Biology subjects

Oomen, P. J. A.

Publications and source records attributed to Oomen, P. J. A..

5 recordsLinked to original sources

bioRxiv2026

Population-level emulation enables fast Bayesian calibration of cardiac digital twins

Purpose: Patient-specific cardiac digital twins have the potential to guide individualized therapy, but generating these in a routine clinical workflow remains challenging. This is especially true when the twins must carry quantified uncertainty: Bayesian calibration requires thousands of simulations per patient, and the emulators that reduce this cost are typically trained one patient at a time. We aimed to train a single emulator across a patient cohort, so that a new patient can be calibrated in minutes without new simulations. Methods: We trained a population-level Gaussian process emulator whose inputs included a 12-dimensional latent encoding of the clinical features of each patient, so that a single emulator represents the whole cohort. We compared spending simulation budgets on resampling the real cohort with augmenting the cohort with digital chimeras, synthetic patients interpolated between real patients. Seven parameters of a closed-loop heart and circulation model were calibrated by Markov chain Monte Carlo to ten clinical biomarkers in 26 patients with left bundle branch block, each using an emulator trained without that patient, and the calibrated posteriors were compared against the full simulator. Results: A single emulator, trained without the held-out patient, calibrated the digital twin of each patient in 59.7-63.0 s per patient, versus 15 h with the simulator. Resampling the real cohort was the more efficient training strategy, bringing every output to R2 greater than or equal to 0.9, which augmentation did not achieve for the same simulation budget. For held-out patients, both strategies performed comparably (median leave-one-out normalized root-mean-square error 11.0% vs. 10.7%; p = 0.745), and error rose with distance from the cohort centroid (Spearman rho = +0.406). The calibrated models showed no cohort-wide bias (median standardized residual -0.12 against the clinical targets), and 95% predictive intervals contained 78.5% of targets, with the largest misfit in systolic blood pressure. Conclusion: Removing the need for new simulations for every new patient is a meaningful step toward uncertainty-aware cardiac digital twins that fit within a routine clinical workflow. Because the simulator, surrogate and sampler are interchangeable, the approach used here can be applied to other (cardiac) models and cohorts.

bioengineering↗

bioRxiv2026

Representing Sex in Cardiovascular Models: Calibrating Reference Parameters from Healthy Cohorts

Reduced-order models are increasingly used to study cardiac physiology and inform patient-specific therapies. However, a model's prediction is only as reliable as its underlying parameters: representative model parameterization is essential to reflect the physiology of the populations these models are meant to represent, including biological sex. Most current models are parameterized from male or sex-agnostic data and/or focus on specific pathologies. Therefore, the goal of this work is to establish a formal parameter estimation pipeline for deriving reduced-order cardiovascular model parameter ranges that are physiologically representative of healthy women and men. We calibrated a closed-loop reduced order model of the heart and circulation separately for healthy female and male populations, using data pooled from eleven healthy cohorts. To account for parameter sensitivity and identifiability, we employed a three-stage parameter subset reduction pipeline: global sensitivity analysis (Sobol's method), collinearity screening (Fisher information matrix), and profile-likelihood identifiability analysis. Sex-specific distributions of parameters that were deemed sensitive and identifiable for each sex, ten for women and 9 for men, were obtained by Hamiltonian Monte Carlo. All the calibrated parameters showed less than 80% overlap between sexes, with the smallest overlap observed in some of the most influential parameters, such as stressed blood volume. Comparing simulations of the calibrated models against allometrically size-matched simulations showed that body size explained some, but not all, of the sex differences. The resulting parameter distributions provide reference ranges usable in future mechanistic and patient-specific simulations to contribute to more inclusive cardiovascular modeling.

bioengineering↗

bioRxiv2025

A computational model to study hemodynamics during atrial fibrillation

Atrial fibrillation (AF) is associated with reduced cardiac output, which is correlated with increased symptomatic burden and declined quality of life. Predicting hemodynamic effects of AF remains challenging due to the complex interplay of multiple contributing mechanisms. Computational modeling offers a valuable tool for simulating hemodynamics. However, existing models are lacking the capabilities to both replicate beat-to-beat hemodynamic variations during AF while being well suited for fitting to clinical data. In this study, we present a computational model comprising: 1) an electrical subsystem that generates uncoordinated atrial and irregular ventricular activation times characteristic of AF, and 2) a mechanical subsystem that simulates hemodynamics using a reduced order model. The model was fitted to replicate individual hemodynamic measurements from 17 patients in the SMURF study during both normal sinus rhythm (NSR) and AF. The fitted model matched a large majority (75%) of blood pressure and intracardiac pressure measurements in both NSR and AF with absolute simulation errors well below 10 mmHg. Furthermore, a large majority of left atrial and left ventricular ejection fraction measurements during NSR were matched with absolute simulation errors well below 10%. The model consistently underestimated right ventricular diastolic pressure during NSR while overestimating right ventricular systolic and mean left atrial pressures during AF. The presented approach of modeling atrial activity in AF as uncoordinated atrial contractions, rather than no atrial contraction, achieved lower overall absolute simulation errors when fitting to individual patients. This computationally efficient model provides a platform for future investigations of patient-specific hemodynamics during AF.

bioengineering↗

bioRxiv2024

Synergistic Biophysics and Machine Learning Modeling to Rapidly Predict Cardiac Growth Probability

AO_SCPLOWBSTRACTC_SCPLOWComputational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to calibrate and run current models while considering data uncertainty and variability makes them impractical for routine clinical use. This study aims to address this need by creating a computational framework to efficiently predict cardiac growth probability. We utilized a biophysics model to rapidly simulate cardiac growth following mitral valve regurgitation (MVR). Here we developed a two-tiered Bayesian History Matching approach augmented with Gaussian process emulators for efficient calibration of model parameters to align with growth outcomes within a 95% confidence interval. We first generated a synthetic data set to assess the accuracy of our framework, and the effect of changes in data uncertainty on growth predictions. We then calibrated our model to match baseline and chronic canine MVR data and used an independent data set to successfully validate the ability of our calibrated model to accurately predict cardiac growth probability. The combined biophysics and machine learning modeling framework we proposed in this study can be easily translated to predict patient-specific cardiac growth.

bioengineering↗

bioRxiv2020

A Rapid Electromechanical Model To Predict Reverse Remodeling Following Cardiac Resynchronization Therapy

Cardiac resynchronization therapy (CRT) is an ef-fective therapy for patients who suffer from heart failure and ventricular dyssynchrony such as left bundle branch block (LBBB). When it works, it reverses adverse left ventricular (LV) remodeling and the progression of heart failure. How-ever, CRT response rate is currently as low as 50-65%. In theory, CRT outcome could be improved by allowing clinicians to tailor the therapy through patient-specific lead locations, timing, and/or pacing protocol. However, this also presents a dilemma: there are far too many possible strategies to test during the implantation surgery. Computational models could address this dilemma by predicting remodeling outcomes for each patient before the surgery takes place. Therefore, the goal of this study was to develop a rapid computational model to predict reverse LV remodeling following CRT. We adapted our recently developed computational model of LV remodeling to simulate the mechanics of ventricular dyssynchrony and added a rapid electrical model to predict electrical activation timing. The model was calibrated to quantitatively match changes in hemodynamics and global and local LV wall mass from a canine study of LBBB and CRT. The calibrated model was used to investigate the influence of LV lead location and ischemia on CRT remodeling outcome. Our model results suggest that remodeling outcome varies with both lead location and ischemia location, and does not always correlate with short-term improvement in QRS duration. The results and time frame required to customize and run this model suggest promise for this approach in a clinical setting.

bioengineering↗