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Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction
Karlyn K Harrod1, Jeffrey L Rogers2, Jeffrey A Feinstein3
1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, United States.
Frontiers in Physiology
|July 19, 2021
Summary
This study developed a computational model to predict pulmonary pressures in heart failure patients. The model accurately estimates these pressures using clinical data, aiding in the diagnosis of diastolic dysfunction and pulmonary hypertension.
Area of Science:
- Cardiovascular Physiology
- Computational Modeling
- Medical Diagnostics
Background:
- Diastolic dysfunction, a common cause of heart failure, often presents subtly and is difficult to diagnose non-invasively.
- Elevated pulmonary pressures are a key indicator of diastolic heart failure (heart failure with preserved ejection fraction, HFpEF) and correlate with mortality.
- Current methods for measuring pulmonary pressures are often invasive and require symptom onset.
Purpose of the Study:
- To develop and validate a differential-algebraic circulation model for predicting pulmonary pressures from clinical data.
- To assess the model's ability to represent diverse pathological conditions and identify its parameters.
- To evaluate the accuracy of non-invasively predicted pulmonary pressures and develop a classifier for pulmonary hypertension.
Main Methods:
- Utilized a differential-algebraic circulation model to assimilate clinical data and predict hemodynamic parameters.
- Investigated model performance across a spectrum of heart failure severity.
- Developed a classifier using assimilated model parameters to detect pulmonary hypertension, addressing missing data issues.
Main Results:
- The model accurately estimated systolic, diastolic, and wedge pulmonary pressures in 82 patients, with average errors of 8, 6, and 6 mmHg, respectively.
- A classifier based on model parameters demonstrated high accuracy in detecting pulmonary hypertension.
- Increased data availability generally led to improved prediction accuracy.
Conclusions:
- A hemodynamic circulation model can effectively predict pulmonary pressures in heart failure patients using clinical data.
- This modeling approach offers a promising non-invasive method for diagnosing diastolic dysfunction and pulmonary hypertension.
- The study highlights the potential of computational modeling in improving cardiovascular diagnostics and patient management.
Keywords:
adaptive Markov chain Monte Carlocomputational physiologydata assimilationlumped parameter hemodynamic modelingmodel-based disease detectionpredictive models for pulmonary pressuresMore Related Videos
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