Diagnosing cardiac disease states using a minimal cardiovascular model Christina Starfinger Wednesday, 21 March 2007 HRSC Scientific Meeting

IntroductionModelIdentificationPredictionResultsPeople Current Clinical Process Measure limited clinical data Diagnose based on data trends observed and approximate mental models of physiological function and pathology – typically based on an “average patient” Treat by selecting a therapy based on standard protocols or methods for diagnosed disease state Problems: –No means of aggregating often conflicting data into a clear picture –Many disease states show very similar measurements clinically confusing the diagnostic picture –Reflex actions can mask disease states until they are acute –Limited measurements are typically used (“task overload”)

IntroductionModelIdentificationPredictionResultsPeople Model-Based Solution 1.Identify patient specific parameters from measured clinical data to create patient specific CVS model 2.Diagnose disease state from patient-specific parameter values 3.Treat using patient-specific model to predict results of different therapeutic strategies/interventions (Therapy Decision Support) Outcomes:  Real-time, patient specific CVS model  Assistance in diagnoses and therapy selection  Uses data and catheters typically found in the ICU

IntroductionModelIdentificationPredictionResultsPeople Model Pulmonary circulation Systemic circulation Left ventricle (LV) Right ventricle (RV) Vena Cava, Aorta, Pulmonary artery/vein

IntroductionModelIdentificationPredictionResultsPeople Model Left ventricle Right ventricle Pulmonary Artery Pulmonary Vein Aorta Vena Cava Smith, BW, Chase, JG, Shaw, GM and Nokes, RI (2006). “Simulating Transient Ventricular Interaction Using a Minimal Cardiovascular System Model,” Physiological Measurement, IOP, Vol 27, pp

IntroductionModelIdentificationPredictionResultsPeople Identification Problem Measurements Required: 1.Max/Min Pressure in aorta (SAP, DAP) 2.Max/Min Pressure in pulmonary artery (SPAP, DPAP) 3.Max/Min Volume in left ventricle (LVEDV,LVESV) 4.Max/Min Volume in right ventricle (RVEDV,RVESV) 5.Heart Rate Model Parameters Identified: Integral-based methods Lav,Lmt,Ltc,Lpv,Eeslvf,Eesrvf,Polvf,Porvf,Rav,Rmt,Rtc,Rpv, Eao,Epa,Evc,Epu,Rsys, Rpul  Measurements required are a minimal set compared to the total number of parameters identified in the model Given or estimated Find Hann, CE, Chase, JG and Shaw, GM (2006). “Integral-based Identification of Patient Specific Parameters for a Minimal Cardiac Model,” Computer Methods and Programs in Biomedicine, Vol 81(2), pp

IntroductionModelIdentificationPredictionResultsPeople Diagnosis from ID Parameters: Lav,Lmt,Ltc,Lpv,Eeslvf,Eesrvf,Polvf,Porvf,Rav,Rmt,Rtc,Rpv, Eao,Epa,Evc,Epu,Rsys, Rpul Contractilities, increased during increased sympathetic activity (HR ↑) Resistances for 4 heart valves, increased for stenosis Pulmonary vascular resistance and systemic vascular resistance, increased for example in PE, and PHT

IntroductionModelIdentificationPredictionResultsPeople Pulmonary Embolism Experiment: 6 pigs, 32.75kg ± 1.83kg pulmonary embolization induced with autologous blood clots clots were injected every two hours with decreasing concentrations Measurements: aortic pressure (P ao ) and pulmonary artery pressure (P pa ) are measured using micromanometer-tipped catheters (Sentron pressure-measuring catheter;Cordis, Miami, FL) right and left ventricle pressures and volumes (V lv,V rv,P lv,P rv ) are measured using 7F, 12 electrodes (8-mm interelectrode distance) conductance micromanometer tipped catheters (CD Leycom, Zoetermeer, The Netherlands) Goal: Accurate identification at all stages of induced embolism with no false parameter value changes Data from Ghuysen et al, Hemodynamics Laboratory, Univ of Liege

IntroductionModelIdentificationPredictionResultsPeople Results for PE (ID) - 1 Left ventricle (30 mins)Right ventricle (30 mins) Errors ~5% and all peak and stroke values captured

IntroductionModelIdentificationPredictionResultsPeople Results for PE (ID) - 2 Pulmonary resistance (Pig 2) Reflex actions (Pig 2) Rpul – All pigs Right ventricle expansion index (RVEDV/LVEDV, Pig 2)

IntroductionModelIdentificationPredictionResultsPeople PEEP Titrations Experiment: 6 pigs (2 used so far for analysis: 21kg) 5 PEEP titrations at different blood volumes: –Baseline –Removal of blood (Hypovolemia) –Reinfusion of blood –Infusion of saline –Infusion of more saline Each PEEP titration included PEEP levels of 0,10 and 20 cmH 2 0 Measurements: Measurements recorded using PiCCO, Servo-i, Vigilance and SC9000 monitors V lv, V rv were estimated based on TBV and GEDV Goal: Predict the effect of PEEP therapy on SV (etc) in presence of different blood volumes Data from Smith et al MMDS, Aalborg University

IntroductionModelIdentificationPredictionResultsPeople Results for PEEP-Prediction Goals: Match peak pressures Match stroke volumes Volumes close Pressures accurate

IntroductionModelIdentificationPredictionResultsPeople Results for PEEP-Prediction Errors ~10% + Trends Captured!

IntroductionModelIdentificationPredictionResultsPeople Conclusions Model-based Identification, Diagnosis and Therapy Decision Support methods presented Validated on two clinical data sets using porcine animal models –Pulmonary embolism –PEEP intervention at different blood volumes Results show good accuracy (<10% error) for critical parameters –No false parameter value changes in identification implies a model of the proper level of complexity for these disease states Future Work = Septic Shock (April 2007) and further disease states

IntroductionModelIdentificationPredictionResultsPeople Acknowledgement Engineers and Docs Dr Geoff ChaseDr Geoff Shaw Denmark (PEEP Data) Prof Steen Andreassen Dr Bram Smith Belgium (PE Data) Dr Thomas Desaive Dr Chris Hann Questions ? ? ? Dr Bram Smith