild, moderately severe, and severe based
on the revised Atlanta criterion, local complications, admission to the ICU, length of stay, and
time to tolerance of oral nutrition. These outcomes were defined in the disease parameters
section. We also studied the relationship between admission PASS and the development of
SIRS. Development of SIRS was defined as the manifestation of >2 SIRS criterion (see disease
parameters section) in those who did not have SIRS at the time of admission.
We also assessed for the relationship between the PASS score 24 hours after admission and
these clinical outcomes.

In order to compare tests characteristics among different scoring systems we also assessed for
the association between admission Ranson’s, Glasgow, Panc3, and HAPS scores for this
cohort with subsequent development of moderately severe and severe pancreatitis according
the Revised Atlanta Criterion. We also determined the relationship between the admission
Glasgow score for patients in our cohort and the requirement for ICU admission and the
development of SIRS and local complications.  

2. PASS score and readmission
An additional aim of the study was to determine the association between discharge PASS score
and readmission. Early readmission was defined as an admission to the inpatient unit <30 days
following discharge from the index hospitalization for persistent or smoldering symptoms related
to the pancreatitis episode, complications of pancreatitis or therapy, or recurrent pancreatitis.
Recurrent pancreatitis was defined as the development of characteristic pain and elevation of
lipase >3 times the upper limit of normal in those whose symptoms resolved and lipase
normalized following discharge from the index hospitalization.  
9

We also studied the relationship between discharge PASS score and early evaluation in the
emergency department (ED) and late readmission. The former was defined as ED evaluation in
<30 days for pancreatitis symptoms, complications, treatment complications, and recurrent
pancreatitis. Late readmission was defined as readmission to the inpatient unit >30 days for
symptoms of pancreatitis, complications of the index episode and its treatment or recurrent
pancreatitis.  

Statistical Analysis
Categorical variables were reported as proportions and continuous parameters as mean (SD/CI)
if normally distributed and medians (IQR) if non-normal. Logistic regression was used to assess
the relationship between PASS score and the clinical outcomes of moderately severe/severe
pancreatitis, local complications, ICU admission, SIRS development as well as early and late
readmission and ER presentation. Receiver operator characteristic (ROC) analysis was used to
define the optimal specific cutoff score for admission PASS score level which predicted
moderately severe and severe pancreatitis. ROC analysis was also used to determine the
optimal discharge PASS score to predict early readmission. We assessed for the bivariate
relationship between other clinical parameters and the outcomes (i.e. moderately severe or
severe pancreatitis, early readmission) using χ
2
, Mann-Whitney U, and linear regression. We
then introduced potential confounders identified by this bivariate analysis as well as several a
priori variables (sex, gender, age, pancreatitis origin) into logistic regression models to define
the relationship between PASS score (using the cutoff values derived from the ROC analysi)
and the clinical outcomes. Locally weighted smoothing (LOWESS) was used to verify linearity of
continuous variables in these models.

10
To adjust for loss to follow-up, we performed a sensitivity analysis by introducing follow-up <30
days as a covariate in the multivariate analysis for early readmission. Because there were a
large number of discharge PASS scores of zero, as an additional sensitivity analysis we ran the
model assessing the relationship between PASS and early readmission after excluding a
discharge PASS score of zero.  

To study the association between length of hospitalization and admission PASS score, we used
multivariate linear regression with logarithmic transformation of the output variable to adjust for
the skewed outcome. The same approach was used to assess for the association between
tolerance of oral nutrition and admission PASS score.  

ROC/AUC analysis was used to assess for the relationship between admission Panc3, HAPS,
Glasgow, and Ranson’s scores with moderately severe and severe pancreatitis when the
algorithms were treated as continuous scales. Published cutoff values were used to compare
their performance characteristics as discrete variables.(6, 18-19) The same approach was used
to study the association between admission Glasgow scores and organ failure, SIRS
development and local complications.  All analyses were performed using SAS 9.4 (Cary, NC)
and STATA. 14.2 (College Station, TX).

SUMMARY RESULTS:
Patients and Outcomes
Between March 2015 and March 2017, 439 unique patients were admitted to the Los Angeles
County Hospital for acute pancreatitis. The most frequent etiology was gallstones; 81% were
Hispanic; and 53% male (Table 1). The median follow up was 4 (range 0-44) months and 3
patients (1%) died during the index hospitalization.  

11
Moderately severe or severe pancreatitis developed in 76 (17%) of patients. Forty-nine (11%)
patients developed local complications including necrosis, pseudocysts, and walled off
pancreatic necrosis.  One hundred seven (24%) patients presented with SIRS and an additional
116 (26%) developed SIRS following admission. The median length of hospitalization was 4 (2-
7) days and ICU admission was necessary in 65 (15%) patients. The median time from
admission to tolerance of oral nutrition was 3 (2-7) days.  
Table 1: Characteristics of Population
Total
Population
N (%)
Moderately Severe or
Severe Pancreatitis    
N (%)
Early (<30 days)
Readmitted  
N (%)
Total 439 76 37  
Female Gender 207(47.1) 29(38.2) 15(40.5)
Hispanic Ethnicity 353(80.8) 58(76.3) 26(70.3)
>20 alcoholic drinks/week 74(16.9) 14(18.4) 6(16.2)
>10 pack years tobacco 31(7.7) 8(10.5) 4(11.1)
Altered mental status 18(4.1) 8(10.5) 1(2.7)
Diabetes Mellitus 115(26.2) 27(35.5) 5(13.5)
Prior Acute Pancreatitis 69(15.7) 12(15.8) 10(27.0)
SIRS on admission  107(24.4) 38(50) 13(35.1)
Alcohol 109(24.8) 20(26.3) 11(29.7)
Gallstone  203(46.2) 30(39.4) 16(43.2)
Other  127(28.9) 26(34.2) 10(27.3)
Comorbidities 167(38.0) 37(48.7) 10(27.0)
Obesity (BMI>30) 149(33.9) 30(39.5) 9(24.3)
TPN 8(1.8) 4(5.3) 3(8.1)
Antibiotics 149(33.9) 43(56.4) 12(32.4)


All Patients
Mean (SD)
Severe or Moderately
Severe
Mean (SD)
Readmitted within 30
days  
Mean (SD)
Age 41.9 (15.3) 47.2(2.0)  41.9 (15.3)
Admission BUN 15.5(10.3) 22.7(17.8) 15.4(9.1)
Admission hematocrit 40.8(6.1) 42.3(7.2) 39.7(7.2)
Discharge BUN 11.7(9.1) 14.7(15.5) 12.1(8.2)
Discharge hematocrit 37.8(7.5) 38.6(8.2) 37.4(6.6)
Median(IQR) Median (IQR) Median(IQR)
Charlson Score 0(0-2) 1(0-3) 0(0-2)


12
Following their index hospitalization 37(9%) patients were readmitted within 30 days for
pancreatitis. Of those, smoldering (persistent) symptoms prompted readmission in 67%; local
complications of pancreatitis or therapy in 25%; and recurrent pancreatitis in 8%. An additional
22(5%) patients were evaluated in the emergency department within 30 days for smoldering
pancreatitis symptoms (90%); complications of therapy (5%); or recurrent pancreatitis (5%).
There were 25 (6%) patients who were readmitted for pancreatitis related problems after 30
days. Patients with late readmission presented for recurrent pancreatitis in 56%; smoldering
symptoms in 32%; and complications of pancreatitis or therapy in 12%.  

PASS Score and In-hospital Patient Outcomes
Moderately Severe and Severe Pancreatitis
The overall median admission PASS score was 130 (IQR 95-174).  We found that admission
PASS score was strongly associated with moderate and severe pancreatitis (Figure 2). The
median admission PASS score among those with moderately severe and severe pancreatitis
was 168 (133-222) compared to 125 (IQR 90-163) for those with mild disease. We observed a
monotonic increase in the development of moderately severe and severe pancreatitis with
higher admission PASS score (Table 2)(SEE APPENDIX 1). Receiver operating characteristic  
Table 2: Admission PASS and Inpatient Outcome
Admission
PASS
Score
N Moderately
Severe or
Severe
Pancreatitis
(%)*
ICU
Admission
(%)
Local
Complications
(%)
Development
of SIRS after
Admission
(%)
SIRS at
Presentation
and after
Admission
(%)
0-50 30 3.3 0 0 3.3 3.3
50-100 96 8.3 5.2 5.2 12.5 21.9
100-150 162 14.8 9.9 9.9 26.5 46.3
150-200 89 20.2 27.0 7.9 46.1 80.9
200-250 38 23.7 26.3 23.7 26.3 84.2
>250 24 66.8 41.7 50 37.5 91.7
* Percentage of patients in each PASS score range who developed the clinical outcome
13
analysis demonstrated that an admission PASS cutoff point of 140 had optimal predictive value
(Figure 2) for moderately severe and severe pancreatitis with a sensitivity and specificity of 65%  
and an area under the curve of 0.7(SEE APPENDIX 2). In univariate analysis BUN>20,
comorbidities, altered mental status, and SIRS all predicted moderately severe or severe
pancreatitis (Table 3) though the latter was collinear with PASS score (SEE APPENDIX 3).
Given that PASS and SIRS measured the same thing we used only the former in our models.
Moderate or severe pancreatitis was not associated with other factors including hematocrit>44,
BMI, tobacco, or heavy alcohol. The a priori variables age, gender, ethnicity, and origin of
pancreatitis also were not associated with moderately severe and severe pancreatitis.  After
controlling for these factors PASS greater than 140 remained a significant predictor of
moderately severe or severe pancreatitis, OR 3.5 (95% CI 2.0-6.3)(SEE APPENDIX 3). PASS  

Figure 2A-B: Receiver Operator Characteristic Analysis for Moderately Severe and
Severe Pancreatitis and Admission PASS Score






14
Table 3: Predictors of Moderately Severe and Severe Pancreatitis
Univariate OR (95% CI) Multivariate OR (95% CI)
Admission PASS > 140 3.2(1.9, 5.4) 3.5(2.0, 6.3)
Altered Mental Status 4.2(1.6, 10.9) 5.3(1.7, 16.7)
Comorbidities 1.7(1.0, 2.8) 1.2(0.7, 2.2)
Admission BUN>20 4.0(2.3,7.0) 3.2(1.7, 5.9)
Age 1.0 (1.0,1.0) 1.0(1.0, 1.0)
Hispanic 0.7(0.4, 1.3) 0.7(0.4, 1.5)
Female 0.6(0.4, 1.1) 0.8(0.4, 1.5)
Etiology Alcohol* 1.0 1.0
Gallstone 0.8(0.4,1.4) 0.7(0.5, 2.4)
Other 1.1(0.6, 2.2) 0.5(0.6,2.9)
*Baseline
scores at 12, 24, 36, and 48 hours after admission predicted moderately severe and severe
pancreatitis (p<0.01) and the PASS scores were higher for patients with moderately severe and
severe disease throughout the hospitalization (Figure 3)(SEE APPENDIX 4).

Figure 3:  PASS Score by Pancreatitis Severity over Time




ICU Admission, Local Complications, SIRS Development
We found that there was a concordance of admission PASS score greater than 140 with ICU
admission (OR 4.9[2.5, 9.4]) and local complications (OR 3.0 [1.6, 5.7]) (Table 2, Table 4)(SEE
15
APPENDIX 5). Admission PASS score greater than 140 was also associated with SIRS
development in those who did not have SIRS at admission (OR 2.9 [1.8, 4.5]) (Table 2, Table 4).  
This analysis was adjusted for a priori etiology and demographic factors as well as confounders
which included hematocrit >44 for SIRS development (OR 1.7 [1.2, 3.3]) and hematocrit>44 (OR
2.4 [1.2, 4.6]), BUN>20 (OR 2.4 [1.2, 4.8]), comorbidities (OR 2.3 [1.2, 4.4]) and altered mental
status (8.9 [2.7, 29.6]) for ICU admission (Table 4)(SEE APPENDIX 5). The likelihood of ICU
admission, local complications, and development of SIRS increased with the level of the
admission PASS score (Table 2).  
 
Table 4: Predictors of ICU Admission, SIRS Development, and Local Complications
ICU Admission SIRS Development Local Complications
Univariate OR
(95% CI)
Multivariate
OR (95%
CI)
Univariate
OR (95%
CI)
Multivariate
OR (95%
CI)
Univariate
OR (95%
CI)
Multivariate
OR (95%
CI)
Admission PASS >
140
4.7 (2.6, 8.5)* 4.9 (2.5,
9.4)**
3.0 (1.9,
4.6)*
2.9 (1.8,
4.5)**
3.1 (1.7,
5.7)*
3.0 (1.6,
5.7)**
Hematocrit > 44 2.4 (1.4, 4.2)* 2.4 (1.2,
4.6)**
2.1 (1.3,
3.2)*
1.7 (1.2,
3.3)**
1.0 (0.5,
1.9)
---
BUN>20 3.4 (1.8, 5.8)* 2.4 (1.2,
4.8)**
0.7 (0.4,
1.2)
--- 0.9 (0.4,
2.0)
---
Comorbidities 2.3 (1.4, 3.9)* 2.3 (1.2,
4.4)**
0.7 (0.5,
1.2)
--- 0.8 (0.4,
1.4)
---
Altered Mental
Status
5.1 (1.8,
13.5)*
8.9 (2.7,
29.6)**
1.4 (0.5,
3.9)
--- 1 (0.2,
4.5)
---
Age 1.0 (1.0, 1.0) 1.0 (1.0, 1.0) 1.0 (1.0,
1.0)
1.0 (1.0, 1.0) 1.0 (1.0,
1.0)
1.0 (1.0, 1.0)
Hispanic 0.8 (0.4, 1.6) 1.9 (0.5, 2.1) 1.6 (0.9,
3.0)
1.7 (0.9, 3.2) 0.8 (0.4,
1.6)
0.8 (0.4, 1.9)
Female 0.5 (0.3, 0.8)* 0.8 (0.4, 1.6) 0.9 (0.6,
1.4)
1.2 (0.7, 2.2) 1 (0.6,
1.8)
1.2 (0.6, 2.4)
Origin Alcohol Baseline 1.0 Baseline 1.0 Baseline
1.0
Baseline 1.0 Baseline
1.0
Baseline 1.0
Gallstone 0.8 (0.4, 1.5) 1.4 (0.5, 3.0) 0.9 (0.6,
1.6)
0.9 (0.5, 1.7) 0.9 (0.4,
1.8)
0.8 (0.4, 1.9)
Other 0.7 (0.4, 1.5) 0.8 (0.3, 2.0) 0.5 (0.3,
1.0)
0.6 (0.3, 1.0) 0.7 (0.3,
1.6)
0.7 (0.3, 1.7)
*p<0.05 in univariate analysis, **p<0.05 in multivariate analysis ---not included in multivariate model given
insignificant in univariate model



16
Length of Stay and Time to Oral Nutrition
The average length of hospitalization was also 1.5 (1.3-1.7) days longer in patients with PASS
score greater than 140 after controlling for age, etiology of pancreatitis, ethnicity and gender
(SEE APPENDIX 6).  The tolerance of oral nutrition was also delayed by an average of 1.3 (1.2,
1.5) days in those with PASS score greater than140 after adjustment for these same covariates.

Comparison of PASS with Established Predictive Scoring Systems in Cohort
The performance of admission PASS (AUC 0.71; OR 3.2 [1.9-5.4]) was comparable to
admission Glasgow (AUC 0.73; OR 4.1 [2.5-6.9]) and Ranson’s (AUC 0.63; OR 2.2 [1.2, 4.0])
and somewhat better than Panc3 and HAPS (Table 5)(SEE APPENDIX 7). Additionally,
admission PASS was comparable to Glasgow for prediction of ICU admission (both AUC 0.74)
but performed somewhat better for prediction of SIRS development (AUC 0.66 versus 0.56) and
local complications (AUC 0.71 versus 0.60) (Table 6).

Table 5: Prediction of Moderately Severe/Severe Pancreatitis by Established
Scoring Systems and PASS
AUC N
1
Cutoff
2
OR 95%
CI
Sensitivity Specificity PPV NPV
Admission
PASS
0.71 439 140 3.2 1.9-
5.4
0.44 0.84 0.37 0.87
Glasgow 0.73 432 2 4.1 2.5-
6.9
0.32 0.92 0.47 0.86
Admission
Ranson’s
0.63 436 2 2.2 1.2-
4.0
0.05 0.99 0.50 0.83
HAPS 0.54 438 1 0.6 0.2-
1.9
0.56 0.50 0.20 0.84
Panc 3 0.57 427 1 1.5 1.1-
2.2
0.23 0.92 0.37 0.85
1. number (N) of patients for which all data for score available
2. cutoffs based on prior literature reports(6, 18-19)





17


Table 6: PASS versus Glasgow for Additional Inpatient Outcomes
Admission
Test
AUC Cutoff OR 95%
CI
Sensitivity Specificity PPV NPV
ICU
Admission
PASS 0.74 140 4.7 2.6-
8.5
0.73 0.64 0.26 0.93
ICU
Admission
Glasgow  0.74 2 4.1 2.5-
6.9
0.38 0.92 0.47 0.89
Local
Complication
PASS 0.71 140 3.1 1.7-
5.7
0.65 0.61 0.18 0.94
Local
Complication
Glasgow  0.60 2 1.6 0.9-
2.9
0.20 0.97 0.03 0.98
SIRS after
Admission
PASS 0.66 140 3.0 1.9-
4.6
0.64 0.63 0.37 0.83
SIRS after
Admission
Glasgow  0.56 2 1.2 0.8-
1.9
0.12 0.85 0.25 0.74

PASS score and Outcomes Following Discharge

Readmission following Index Hospitalization
Among the surviving patients the discharge PASS score ranged from 0 to 373 with a median of
40 (IQR 0-73.3); 125 patients had a PASS score of 0 (SEE APPENDIX 8). The median
discharge PASS score for patients readmitted <30 days (early readmission) was 75 (IQR 50-
105) versus 39 (IRQ 0-68) (p<0.001) for patients not readmitted. The median discharge PASS
score for patients presenting to the emergency department within 30 days for pancreatitis
symptoms was 65 (IQR 50-104) versus 40 (IQR 0-7)) (p<0.001) for those who did not.

Discharge PASS score was significantly correlated with early readmission (Table 7)(p<0.001).
ROC analysis revealed that a discharge PASS score of >60 optimized performance
characteristics (Figure 4) with a sensitivity of 68%, specificity of 71%, and area under the curve
of 0.75 (SEE APPENDIX 8). When adding in potential covariates, we found that the use of total
parenteral nutrition during hospitalization and prior pancreatitis also predicted readmission
18
though the former was collinear with discharge PASS score. The severity of pancreatitis,
development of local complications, ICU admission, Charlson score, obesity (BMI>30), heavy
alcohol use (>20 drinks/week), biochemical profile, and length of hospitalization did not predict
readmission. Same admission cholecystectomy also did not predict early readmission, OR 0.6
(0.2, 1.7), but was included in the multivariate analysis given clinical importance. The a priori
variables age, gender, ethnicity, origin of pancreatitis, and same admission cholecystectomy
were not associated with pancreatitis readmission; after adjusting for these factors discharge
PASS score >60 remained a significant predictor of early readmission (OR 5.0 [2.4, 10.7])
showing strength of this cutoff (Table 8).  


Table 7: Discharge PASS Score and Readmission for Pancreatitis
Discharge
PASS Score
N Readmitted <30
days (%)*
Readmitted or ED
visit <30 days (%)
Readmitted >30
days (%)
0 125 0.8 0.8 8.8
0-25 64 3.1 4.7 3.1
25-50 73 11.0 20.6 4.1
50-75 84 9.5 17.9 6.0
75-100 37 18.9 24.3 8.3
100-125 28 21.4 32.1 3.6
125-150 11 36.4 36.4 0
>150 14 7.1 21.4 0
* Percentage of patients in each PASS score range who developed the clinical outcome




19
Figures 4A-B: Receiver Operator Characteristic Analysis for Early Readmission and
Discharge PASS Score



Table 8: Predictors of Early Readmission
Univariate OR (95% CI) Multivariate OR (95% CI)
Discharge PASS > 60 4.7 (2.3, 9.8) 5.0 (2.4, 10.7)
Prior Acute Pancreatitis 2.1 (1.0, 4.7) 1.7 (0.7, 4.2)
Age 1.0(1.0, 1.0) 1.0(1.0,1.0)
Hispanic 0.5 (0.3, 1.1) 0.6 (0.3, 1.3)
Female 0.7 (0.4, 1.5) 0.7(0.3, 1.6)
Same Admission
Cholecystectomy
0.6 (0.2, 1.7) 0.8 (0.3, 2.5)
Etiology Alcohol* 1.0 1.0
Gallstone 0.8(0.3, 1.7) 1.0(0.4, 2.9)
Other 0.8(0.3, 1.9) 0.8(0.3, 2.4)
*Baseline
Early (<30 days) ED Presentation and Late Readmission
Discharge PASS score >60 was also correlated with ED presentation within <30 days of
discharge for pancreatitis symptoms (OR 3.2[1.3, 7.7]) (Table 9); severity of pancreatitis, local
complications, and admission clinical parameters did not.  There was no significant relationship
between discharge PASS score and late readmission whether treated as a continuous (p=0.8)
or categorical (PASS>60) variable (Table 9).  Heavy alcohol use was associated with late
readmission (OR 4.1 [1.8, 9.4] as was alcoholic pancreatitis relative to gallstone pancreatitis
(Table 9).
20
Table 9: Predictors of Early ED, Late (>30 days) Readmission
Early (<30 days) ER Readmission Late (>30 days) Readmission
Univariate OR
(95% CI)
Multivariate OR
(95% CI)
Univariate OR
(95% CI)
Multivariate OR
(95% CI)
Discharge PASS  
> 60
3.3 (1.4, 7.8) 3.2 (1.3, 7.7) 0.7(0.3, 1.7) 0.8(0.3, 1.9)
Age 1.0 (1.0, 1.0) 1.0 (1.0, 1.0) 1.0(1.0, 1.0) 1.0(0.9, 1.0)
Hispanic 0.5 (0.2, 1.3) 0.5(0.2, 1.4) 1.0 (0.3, 2.6) 1.0(0.4, 3.0)
Female 1.2 (0.5, 2.9) 1.6(0.6, 4.4) 0.6(0.2, 1.3) 1.2(0.4, 3.4)
Etiology Alcohol* 1.0 1.0 1.0 1.0
Gallstone 0.4 (0.1, 1.2) 0.3 (0.1, 1.1) 0.1 (0, 0.4) 0.1(0, 0.3)
Other 1.4 (0.5, 3.7) 0.9 (0.3, 2.8) 0.2 (0.2, 1.2) 0.4(0.2, 1.0)
*Baseline

Sensitivity Analyses:
When follow-up of <30 days was included as a cofactor in the multivariate analysis the
relationship between discharge PASS score >60 and early readmission remained significant
(OR 5.1 [95% CI 2.4-10.7])(SEE APPENDIX 9). Only one of the 125 patients with a discharge
PASS score of zero was readmitted within 30 days. We repeated the analysis including ROC
after excluding all patients with a score of zero and PASS score remained a significant predictor
of early readmission (p<0.01) (SEE APPENDIX 9).

DISCUSSION
We have performed prospective external validation of a newly developed disease-activity
instrument in acute pancreatitis.  In this well characterized cohort of patients with acute
pancreatitis the PASS score was strongly associated with sentinel clinical events that occur
during hospitalization as well as following discharge. A PASS score >140 at admission was
associated with the development of moderately severe and severe pancreatitis, systemic
inflammatory response syndrome, and local complications as well as prolonged length of stay
and delayed resumption of oral nutrition. Meanwhile a PASS score at discharge >60 was
strongly correlated with readmission and emergency department presentation for smoldering
symptoms and complications of pancreatitis.  Promising performance at multiple points in the
course of acute pancreatitis suggest its role as a true measurement of disease activity. This has
21
important implications for its role in clinical care as well as prospective intervention trials for new
treatments for patients with acute pancreatitis.

The development of pharmacologic and other therapy for acute pancreatitis requires the
demonstration of quantifiable improvement in clinical outcomes.(20) Given its correlation with
multiple aspects of the clinical course of pancreatitis PASS represents a promising tool to gauge
responses to therapy as well as to assess for the resolution of disease. Similarly, it may
potentially be used as a system to determine eligibility; i.e. predicted severe could be defined as
a PASS>140 or another cutoff.  

Admission and subsequent PASS score levels correlated with the development of established
clinical outcomes in acute pancreatitis.  Specifically, an admission score >140 was associated
with substantially increased risk of transient and persistent organ failure (moderately severe and
severe pancreatitis) as well as other clinical outcomes with high face validity including the
development of local complications. Elevated admission PASS score was also linked to
additional parameters such as increased length of hospitalization and need for intensive care
unit admission, both of which have tangible financial and clinical implications.  

As a measure of disease activity, the PASS score at discharge was linked to post-hospital
outcome, in particular risk of 30-day readmission.   The thirty-day readmission rate has received
widespread attention as a correlate with adverse outcomes and death in congestive heart failure,
coronary artery disease, and medical illnesses in general(21-22).  It is a core quality metric of
the Affordable Care Act and Centers for Medicare and Medicaid Services.(23) It is also the
strongest predictor of death at one year following acute pancreatitis hospitalization.(12)  
Interestingly, discharge PASS score was associated with early readmission whereas other
parameters such as disease severity, length of stay, ICU admission, and local complications
22
were not. This is consistent with previous literature that has linked specific components of the
PASS including intolerance of oral nutrition, pain, abnormal vital signs, and high opiate
requirements with increased risk of early readmission.(7, 24) Contrary to findings with respect to
early readmission, the discharge PASS score was not associated with late readmission. The
likely explanation is that late as opposed to early readmission was driven by recurrent episodes
of acute pancreatitis in those who had recovered at the end of the index hospitalization. Our
findings provide a quantitative correlate with prior reports that smoldering symptoms dominate
early while recurrent pancreatitis episodes, particularly among those with alcoholic disease,
account for late readmission. (24-25)

The Pancreatitis Activity Scoring System is distinct from prior prognostic scores that have
focused on tools to predict outcomes at specific time points in the course of pancreatitis. A prior
instrument to predict early readmission awarded points for ongoing symptoms, necrosis,
antibiotic use and pain (SNAP) at discharge.(26) In the validation cohort, which defined
readmission as re-hospitalization and presentation to the emergency department, the score had
a 71% sensitivity and 87% specificity for readmission.(26) Nevertheless, in a subsequent cohort
at another academic medical center the specificity decreased to 56%.(24) At least nine scoring
systems have been developed to predict severe pancreatitis and other adverse outcomes.(6)
Laboratories, including blood urea nitrogen and creatinine are also correlated with adverse
outcomes.(27-28) Nevertheless, work by Mounzer et al. comparing these individual algorithms
found that they were equivalent and while combinations could yield superior results the requisite
complexity made them impractical for clinical use.(6) Though not directly comparable to the
study by Mounzer et al given that our severity outcome was moderately severe and severe
pancreatitis rather than persistent organ failure, our results demonstrated a similar trend,
namely that Glasgow (AUC=0.73) performed slightly better than Ranson’s criterion (AUC=0.63).
PASS was comparable to both scoring systems (AUC=0.71). Nevertheless, admission PASS
23
performed somewhat better than Glasgow when compared across a wider range of outcomes.  
Furthermore, PASS has the advantage of being correlated with various pancreatitis outcomes at
progressive stages of the disease and the ability to provide ongoing assessment of disease
activity.  

In addition, the development of the scoring system through a consensus-based process helps to
ensure that routinely used clinical parameters predominated in the PASS score. The importance
of qualitative metrics including pain and the ability to tolerate oral nutrition were recognized in
it’s development and included in the scoring system. We theorize that inclusion of elements
which reflect patient symptoms in addition to biochemical parameters underlies the score’s
ability to predict length of hospitalization and early readmission. Future studies of PASS will be
improved by correlating the score with additional quality of life measures.

The strength of our design is that cases were prospectively identified, carefully verified and
comprehensively characterized. This provided a much greater richness to the data set
compared to those generated by interrogation of administrative data using International
Classification of Disease (ICD) or Current Procedural Terminology (CPT) codes. Thus, we were
able to test and control for numerous potential confounders including quantitative extent of
alcohol use, body mass index, and personal history of pancreatitis. Additionally, very few
patients were transferred to the Los Angeles County Hospital whose primary mission is to
provide inpatients services for a million patient community health network. Thus, our findings
may provide greater generalizability than studies conducted exclusively at tertiary centers where
most patients are referred for higher levels of care. Indeed, the somewhat lesser performance
characteristics of HAPS and Panc3 relative to PASS in our cohort may reflect their development
and validation at referral centers rather than a large community medical center.

24
There were several limitations to the present study. We did not contact patients to assess for
readmission at other institutions, which may in part account for our lower rate of readmission
(9%) than prior reports which suggests a 15-16% readmission rate.(7, 12, 24) Nevertheless, our
sensitivity analysis controlling for follow-up of less than 30 days did not materially alter our
results. Additionally, our median follow up was only four months. Thus, while we have detailed
information regarding hospitalization and the immediate period following discharge, we were
unable to assess correlation between PASS score and critical long term outcomes including
chronic pancreatitis. Our population was primarily of Hispanic ethnicity which may not reflect
patients at other centers. Nevertheless, this group represents a rapidly increasing proportion of
the United States population and has a diverse European, Amerindian, and African genetic
admixture.(29)  The discharge PASS cutoff of 60 derived to predict early readmission was used
to assess other discharge outcomes and the admission cutoff of 140 derived to forecast
moderately severe and severe pancreatitis was used for other inpatient outcomes such as
length of stay. Our rationale was that simplification tends to favor utilization of clinical tools.
Finally, the area-under-curves for the various clinical outcomes was in the range of 0.7-0.8,
nevertheless this is similar to what has been observed for scores designed to predict specific
outcomes at predefined time points.(6, 11)

In summary, in this large prospective cohort of patients hospitalized for acute pancreatitis the
Pancreatitis Activity Scoring System performed well at admission as well as at discharge in
identifying patients at increased risk for multiple adverse in-hospital outcomes as well as early
readmission, respectively.  It appears to be a promising system to quantitatively gauge disease
activity in patients with acute pancreatitis.


25
APPENDICES: DESCRIPTION OF STATISTICAL APPROACH, CODE AND OUTPUT FOR
ANALYSES  

Appendix 1: Distribution of Admission Pass Scores
APPROACH: The first step of the analysis was to compare the continuous distributions of
PASS scores at admission among those who developed moderately severe/severe pancreatitis
and those who did not using the program SAS. As the distributions were non parametric the
medians and interquartile ranges are reported in the summary results.  

Scores were than converted to categorical variables using the generate/replace commands of
STATA. These were used to generate a logistic regression analysis using 50 point increments
of admission PASS as the independent variable and development of moderately severe/severe
pancreatitis as the dependent variable.  

Both approaches indicated an association between admission PASS and development of
moderately severe and severe pancreatitis.

CODE:
**SAS: Admission PASS as continuous variable
data panc;
set work.panc;
run;
data livepanc;
set panc;
if death = '0';
run;
proc univariate plot normal data=livepanc;
var AdmitPASSscore;
run;
proc print data=panc;
run;
data MODSEVPANC;
set livepanc;
26
if ModSevPanc = '1';
run;
proc univariate plot normal data=MODSEVPANC;
var AdmitPASSScore;
run;
data NOMODSEVPANC;
set livepanc;
if ModSevPanc='0';
run;
proc univariate plot normal data=NOMODSEVPANC;
var AdmitPASSscore;
run;

**STATA: Admission PASS as categorical variable
generate admpasscategory=0
replace admpasscategory=1 if AdmitPASSscore >0
replace admpasscategory=2 if AdmitPASSscore >50
replace admpasscategory=3 if AdmitPASSscore >100
replace admpasscategory=4 if AdmitPASSscore >150
replace admpasscategory=5 if AdmitPASSscore >200
replace admpasscategory=6 if AdmitPASSscore >250

tabulate admpasscategory ModSevPanc, chi row
xi: logit ModSevPanc i.admpasscategory, or
logit ModSevPanc AdmitPASSscore, or

OUTPUT:
*Distribution of all admission PASS scores as continuous variables
Distribution Admit PASS  

The SAS System

The UNIVARIATE Procedure
Variable: AdmitPASSscore (AdmitPASSscore)
Moments
N 439 Sum Weights 439
Mean 139.224146 Sum Observations 61119.4
Std Deviation 64.7778706 Variance 4196.17252
Skewness 1.07224021 Kurtosis 1.85503033
Uncorrected SS 10347219.8 Corrected SS 1837923.56
Coeff Variation 46.5277558 Std Error Mean 3.09167908

Basic Statistical Measures
Location Variability
27
Basic Statistical Measures
Location Variability
Mean 139.2241 Std Deviation 64.77787
Median 130.0000 Variance 4196
Mode 130.0000 Range 405.20000
   Interquartile Range 78.50000

Tests for Location: Mu0=0
Test Statistic P Value
Student's t t 45.03189 Pr > |t| <.0001
Sign M 219.5 Pr >= |M| <.0001
Signed Rank S 48290 Pr >= |S| <.0001

Tests for Normality
Test Statistic p Value
Shapiro-Wilk W 0.940359 Pr < W <0.0001
Kolmogorov-Smirnov D 0.093177 Pr > D <0.0100
Cramer-von Mises W-Sq 0.797493 Pr > W-Sq <0.0050
Anderson-Darling A-Sq 5.188536 Pr > A-Sq <0.0050

Quantiles (Definition 5)
Level Quantile
100% Max 423.5
99% 355.0
95% 275.0
90% 216.7
75% Q3 173.5
50% Median 130.0
25% Q1 95.0
10% 65.0
5% 48.3
1% 40.0
0% Min 18.3

Extreme Observations
Lowest Highest
Value Obs Value Obs
18.3 20 355.0 362
25.0 418 365.0 161
35.0 9 367.5 367
28
Extreme Observations
Lowest Highest
Value Obs Value Obs
40.0 360 385.0 154
40.0 359 423.5 397

Missing Values
Missing
Value
Count Percent Of
All Obs Missing Obs
. 116 20.90 100.00



*Distribution of Admit PASS among those who developed Moderate/Severe Pancreatitis



The SAS System

The UNIVARIATE Procedure
Variable: AdmitPASSscore (AdmitPASSscore)
Moments
N 76 Sum Weights 76
Mean 185.810526 Sum Observations 14121.6

Basic Statistical Measures
Location Variability
Mean 185.8105 Std Deviation 80.01736
Median 167.5000 Variance 6403
29
Basic Statistical Measures
Location Variability
Mode 145.0000 Range 327.50000
   Interquartile Range 99.15000


Quantiles (Definition 5)
Level Quantile
100% Max 367.50
99% 367.50
95% 343.40
90% 305.00
75% Q3 234.15
50% Median 167.50
25% Q1 135.00
10% 95.00
5% 65.00
1% 40.00
0% Min 40.00




30
*Distribution Admit PASS among those who did not develop Moderate/Severe
Pancreatitis

The SAS System

The UNIVARIATE Procedure
Variable: AdmitPASSscore (AdmitPASSscore)
Moments
N 363 Sum Weights 363
Mean 130.13168 Sum Observations 47237.8
Std Deviation 57.4618592 Variance 3301.86526
Skewness 1.02801384 Kurtosis 2.62039842
Uncorrected SS 7342409.52 Corrected SS 1195275.23
Coeff Variation 44.1567027 Std Error Mean 3.01596544

Basic Statistical Measures
Location Variability
Mean 130.1317 Std Deviation 57.46186
Median 125.0000 Variance 3302
Mode 130.0000 Range 405.20000
   Interquartile Range 72.50000

Tests for Location: Mu0=0
Test Statistic p Value
Student's t t 43.1476 Pr > |t| <.0001
Sign M 181.5 Pr >= |M| <.0001
Signed Rank S 33033 Pr >= |S| <.0001

Tests for Normality
Test Statistic p Value
Shapiro-Wilk W 0.949918 Pr < W <0.0001
Kolmogorov-Smirnov D 0.072302 Pr > D <0.0100
Cramer-von Mises W-Sq 0.361679 Pr > W-Sq <0.0050
Anderson-Darling A-Sq 2.364551 Pr > A-Sq <0.0050

Quantiles (Definition 5)
Level Quantile
100% Max 423.5
99% 313.3
95% 220.0
90% 201.7
75% Q3 162.5
50% Median 125.0
31
Quantiles (Definition 5)
Level Quantile
25% Q1 90.0
10% 65.0
5% 45.0
1% 40.0
0% Min 18.3

Extreme Observations
Lowest Highest
Value Obs Value Obs
18.3 14 295.0 142
25.0 346 313.3 107
35.0 8 328.4 275
40.0 295 385.0 128
40.0 294 423.5 325













32


*Admission PASS by 50 point categories
. tabulate admpasscategory ModSevPanc, chi row
+----------------+

admpasscat |      ModSevPanc
    egory |         0          1 |     Total
-----------+----------------------+----------
        1 |        29          1 |        30  
          |     96.67       3.33 |    100.00  
-----------+----------------------+----------
        2 |        88          8 |        96  
          |     91.67       8.33 |    100.00  
-----------+----------------------+----------
        3 |       138         24 |       162  
          |     85.19      14.81 |    100.00  
-----------+----------------------+----------
        4 |        71         18 |        89  
          |     79.78      20.22 |    100.00  
-----------+----------------------+----------
        5 |        29          9 |        38  
          |     76.32      23.68 |    100.00  
-----------+----------------------+----------
        6 |         8         16 |        24  
          |     33.33      66.67 |    100.00  
-----------+----------------------+----------
    Total |       363         76 |       439  
          |     82.69      17.31 |    100.00  

         Pearson chi2(5) =  52.6516   Pr = 0.000


*Logistic regression to test association of PASS categories and pancreatitis severity

xi: logit ModSevPanc i.admpasscategory, or
i.admpasscate~y   _Iadmpassca_1-6     (naturally coded; _Iadmpassca_1 omitted)

Logistic regression                             Number of obs     =        439
                                               LR chi2(5)        =      43.05
                                               Prob > chi2       =     0.0000
Log likelihood = -180.76659                     Pseudo R2         =     0.1064

-------------------------------------------------------------------------------
  ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
_Iadmpassca_2 |   2.636364   2.852694     0.90   0.370     .3161965    21.98131
_Iadmpassca_3 |   5.043478    5.24957     1.55   0.120     .6557616    38.78951
_Iadmpassca_4 |   7.352112   7.725397     1.90   0.058     .9375469    57.65424
_Iadmpassca_5 |          9   9.776819     2.02   0.043     1.070461    75.66831
_Iadmpassca_6 |         58   64.11513     3.67   0.000     6.644793     506.261
       _cons |   .0344828   .0350723    -3.31   0.001     .0046973    .2531367
-------------------------------------------------------------------------------










33
Appendix 2 : Determination of  Admission PASS Cutoff to Predict Severe Pancreatitis

APPROACH: Logistic regression was performed using the program STATA with admission
PASS score as the independent continuous variable and development of moderately
severe/severe pancreatitis as the dependent variable. Screenshots of the STATA data editor
show that a cutoff of 140 optimizes sensitivity and specificity. The data editor is used to find the
probability which corresponds to a PASS of 140 which is used to determine the resulting
performance characteristics (sens/spec/PPV/NPV). Simple logistic regression is used in a
categorical model using PASS>140 as the independent variable to determine the associated
OR (95%CI) of developing moderately severe/severe pancreatitis.  


CODE:
**STATA: ROC analysis to determine binary cutoff
logit ModSevPanc AdmitPASSscore, or
lsens, genprob(probcut) genspec(spec) gensens (sens)
lroc
estat classification, cutoff(0.153160)

**STATA: Simple logistic regression model for PASS140 and severe pancreatitis
generate pass140=0
replace pass140=1 if AdmitPASSscore>140
logit ModSevPanc pass140, or

OUTPUT :
*Identification of cutoff for AdmitPASS and moderately severe/severe pancreatitis

logit ModSevPanc AdmitPASSscore, or
Iteration 0:   log likelihood = -202.29128  
Iteration 1:   log likelihood = -184.64862  
Iteration 2:   log likelihood = -183.45602  
Iteration 3:   log likelihood = -183.45462  
Iteration 4:   log likelihood = -183.45462  

Logistic regression                             Number of obs     =        439
                                               LR chi2(1)        =      37.67
                                               Prob > chi2       =     0.0000
Log likelihood = -183.45462                     Pseudo R2         =     0.0931

--------------------------------------------------------------------------------
   ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
AdmitPASSscore |   1.011485   .0019746     5.85   0.000     1.007622    1.015362
        _cons |   .0363543   .0126117    -9.55   0.000     .0184189    .0717542
--------------------------------------------------------------------------------
34

ROC and sensitivity/specificity plots shown in summary results section



. estat classification, cutoff(0.153160)
Logistic model for ModSevPanc
             -------- True --------
Classified |         D            ~D  |      Total
35
-----------+--------------------------+-----------
    +     |        49           131  |        180
    -     |        27           232  |        259
-----------+--------------------------+-----------
  Total   |        76           363  |        439
Classified + if predicted Pr(D) >= .15316
True D defined as ModSevPanc != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   64.47%
Specificity                     Pr( -|~D)   63.91%
Positive predictive value       Pr( D| +)   27.22%
Negative predictive value       Pr(~D| -)   89.58%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   36.09%
False - rate for true D         Pr( -| D)   35.53%
False + rate for classified +   Pr(~D| +)   72.78%
False - rate for classified -   Pr( D| -)   10.42%
--------------------------------------------------
Correctly classified                        64.01%
OUTPUT:  
*Simple logistic regression model for PASS >140 and moderately severe/severe
pancreatitis
logit ModSevPanc pass140, or
Logistic regression                             Number of obs     =        438
                                               LR chi2(1)        =      21.53
                                               Prob > chi2       =     0.0000
Log likelihood = -194.42681                     Pseudo R2         =     0.0525
------------------------------------------------------------------------------
 ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pass140 |   3.262626   .8545752     4.51   0.000       1.9526    5.451567
      _cons |   .1184211   .0241018   -10.48   0.000      .079467      .17647
-----------------------------------------------------------------------------

36
Appendix Part 3: Multivariate Model for Admission PASS and Severe Pancreatitis
APPROACH: We then assessed whether a number of categorical variables also predicted
moderately severe/severe pancreatitis (potential confounders) using chi squared and logistic
regression. Continuous variables were assessed for association with moderately severe/severe
pancreatitis by ttest/linear regression if normally distributed. If their distribution was not normal a
transformation (typically logarithmic) was attempted but if the distribution was still non-normal
distribution then the non parametric Wilcoxon Rank Sum was used to determine if there was an
association. Altered mental status, comorbidities BUN >20 were significantly associated with
moderately severe/severe pancreatitis and were not collinear with PASS. Univariate logistic
regression was used to determine OR of their association with moderately severe/severe
pancreatitis as single variables and then they were introduced into the multivariate model along
with the a priori variables of age, ethnicity, and gender.
 
CODE:
**STATA: Association of categorical variables with moderately severe/severe pancreatitis
tabulate ModSevPanc Female, chi2 row
tabulate ModSevPanc Hispanic, chi2 row
tabulate ModSevPanc Alctwentydrinks, chi2 row
tabulate ModSevPanc Smok10py, chi2 row
tabulate ModSevPanc AMS, chi2 row
tabulate ModSevPanc CKD, chi2 row
tabulate ModSevPanc DM, chi2 row
tabulate ModSevPanc AnyComorbidities, chi2 row
tabulate ModSevPanc PriorAP, chi2 row
tabulate ModSevPanc Admit_Hct44, chi2 row
tabulate ModSevPanc Admit_BUN20, chi2 row
xi: lo git ModSevPanc i.Etiology_ETOH1_Gallstones2_Other, or
tabulate ModSevPanc AnyCommorbidities, chi2 row

**STATA: Association of continuous variables with moderately severe/severe
pancreatitis

histogram Age
ttest Age, by(ModSevPanc)
histogram CharlstonAdmit
ranksum CharlstonAdmit, by(ModSevPanc)
histogram ADMVAS
37
ranksum ADMVAS, by(ModSevPanc)
histogram Adm_BUN
generate logBUN=log(Adm_BUN)
histogram logBUN
ttest logBUN, by(ModSevPanc)
histogram ADMCr
generate logADMCr=log(Adm_Creatinine)
histogram logADMCr
ttest logADMCr, by(ModSevPanc)
histogram AdmHct
ttest AdmHct, by(ModSevPanc)
histogram AdmHgb
ttest AdmHgb, by(ModSevPanc)

**STATA Univariate logistic regression to determine OR for association with moderately
severe/severe pancreatitis (example using the variable “any comorbidities”).

logit ModSevPanc AnyCommorbidities, or

**STATA: Multivariate logistic regression model
generate pass140=0
replace pass140=1 if AdmitPASSscore>140
xi: logit ModSevPanc pass140 AMS AnyCommorbidities Admit_BUN20 Hispanic Female Age
i.Etiology_ETOH1_Gallstones2_Other, or

OUTPUT:
*Potential predictors of moderately/severe severe pancreatitis

. tabulate ModSevPanc  Female, chi2 row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

          |        Female
ModSevPanc |         0          1 |     Total
-----------+----------------------+----------
        0 |       184        177 |       361  
          |     50.97      49.03 |    100.00  
-----------+----------------------+----------
        1 |        48         30 |        78  
          |     61.54      38.46 |    100.00  
-----------+----------------------+----------
    Total |       232        207 |       439  
          |     52.85      47.15 |    100.00  

         Pearson chi2(1) =   2.8752   Pr = 0.090

.  
. tabulate ModSevPanc  Hispanic, chi2 row

          |       Hispanic
ModSevPanc |         0          1 |     Total
38
-----------+----------------------+----------
        0 |        66        293 |       359  
          |     18.38      81.62 |    100.00  
-----------+----------------------+----------
        1 |        18         60 |        78  
          |     23.08      76.92 |    100.00  
-----------+----------------------+----------
    Total |        84        353 |       437  
          |     19.22      80.78 |    100.00  

         Pearson chi2(1) =   0.9087   Pr = 0.340

.  
. tabulate ModSevPanc  Alctwentydrinks, chi2 row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

          |    Alctwentydrinks
ModSevPanc |         0          1 |     Total
-----------+----------------------+----------
        0 |       301         60 |       361  
          |     83.38      16.62 |    100.00  
-----------+----------------------+----------
        1 |        64         14 |        78  
          |     82.05      17.95 |    100.00  
-----------+----------------------+----------
    Total |       365         74 |       439  
          |     83.14      16.86 |    100.00  

         Pearson chi2(1) =   0.0807   Pr = 0.776

.  
. tabulate ModSevPanc  Smok10py, chi2 row


          |       Smok10py
ModSevPanc |         0          1 |     Total
-----------+----------------------+----------
        0 |       309         23 |       332  
          |     93.07       6.93 |    100.00  
-----------+----------------------+----------
        1 |        65          8 |        73  
          |     89.04      10.96 |    100.00  
-----------+----------------------+----------
    Total |       374         31 |       405  
          |     92.35       7.65 |    100.00  

         Pearson chi2(1) =   1.3758   Pr = 0.241

.  
. tabulate ModSevPanc  AMS, chi2 row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

          |          AMS
ModSevPanc |         0          1 |     Total
-----------+----------------------+----------
        0 |       351         10 |       361  
          |     97.23       2.77 |    100.00  
-----------+----------------------+----------
39
        1 |        70          8 |        78  
          |     89.74      10.26 |    100.00  
-----------+----------------------+----------
    Total |       421         18 |       439  
          |     95.90       4.10 |    100.00  

         Pearson chi2(1) =   9.1422   Pr = 0.002

.  
. tabulate ModSevPanc  CKD, chi2 row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

          |               CKD
ModSevPanc |         0          1          2 |     Total
-----------+---------------------------------+----------
        0 |       358          0          3 |       361  
          |     99.17       0.00       0.83 |    100.00  
-----------+---------------------------------+----------
        1 |        75          2          1 |        78  
          |     96.15       2.56       1.28 |    100.00  
-----------+---------------------------------+----------
    Total |       433          2          4 |       439  
          |     98.63       0.46       0.91 |    100.00  

         Pearson chi2(2) =   9.4587   Pr = 0.009

.  
. tabulate ModSevPanc  DM, chi2 row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

          |          DM
ModSevPanc |         0          1 |     Total
-----------+----------------------+----------
        0 |       275         86 |       361  
          |     76.18      23.82 |    100.00  
-----------+----------------------+----------
        1 |        49         29 |        78  
          |     62.82      37.18 |    100.00  
-----------+----------------------+----------
    Total |       324        115 |       439  
          |     73.80      26.20 |    100.00  

         Pearson chi2(1) =   5.9187   Pr = 0.015

.
. tabulate ModSevPanc  PriorAP, chi2 row

+----------------+
----------------+

          |        PriorAP
ModSevPanc |         0          1 |     Total
-----------+----------------------+----------
        0 |       304         57 |       361  
          |     84.21      15.79 |    100.00  
-----------+----------------------+----------
        1 |        66         12 |        78  
          |     84.62      15.38 |    100.00  
40
-----------+----------------------+----------
    Total |       370         69 |       439  
          |     84.28      15.72 |    100.00  

         Pearson chi2(1) =   0.0079   Pr = 0.929

.  
. tabulate ModSevPanc  Admit_Hct44, chi2 row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

          |     Admit_Hct>44
ModSevPanc |         0          1 |     Total
-----------+----------------------+----------
        0 |       254        107 |       361  
          |     70.36      29.64 |    100.00  
-----------+----------------------+----------
        1 |        52         26 |        78  
          |     66.67      33.33 |    100.00  
-----------+----------------------+----------
    Total |       306        133 |       439  
          |     69.70      30.30 |    100.00  

         Pearson chi2(1) =   0.4143   Pr = 0.520

.  
. tabulate ModSevPanc  Admit_BUN20, chi2 row

+----------------+
| Key            |
|----------------|
          |     Admit_BUN>20
ModSevPanc |         0          1 |     Total
-----------+----------------------+----------
        0 |       315         46 |       361  
          |     87.26      12.74 |    100.00  
-----------+----------------------+----------
        1 |        47         31 |        78  
          |     60.26      39.74 |    100.00  
-----------+----------------------+----------
    Total |       362         77 |       439  
          |     82.46      17.54 |    100.00  

         Pearson chi2(1) =  32.3321   Pr = 0.000

 
. xi: logit ModSevPanc   i.Etiology_ETOH1_Gallstones2_Other, or
i.Etiology_ET~r   _IEtiology__1-3     (naturally coded; _IEtiology__1 omitted)

Iteration 0:   log likelihood = -205.38701  
Iteration 1:   log likelihood =  -203.9814  
Iteration 2:   log likelihood = -203.97225  
Iteration 3:   log likelihood = -203.97225  

Logistic regression                             Number of obs     =        439
                                               LR chi2(2)        =       2.83
                                               Prob > chi2       =     0.2430
Log likelihood = -203.97225                     Pseudo R2         =     0.0069

-------------------------------------------------------------------------------
  ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
_IEtiology__2 |   .7716763   .2444517    -0.82   0.413     .4147548     1.43575
_IEtiology__3 |   1.258586   .4117928     0.70   0.482     .6627975    2.389928
       _cons |   .2247191   .0556088    -6.03   0.000     .1383574    .3649871.  
41
. tabulate ModSevPanc  AnyCommorbidities, chi2 row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

          |   AnyCommorbidities
ModSevPanc |         0          1 |     Total
-----------+----------------------+----------
        0 |       233        128 |       361  
          |     64.54      35.46 |    100.00  
-----------+----------------------+----------
        1 |        39         39 |        78  
          |     50.00      50.00 |    100.00  
-----------+----------------------+----------
    Total |       272        167 |       439  
          |     61.96      38.04 |    100.00  

         Pearson chi2(1) =   5.7555   Pr = 0.016


. ttest Age, by(ModSevPanc)

Two-sample t test with equal variances
------------------------------------------------------------------------------
  Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      0 |     361    44.26039     .767559    14.58362    42.75093    45.76985
      1 |      78    47.65385    1.961232    17.32113    43.74854    51.55916
---------+--------------------------------------------------------------------
combined |     439    44.86333    .7226202    15.14057    43.44309    46.28356
---------+--------------------------------------------------------------------
   diff |           -3.393458    1.885674               -7.099577    .3126601
------------------------------------------------------------------------------
   diff = mean(0) - mean(1)                                      t =  -1.7996
Ho: diff = 0                                     degrees of freedom =      437

   Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.0363 Pr(|T| > |t|) = 0.0726          Pr(T > t) = 0.9637

.  
. ranksum CharlstonAdmit, by(ModSevPanc)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

 ModSevPanc |      obs    rank sum    expected
-------------+---------------------------------
          0 |      360       76290       79020
          1 |       78       19851       17121
-------------+---------------------------------
   combined |      438       96141       96141

unadjusted variance  1027260.00
adjustment for ties  -145063.94
                    ----------
adjusted variance     882196.06

Ho: Charls~t(ModSev~c==0) = Charls~t(ModSev~c==1)
            z =  -2.907
   Prob > |z| =   0.0037

.  
. ranksum ADMVAS, by(ModSevPanc)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

 ModSevPanc |      obs    rank sum    expected
42
-------------+---------------------------------
          0 |      358       76332       77865
          1 |       76       18063       16530
-------------+---------------------------------
   combined |      434       94395       94395

unadjusted variance   986290.00
adjustment for ties   -75087.46
                    ----------
adjusted variance     911202.54

Ho: ADMVAS(ModSev~c==0) = ADMVAS(ModSev~c==1)
            z =  -1.606
   Prob > |z| =   0.1083
.  
. ranksum Hosp_Days, by(ModSevPanc)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

 ModSevPanc |      obs    rank sum    expected
-------------+---------------------------------
          0 |      361       73758       79420
          1 |       78       22822       17160
-------------+---------------------------------
   combined |      439       96580       96580

unadjusted variance  1032460.00
adjustment for ties   -10923.56
                    ----------
adjusted variance    1021536.44

Ho: Hosp_D~s(ModSev~c==0) = Hosp_D~s(ModSev~c==1)
            z =  -5.602
   Prob > |z| =   0.0000

.  
. ranksum Daydietadvanced, by(ModSevPanc)

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

 ModSevPanc |      obs    rank sum    expected
-------------+---------------------------------
          0 |      359     74965.5     78441.5
          1 |       77     20300.5     16824.5
-------------+---------------------------------
   combined |      436       95266       95266

unadjusted variance  1006665.92
adjustment for ties   -34170.65
                    ----------
adjusted variance     972495.27

Ho: Daydie~d(ModSev~c==0) = Daydie~d(ModSev~c==1)
            z =  -3.525
   Prob > |z| =   0.0004


43
0 .2 .4 .6 .8 1
Density
1 2 3 4 5
logBUN

.  
.
ttest logBUN, by(ModSevPanc)

Two-sample t test with equal variances
------------------------------------------------------------------------------
  Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      0 |     361    2.529327    .0232572    .4418862     2.48359    2.575064
      1 |      78     2.90053     .077193    .6817505    2.746819    3.054241
---------+--------------------------------------------------------------------
combined |     439    2.595281    .0244473    .5122279    2.547232    2.643329
---------+--------------------------------------------------------------------
   diff |           -.3712035    .0615197               -.4921146   -.2502923
------------------------------------------------------------------------------
   diff = mean(0) - mean(1)                                      t =  -6.0339
Ho: diff = 0                                     degrees of freedom =      437

   Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000



0 .5 1 1.5
Density
-1 0 1 2
logADMCr



Two-sample t test with equal variances
------------------------------------------------------------------------------
  Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      0 |     361   -.3226131    .0190716    .3623595   -.3601187   -.2851074
      1 |      78    .1086059    .0770581     .680559   -.0448364    .2620483
---------+--------------------------------------------------------------------
combined |     439   -.2459956    .0222112    .4653766   -.2896494   -.2023418
---------+--------------------------------------------------------------------
   diff |            -.431219    .0543944               -.5381262   -.3243118
------------------------------------------------------------------------------
44
   diff = mean(0) - mean(1)                                      t =  -7.9276
Ho: diff = 0                                     degrees of freedom =      437

   Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000







0 .02 .04 .06 .08
Density
10 20 30 40 50 60
AdmHct


. ttest AdmHct, by(ModSevPanc)
Two-sample t test with equal variances
------------------------------------------------------------------------------
  Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      0 |     360    40.56389    .3090552    5.863909     39.9561    41.17167
      1 |      78    42.05641    .8156899    7.203978    40.43216    43.68066
---------+--------------------------------------------------------------------
combined |     438    40.82968    .2934567    6.141593    40.25292    41.40644
---------+--------------------------------------------------------------------
   diff |           -1.492521    .7645879               -2.995258    .0102149
------------------------------------------------------------------------------
   diff = mean(0) - mean(1)                                      t =  -1.9521
Ho: diff = 0                                     degrees of freedom =      436
   Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.0258 Pr(|T| > |t|) = 0.0516          Pr(T > t) = 0.974
0 .05 .1 .15 .2
Density
0 10 20 30 40
AdmHgb


. ttest AdmHgb, by(ModSevPanc)
Two-sample t test with equal variances
------------------------------------------------------------------------------
  Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
45
---------+--------------------------------------------------------------------
      0 |     359    13.42646    .1285893     2.43642    13.17358    13.67935
      1 |      78    14.28333    .5081173    4.487571    13.27154    15.29512
---------+--------------------------------------------------------------------
combined |     437    13.57941    .1397825     2.92209    13.30467    13.85414
---------+--------------------------------------------------------------------
   diff |           -.8568709    .3631422               -1.570602   -.1431395
------------------------------------------------------------------------------
   diff = mean(0) - mean(1)                                      t =  -2.3596
Ho: diff = 0                                     degrees of freedom =      435

   Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0094 Pr(|T| > |t|) = 0.0187          Pr(T > t) = 0.9906


*Univariate OR example
. logit ModSevPanc AnyCommorbidities, or

Iteration 0:   log likelihood = -202.29128  
Iteration 1:   log likelihood = -200.14735  
Iteration 2:   log likelihood = -200.12882  
Iteration 3:   log likelihood = -200.12882  

Logistic regression                             Number of obs     =        439
                                               LR chi2(1)        =       4.32
                                               Prob > chi2       =     0.0376
Log likelihood = -200.12882                     Pseudo R2         =     0.0107

-----------------------------------------------------------------------------------
      ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
AnyCommorbidities |   1.700395   .4323559     2.09   0.037     1.033038    2.798872
           _cons |    .167382    .028959   -10.33   0.000     .1192453    .2349503
-----------------------------------------------------------------------------------

*Multivariate model for admission PASS>140 and development of moderately
severe/severe pancreatitis

. xi: logit ModSevPanc pass140 AMS AnyCommorbidities Admit_BUN20 Hispanic Female Age
i.Etiology_ETOH1_Gallstones2_Other, or
i.Etiology_ET~r   _IEtiology__1-3     (naturally coded; _IEtiology__1 omitted)

Iteration 0:   log likelihood = -201.91013  
Iteration 1:   log likelihood = -178.74963  
Iteration 2:   log likelihood = -176.45721  
Iteration 3:   log likelihood = -176.44665  
Iteration 4:   log likelihood = -176.44665  

Logistic regression                             Number of obs     =        437
                                               LR chi2(9)        =      50.93
                                               Prob > chi2       =     0.0000
Log likelihood = -176.44665                     Pseudo R2         =     0.1261

-----------------------------------------------------------------------------------
      ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         pass140 |   3.459152   .9937101     4.32   0.000     1.969904    6.074271
             AMS |   5.257271   3.105009     2.81   0.005     1.652077    16.72979
AnyCommorbidities |   1.231167   .3586927     0.71   0.475     .6955447    2.179261
     Admit_BUN20 |   3.169891   1.012599     3.61   0.000     1.694861    5.928632
        Hispanic |   .8826459    .295957    -0.37   0.710     .4574809    1.702943
          Female |   .8037837   .2486557    -0.71   0.480     .4383434    1.473886
             Age |   1.002345   .0095138     0.25   0.805     .9838706    1.021166
   _IEtiology__2 |   1.135124   .4358464     0.33   0.741     .5348265    2.409205
   _IEtiology__3 |   1.303551   .5242529     0.66   0.510     .5926493    2.867202
           _cons |   .0673191   .0406166    -4.47   0.000     .0206334    .2196374
46
Appendix 4: PASS at Subsequent Inpatient Time Points and Severe Pancreatitis
APPROACH: We similarly assessed using logistic regression models whether PASS at 24, 48,
36, and 48 hours correlated with moderately/severe and severe pancreatitis. The approach to
determine the optimal cutoff by ROC analysis is shown for the 24 hour time point.  The
measurements of central tendency and dispersion for the PASS score at 24, 36, 48, 60, 72, 84,
96 hours for patients who did and did not develop moderately severe/severe pancreatitis was
determined and presented in Figure 3 of the summary results.  
CODE:
**STATA: PASS cutoff at 24 hours in comparison of association with moderately
severe/severe pancreatitis
logit ModSevPanc PASS24hr, or
lsens, genprob(probcut) genspec(spec) gensens(sens)
lroc  
generate pass112=0
replace pass112=1 if PASS24hr > 112
logit ModSevPanc pass112, or

**STATA: measures of central tendency and dispersion of PASS at variable time points in
those who did and did not develop moderately severe/severe pancreatitis

tabstat  AdmitPASSscore, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)
tabstat  PASS24hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)
tabstat  PASS36hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)
tabstat  PASS48hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)
tabstat  PASS72hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)
tabstat: PASS60hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)
tabstat  PASS84hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)
tabstat  PASS96hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)

OUTPUT:
*Determination of cutoff for 24 hour PASS and association  with moderately
severe/severe pancreatitis

.logit ModSevPanc PASS24hr, or

Logistic regression                             Number of obs     =        399
                                               LR chi2(1)        =      34.41
                                               Prob > chi2       =     0.0000
Log likelihood = -177.07075                     Pseudo R2         =     0.0886

------------------------------------------------------------------------------
 ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
47
-------------+----------------------------------------------------------------
   PASS24hr |   1.010746   .0019892     5.43   0.000     1.006855    1.014653
      _cons |   .0697216   .0192127    -9.66   0.000     .0406263    .1196541
------------------------------------------------------------------------------

lsens, genprob(probcut) genspec(spec) gensens(sens)
lroc  

0.00 0.25 0.50 0.75 1.00
Sensitivity/Specificity
0.00 0.25 0.50 0.75 1.00
Probability cutoff
Sensitivity Specificity


lroc

Logistic model for ModSevPanc

number of observations =      399
area under ROC curve   =   0.6962

0.00 0.25 0.50 0.75 1.00
Sensitivity
0.00 0.25 0.50 0.75 1.00
1 - Specificity
Area under ROC curve = 0.6962



.generate pass112=0

replace pass112=1 if PASS24hr > 112

.logit ModSevPanc pass112, or

Logistic regression                             Number of obs     =        439
                                               LR chi2(1)        =      13.94
                                               Prob > chi2       =     0.0002
Log likelihood = -195.31928                     Pseudo R2         =     0.0345

------------------------------------------------------------------------------
 ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    pass112 |   2.606513   .6838284     3.65   0.000     1.558633    4.358889
      _cons |   .1261682   .0257673   -10.14   0.000     .0845491    .1882743
48

*Measures of central tendency/dispersion of PASS at variable time points in those with
and without moderately severe/severe pancreatitis

tabstat  AdmitPASSscore, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)

Summary for variables: AdmitPASSscore
    by categories of: ModSevPanc (ModSevPanc)

ModSevPanc |         N      mean        sd       p50       p25       p75
-----------+------------------------------------------------------------
        0 |       363  129.9829  57.71327       125        90     162.5
        1 |        76  182.6526  79.10327     167.5     132.5    221.65
-----------+------------------------------------------------------------
    Total |       439  139.1011  64.97961       130        95     173.5
------------------------------------------------------------------------

.  
. tabstat  PASS24hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)

Summary for variables: PASS24hr
    by categories of: ModSevPanc (ModSevPanc)

ModSevPanc |         N      mean        sd       p50       p25       p75
-----------+------------------------------------------------------------
        0 |       363  89.84559  56.92462        75        40     128.4
        1 |        76  144.3368  87.04205    136.65        90       185
-----------+------------------------------------------------------------
    Total |       439  99.27916  66.34372        87        45     138.3
------------------------------------------------------------------------

.  
. tabstat  PASS36hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)

Summary for variables: PASS36hr
    by categories of: ModSevPanc (ModSevPanc)

ModSevPanc |         N      mean        sd       p50       p25       p75
-----------+------------------------------------------------------------
        0 |       363    78.423  57.34173        65        40       105
        1 |        76  131.7921  84.72503       115        65       190
-----------+------------------------------------------------------------
    Total |       439   87.6623   65.9952        70        40       115
------------------------------------------------------------------------

.  
. tabstat  PASS48hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)

Summary for variables: PASS48hr
    by categories of: ModSevPanc (ModSevPanc)

ModSevPanc |         N      mean        sd       p50       p25       p75
-----------+------------------------------------------------------------
        0 |       363  73.59788  59.79581        60        40       104
        1 |        76  122.2197  80.60241     103.5        55       175
-----------+------------------------------------------------------------
    Total |       439  82.01533  66.38354        65        40       115
------------------------------------------------------------------------


. tabstat PASS60hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)

Summary for variables: PASS60hr
    by categories of: ModSevPanc (ModSevPanc)  

ModSevPanc |         N      mean        sd       p50       p25       p75
49
-----------+------------------------------------------------------------
        0 |       363  71.02515  65.35127        55        40        95
        1 |        76     110.6  75.35633        90        50     159.2
-----------+------------------------------------------------------------
    Total |       439  77.87638  68.75177        65        40       104
------------------------------------------------------------------------

.. tabstat  PASS72hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)

Summary for variables: PASS72hr
    by categories of: ModSevPanc (ModSevPanc)

ModSevPanc |         N      mean        sd       p50       p25       p75
-----------+------------------------------------------------------------
        0 |       363  66.10372  57.26944        50        35        90
        1 |        76  108.8474  78.60227        90        50     158.5
-----------+------------------------------------------------------------
    Total |       439  73.50353  63.48826        60        40       100
------------------------------------------------------------------------

.  

.  
. tabstat  PASS84hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)

Summary for variables: PASS84hr
    by categories of: ModSevPanc (ModSevPanc)

ModSevPanc |         N      mean        sd       p50       p25       p75
-----------+------------------------------------------------------------
        0 |       363  66.14063  66.19763        50        25        95
        1 |        76  97.35789  82.06958      81.7        40     137.5
-----------+------------------------------------------------------------
    Total |       439  71.54499  70.10636      53.5        25       101
------------------------------------------------------------------------

.  
. tabstat  PASS96hr, by(ModSevPanc) stat(n, mean, sd, median, p25, p75)

Summary for variables: PASS96hr
    by categories of: ModSevPanc (ModSevPanc)

ModSevPanc |         N      mean        sd       p50       p25       p75
-----------+------------------------------------------------------------
        0 |       363  61.77231  63.07042        50        25        90
        1 |        76  94.01711  78.95505     83.35        40    135.85
-----------+------------------------------------------------------------
    Total |       439  67.35456  67.11391        50        25        95
------------------------------------------------------------------------














50
Appendix 5: PASS and SIRS Development, ICU Admission, and Local Complications
APPROACH: By categorizing admission PASS score in increments of 50 we determined the
proportion and odds (using logistic regression) of developing the following outcomes: 1) local
complications (pancreatic necrosis, pseudocyst, abscess); 2) SIRS in those who did not have
SIRS at admission; and 3) ICU admission.  We measure the association of Admission
PASS>140 with each of these outcomes. Using the same comprehensive panel of categorical
and continuous variables (potential confounders) described in analysis Appendix 3 we
individually tested for association with each of the three outcomes using chi squared/logistic
regression and continuous parametric/nonparametric tests as described above. We then
introduced variables significantly associated with each outcome and the a priori variables of age,
gender, and ethnicity to generate a multivariate model. The code used for the analysis of the
outcome of ICU admission is presented below. Given that the approach is similar ot association
between admission PASS and moderately severe/severe pancreatitis the output is not shown.
CODE:
**STATA: Association of admission PASS as categorical variable and ICU admission
generate admpasscategory=0
replace admpasscategory=1 if AdmitPASSscore >0
replace admpasscategory=2 if AdmitPASSscore >50
replace admpasscategory=3 if AdmitPASSscore >100
replace admpasscategory=4 if AdmitPASSscore >150
replace admpasscategory=5 if AdmitPASSscore >200
replace admpasscategory=6 if AdmitPASSscore >250

tabulate admpasscategory ICUStay, chi row
xi: logit ICUStay i.admpasscategory, or

**STATA: Association of admission PASS>140  and additional variables with ICU
admission
generate pass140=0
replace pass140=1 if AdmitPASSscore>140
logit ICUStay pass140, or
tabulate ICUStay Female, chi2 row
tabulate ICUStay Hispanic, chi2 row
tabulate ICUStay Alctwentydrinks, chi2 row
tabulate ICUStay Smok10py, chi2 row
tabulate ICUStay AMS, chi2 row
51
tabulate ICUStay CKD, chi2 row
tabulate ICUStay DM, chi2 row
tabulate ICUStay PriorAP, chi2 row
tabulate ICUStay Admit_Hct44, chi2 row
tabulate ICUStay Admit_BUN20, chi2 row
xi: logit ICUStay i.Etiology_ETOH1_Gallstones2_Other, or
tabulate ICUStay AnyCommorbidities, chi2 row

ttest Age, by(ICUStay)
*Charlston, VAS, Hospital Days non-normal
tabstat CharlstonAdmit, by(ICUStay) stat (n, mean, sd, median, p25, p75, min, max)
ranksum CharlstonAdmit, by(ICUStay)
tabstat ADMVAS, by(ICUStay) stat (n, mean, sd, median, p25, p75, min, max)
ranksum ADMVAS, by(ICUStay)

*BUN, Cr, normal after log transform
*HCT normal
generate logADMCr=log(Adm_Creatinine)
ttest logADMCr, by(ICUStay)
ttest AdmHgb, by(ICUStay)
generate logBUN=log(Adm_BUN)
ttest logBUN, by(ICUStay)
ttest Adm_HCT, by(ICUStay)

**STATA: Multivariate model of admission PASS>140 and ICU admission
xi: logit ICUStay Pass140 Age Female Hispanic Admit_Hct44 AnyCommorbidities
Admit_BUN20 AMS Etiology_ETOH1_Gallstones2_Other, or






















52
Appendix 6: PASS and Length of Hospitalization and Time to Tolerance of Oral Nutrition  

APPROACH: In order to determine the relationship between Admisson PASS and length of
hospitalization the SAS program, GLM procedure was utilized. Initial linear regression indicated
a significant association, admission PASS >140 was associated with a 1.96 (95% CI 0.78-3.13)
day increase in the length of the hospitalization. However, assessment of the residuals of this
model for the line assumptions (linearity, independence, normality, equal
variance/homoscedasticity) revealed that the residuals were not normally distributed. Therefore
a logarithmic transform of the output variable (days of hospitalization) was performed and the
updated model fulfilled the LINE assumptions. The a priori variables of age, gender, and
ethnicity were integrated into the model. Exponentiation was used to interpret the results in a
more meaningful manner (geometric scale). Using this model which adjusted for the additional
variables, admission PASS >140 conferred a mean increase in hospitalization of 1.5 (1.3-1.7)
days. The analysis for time to tolerance of oral nutrition required a similar approach with
logarithmic transformation of the output variable in order for the multivariate linear regression
model to fulfill the LINE assumptions.

CODE:  
**SAS linear regression model with the input being admission pass>140 and output the
length of hospitalization

data panc;  
set panc;
run;
data panc;
set panc;
if AdmitPASSscore > 140 then pass140=1;
else pass140=0;
run;
proc glm data=panc;
model Hosp_Days=pass140 / solutions CLPARM;
output out=modeltst r=resid p=pred;
run;

**SAS Assessment for LINE assumptions

proc sgplot data=modeltst;
scatter y=resid x=pred;
refline 0;
run;
proc univariate plot normal data=modeltst;
var resid;
run;

**SAS logarithmic transportion of the output variable

data panc;
set panc;
loghosp=log (Hosp_Days);  
run;
53
proc glm data=panc;
model loghosp=pass140 / solutions CLPARM;
output out=modeltst r=resid p=pred;
run;

**SAS reassessment of LINE assumptions
proc sgplot data=modeltst;
scatter y=resid x=pred;
refline 0;
run;
proc univariate plot normal data=modeltst;
var resid;
run;
proc glm data=panc;
model loghosp=pass140 / solutions CLPARM;
ods output parameterestimates=param_est;
run;
data param_est;
set param_est;
est_exp=exp(estimate);
estlowerCL_exp=exp(lowercl);
estupperCL_exp=exp(uppercl);
run;

**SAS final multivariate linear regression model (with log transform)

proc print data=param_est;
run;  
proc glm data=panc;
model loghosp=pass140 Etiology_ETOH1_Gallstones2_Other Age Hispanic  / solutions
CLPARM;
class Etiology_ETOH1_Gallstones2_Other
ods output parameterestimates=param_est;
run;
data param_est;
set param_est;
est_exp=exp(estimate);
estlowerCL_exp=exp(lowercl);
estupperCL_exp=exp(uppercl);
run;
proc print data=param_est;
run;  

OUTPUT:
*Initial linear regression model and assessment of LINE assumptions

 
Dependent Variable: Hosp_Days Hosp_Days
Source DF Sum of Squares Mean Square F Value Pr > F
54
Source DF Sum of Squares Mean Square F Value Pr > F
Model 1 406.11410 406.11410 10.75 0.0011
Error 437 16503.64444 37.76578    
Corrected Total 438 16909.75854      

R-Square Coeff Var Root MSE Hosp_Days Mean
0.024017 105.9217 6.145387 5.801822

Source DF Type I SS Mean Square F Value Pr > F
pass140 1 406.1140977 406.1140977 10.75 0.0011

Source DF Type III SS Mean Square F Value Pr > F
pass140 1 406.1140977 406.1140977 10.75 0.0011

Parameter Estimate Standard
Error
t Value Pr > |t| 95% Confidence Limits
Intercept 5.000000000 0.38185574 13.09 <.0001 4.249497919 5.750502081
pass140 1.955555556 0.59634192 3.28 0.0011 0.783500781 3.127610330



55




Moments
N 439 Sum Weights 439
Mean 0 Sum Observations 0
Std Deviation 6.13836733 Variance 37.6795535
Skewness 3.80215449 Kurtosis 21.0020832
Uncorrected SS 16503.6444 Corrected SS 16503.6444
Coeff Variation . Std Error Mean 0.29296829

Basic Statistical Measures
Location Variability
Mean 0.00000 Std Deviation 6.13837
Median -1.95556 Variance 37.67955
Mode -4.00000 Range 60.00000
   Interquartile Range 4.00000

Tests for Location: Mu0=0
Test Statistic p Value
Student's t t 0 Pr > |t| 1.0000
Sign M -59.5 Pr >= |M| <.0001
Signed Rank S -10633 Pr >= |S| <.0001

Tests for Normality
Test Statistic p Value
Shapiro-Wilk W 0.655222 Pr < W <0.0001
56
Tests for Normality
Test Statistic p Value
Kolmogorov-Smirnov D 0.204656 Pr > D <0.0100
Cramer-von Mises W-Sq 6.163106 Pr > W-Sq <0.0050
Anderson-Darling A-Sq 35.07121 Pr > A-Sq <0.0050




*Linear regression model with logarithmic transform of the output variable

Dependent Variable: loghosp  
Source DF Sum of Squares Mean Square F Value Pr > F
Model 1 13.7246699 13.7246699 21.03 <.0001
Error 427 278.7266628 0.6527557    
Corrected Total 428 292.4513327      

R-Square Coeff Var Root MSE loghosp Mean
0.046930 56.39965 0.807933 1.432514

Source DF Type I SS Mean Square F Value Pr > F
pass140 1 13.72466992 13.72466992 21.03 <.0001

Source DF Type III SS Mean Square F Value Pr > F
pass140 1 13.72466992 13.72466992 21.03 <.0001

Parameter Estimate Standard
Error
t Value Pr > |t| 95% Confidence Limits
57
Parameter Estimate Standard
Error
t Value Pr > |t| 95% Confidence Limits
Intercept 1.281889625 0.05099628 25.14 <.0001 1.181654652 1.382124598
pass140 0.363021827 0.07916935 4.59 <.0001 0.207411677 0.518631978






The UNIVARIATE Procedure
Variable: resid
Moments
N 429 Sum Weights 429
Mean 0 Sum Observations 0
Std Deviation 0.80698855 Variance 0.65123052
Skewness 0.10266721 Kurtosis 0.09181502
Uncorrected SS 278.726663 Corrected SS 278.726663
Coeff Variation . Std Error Mean 0.03896177

Basic Statistical Measures
Location Variability
58
Basic Statistical Measures
Location Variability
Mean 0.00000 Std Deviation 0.80699
Median -0.03547 Variance 0.65123
Mode -1.28189 Range 4.09434
   Interquartile Range 1.05617

Tests for Location: Mu0=0
Test Statistic p Value
Student's t t 0 Pr > |t| 1.0000
Sign M -0.5 Pr >= |M| 1.0000
Signed Rank S 451.5 Pr >= |S| 0.8607

Tests for Normality
Test Statistic p Value
Shapiro-Wilk W 0.978031 Pr < W <0.0001
Kolmogorov-Smirnov D 0.08293 Pr > D <0.0100
Cramer-von Mises W-Sq 0.355379 Pr > W-Sq <0.0050
Anderson-Darling A-Sq 2.659942 Pr > A-Sq <0.0050






59
The GLM Procedure
 
Dependent Variable: loghosp  
Source DF Sum of Squares Mean Square F Value Pr > F
Model 1 13.7246699 13.7246699 21.03 <.0001
Error 427 278.7266628 0.6527557    
Corrected Total 428 292.4513327      

R-Square Coeff Var Root MSE loghosp Mean
0.046930 56.39965 0.807933 1.432514

Source DF Type I SS Mean Square F Value Pr > F
pass140 1 13.72466992 13.72466992 21.03 <.0001

Source DF Type III SS Mean Square F Value Pr > F
pass140 1 13.72466992 13.72466992 21.03 <.0001

Parameter Estimate Standard
Error
t Value Pr > |t| 95% Confidence Limits
Intercept 1.281889625 0.05099628 25.14 <.0001 1.181654652 1.382124598
pass140 0.363021827 0.07916935 4.59 <.0001 0.207411677 0.518631978




Obs Dependent Parameter Estimate StdErr tValue Probt LowerCL UpperCL est_exp estlowerCL_exp estupperCL_exp
1 loghosp Intercept 1.281889625 0.05099628 25.14 <.0001 1.181654652 1.382124598 3.60344 3.25976 3.98336
2 loghosp pass140 0.363021827 0.07916935 4.59 <.0001 0.207411677 0.518631978 1.43767 1.23049 1.67973


*Multivariate model with logarithmic transform of the output variable

Dependent Variable: loghosp  
Source DF Sum of Squares Mean Square F Value Pr > F
Model 5 41.9089557 8.3817911 14.21 <.0001
60
Source DF Sum of Squares Mean Square F Value Pr > F
Error 421 248.3585255 0.5899252    
Corrected Total 426 290.2674812      

R-Square Coeff Var Root MSE loghosp Mean
0.144380 53.52273 0.768066 1.435028

Source DF Type I SS Mean Square F Value Pr > F
pass140 1 14.52284589 14.52284589 24.62 <.0001
Etiology_ETOH1_Galls 2 23.67196571 11.83598285 20.06 <.0001
Age 1 3.67559817 3.67559817 6.23 0.0129
Hispanic 1 0.03854599 0.03854599 0.07 0.7984

Source DF Type III SS Mean Square F Value Pr > F
pass140 1 15.43650306 15.43650306 26.17 <.0001
Etiology_ETOH1_Galls 2 24.14522367 12.07261184 20.46 <.0001
Age 1 3.64779585 3.64779585 6.18 0.0133
Hispanic 1 0.03854599 0.03854599 0.07 0.7984

Parameter Estimate   Standard
Error
t Value Pr > |t| 95% Confidence Limits
Intercept 0.8450670365 B 0.16012361 5.28 <.0001 0.5303257063 1.1598083668
Pass140 0.3875841471   0.07576873 5.12 <.0001 0.2386520023 0.5365162919
Etiology_ETOH1_Galls 1 -.1053019537 B 0.10347367 -1.02 0.3094 -.3086913264 0.0980874190
Etiology_ETOH1_Galls 2 0.4239490324 B 0.08929002 4.75 <.0001 0.2484392399 0.5994588250
Etiology_ETOH1_Galls 3 0.0000000000 B . . . . .
Age 0.0061349594   0.00246715 2.49 0.0133 0.0012855000 0.0109844188
Hispanic -.0246119944   0.09628435 -0.26 0.7984 -.2138699397 0.1646459508

The SAS System

Obs Dependent Parameter Estimate Biased StdErr tValue Probt LowerCL UpperCL est_exp estlowerCL_exp estupperCL_exp
1 loghosp Intercept 0.8450670365 1 0.16012361 5.28 <.0001 0.5303257063 1.1598083668 2.32813 1.69949 3.18932
2 loghosp pass140 0.3875841471 0 0.07576873 5.12 <.0001 0.2386520023 0.5365162919 1.47342 1.26954 1.71004
3 loghosp Etiology_ETOH1_Galls 1 -.1053019537 1 0.10347367 -1.02 0.3094 -.3086913264 0.0980874190 0.90005 0.73441 1.10306
4 loghosp Etiology_ETOH1_Galls 2 0.4239490324 1 0.08929002 4.75 <.0001 0.2484392399 0.5994588250 1.52798 1.28202 1.82113
5 loghosp Etiology_ETOH1_Galls 3 0.0000000000 1 . . . . . 1.00000 . .
6 loghosp Age 0.0061349594 0 0.00246715 2.49 0.0133 0.0012855000 0.0109844188 1.00615 1.00129 1.01104
7 loghosp Hispanic -.0246119944 0 0.09628435 -0.26 0.7984 -.2138699397 0.1646459508 0.97569 0.80745  



61
Appendix 7: Comparison of PASS with Established Predictive Scoring Systems
APPROACH: In order to compare the strength of association of PASS with other available
scoring systems logistic regression modeling was performed with the independent variables
being admission Glasgow, Ranson’s, HAPS and Panc3 scores and the dependent variable
being the development of moderately severe/severe pancreatitis. Each score was modeled as a
continuous variable (independent) to determine the area under the curve for prediction of
moderately severe/severe pancreatitis.  

However, in order to generate the tests characteristics (sensitivity, specificity, PPV, NPV) as
well as the OR (95%CI) we used the cutoffs for the scores which have been defined in prior
publications as predictive of moderately severe/severe pancreatitis.  These included a Glasgow
score of 2, Ranson’s score of 2, HAPS score of 1, and Panc3 score of 1.  The code, output
(including probability from STATA data editor) for the Glasgow score are shown below. The
same approach was used for Ranson’s, HAPS, and Panc3.  

We used a similar approach to compare Glasgow with PASS score for three additional
outcomes (dependent variables in the models) of ICU admission, local complications, and SIRS
after admission.
CODE:
**STATA: Logistic regression modeling of continuous Glasgow score to generate AUC
and test characteristics for prediction of moderately severe/severe pancreatitis.
logit ModSevPanc GlasgowonAdmission, or
lsens, genprob(probcut) genspec(spec) gensens(sens)
lroc
estat classification, cutoff (0.43577)


**STATA: Logistic regression modeling of categorical Glasgow score to generate OR for
development of moderately severe/severe pancreatitis.
generate glasgow2=0
replace glasgow2=1 if GlasgowonAdmission>1
logit ModSevPanc glasgow2, or
62

OUTPUT:
*Logistic Regression and generation of ROC, AUC and test characteristics
logit ModSevPanc GlasgowonAdmission, or

Iteration 0:   log likelihood = -204.00546  
Iteration 1:   log likelihood = -180.86701  
Iteration 2:   log likelihood = -179.05551  
Iteration 3:   log likelihood = -179.05208  
Iteration 4:   log likelihood = -179.05208  

Logistic regression                             Number of obs     =        432
                                               LR chi2(1)        =      49.91
                                               Prob > chi2       =     0.0000
Log likelihood = -179.05208                     Pseudo R2         =     0.1223

------------------------------------------------------------------------------------
       ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
GlasgowonAdmission |   2.150779   .2471464     6.66   0.000     1.717055    2.694062
            _cons |   .0776294   .0176382   -11.25   0.000     .0497307    .1211792
------------------------------------------------------------------------------------




0.00 0.25 0.50 0.75 1.00
Sensitivity/Specificity
0.00 0.25 0.50 0.75 1.00
Probability cutoff
Sensitivity Specificity


63
0.00 0.25 0.50 0.75 1.00
Sensitivity
0.00 0.25 0.50 0.75 1.00
1 - Specificity
Area under ROC curve = 0.7295


*Identification of moderately severe/severe pancreatitis for Glasgow 2 to generate test
characteristics




64


estat classification, cutoff (0.43577)

Logistic model for ModSevPanc

             -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
    +     |        25            28  |         53
    -     |        53           326  |        379
-----------+--------------------------+-----------
  Total   |        78           354  |        432

Classified + if predicted Pr(D) >= .43577
True D defined as ModSevPanc != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   32.05%
Specificity                     Pr( -|~D)   92.09%
Positive predictive value       Pr( D| +)   47.17%
Negative predictive value       Pr(~D| -)   86.02%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    7.91%
False - rate for true D         Pr( -| D)   67.95%
False + rate for classified +   Pr(~D| +)   52.83%
False - rate for classified -   Pr( D| -)   13.98%
--------------------------------------------------
Correctly classified                        81.25%
--------------------------------------------------

OR of moderately severe/severe pancreatitis for Glasgow >2

. generate glasgow2=0
. replace glasgow2=1 if GlasgowonAdmission>1

. logit ModSevPanc glasgow2, or

Iteration 0:   log likelihood = -205.19114  
Iteration 1:   log likelihood = -190.89766  
Iteration 2:   log likelihood = -189.97719  
Iteration 3:   log likelihood = -189.97631  
Iteration 4:   log likelihood = -189.97631  

Logistic regression                             Number of obs     =        438
65
                                               LR chi2(1)        =      30.43
                                               Prob > chi2       =     0.0000
Log likelihood = -189.97631                     Pseudo R2         =     0.0741

------------------------------------------------------------------------------
 ModSevPanc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   glasgow2 |   4.127016   1.072128     5.46   0.000     2.480325    6.866948
      _cons |   .1198502   .0224204   -11.34   0.000     .0830623    .1729312






















66
Appendix 8: Discharge PASS Score and Early Readmission <30 Days
APPROACH: The distribution of discharge PASS score was first modeled as a continuous
variable using SAS. This was assessed by evaluating the distribution of discharge PASS scores
as a whole and then by the subset readmitted in <30 days or not readmitted within 30 days.
STATA was used to model discharge PASS as a categorical variable. Initially association of
pass score in increments of 25 was correlated with early readmission. Subsequently, a logistic
regression model was used with discharge PASS as the independent and early readmission as
the dependent variable. ROC analysis was used to identify the score which optimized sensitivity
and specificity which occurred for a discharge PASS of 60. The associated probability identified
using the STATA data editor was then used to determine the test characteristics (sensitivity,
specificity, PPV, NPV).  A similar approach was used to evaluate the association between
discharge PASS and early presentation to the emergency room for pancreatitis (<30 days) or
late (>30 day) readmission (not shown).  

CODE:
**SAS is used to compare distributions of discharge PASS overall and for those who
were readmitted within 30 days or not readmitted within 30 days.

data panc;
set work.panc;
run;
data livepanc;
set panc;
if death = '0';
run;
proc univariate plot normal data=livepanc;
var DCSCORE;
run;
proc print data=panc;
run;
data readmitpanc;
set livepanc;
if Readmit_Pancreatitis_30 = '1';
run;
proc univariate plot normal data=readmitpanc;
var DCSCORE;
67
run;
data notreadmitpanc;
set livepanc;
if Readmit_Pancreatitis_30='0';
run;
proc univariate plot normal data=notreadmitpanc;
var DCSCORE;
run;

**Modeling association of discharge PASS and early readmission as a categorical
variable

drop if StudyNumber==45
drop if StudyNumber==58
drop if StudyNumber==47
generate passcategory=0
replace passcategory=1 if DCSCORE>0
replace passcategory=2 if DCSCORE>25
replace passcategory=3 if DCSCORE>50
replace passcategory=4 if DCSCORE>75
replace passcategory=5 if DCSCORE>100
replace passcategory=6 if DCSCORE>125
replace passcategory=7 if DCSCORE>150
tabulate passcategory Readmit_Pancreatitis30, chi2 row
xi: logit Readmit_Pancreatitis30 i.passcategory, or

**Logistic regression modeling of continuous discharge PASS with early readmission  to
determine optimal cutoff and test characteristics
 
logit Readmit_Pancreatitis30 DCSCORE, or
lsens, genprob(probcut) genspec(spec) gensens(sens)
lroc
estat classification, cutoff(0.072263)


Code not shown for evaluation of potential covariates

**Multivariate logistic regression model
generate pass60=0
replace pass60=1 if DCSCORE>60
logit Readmit_Pancreatitis30 Choley, or
xi: logit Readmit_Pancreatitis30 pass60 PriorAP Hispanic Female
i.Etiology_ETOH1_Gallstones2_ Age Choley, or

OUTPUT:
*Continuous distribution of discharge PASS for all patients
The UNIVARIATE Procedure
Variable: DCSCORE (DCSCORE)
Moments
68
Moments
N 437 Sum Weights 437

Basic Statistical Measures
Location Variability
Mean 48.38455 Std Deviation 49.00981
Median 40.00000 Variance 2402
Mode 0.00000 Range 372.50000
   Interquartile Range 73.30000


Quantiles (Definition 5)
Level Quantile
100% Max 372.50
99% 202.95
95% 136.40
90% 110.00
75% Q3 73.30
50% Median 40.00
25% Q1 0.00
10% 0.00
5% 0.00
1% 0.00
0% Min 0.00


69




*Discharge PASS score paitents readmitted (<30 days) for pancreatitis



Moments
N 37 Sum Weights 37
Mean 78.5432432 Sum Observations 2906.1

Basic Statistical Measures
Location Variability
Mean 78.54324 Std Deviation 37.94642
Median 75.00000 Variance 1440
Mode 65.00000 Range 153.30000
   Interquartile Range 55.00000

Note: The mode displayed is the smallest of 2 modes with a count of 3.



Quantiles (Definition 5)
Level Quantile
100% Max 153.3
99% 153.3
70
Quantiles (Definition 5)
Level Quantile
95% 150.0
90% 135.0
75% Q3 105.0
50% Median 75.0
25% Q1 50.0
10% 33.3
5% 25.0
1% 0.0
0% Min 0.0







*Distribution of  discharge PASS score for patients not readmitted for pancreatitis  

The UNIVARIATE Procedure
Variable: DCSCORE (DCSCORE)
Moments
N 400 Sum Weights 400
Mean 45.594875 Sum Observations 18237.95

Basic Statistical Measures
Location Variability
71
Basic Statistical Measures
Location Variability
Mean 45.59488 Std Deviation 49.01669
Median 39.20000 Variance 2403
Mode 0.00000 Range 372.50000
   Interquartile Range 67.65000

Quantiles (Definition 5)
Level Quantile
100% Max 372.500
99% 205.225
95% 132.500
90% 106.700
75% Q3 67.650
50% Median 39.200
25% Q1 0.000
10% 0.000
5% 0.000
1% 0.000
0% Min 0.000




* Discharge PASS and early readmission modeled as categorical variables

72
. tabulate passcategory Readmit_Pancreatitis30, chi2 row


          | Readmit_Pancreatitis<
passcatego |          30
       ry |         0          1 |     Total
-----------+----------------------+----------
        0 |       107          1 |       108  
          |     99.07       0.93 |    100.00  
-----------+----------------------+----------
        1 |        56          2 |        58  
          |     96.55       3.45 |    100.00  
-----------+----------------------+----------
        2 |        51          7 |        58  
          |     87.93      12.07 |    100.00  
-----------+----------------------+----------
        3 |        68          6 |        74  
          |     91.89       8.11 |    100.00  
-----------+----------------------+----------
        4 |        23          4 |        27  
          |     85.19      14.81 |    100.00  
-----------+----------------------+----------
        5 |        21          4 |        25  
          |     84.00      16.00 |    100.00  
-----------+----------------------+----------
        6 |         5          2 |         7  
          |     71.43      28.57 |    100.00  
-----------+----------------------+----------
        7 |        10          1 |        11  
          |     90.91       9.09 |    100.00  
-----------+----------------------+----------
    Total |       341         27 |       368  
          |     92.66       7.34 |    100.00  

         Pearson chi2(7) =  19.4670   Pr = 0.007


xi: logit Readmit_Pancreatitis30 i.passcategory, or

----------------------------------------------------------------------------------------
Readmit_Pancreatitis30 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
        _Ipasscateg_1 |          4   4.938329     1.12   0.261     .3557801     44.9716
        _Ipasscateg_2 |   15.26154   16.35514     2.54   0.011     1.868108    124.6794
        _Ipasscateg_3 |   13.05263   13.97438     2.40   0.016     1.600982    106.4166
        _Ipasscateg_4 |   28.93333   31.48626     3.09   0.002     3.428399    244.1775
        _Ipasscateg_5 |   32.34783   35.70311     3.15   0.002     3.718369    281.4088
        _Ipasscateg_6 |   70.85714   83.86689     3.60   0.000     6.964612    720.8922
        _Ipasscateg_7 |   9.538462   13.77305     1.56   0.118     .5628427    161.6477
                _cons |   .0080645    .008097    -4.80   0.000     .0011271    .0577039
----------------------------------------------------------------------------------------


*Discharge PASS modeled as continuous variable with early readmission to determine
optimal cutoff and performance characteristics by ROC analysis

. logit Readmit_Pancreatitis30 DCSCORE, or

Iteration 0:   log likelihood = -129.71459  
Iteration 1:   log likelihood = -125.29106  
Iteration 2:   log likelihood = -124.00627  
Iteration 3:   log likelihood = -124.00273  
Iteration 4:   log likelihood = -124.00273  

Logistic regression                             Number of obs     =        472
                                               LR chi2(1)        =      11.42
                                               Prob > chi2       =     0.0007
Log likelihood = -124.00273                     Pseudo R2         =     0.0440
73

----------------------------------------------------------------------------------------
Readmit_Pancreatitis30 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
              DCSCORE |   1.009919   .0028568     3.49   0.000     1.004336    1.015534
                _cons |   .0467595   .0124641   -11.49   0.000     .0277316    .0788433
----------------------------------------------------------------------------------------






estat classification, cutoff(0.072263)
estat classification, cutoff(0.072263)

Logistic model for Readmit_Pancreatitis30

             -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
    +     |        25           168  |        193
    -     |        12           267  |        279
-----------+--------------------------+-----------
  Total   |        37           435  |        472

Classified + if predicted Pr(D) >= .072263
True D defined as Readmit_Pancreatitis30 != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   67.57%
Specificity                     Pr( -|~D)   61.38%
Positive predictive value       Pr( D| +)   12.95%
Negative predictive value       Pr(~D| -)   95.70%


* Multivariate logistic regression model for discharge PASS and early readmission

. xi: logit Readmit_Pancreatitis30 pass60 PriorAP Hispanic Female i.Etiology_ETOH1_Gallstones2_ Age Choley, or
i.Etiology_ET~r   _IEtiology__1-3     (naturally coded; _IEtiology__1 omitted)
Logistic regression                             Number of obs     =        433
                                               LR chi2(8)        =      27.32
                                               Prob > chi2       =     0.0006
Log likelihood = -110.33867                     Pseudo R2         =     0.1102

----------------------------------------------------------------------------------------
Readmit_Pancreatitis30 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
               pass60 |   5.046515   1.932915     4.23   0.000     2.382125    10.69101
              PriorAP |   1.742322   .7789834     1.24   0.214     .7253714    4.185009
             Hispanic |   .5899143   .2444036    -1.27   0.203     .2618986    1.328754
               Female |   .7029043   .2880294    -0.86   0.390     .3148466    1.569255
74
        _IEtiology__2 |   1.054649   .5413892     0.10   0.917     .3856167    2.884432
        _IEtiology__3 |   .8614104   .4416817    -0.29   0.771     .3153282    2.353192
                  Age |   .9861546   .0130472    -1.05   0.292     .9609113    1.012061
               Choley |   .8045366   .4679583    -0.37   0.708      .257303     2.51563
                _cons |   .1213207   .1007283    -2.54   0.011     .0238348    .6175293
---------------------------------------------------------------------------------------
























75
Appendix 9: Sensitivity Analyses for Follow up <30 Days and Zero Scores
APPROACH: A sensitivity analysis for the relationship between discharge PASS and early
readmission to adjust for inadequate follow up was performed. A descriptive analysis of the
follow up time of the whole cohort, those who were readmitted in <30 days and those not
readmitted was first performed. The multivariate logistic regression model used to measure the
relationship between discharge PASS and early readmission was then adjusted for follow up by
including follow up time <30 days as a categorical variable.

To assess for the presence of zero scores on the observed relationship between discharge
PASS and early readmission, the subjects with scores of zero were deleted from the dataset.
The entire analysis was then repeated including analysis of the categorical variables in
increments of 25 and generation of discharge PASS cutpoint (code/output not shown). This
revealed a PASS 67 as optimal for the cohort in which discharge PASS>1. Logistic regression
demonstrated that a significant relationship between discharge PASS and early readmission
remained.

CODE:  

**STATA: Generate distributions of follow-up time for cohort as a whole and
dichotomized by early readmission  

Histogram FollowUpDays
tabstat FollowUpDays, by(Readmit_Pancreatitis30) stat (n, mean, sd, median, p25, p75, min,
max)

**STATA: Multivariate analysis was repeated after introducing a categorical variable
coding for a follow up time of <30 days

xi: logit Readmit_Pancreatitis30 pass60 PriorAP Hispanic Female
i.Etiology_ETOH1_Gallstones2_ Age ICUStay Less30FU, or

** STATA: repeated analysis assessing for association between early readmission and
discharge PASS including ROC analysis revealing discharge pass67 as optimal cutoff

76
logit Readmit_Pancreatitis30 DCSCORE, or
lsens, genprob(probcut) genspec(spec) gensens(sens)
lroc
estat classification, cutoff(0.112213)
logit Readmit_Pancreatitis30 pass67, or

OUTPUT:
*Assessment of central tendencies and distribution of follow up<30 days
Summary for variables: FollowUpDays
    by categories of: Readmit_Pancreatitis30 (Readmit_Pancreatitis<30)


. tabstat FollowUpDays, stat (n, mean, sd, median, p25, p75, min, max)

   variable |         N      mean        sd       p50       p25       p75       min       max
-------------+--------------------------------------------------------------------------------
FollowUpDays |       428  212.9603  235.0369       123        10       380         0      1316
----------------------------------------------------------------------------------------------

Readmit_Pancreatitis30 |         N      mean        sd       p50       p25       p75       min       max
-----------------------+--------------------------------------------------------------------------------
                    0 |       391  202.9795  234.0412       102         9       360         0      1316
                    1 |        37  318.4324  222.0848       322       105       461         8       762
-----------------------+--------------------------------------------------------------------------------
                Total |       428  212.9603  235.0369       123        10       380         0      1316
--------------------------------------------------------------------------------------------------------

* Multivariate model for discharge PASS and early readmission adjusted for follow up
time <30 days

. xi: logit Readmit_Pancreatitis30 pass60 PriorAP Hispanic Female i.Etiology_ETOH1_Gallstones2_ Age ICUStay Less30FU, or
i.Etiology_ET~r   _IEtiology__1-3     (naturally coded; _IEtiology__1 omitted)

Logistic regression                             Number of obs     =        426
                                               LR chi2(9)        =      47.18
                                               Prob > chi2       =     0.0000
Log likelihood = -102.16645                     Pseudo R2         =     0.1876

----------------------------------------------------------------------------------------
Readmit_Pancreatitis30 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
               pass60 |   5.116568    1.96919     4.24   0.000     2.406472    10.87869
              PriorAP |   1.552615   .7045541     0.97   0.332     .6379705    3.778563
             Hispanic |   .6089126   .2642758    -1.14   0.253     .2600895    1.425566
               Female |   .6965061   .2966706    -0.85   0.396     .3022478    1.605043
        _IEtiology__2 |   1.271051   .6434342     0.47   0.636      .471266    3.428152
        _IEtiology__3 |   .8673785   .4706616    -0.26   0.793     .2994506     2.51242
                  Age |   .9739568   .0134225    -1.91   0.056     .9480013    1.000623
              ICUStay |   1.308905   .6398564     0.55   0.582     .5021108    3.412061
             Less30FU |   .1038984   .0656139    -3.59   0.000     .0301341     .358228
                _cons |    .306716   .2667128    -1.36   0.174     .0557895    1.686245

77
*Analysis for optimal cutpoint, test characteristics, and association of discharge PASS
and readmission when subjects with discharge PASS=0 are exempted


0.00 0.25 0.50 0.75 1.00
Sensitivity/Specificity
0.00 0.25 0.50 0.75 1.00
Probability cutoff
Sensitivity Specificity

0.00 0.25 0.50 0.75 1.00
Sensitivity
0.00 0.25 0.50 0.75 1.00
1 - Specificity
Area under ROC curve = 0.6571

. estat classification, cutoff(0.112213)

Logistic model for Readmit_Pancreatitis30

             -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
    +     |        22           100  |        122
    -     |        14           176  |        190
-----------+--------------------------+-----------
  Total   |        36           276  |        312

Classified + if predicted Pr(D) >= .112213
True D defined as Readmit_Pancreatitis30 != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   61.11%
78
Specificity                     Pr( -|~D)   63.77%
Positive predictive value       Pr( D| +)   18.03%
Negative predictive value       Pr(~D| -)   92.63%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   36.23%
False - rate for true D         Pr( -| D)   38.89%
False + rate for classified +   Pr(~D| +)   81.97%
False - rate for classified -   Pr( D| -)    7.37%
--------------------------------------------------
Correctly classified                        63.46%
--------------------------------------------------

. generate pass67=0
. replace pass67=1 if DCSCORE>67
. logit Readmit_Pancreatitis30 pass67, or

Iteration 0:   log likelihood = -111.57967  
Iteration 1:   log likelihood = -107.98038  
Iteration 2:   log likelihood = -107.82512  
Iteration 3:   log likelihood = -107.82494  
Iteration 4:   log likelihood = -107.82494  

Logistic regression                             Number of obs     =        312
                                               LR chi2(1)        =       7.51
                                               Prob > chi2       =     0.0061
Log likelihood = -107.82494                     Pseudo R2         =     0.0337

----------------------------------------------------------------------------------------
Readmit_Pancreatitis30 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
               pass67 |   2.667368   .9629787     2.72   0.007      1.31456    5.412345
                _cons |   .0828729   .0222667    -9.27   0.000     .0489449    .1403194
----------------------------------------------------------------------------------------
























79

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80
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Abstract (if available)
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Asset Metadata
Creator Buxbaum, James Leonard (author) 
Core Title Validation of the pancreatitis activity scoring system in a large prospective cohort 
School Keck School of Medicine 
Degree Master of Science 
Degree Program Clinical, Biomedical and Translational Investigations 
Publication Date 05/01/2018 
Defense Date 04/30/2018 
Publisher University of Southern California (original), University of Southern California. Libraries (digital) 
Tag Apache,OAI-PMH Harvest,pancreatitis,pancreatitis, acute necrotizing,patient readmission 
Format application/pdf (imt) 
Language English
Contributor Electronically uploaded by the author (provenance) 
Advisor Lane, Christianne Joy (committee chair), Cozen, Wendy (committee member), Stolz, Andrew Abba (committee member) 
Creator Email james.buxbaum@med.usc.edu,jbuxbaum@usc.edu 
Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-497836
Unique identifier UC11266339 
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Legacy Identifier etd-BuxbaumJam-6291.pdf 
Dmrecord 497836 
Document Type Thesis 
Format application/pdf (imt) 
Rights Buxbaum, James Leonard 
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Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law.  Electronic access is being provided by the USC Libraries in agreement with the a... 
Repository Name University of Southern California Digital Library
Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Tags
pancreatitis
pancreatitis, acute necrotizing
patient readmission