Diagnosing cardiac
disease states
using a minimal
cardiovascular
model
Christina Starfinger
Wednesday, 21 March 2007
HRSC Scientific Meeting
Current Clinical Process
Introduction
Model
Identification
Prediction
Results
People
•
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”)
Model-Based Solution
Introduction
Model
Identification
Prediction
Results
People
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
Model
Introduction
Model
Identification
Prediction
Results
People
Left ventricle (LV)
Right ventricle (RV)
Vena Cava, Aorta,
Pulmonary artery/vein
Pulmonary circulation
Systemic circulation
Model
Introduction
Model
Identification
Prediction
Results
People
Right ventricle
Pulmonary
Artery
Vena
Cava
Pulmonary
Vein
Aorta
Left ventricle
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. 165-179
Identification Problem
Introduction
Model
Identification
Prediction
Results
People
Measurements Required:
1.
2.
3.
4.
5.
Max/Min Pressure in aorta (SAP, DAP)
Max/Min Pressure in pulmonary artery (SPAP, DPAP)
Max/Min Volume in left ventricle (LVEDV,LVESV)
Max/Min Volume in right ventricle (RVEDV,RVESV)
Heart Rate
Given or
estimated
Model Parameters Identified: Integral-based methods
Lav,Lmt,Ltc,Lpv,Eeslvf,Eesrvf,Polvf,Porvf,Rav,Rmt,Rtc,Rpv,
Eao,Epa,Evc,Epu,Rsys, Rpul
Find
 Measurements required are a minimal set compared to the total
number of parameters identified in the model
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. 181-192
Diagnosis from ID
Introduction
Model
Parameters:
Identification
Prediction
Results
People
Contractilities, increased
during increased sympathetic
activity (HR ↑)
Lav,Lmt,Ltc,Lpv,Eeslvf,Eesrvf,Polvf,Porvf,Rav,Rmt,Rtc,Rpv,
Eao,Epa,Evc,Epu,Rsys, Rpul
Pulmonary vascular
resistance and
systemic vascular
resistance,
increased for
example in PE, and
PHT
Resistances for 4
heart valves,
increased for
stenosis
Pulmonary Embolism
Introduction
Model
Identification
Prediction
Results
People
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 (Pao) and pulmonary artery pressure (Ppa) are measured using
micromanometer-tipped catheters (Sentron pressure-measuring catheter;Cordis,
Miami, FL)
right and left ventricle pressures and volumes (Vlv,Vrv,Plv,Prv) 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
Results for PE (ID) - 1
Introduction
Model
Left ventricle (30 mins)
Identification
Prediction
Results
People
Right ventricle (30 mins)
Errors ~5% and all peak and stroke values captured
Results for PE (ID) - 2
Introduction
Model
Identification
Rpul – All pigs
Prediction
Results
People
Pulmonary resistance
(Pig 2)
Right ventricle expansion index
(RVEDV/LVEDV, Pig 2)
Reflex actions
(Pig 2)
PEEP Titrations
Introduction
Model
Identification
Prediction
Results
People
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 cmH20
Measurements:
•
•
Measurements recorded using PiCCO, Servo-i, Vigilance and SC9000 monitors
Vlv, Vrv 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
Results for PEEP-Prediction
Introduction
Model
Identification
Prediction
Results
People
Goals:
• Match peak pressures
• Match stroke volumes
• Volumes close
• Pressures accurate
Results for PEEP-Prediction
Introduction
Model
Identification
Prediction
Results
Errors ~10% + Trends Captured!
People
Conclusions
Introduction
Model
Identification
Prediction
Results
People
• 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
Acknowledgement
Introduction
Model
Identification
Prediction
Results
People
Engineers and Docs
Questions
???
Dr Chris Hann
Dr Geoff Chase
Dr Geoff Shaw
Denmark (PEEP Data)
Prof Steen
Andreassen
Belgium (PE Data)
Dr Bram Smith
Dr Bram Smith
Dr Thomas Desaive