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Computation and validation of circulating blood volume with the indocyanine green dilution technique

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Computation and validation of circulating blood volume with the indocyanine green dilution technique

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Content COMPUTATION AND VALIDATION OF CIRCULATING BLOOD VOLUME WITH THE INDOC YANINE GREEN DILUTION TECHNIQUE by Matthew Paul Sullivan A Thesis Presented to the FACULTY OF THE VITERBI SCHOOL OF ENGINEERING UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE BIOMEDICAL ENGINEERING December 2005 Copyright 2005 Matthew Paul Sullivan Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UMI Number: 1435092 Copyright 2006 by Sullivan, M atthew Paul All rights reserved. IN F O R M A T IO N TO U S E R S The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignm ent can adversely affect reproduction. In the unlikely event that the author did not send a com plete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. ® UMI UMI Microform 1435092 Copyright 2006 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Com pany 300 North Zeeb Road P.O. Box 1346 Ann Arbor, Ml 481 06 -1 34 6 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Dedication “You cannot teach a man anything; you can only help him fin d it within himself.” -Galileo Galilei (1564-1642) Italian physicist and astronomer. This thesis is dedicated to both of my late grandfathers: Leo Sullivan and Paul DeCaporale. Life’s a journey, not a destination. See you both when the trip is over. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Acknowledgements I would like to thank Prof. Jean-Michel Maarek for his guidance, assistance, and encouragement. Without his help and constructive criticism this thesis would never have taken shape. I would also like to extend thanks to the other two members of my committee: Prof. David D’Argenio and Dr. Daniel Holschneider. Lastly, special thanks to both Cambria Odle and Kimberly Lau, whose willingness to run errands for me was beyond valuable. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table of Contents Dedication......................................................................................................................................ii Acknowledgements..................................................................................................................... iii List of Tables................................................................................................................................ v List of Figures.............................................................................................................................. vi Abstract........................................................................................................................................vii Chapter 1: Background..............................................................................................................1 Chapter 2: Methods and Materials........................................................................................... 9 Chapter 3: Results.................................................................................................................... 21 Chapter 4: Discussion..............................................................................................................30 References....................................................................................................................................34 Appendix......................................................................................................................................37 iv Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. List of Tables Table 1: Mean blood volumes using both ICG and EB technique ............................... 22 Table 2: Three-Factor ANOVA ....................................................................................28 Table 3: Two-Factor ANOVA ......................................................................................29 v Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. List of Figures Figure 1: Indocyanine green (C43Fl47N2Na06S2).....................................................................5 Figure 2: Pharmacokinetic Compartmental Recirculation Model of ICG........................... 7 Figure 3: Experimental Setup............................ 10 Figure 4: ICG Concentration from Time of Injection........................................................... 16 Figure 5: Circulating Blood Volume Computation Algorithm............................................17 Figure 6: Ratio of Baseline Circulating Blood Volume to Body M ass............................. 23 Figure 7: Mean of ICG vs. EB Blood Volume Estimates.................................................... 24 Figure 8: Mean of ICG and EB Volume Estimates vs. Difference o f ICG and EB Volume Estimates....................................................................................... 25 vi Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A bstract BACKGROUND: Circulating blood volume (CBV) is an important parameter in assessing hemorrhages, investigating low cardiac function, and tracking acute circulatory failure. This study will develop an automated method for computing CBV using the indocyanine green (ICG) dye dilution technique and compare the accuracy of the ICG and Evans blue (EB) dilution methods METHODS: CBV was computed in nine rabbits with both dilution methods. ICG and EB dye concentrations were obtained for both baseline and hypovolemic conditions and CBV was then calculated. RESULTS: Mean baseline CBV for the ICG method was 210.19 +/- 31.10 ml and for the hypovolemic state was 157.40 +/- 28.03. CBV values for the EB method were 198.72 +/- 17.64 and 173.89 +/- 19.97 ml respectively. Analysis of variables shows that CBV between methods does not significantly differ (p>0.05). CONCLUSIONS: CBV estimated through repeated ICG dilution measurements is statistically comparable to traditional EB techniques. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. C hapter 1: Background Introduction Motivation Computation o f circulating blood volume (CBV) in a patient is of use for several reasons. First, low cardiac output can be investigated. Second, the extent of massive trauma to an individual (hemorrhage for example) can be monitored and deficient levels of CBV can be treated. Additionally, conditions such as acute circulatory failure and sepsis that may result in depleted blood volume can be tracked. The ability to conduct serial measurements of CBV is currently lacking in the clinical setting and would allow physicians to more accurately monitor a patient. Aims o f This Study I. To develop an automated method by which circulating blood volume can be accurately computed from measurements of blood levels of indocyanine green after intravenous injection of the dye. II. To assess the accuracy of blood volumes obtained from indocyanine green measurements by comparison to those determined by a classical method with assesses blood concentrations of the dye Evans blue. 1 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. History Calculation of CBV has evolved considerably over time. The first forays into finding CBV occurred by draining animals and executed criminals; such methods were hardly accurate or precise. Indirect measurements are first credited to Valentin in 1838 who proposed using a known volume of water to dilute blood and then compute the resulting diluted blood volume from concentration [2,3]. CBV estimates are by nature complicated in that plasma volume and the hematocrit need to be measured separately. Modem indicators to measure plasma include Evans’s Blue, Iodine-131, Chromium-51 and Indo-cyanine green (ICG). Radioactive isotopes of iron, chromium, and phosphorus are used to measure hematocrit volume [2,5]. Differing Techniques for measuring Total Blood Volume Direct measurement of CBV is not practical for clinical use for obvious reasons. As such, a number of direct indicator methods have been developed based on the theory of mass conservation. If a known volume of indicator has been injected into the systemic blood flow of a subject and complete mixing occurs, then the detected concentration (obtained through either blood withdrawal, spectroscopy, or fluorescence) can be obtained. Dividing the original known mass by the 2 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. concentration (specifically the concentration at the time of injection To) yields blood volume as shown in Equation 1. Traditionally, radio-isotopes of Iodine and Chromium have been used as tracers in the blood when finding CBV [5]. Radio-active albumin has also been employed. The disadvantages to this are many, however. Radiation poisoning is a prime concern, as well as the length of which these isotopes stay in the body [2, 3, 4, 5, 7, 10]. Evans blue dye, while not radioactive, suffers from many of the same deficiencies as radio-isotopes. Retention of Evans blue in the body lasts for several days, preventing serial measurements of blood volume. Evans blue also may have some mutagenic potential [4,6]. In addition, discoloration of the skin can occur [5]. ICG dye holds two advantages over traditional tracers used to compute CBV. First, it has a half-life of 4-5 mins (compared to Albumin, which has a half life lasting 2-3 days of Cr-51, having a half-life of 27.8 days), so serial measurements at a bedside setting are very feasible [9,10,11]. ICG also has virtually no permeability outside of the blood stream and is only removed in the hepatic circulation [4,15]. ICG does 3 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. have one major limitation: as mentioned earlier it is removed from the blood stream at an exponential rate making measurement challenging. Several techniques, such as semilog scaling and back extrapolation need to be employed and are discussed later in this paper. Rationale for using ICG ICG dye has a half-life o f 4-5 minutes in the body allowing for serial measurements to be obtained. ICG is often at 1% of its injected concentration after 20 minutes, making repeated measurements on a 20 minute cycle feasible. This makes ICG an ideal tracer for use in on operating room where fluid management of a patient may be vital. This, coupled with ICG’s low impact on the body (non-radioactive, minimally invasive, and approved by the U.S. Food and Drug Administration for intravenous use) make it ideal for computation o f CBV [7,9,10]. Chemistry o f ICG ICG is a tricarbocyanine dye with a molecular weight of 775. Its peak optical absorption when bound to serum is 805 nm and significant absorption is not present at 820nm. ICG binds to plasma proteins with high affinity, making it almost exclusively an intravascular substance, only being removed in the liver [4,5,7,9,10,11,14,15,16,20]. It is due to this lack of leakage that makes ICG 4 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. spectroscopy such a reliable technique [12]. The molecular structure of ICG is shown in Figure 1. ^CH3 F 'gure 1 - C C Indocyanine green /V(CH=CHI3 -CH=< J T (CvHMNaOM. n+ v r ^ (CH2U tCH2> 4 S03 S03Nq Limits o f ICG ICG’s short half-life, while a great advantage, also limits its accuracy. If poor perfusion exists in a subject, uniform mixing of ICG in the bloodstream may not happen before the majority of the tracer has been removed by hepatic clearance. Picker et al report up to a 40% underestimation of blood volume when compared to Evans blue in canines [17]. Pharmacokinetics Compartmental Modeling Upon injection into systemic circulation ICG dye begins to mix until a uniform concentration is reached. This is complicated by the almost immediate onset of elimination by the liver however. Several peaks of continually decreasing size Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (essentially a damped oscillation) are observed as the dye continues to circulate in a state preceding uniform mixing [7]. Thus, as ICG dye mixes in the bloodstream, an exponential decay of ICG is observed. A uniform concentration in the circulating blood volume is generally observed between 3-5 minutes in humans [18]. The precise model of elimination has evolved since ICG appeared in studies in the 1960’s. The simplest representation of systemic blood flow involves a single compartment and an elimination rate constant. Such a model possesses a differential relationship between ICG concentration C(t) and time t whose solution is of the form: C (0 = Ae(~Bt} (2) Where A and B are parameters of the differential solution. While early studies utilized this single compartment model [3,4,7], evidence in recent years has led to more complex compartmental models being adopted. Due to more distal branches o f the circulatory system having lower perfusion rates than larger, more central vessels (such as the aorta and the vena cavae), a theory of fast and slow compartments became commonplace. In its most simple form, two parallel pathways (each consisting of a series of lumped components) represent these two compartments. Models derived from this theory have two sets of parallel paths 6 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. representing the central and peripheral circulations. These recirculatory models allow for differentiation between both central and peripheral compartments (with fast and slow branches) as well as the intra and extra vascular portions of the body [7,10,13,14,16]. This compartmental model is shown in Figure 2. Distribution of blood between the fast and slow compartments is not equal; it has been reported that -30% of the circulating blood volume exists in the central compartment and the fast circuit o f peripheral circulation contains 14% of the remaining 70% [8]. While the vast majority of the body’s blood is contained in the slow peripheral compartment, the fast compartment receives over 50% of the cardiac output [8]. Right Atrial Arterial — I ™ — injection ooooo CV -F a st O O O O O Elimination Clearance CV- Slow Fast Clearance o —looooo P V -F ast U —looooo Slow Clearance P V -Slow Figure 2. A pharmacokinetic compartmental recirculation model o f ICG. Both central (CV) and peripheral (PV) compartments are shown with each having a fa st and a slow circulation path. Mass conservation means that the sum o f the peripheral compartments must equal C. O. [13] 1 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. This study has two goals. First, an automated method for the computation of blood volume from ICG dilution techniques will be created. Second, circulating blood volumes determined from ICG dye fluorescence will be compared to volumes computed from Evans blue dye and ICG dye accuracy will be assessed. 8 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. C hapter 2: M ethods and M aterials Animal Preparation and Instrumental Setup This study adhered to all guidelines set forth by the Institutional Animal Care and Use Committee at the University of Southern California (Los Angeles, California). Instrumentation Nine adult New Zealand White rabbits were anesthetized with isoflourane (concentrations of 1.5% during surgical procedures and 0.8-1.0% during experimentation. Intravenous pancuronium bromide (0.1 mg kg-1 h-1) was used for neuromuscular block purposes; subsequent ventilation was achieved mechanically with pure oxygen via tracheostomy. End tidal partial pressure PC02 (monitored with a capnometer, model 254; Datex, Andover, Ma) was maintained between 32-36 mmHg. Blood pressure was continuously monitored via an 18 gauge catheter inserted into the left axillary artery. Fluid injections were delivered to the left axillary vein. A constant core temperature of 40 degrees Celcius was maintained through heat lamps. The entire experimental setup is shown in Figure 3. 9 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Fluorescence emission Calibration cell measured in cell Lock-in amplifier Excitation light PMT 830 nm interferential filters Lock-in amplifier PMT Laser diode Excitation light Fluorescence emission measured in-vivo Fiber optic probe Beam splitter Thermodilution cardiac output (CO 0.42 l/min Fluorescence dilution cardiac output (CO IC G ) Arterial blood sampling Iced ICG/dextrose injection Figure 3. The instrumental setup is shown. Thermodilution Technique A 4-French thermodilution balloon catheter was inserted into the right femoral vein and extended proximally until the thermistor at the tip was positioned inside the main pulmonary artery. This thermodilution catheter was connected to a cardiac output computer (Sat 2; Baxter, Irvine, Ca); thermodilution cardiac output (C O td) was measured from this. 10 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Animal Positioning and Optical Hardware The rabbit was placed in the prone position. An optic probe was positioned on the surface of the rabbit’s ear. Near-infrared light from a 782-nm laser LED (SRT- F785S; Micro Laser Systems, Garden Grove, CA) at a frequency of 7.7 kHz was emitted onto the blood vessels of the ear through a 400|j,m optic excitation fiber forming the core of a bifurcated multifiber optic probe (R400.7; Ocean Optics, Dunedin, FL). Six 400|im detection fibers surrounded the central excitation fiber and inputted fluorescent signals were then processed through a 830-nm interferential filter (079-2230; OptoSigma, Santa Ana, CA) and finally through a photomultiplier tube (H7732-10; Hamamatsu, Bridgewater, NJ). Further amplification and demodulation via a lock-in amplifier (SR 830; Stanford Research Systems, Sunnyvale, CA) was then achieved. Wavelengths of excitation (720-nm) and detection (830-nm) were used to obtain maximal ICG fluorescence. An articulated manipulator held the optic probe flush with the ear and thus prevented slipping during experimentation. Signal Acquisition A four channel A/D converter module (Powerlab/4SP; AD Instruments, Colorado Springs, CO) enabled for continuous recording and display o f heart rate, arterial blood pressure, expired C 02 concentration, fluorescence, and thermodilution data. Signals were processed on-line via LaBWindows CVI (National Instruments, Austin 11 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. TX) to compute and output estimation of fluorescence dilution cardiac output (COicg). Experimental Protocol ICG Transcutaneous Fluorescence Calibration and Relation to ICG Concentration in the Blood The animal was allowed between 15-20 min to stabilize after surgical preparation. ICG transcutaneous fluorescence required calibration; a 1,000 pg dose of ICG solution (1 mg of 1 ml/mg in 5% dextrose solution) was infused as a bolus through the distal port of the thermodilution catheter. After ICG had homogeneously mixed (between 1-5 min) five 1.5 ml blood samples were drawn and placed into a calibration cell and fluorescence was measured. A linear relationship exists between transcutaneous ICG fluorescence and fluorescence measured directly from the blood. ICG fluorescence has a semi-linear relation to ICG concentration in blood, specifically, in low concentrations (<lpg/ml) in which emission and absorption spectra do not overlap; changes of ICG concentration will result in a linear change of fluorescence. It is in the range that this study was conducted. ICG fluorescence at higher concentrations of ICG (>1 pg/ml) will not increase linearly but instead follow a quadratic model. 12 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Validation o f Circulating Blood Volume Measurement Circulation blood volume using ICG dilution was estimated in nine rabbits (B V icg) while estimates using the Evans blue technique (B V eb) were performed in eight of the subjects. Measurements using both techniques were taken in both baseline (normovolemic) and hypovolemic conditions. Baseline cardiac output was obtained through a combination of thermodilution and fluorescence dilution methods a total of six times (three prior to the introduction of Evans blue and three after). 1.5 ml of iced ICG solution (containing 45 pg of ICG, with 0.3 mg/ml in 5% dextrose solution) was injected as a bolus into the proximal port of the thermodilution catheter. ICG solution was iced since thermodilution techniques were independently being employed to compute C O jd using a cardiac output computer. The ventilator was stopped at end expiration during this injection. Each measurement of C O icg, C O td, and B V icg was separated by an interval of 3-5 minutes to let ICG fluorescence return to baseline. Upon return to baseline, a 1.5 ml sample of arterial blood was drawn to measure central blood hematocrit and to serve as a blank for spectrophotometric measurement (Spectronic 20; Bausch and Lomb, Rochester, NY) that the Evans blue dilution technique required for purposes of plasma optical density. After three successive injections of ICG dye, a 6mg bolus of Evans blue (1 ml of 6 mg/ml solution in 0.9% saline) was added intravenously. Blood samples were withdrawn as 90 s, 3 min, and 5 min intervals after the injection of Evans blue and subsequently centrifuged (3,000 rpm). After mixing samples with 13 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2.5 ml o f distilled water, optical density was measured at 620 nm. Upon the 5 min sampling of Evan blue, three more baseline ICG injections and measurement of C O icg were performed to rule out major shifts in cardiac output due to Evan’s blue injection and blood sampling that may have affected the measurement of blood volume. After completion of baseline readings, blood (30-50 ml) was removed via the arterial catheter until a drop to 28-29 mmHg was observed in Pco2 which was indicative of diminished cardiac output. Calibration of cutaneous ICG vs. blood ICG fluorescence was repeated to determine that hypovolemia had an effect on the scaling factor. As above, six measurements were taken of C O icg, C O td, and B V icg in the hypovolemic condition with three being before the addition of Evans blue dye and three after. Evans blue samples were again taken at 90 s, 3 min, and 5 min. Data Analysis Estimation o f Cardiac Output from ICG and Thermodilution Curves After transcutaneous ICG data was transformed into blood ICG concentration, ICG traces were used to compute C O icg- In addition, C O td was found through traditional thermodilution techniques. 14 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Estimation o f Blood Volume Using the ICG Tracer Technique Upon injection into the inferior vena cava, ICG begins to mix in the bloodstream until it achieves a relatively uniform concentration. As noted above, before complete mixing has occurred, ICG concentration in the blood will appear as a damped oscillation. The interval between peaks can be found and from this circulation time can be reasonably approximated. Once thorough mixing has taken place, an exponential decay in ICG concentration can be observed (See Figure 4). The first ICG circulation peak was considered to begin when the signal exceeded more than two standard deviations of the mean of the baseline. This baseline region was defined as the two seconds preceding the time of ICG injection. ICG injection time (To) was considered to be one half the circulation time before the appearance of the first (and greatest) ICG peak. Once a baseline has been removed from an ICG fluorescence signal and it has been converted into ICG concentration, signal parameters such as circulation time are extracted. Two circulation times (estimated as the time interval between the first and second ICG concentration peaks) beyond the second ICG peak (that is, the first instance o f recirculation) are ignored before an exponential decay is assumed to be present. This exponential decay o f the signal is tracked for 45 seconds. 15 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2.5 A t = 13 sec circ 1 [ICG]0 = 0.21 microg/ml BV™ = 215 ml 1.5 AT, circ E B) Exponential fit 45 sec 0 O [IC G ], 0.5 100 120 20 40 exp Time (sec) Figure 4. ICG concentration is shown with respect to time. Circulation time is computed from taking the peak-to-peak interval; time o f injection is found by finding one half o f the circulation time prior to the first peak. After three circulation times have elapsed an exponential fit is applied and initial concentration can be determined through back-extrapolation at the time o f injection. Least squares regression is used on the exponential portion of the signal. Once coefficients for equation 2 are found, the function can be back extrapolated to the point of injection. Since a uniform mixture of ICG in the blood stream is assumed at this point, blood volume can be found using the equation 1, where m is the injected mass of ICG dye (45pg). The sequence o f steps, from offline signal acquisition to CBV estimation, are shown in Figure 5. 16 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Circulating Blood Volume Computation Load ICG Waveform ICG fluorescence is converted to ICG concentration. Using the exponential decay equation, the concentration of ICG at the time o f injection is computed. A period o f 45 seconds beginning two circulation times after the second ICG peak is fit to an exponential decay using linear regression. A baseline is computed by taking two seconds worth o f data prior to the time of injection and averaging. This value is subtracted from the entire waveform. Determine Peak Representing the first and second ICG passes through the circulatory system. The interval between these peaks corresponds to ICG circulatory * time. Figure 5. Shown are the steps in the algorithm to compute circulating blood volume. An ICG waveform is extracted and loaded into analysis software where the first and second peaks are found. The interval between these two peaks corresponds to circulation time. One half o f this time prior to the first peak is assumed to be the time o f ICG dye injection. ICG fluorescence is then converted into ICG concentration and an interval o f 45 s after two circulation times past the second peak exponential curve fitting is performed by linear regression. This fitted curve is then back-extrapolated to the time o f injection, To, and circulating blood volume is computed. 17 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. All programming was coded in MATLAB; see Appendix 1 for the code in its entirety. MATLAB was chosen primarily for the ease at which imported data could be analyzed and displayed. Estimation o f Evan’ s Blue Blood Volume Evans Blue concentration, estimated from the instant of injection though back- extrapolation methods similar to those used with ICG dye. BVE b was found with the following formula: d t / m E B 1 0 0 / " n B V eb = j— = p • --------------------------- (3) [EB}> 100-0.96x0.9 xH ct where mE B is the injected mass of Evans blue dye, [EB]o is the plasma concentration of Evans blue at the time of injection, and Hct is the central hematocrit. 0.96 and 0.9 are correction factors representing plasma volume between erythrocytes (following centrifugation) and for the microvascular/central hematocrit ratio. Since Evans blue only mixes in the plasma and not in the red blood cells, the calculation o f CBV must be adjusted to account for not including the hematocrit. 18 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Analysis o f Variables (ANOVA) ANOVA is a technique in which a linear model is used to compute regression and to analyze variance in a number of variable factors. This linear model is shown in equation 4: y = Xp + s (4) Where y is an n-by-1 vector of observations of the response variable, X is an n-by-p matrix determined by the predictors, P is p-by-one vector of parameters, and e is an n-by-1 vector of normally distributed random disturbances or noise [19]. In this study, y is equal to the predicted blood volumes B V icg and B V eb, X is equal to the correlation terms used to compute the p-values. Bland-Altman Plotting Plotting techniques were developed by Bland and Altman for assessing the agreement between two distinct methods of clinical measurement. Means for both measurement techniques are computed and then averaged for each subject. These averaged values are then plotted against the difference between the means of each measurement technique. A complete plot will display the average of the means for multiple subjects on the abscissa, while showing the specific difference in a subjects mean measurements on the ordinate. In a study where both techniques yielded very 19 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. similar measurements and there was little variation among subjects a Bland-Altman plot would consist of a series of points clustered around one value on the abscissa and would have values close to zero on the ordinate [1] 20 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 3: Results Mean baseline and bleeding volumes determined through ICG and EB techniques (B V jcg and B V eb) fo r all subjects. Each animal’s blood volume was computed a total of six times with the ICG technique for each separate condition (normovolemia and hypovolemia). Three estimates were found before the injection of Evans blue and three were found after its introduction to the circulatory system. The final two measurements of the baseline before the injection of Evans blue and the first two measurements after its infusion were used to compute a mean estimated blood volume for each animal. Other measurements were discarded because each trial had differing quantities of samples and a consistent approach was desired when comparing computed blood volumes between all the animals. A similar method was used when bleeding was performed. The mean estimated blood volumes using both the ICG and EB techniques for all animals are shown in Table 1. 21 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. BASELINE (ml) BLEEDING (ml) ICG 210 .19 + /-3 1.1 0 157 .40 + /-2 8.0 3 EB 198.72+/-17.64 173.89+/- 19.97 Table 1. Mean blood volumes using both ICG and EB techniques. Values fo r eight rabbits are shown.. Both baseline and bleeding conditions are given. Mean ICG baseline data exceeds that o f baseline EB data, whereas in the case o f bleeding these observations are reversed. Relation between ICG blood volume (B V jcq) and body mass. As body mass increases it is reasonable to assume that CBV would as well. Due to the fluctuation of mass from rabbit to rabbit, it made more sense to interpret the relation between blood volume and body mass as a ratio. These ratios are shown in Figure 6. The four BVicg estimates used earlier for each condition were averaged and the mean of this value and the single B V eb measurement were used as the single blood volume measurement for each animal. 22 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Ratio of Baseline Circulating Blood Volume to Body Mass 90.00 80.00 ® 70.00 1 60.00 I 2 50 00 > 5 5 - 2 40.00 * S 30.00 0Q J 2Q00 1 1Q00 “ 0.00 1 2 3 4 5 6 7 8 Subject Figure 6. Each animal’ s normovolemic blood volume (in ml) was divided by its mass, measured in kg. This allows fo r a more meaningful comparison o f the two parameters when comparing all animals used in this study. Mean animal mass was 3.197 kg, ± 0.399. Blood volume was determined by averaging B V icg (from the four values used earlier) and B V eb- Comparison between B V icg and B V eb in both Normovolemia and Hypovolemia Direct comparison of the mean BV icg and BVE b for each animal is shown in Figure 7. Linear regression reveals and hypovolemic conditions, through the origin. 23 similar relationships between both the normovolemic Regression lines were computed such that they pass Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Mean ICG vs EB Blood Volume Estimates 240.00 -i ♦ Baseline ■ Bleeding — Linear (Baseline) — Linear (Bleeding) y = 1.0663x y = 0.8942x in 120.00 ------------------------------------------------------------------------ 100.00 - I , -----------------, ------------------------- , 100.00 150.00 200.00 250.00 300.00 Mean ICG Blood Volume Figure 7. Mean BVicg and BVeb data from each animal were directly compared. Similar linear relations exist between normovolemic and hypovolemic conditions. Trendlines have been computes such that they pass through the origin. Figure 8 shows the difference between the average BVicg and BVeb plotted as a function of the mean o f the averaged of BVicg and BVE b- This Bland-Altman plot allows for visualization of how greatly measurements of ICG and Evans blue dilution differ from each other and at the same time demonstrates how variable each CBV measurement can be from subject to subject [1], 24 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Mean of ICG and EB Volume Estimates vs. Difference of ICG and EB Volume Estimates 60 40 1 3 § 20 o n o u -20 *5 0 200 250 300 100 -60 -80 (BVicg +BVffiy2 ♦ Baseline ■ Bleeding Figure 8. The difference between the average o f B V icg and B V eb is plotted as a function o f the mean o f the averages o f the B V icg and B V eb [1]. Analysis o f Variables (ANOVA) In order for analysis of variable (ANOVA) to be possible, an equal number of B V eb data were needed to directly compare to the BVicg counterparts. Since only one BVeb was obtained for each normovolemic and hypovolemic state per animal, synthetic data was created. It was assumed the coefficient of variation was the same for both the ICG and E B methods and that the B V eb obtained would serve as a mean. From this, standard deviation could be computed for each animal and normalized BVeb data could be generated. The median coefficient of variation from the ICG data across all animals was chosen to compute standard deviations for the E B data to be created. 25 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Three independent variables were used for ANOVA. ‘Rabbit’ refers to the fact that each subject was o f a differing mass and this might possibly be a factor influencing CBV estimation. ‘Method’ (ICG or EB used as the dilution agent) and ‘Condition’ (normovolemic or hypovolemic) were the remaining two independent variables used for ANOVA. ANOVA results are shown in Tables 2 and 3. When the factors used in the analysis were assessed, it was found that ICG and Evans blue do not significantly differ (P = 0.395). P-values for each rabbit and condition (both zero) indicate that those factors significantly affected the blood volume measurements, as would be expected. A total of sixteen CBV estimates from each subject were used in the ANOVA. Eight of these CBV estimates were values obtained using ICG dilution and the remaining eight utilized the Evans blue technique. For the eight ICG CBV estimates, four were from the normovolemic condition and four were from the hypovolemic condition. EB data was grouped in the same manner. Three-variable ANOVA with interactions (Table 2b) yields similar results as above for each individual factor; rabbit and condition have p-values equaling zero and method is not significant as above (p = 0.2313). Interactions between rabbit and method and condition and method have p-values equal to zero indicating that each individual rabbit’s mass is variable enough to result in significant differences when 26 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. interacted with method; a similar relation exists when interacting method and condition. Rabbit and condition have a p- value of 0.0783, which falls outside of the P < 0.05 boundary. Thus, the interaction of rabbit and condition is not significant. In order to account for a potential systematic difference in the ICG and Evans Blue dilution techniques, a new variable was created where V = mean(BVicG) - BVeb- Establishing this new quantity effectively removed one variable from the ANOVA computations. These ANOVA computations are shown in Table 3. AVOVA data for two variables shows that the p-value for the rabbit was 0.0245, indicating volume data depends significantly on the rabbit in question. Condition has a p-value of 0.0092, meaning normovolemia or hypovolemia plays a significant role in predicting blood volumes. These findings remain consistent with those above in the ANOVA where the difference between ICG and Evans blue dilution was not taken into account. When interactions are considered (Table 3b), condition becomes significant, as its p- value falls to 0.0338. Rabbit becomes marginally significant (p = .0522) and the interaction of rabbit and condition is not significant (p = .6353). 27 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Source Sum Sq. d.f. Mean Sq. F Prob > F Rabbit 41849.5 7 14.39 0 Method 302.8 1 0.73 0.395 Condition 49371.1 1 118.85 0 Error 49016.1 118 415.4 Total 140539 127 Table 2a. Shown here are the ANOVA values fo r three factors: Rabbit, Method, and Condition. Source Sum Sq. d.f. Mean Sq. F Prob > F Rabbit 41849.5 7 5978.5 28.63 0 Method 302.8 1 302.8 1.45 0.2313 Condition 49371.1 1 49371.1 236.43 0 Rabbit and Method 18896.7 7 2699.1 12.93 0 Rabbit and Condition 2765.6 7 395.1 1.89 0.0783 Method and Condition 5845.3 1 5845.3 27.99 0 Error 21508.5 103 208.8 Total 140539.6 137 Table 2b. Shown here are the ANOVA values fo r three factors (Rabbit, Method, and Condition) with interactions. 28 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. source Sum Sq. d.f. Mean Sq. F Prob > F rabbit 8682.9 7 1240.41 5.03 0.0245 condition 3126.3 1 3126.29 12.68 0.092 error 1725.6 7 246.51 total 13534.7 15 Table 3a. Shown here are the ANOVA values fo r two factors (Rabbit and Method) when analyzing the mean difference o f the ICG and Evans Blue tracer techniques. source Sum Sq. d.f. Mean Sq. F Prob > F rabbit 5725.7 3 1908.57 3.99 0.0522 condition 3126.3 1 3126.29 6.53 0.0338 rabbit and condition 855.2 3 285.07 0.6 0.6353 error 3827.5 8 478.44 total 13534.7 15 Table 3b. Shown here are the ANOVA values fo r two factors (Rabbit and Method) with interactions when analyzing the mean difference o f the ICG and Evans Blue tracer techniques. 29 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 4: Discussion The main findings of this study were that: (1) circulating blood volume could be repeatedly estimated from serial ICG fluorescence traces which are linearly related to the concentration o f the ICG tracer in the circulating blood volume and that (2) circulating blood volumes estimated from the ICG tracer technique are comparable to those obtained from Evans blue. Comparing Circulating Blood Volumes Estimated from ICG Dilution and Evans Blue Dilution Techniques Mean CBV obtained from ICG dilution during a state of normovolemia slightly exceeds that of estimates from use of Evans blue dilution (Table 1). This relationship is reversed when the animal is bled and hypovolemia occurs. Table 3a shows that condition is significant when looking at the difference of the two tracer techniques so such a discrepancy can be explained due to this. One possible explanation for this may be that when bleeding occurs portions of the circulatory system become shunted off via vasoconstriction in an attempt to maintain blood pressure ( and thus less perfusion can occur, especially when the life of ICG in the bloodstream is so short). When perfusion (and thus total mixing) is decreased from vasoconstriction, the concentration of ICG detected through fluorescence will increase and a lower volume o f circulating blood volume will be estimated. In summary, hypovolemic conditions cause an increase in vasoconstriction and ICG 30 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. dye will have a reduced perfusion in the periphery leading to an underestimation in CBV. Underestimation of CBV due to hypovolemia would present less of a problem with Evans blue, since it is not being eliminated at any relevant rate and may have more time to perfuse into isolated blood vessels. Direct comparison between each measured Evans blue volume estimation and the mean of four ICG volume estimates was presented in Figure 7. Despite low R values, especially for the hypovolemic case, a fair linear relationship between BVicg and B V eb was found. It should be noted that data from one rabbit was not used as it had abnormally high B V eb values and an error on calibration was suspected. ANOVA shows that there is no significant difference between B V icg and B V eb- In a three-variable ANOVA, subject and condition were shown to be significantly different, as would be expected. When interactions between the three variables were included, only the condition and subject interaction were not found to be significantly different (aside from method by itself). This is puzzling, as both subject and condition were independently significantly different. One explanation might be that the change in estimated circulating blood volume might change in such a very predictable way relative to body mass as the subject is bled. It is interesting that method changes as it is interacted with both method and condition such that together they are significantly different. This may be interpreted as the differences between subjects or in the change in blood volume estimation accompanying a shift from 31 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. normovolemia to hypovolemia far exceed any similarity between estimates obtained from the two dilution methods. The difference between ICG and Evans blue CBV estimates was next looked at in an ANOVA format. The possibility of there being a systematic difference between techniques was best approached with this strategy. Once again, both the subject and the condition proved to be significantly different with p-values falling below 0.05. Clinical Significance Accurate and rapidly repeatable circulating blood volume measurement provides physicians with a means of dependably tracking a patient’s internal circulatory stability in the event of trauma. Current clinical techniques rely on tracers such as Evans blue that while minimally invasive, remain in the body for a period on the order of days. Thus, a tracer such as ICG with a half-life orders of magnitude lower than alternative tracers can be utilized in serial applications and a patient’s CBV can be analyzed over the course o f a surgery and recovery. In addition to ICG dilution being minimally invasive, equipment to measure its fluorescence need only make contact with thin and moderately vascularized regions of the skin. Examples of this include the nose, the ear, and possibly the finger - a region in which pulse oximeters have already capitalized on. Such a fixture could be positioned with little-to-no interference during surgery and subsequent recovery. 32 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Another potential use of the ICG tracer technique in computing CBV is replacing the often inaccurate method of using pulmonary capillary wedge pressure as an approximation for blood volume. Since this method is at best a crude estimate and does not take into account factors such as temperature fluctuations or chronic abnormalities in blood pressure, the ICG tracer alternative would be a much more dependable and versatile replacement. 33 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. References 1. Bland JM, Altman DG. Statistical methods fo r assessing agreement between two methods o f clinical measurement. Lancet. 1(1986). 307-310. 2. Bloomfield, DA and Khan AMS. Measurement o f blood volume and effusion volume. NAME OF BOOK. 231-243. 3. Bradley EC, Barr JW. Determination o f blood volume using indocyanine green (cardio-green) dye. Life Sciences. 7 (1968). 1001-1007. 4. Haller M, Akbulut C, et al. Determination o f plasma volume with indocyanine green in man. Life Sciences. 53 (1993). 1597-1604. 5. Haneda K, and Horiuchi T. A method fo r measurement o f total circulating blood volume using indocyanine green. Tohoku Journal of Experimental Medicine. 148 (1986). 49-56. 6. Haruna M, Kumon K, et al. Blood volume measurement at the bedside using ICG pulse spectroscopy. Anesthesiology 89(61 (19981. 1322-1328. 7. He Y, Tanigami H, et al. Measurement o f blood volume using indocyanine green measured with pulse spectrophotometry: its reproducibility and reliability. Critical Care Medicine. 26(1998). 1446-1451. 8. Henthom TK, Avram MJ, et al. Minimal compartmental model of circulatory mixing of indocyanine green. American Journal of Physiology. (1992). H903-910. 9. Iijima T, Aoyagi T, et al. Cardiac output and circulating blood volume analysis by pulse dye-densitometry. Journal of clinical monitoring. 13 (1997). 81-89. 34 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 10. Iijima T, Iwao Y, Sankawa H. Circulating blood volume measured by pulse /?/ • dy e-densitometry: comparison with I-HAS analysis. Anesthesiology. 89 (1998). 1329-1335. 11. Imai T, Mitaka C, et al. Accuracy and repeatability o f blood volume measurement by pulse dye densitometry compared to the conventional method using 5 1 Cr-labeled red blood cells. Intensive Care Medicine. 26 (2000). 1343-1349. 12. Ishihara H, Iwakawa T, et al. Does indocyanine green accurately measure plasma volume independently o f its disappearance rate from plasma in critically ill patients? Intensive Care Medicine 25 (1999). 1252-1258. 13. Kuipers J, Boer F, et al. First-pass lung uptake and pulmonary clearance o f propofol. Anesthesiology. 91 (1999). 1780-1787. 14. Kuipers J, Boer F, et al. Recirculatory pharmacokinetics and pharmacodynamics o f rocuronium in patients: the influence o f cardiac output. Anesthesiology 94 (2001). 47-55. 15. Maarek, JI, Holschneider DP, Harimoto J. Fluorescence o f indocyanine green in blood: intensity dependence on concentration and stabilization with sodium polyaspartate. Journal of Photochemistry and Photobiology B: Biology. 65 (2001). 157-164. 16. Niemann CU, Henthom TK, et al. Indocyanine green kinetics characterize blood volume and flow distribution and their alteration by propranolol. Clinical Pharmacology and Therapeutics. 67 (2000). 342-350. 17. Picker O, Wietasch G, Scheeren TWL, Arndt JO. Determination o f total blood volume by indicator dilution: a comparison o f mean transit time and mass conservation principle. Intensive Care Medicine. 27 (2001). 767-774. 18. Sekimoto M, Fukui M, and Fujita K. Plasma volume estimation using indocyanine green with biexponential regression analysis of decay curves. Anaesthesia. 52(1997). 1166-1172. 35 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 19. The Mathworks. © 1994-2005. Statistics Toolbox: Linear Models. Retrieved 8/30/05 from http://www.mathworks.com/access/helpdesk/help/toolbox/stats/stats.html. 20. Thiessen JJ, Rappaport PL, and Eppel JG. Indocyanine green pharmacokinetics in the rabbit. Canadian Journal of Physiological Pharmacology. 62(1984). 1078-1085. 36 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Appendix: MATLAB Source Code flour oanalysis. m This program is designed to receive ICG fluorescence waveforms and analyze them. After analysis predictions on CBV are made from unconstrained nonlinear optimization In the form o f the fminsearch function. %MODIFIED PORTION OF CURVE TO BE ANALYZED FOR EXPONENTIAL APPROXIMATION 2/13/05 clear all close all %Load waveform. Make sure to set directory to correct folder. global concentration; global concentration2; global exp_start2; global exp_end2; Ear_flou = load('il.txf); % Samping rate of signal sampling_rate = 100; %injected dose (in micrograms) injected_dose = 45; %scale time vector time - (l:length(Ear_flou))./sampling_rate; plot(time,Ear_flou) hold on %Find peak of first passing of dye max_peak = m ax(Earflou); max_peak_index = find(Ear_flou=: =(max(Ear_flou))); %In case peak is a plateau max_peak_index = max_peak_index(l); % Identify secondary peak - Begin searching 250 points after the first peak 37 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. i = 0; for i = (max_peak_index + 90):(length(Ear_flou)-90) ir flou(i) >= Ear_flou(i+l) & E arjlou(i) >= Ear_flou(i+2) & E arflo u )> = Ear_flou(i+3) & Earflou(i) >= Ear_flou(i+4) & E arflo u >= Ear_flou(i+5) & Ear_flou(i) >= Ear_flou(i+6) & E arflo u >= Ear_flou(i+7) & E arflou(i) >= Ear_flou(i+8) & Ear_flou(i >= Ear_flou(i+9) & Ear_flou(i) >= Ear_flou(i+10) &Ear_flou >= Ear_flou(i+ll) & E arflou(i) >= Ear_flou(i+12) &Ear_flou >= Ear_flou(i+13) &Ear_flou(i) >= Ear_flou(i+14) & E arflou >= Ear_flou(i+15) & Ear_flou(i) >= Ear_flou(i+16) & E arflo u >= Ear flou(i+17) & Earflou(i) >= Ear_flou(i+18) & E arflou >= Ear_flou(i+19) & E arflou(i) >= Ear_flou(i+20) & E arflou >= Ear flou(i+21) & Ear_flou(i) >= Ear_flou(i+22) & E arflou >= Ear_flou(i+23) & E arflou(i) >= Ear_flou(i+24) & Ear_flou >= Ear_flou(i+25) & Earflou(i) >= Ear_flou(i+26) & E arflou >= Ear_flou(i+27) & E arflou(i) >= Ear_flou(i+28) & E arflou >= Ear_flou(i+29) & Ear_flou(i) >= Ear_flou(i+30) & E arflo u >= Ear flou(i+31) & Ear_flou(i) >= Ear_flou(i+32) & E arflo u >= Ear_flou(i+33) & E arflou(i) >= Ear_flou(i+34) & E arflou >= Ear flou(i+35) & E arflou(i) >= Ear_flou(i+36) & E arflou >= Ear_flou(i+37) & E arflou(i) >= Ear_flou(i+38) & E arflou >= Ear_flou(i+39) & E arflou(i) >= Ear_flou(i+40) &Ear_flou >= Ear_flou(i+41) & Ear_flou(i) >= Ear_flou(i+42) &Ear_flou >= Ear_flou(i+43) &Ear_flou(i) >= Ear_flou(i+44) & E arflou >= Ear_flou(i+45) & Ear_flou(i) >= Ear_flou(i+46) & E arflo u >= Ear flou(i+47) & E arflou(i) >= Ear_flou(i+48) & Ear_flou >= Ear_flou(i+49) & Earflou(i) >= Ear_flou(i+50) & E arflou >= Ear_flou(i+51) & E arflou(i) >= Ear_flou(i+52) & E arflou >= Ear_flou(i+53) & E arflou(i) >= Ear_flou(i+54) & E arflo u >= Ear_flou(i+55) & Ear_flou(i) >= Ear_flou(i+56) & E arflo u >= Ear_flou(i+57) & E arflou(i) >= Ear_flou(i+58) & E arflou >= Ear_flou(i+59) & Ear_flou(i) >= Ear_flou(i+60) & E arflo u >= Ear_flou(i+61) & Ear_flou(i) >= Ear_flou(i+62) & E arflou >= Ear_flou(i+63) & E arflou(i) >= Ear_flou(i+64) & E arflo u >= Ear_flou(i+65) & Ear flou(i) >= Ear_flou(i+66) & E arflou >= Ear_flou(i+67) & E arflou(i) >= Ear_flou(i+68) & E arflou >= Ear_flou(i+69) & Ear_flou(i) >= Ear_flou(i+70) &Ear_flou >= Ear_flou(i+71) & E arflou(i) >= Ear_flou(i+72) &Ear flou >= Ear_flou(i+73) &Ear_flou(i) >= Ear_flou(i+74) & E arflou >= Ear_flou(i+75) & E arflou(i) >= Ear_flou(i+76) & E arflou >= Ear_flou(i+77) & Ear_flou(i) >= Ear_flou(i+78) & E arflo u >= Ear_flou(i+79) & E arflou(i) >= Ear_flou(i+80) & E arflo u >= 38 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Ear_flou(i+81) & E arflou(i) >= Ear_flou(i+82) & Ear_flou(i) >= Ear flou(i+83) & Ear_flou(i) >= Ear_flou(i+84) & Ear flou(i) >= Ear_flou(i+85) & E arflou(i) >= Ear_flou(i+86) & Ear flou(i) >= Ear_flou(i+87) & E arflou(i) >= Ear_flou(i+88) & Ear_flou(i) >= Ear_flou(i+89) & Ear_flou(i) >= Ear_flou(i+90) & E arflou(i) > Ear_flou(i- 1) & Ear_flou(i) > Ear_flou(i-2) & Ear_flou(i) > Ear_flou(i-3) & E arflou(i) > Ear_flou(i-4) & E arflou(i) > Ear_flou(i-5) & Ear flou(i) > Ear_flou(i-6) & Ear_flou(i) > Ear flou(i-7) & Ear_flou(i) > Ear_flou(i-8) & Ear flou(i) > Ear_flou(i-9) & Ear_flou(i) > Ear flou(i-lO) & Ear_flou(i) > Ear_flou(i-ll) & Ear flou(i) > Ear_flou(i-12) & Ear flou(i) > Ear_flou(i-13) & Ear flou(i) > Ear_flou(i-14) & Ear flou(i) > Ear_flou(i-15) & Ear flou(i) > Ear_flou(i- 16) & Ear_flou(i) > Ear_flou(i-17) & Ear flou(i) > Ear_flou(i-18) & Ear_flou(i) > Ear_flou(i-19) & Ear flou(i) > Ear_flou(i-20) & Ear_flou(i) > Ear_flou(i-21) & Ear_flou(i) > Ear_flou(i-22) & Ear flou(i) > Ear_flou(i-23) & Ear flou(i) > Ear_flou(i-24) & Ear_flou(i) > Ear_flou(i-25) & Ear flou(i) > Ear_flou(i-26) & Ear_flou(i) > Ear_flou(i-27) & Ear flou(i) > Ear_flou(i- 28) & Ear flou(i) > Ear_flou(i-29) & Ear flou(i) > Ear_flou(i-30) & Ear flou(i) > Ear_flou(i-31) & Ear flou(i) > Ear_flou(i-32) & Ear_flou(i) > Ear_flou(i-33) & Ear flou(i) > Ear_flou(i-34) & Ear_flou(i) > Ear_flou(i- 35) & Ear_flou(i) > Ear_flou(i-36) & Ear_flou(i) > Ear_flou(i-37) & Ear flou(i) > Ear_flou(i-38) & Ear flou(i) > Ear_flou(i-39) & Ear_flou(i) > Ear_flou(i-40) & Ear_flou(i) > Ear_flou(i-41) & Ear flou(i) > Ear_flou(i-42) & Ear flou(i) > Ear_flou(i-43) & Ear flou(i) > Ear_flou(i-44) & Ear flou(i) > Ear_flou(i-45) & Ear flou(i) > Ear_flou(i-46) & Ear flou(i) > Ear_flou(i- 47) & Ear flou(i) > Ear_flou(i-48) & Ear flou(i) > Ear_flou(i-49) & Ear flou(i) > Ear_flou(i-50) & Ear_flou(i) > Ear_flou(i-51) & Ear_flou(i) > Ear_flou(i-52) & Ear flou(i) > Ear_flou(i-53) & Ear_flou(i) > Ear_flou(i-54) & Ear flou(i) > Ear_flou(i-55) & Ear flou(i) > Ear_flou(i-56) & Ear flou(i) > Ear_flou(i-57) & Ear flou(i) > Ear_flou(i-58) & Ear flou(i) > Ear_flou(i- 59) & Ear_flou(i) > Ear_flou(i-60) & Ear flou(i) > Ear_flou(i-61) & Ear_flou(i) > Ear_flou(i-62) & Ear_flou(i) > Ear_flou(i-63) & Ear_flou(i) > Ear_flou(i-64) & Ear flou(i) > Ear_flou(i-65) & Ear_flou(i) > Ear_flou(i- 66) & Ear flou(i) > Ear_flou(i-67) & Ear flou(i) > Ear_flou(i-68) & Ear_flou(i) > Ear_flou(i-69) & Ear_flou(i) > Ear_flou(i-70) & Ear_flou(i) > Ear_flou(i-71) & Ear_flou(i) > Ear_flou(i-72) & Ear flou(i) > Ear_flou(i-73) & Ear_flou(i) > Ear_flou(i-74) & Ear_flou(i) > Ear_flou(i-75) & Ear flou(i) > Ear_flou(i-76) & Ear flou(i) > Ear_flou(i-77) & Ear_flou(i) > Ear_flou(i- 78) & Ear flou(i) > Ear_flou(i-79) & Ear flou(i) > Ear_flou(i-80) & Ear flou(i) > Ear_flou(i-81) & Ear flou(i) > Ear_flou(i-82) & Ear_flou(i) > Ear_flou(i-83) & Ear_flou(i) > Ear_flou(i-84) & Ear flou(i) > Ear_flou(i-85) & Ear flou(i) > Ear_flou(i-86) & Ear_flou(i) > Ear_flou(i-87) & Ear_flou(i) > Ear_flou(i-88) & Ear_flou(i) > Ear_flou(i-89) & Ear flou(i) > Ear_flou(i- 90) 39 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. second_peak_index(i) = 1; else secondj3eak_index(i) = 0; end end second_peak_index = find(second_peak_index = 1); %Find true 2nd maxpeak true_second_peak = max(Ear_flou(second_peak_index)); true_second_peak_index = second_peak_index(find(EarjElou(second_peak_index)=: =true_second_peak) ); true_second_peak_index = true_second_peak_index(l); %FIND CIRCULATION TIME circulation_time = true_second_peak_index - max_peak_index; circulationtim eseconds = circulation time/sampling rate h a lfc irc tim e = floor(circulation_time/2); tim ezero in d ex = max_peak_index - half circ time; %COMPUTE BASELINE %%%Choose two seconds to use for first calculation firstbaselinestart = time_zero_index-201; first baseline end = time_zero_index -1 ; %first_baseline_start =1 ; %first_baseline_end = 200 ; firstbaseline = [first_baseline_start:first_baseline_end]; m eanfirstbaseline = mean(Ear_flou(firstbaseline)); stdfirstbaseline = std(Ear_flou(firstbaseline)); new_baseline_criteria = mean_first_baseline + (2 * std first baseline); j = 0; 40 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. % find true baseline value % (loop runs backwards) for j= (firstbaselinestart + 150):max_peak_index if Ear_flou(j) < new baseline criteria baseline_matrix(j) = 0; else baseline_matrix(j)=l; end end truebaselinevalues = find(baseline_matrix = 1 ) ; truebaselineindex = true_baseline_values(l); % curve departs baseline here % plot(966/sampling rate,Ear flou(966),’ kv') % hold on % plot(l 166/sampling_rate,Ear_flou( 1166),'kv') % hold on true_baseline = mean(Ear_flou((true_baseline_index-200) :true_baseline_index)); zero ed earflo u = Earflou-truebaseline; plot(time,Ear_flou) hold on plot(max_peak_index/sampling_rate,max_peak,'rv') hold on plot(true_second_peak_index/sampling_rate,Ear_flou(true_second_peak_mdex),'rv') hold on plot(true_baseline_index/sam plingrate,Earflou(truebaselineindex),'rv') hold on plot(time, zeroed_ear_flou,'r') % % % convert data from flouro into concentration % scaling =1.036190455; 41 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. linearterm =0.76888304; quadraticterm = 0.055460758; scaleddata = z e ro ed e arflo u * scaling; concentration = (linear term .* scaled_data) + ((quadratic_term .* scaled_data).A 2); figure(2) plot(time, concentration) % find 3 mixing times into curve %CHANGED TO 2 MIXING TIMES 2/13/05 %exp_startl = (floor(circulation_time/2)) + 3 * circulation_time; %NUMBER OF CIRCULATION TIMES AFTER SECOND PEAK TO BE IGNORED BEFORE EXPONENTIAL APPROXIMATION circnum ber = 2; ex p startl = ((circ_number*circulation_time) + truesecondjpeakindex); e x p en d l = exp_startl + (45*sampling_rate); concentration2 = concentration(exp_startl:exp_endl); exp_end2 = e x p e n d l-(e x p sta rtl-l); exp_start2 = exp_startl - (exp_startl-l); x = fminsearch(@expfit2,[l 0.1]) time2 = exp_start2:exp_end2; fitted_line(exp_startl:exp_endl) = x(l)*exp(-(x(2)).*(time2)); %need to backtrack, put in negative time numbers to generate the rest of the curve backtrack time length = length(l:(exp_startl -1)); backtracktim e = (-backtrack_time_length +1):1:0; fitted_line(l:(exp_startl -1)) = x(l)*exp(-(x(2)).*(backtrack_time)); hold on 42 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. fittime = (1 :exp_endl)/100; plot(fit_time, fitted_line,'r') hold on concentrationtim ezero = fitted_line(time_zero_index) plotftime zero index/sampling rate,concentrationtime zero,'gv') volume_prediction = injected_dose/concentration_time_zero expfit.m This function is the subfuction called by the main program and in specific the fminsearch function. It returns the local minimums for most accurate exponential curvefitting. function f = expfit2(x) global concentration global concentration global exp_start2 global exp_end2 f=0; k = 0; time_vector= [exp_start2:exp_end2]; for k = 1 :length(time_vector) y(k) = x(l) * exp(-(x(2))*(k)); f = f + ((y(k) - concentration2((k)))A 2); end icganova.m This function performs ANOVA on computed CBV estimates for three inputted variables. clear all close all 43 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. %Load in output data, in this case the computed blood volumes y= load('volumes.txt'); %load in factors %Factor A - animal number gl = load('rabbitn umbers.txt'); %Factor B - method used (ICG or EB) g2 = importdata('methods.txt'); %Factor C - condition - baseline or bleeding g3 = importdataCconditions.txt'); pi = anovan(y, {gl g2 g3} ) p2 = anovan(y, {gl g2 g3} ,'interaction') icganova2factor2. m This program is similar to icganova.m except it runs ANOVA analysis for two variables. clear all close all %Load in output data, in this case the computed blood volumes %in a matrix table anovamatrix = loadCanova2factormatrix.txt'); pi = anova2(anovamatrix) p2 = anova2(anovamatrix,2) y= loadCICG_minus_EB_volumes.txt'); %load in factors %Factor A - animal number gl = load('ICG_minus_EB_rabbitnumbers.txf); 44 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. %Factor B - condition - baseline or bleeding g2 = importdataCICG_minus_EB_conditions.txt'); p3 = anovan(y, {gl g2}) p4 = anovan(y, {gl g2},'interaction') Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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Asset Metadata

Creator Sullivan, Matthew Paul (author)

Core Title Computation and validation of circulating blood volume with the indocyanine green dilution technique

School Viterbi School of Engineering

Degree Master of Science

Degree Program Biomedical Engineering

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag engineering, biomedical,OAI-PMH Harvest

Language English

Contributor Digitized by ProQuest (provenance)

Advisor Maarek, Jean-Michel (committee chair), D'Argenio, David (committee member), Holschneider, Daniel (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-49063

Unique identifier UC11338109

Identifier 1435092.pdf (filename),usctheses-c16-49063 (legacy record id)

Legacy Identifier 1435092.pdf

Dmrecord 49063

Document Type Thesis

Rights Sullivan, Matthew Paul

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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 au...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA