Feng Gu

Moderate to extreme preterm birth (< 32 weeks gestation) affects cardiopulmonary structure and function and is associated with increased risk of heart failure through adulthood. The rat hyperoxia (Hx) model (term born; postnatal Hx exposure) captures biventricular changes, including at the cell and organ scale, and pulmonary vascular remodeling seen in preterm humans. However, synthesizing these measures across scales and organ systems is challenging. We hypothesized that in silico modeling of biventricular mitochondrial, myofiber, and organ-scale function plus circulatory function could capture key features of cardiopulmonary abnormalities due to preterm birth. Therefore, we calibrated a multiscale model to subject-specific biventricular pressure–volume data previously obtained from Hx rats alongside normoxic (Nx) controls to investigate the abnormalities in cardiopulmonary function at multiple scales in this animal model of human preterm birth. The calibrated model demonstrates excellent agreement with the data and captures the expected pulmonary vascular changes and right ventricular dilation seen in preterm-born children. Our multiscale modeling approach captures cardiopulmonary abnormalities across spatial scales and provides an innovative approach to explore the consequences of preterm birth beyond preclinical experimental data alone. This is a foundational step in understanding the impact of preterm birth on cardiopulmonary disease in childhood as well as adulthood.

Background and Aims: Heart failure with preserved ejection fraction (HFpEF) exhibits profound phenotypic heterogeneity, which likely contributes to variable therapeutic response. We developed a physiology-informed digital twin–AI framework to predict individual hemodynamic and myocardial energetic responses to accelerated atrial pacing and tested whether simulated physiologic response corresponds to responders in the myPACE randomized clinical trial. Methods: Patient-specific digital twins were constructed for 146 HFpEF patients and used to train a variational autoencoder that generated a virtual HFpEF population (n = 25,000). The model simulated pacing-induced changes in left atrial pressure (LAP), systolic blood pressure (SBP), cardiac output (CO), and cardiac efficiency (CE; derived from myocardial oxygen-demand estimates). These simulations served as labels to train classifiers based on clinical variables available in myPACE, allowing us to examine associations with clinical end points and test a hypothesized relationship between CE and treatment response. Results: Simulations revealed heterogeneous physiological responses, with 95.6% of virtual patients showing reduced LAP, 47.0% an SBP reduction greater than 8.5 mmHg, 93.8% increased CO, and 36.1% improved CE. Classifiers reproduced these patterns with high fidelity. In the myPACE trial, patients classified as having CE improvement or a larger SBP reduction experienced significantly greater 1-month improvements in quality-of-life scores and larger NT-proBNP reductions. Conclusions: A physiology-informed digital twin–AI framework can predict hemodynamic and energetic responses corresponding to clinical benefit in HFpEF patients receiving accelerated atrial pacing. CE improvement functioned as a mechanistic indicator, while SBP reduction served as an accessible clinical correlate, offering mechanistically grounded guidance for patient-specific pacing and motivating prospective validation. Graphical

Moderate to extreme preterm birth (<32 weeks gestation) affects cardiopulmonary structure and function and is associated with increased risk of heart failure through adulthood. The rat hyperoxia (Hx) model (term born; postnatal Hx exposure) captures biventricular changes, including at the cell- and organ-scale, and pulmonary vascular remodeling seen in preterm humans. However, synthesizing these measures across scales and organ systems is challenging. We hypothesized that in-silico modeling of biventricular mitochondrial, myofiber, and organ-scale function plus circulatory function could capture key features of cardiopulmonary abnormalities due to preterm birth. Therefore, we calibrated a multiscale model to subject-specific biventricular pressure-volume data previously obtained from Hx rats alongside normoxic (Nx) controls to investigate the abnormalities in cardiopulmonary function at multiple scales in this animal model of human preterm birth. The calibrated model demonstrates excellent agreement with the data and captures the expected pulmonary vascular changes and right ventricular dilation seen in preterm born children. Our multiscale modeling approach captures cardiopulmonary abnormalities across spatial scales and provides an innovative approach to explore the consequences of preterm birth beyond preclinical experimental data alone. This is a foundational step in understanding the impact of preterm birth on cardiopulmonary disease in childhood as well as adulthood.

Summary: Background: Heart failure (HF) is a highly heterogeneous and complex condition. Although patient care generates vast amounts of clinical data, robust methods to synthesize available data for individualized management are lacking. Methods: A mechanistic computational model of cardiac and cardiovascular system mechanics was identified for each individual in a cohort of 343 patients with HF. The identified digital twins — comprising optimized sets of parameters and corresponding simulations of cardiovascular system function—for patients with HF in the cohort is used to inform both supervised and unsupervised approaches in identifying phenogroups and novel mechanistic drivers of cardiovascular death risk. Findings: The integration of digital twins into AI-based analyses of patient data enhances the performance and interpretability of prognostics AI models. Prognostics AI models trained with digital twin features are more generalizable than models trained with only clinical variables, as evaluated using an independent prospective cohort. In addition, the digital twin-based approach to phenomapping and predictive AI helps address inconsistencies and inaccuracies in clinical measurements, enables imputation of missing data, and estimates functional parameters that are otherwise unmeasurable directly. This approach provides a more comprehensive and accurate representation of the patient’s disease state than raw clinical data alone. Interpretation: The developed and validated digital twin-based AI framework has the potential to simulate patient-specific pathophysiologic parameters, thereby informing prognosis and guiding therapeutic options.Ultimately, this approach has the potential to enhance the ability to focus on the most critical aspects of a patient’s condition, leading to individualized care and management. Funding: National Institutes of Health and Joint Institute for Translational and Clinical Research (University of Michigan and Peking University Health Science Center)