Individual hearts: computational models for improved management of cardiovascular disease

Clinical Snapshot

Key Findings

• P Value: Not applicable - narrative review

• Effect Size: Not quantified - qualitative synthesis only

• Primary Outcome: Computational models show promise for personalised cardiovascular care across diagnostic, mechanistic, and therapeutic applications

• Nnt Or Sensitivity: Not reported - implementation challenges noted including validation, regulatory approval, and workflow integration

• Confidence Interval: Not provided

Clinical Application

Currently limited by validation challenges, regulatory barriers, and clinical workflow integration issues Relevant to Australian cardiology practice but would require TGA approval for clinical decision support tools and integration with existing PBS-funded cardiovascular therapies and RACGP guidelines Patients with complex cardiovascular disease requiring personalised management approaches

Abstract

Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, with conventional management often applying standardised approaches that struggle to address individual variability in increasingly complex patient populations. Computational models, both knowledge-driven and data-driven, have the potential to reshape cardiovascular medicine by offering innovative tools that integrate patient-specific information with physiological understanding or statistical inference to generate insights beyond conventional diagnostics. This review traces how computational modelling has evolved from theoretical research tools into clinical decision support systems that enable personalised cardiovascular care. We examine this evolution across three key domains: enhancing diagnostic accuracy through improved measurement techniques, deepening mechanistic insights into cardiovascular pathophysiology and enabling precision medicine through patient-specific simulations. The review covers the complementary strengths of data-driven approaches, which identify patterns in large clinical datasets, and knowledge-driven models, which simulate cardiovascular processes based on established biophysical principles. Applications range from artificial intelligence-guided measurements and model-informed diagnostics to digital twins that enable in silico testing of therapeutic interventions in the digital replicas of individual hearts. This review outlines the main types of cardiovascular modelling, highlighting their strengths, limitations and complementary potential through current clinical and research applications. We also discuss future directions, emphasising the need for interdisciplinary collaboration, pragmatic model design and integration of hybrid approaches. While progress is promising, challenges remain in validation, regulatory approval and clinical workflow integration. With continued development and thoughtful implementation, computational models hold the potential to enable more informed decision-making and advance truly personalised cardiovascular care.

References

1. 1.van Osta, N., van Loon, T., & Lumens, J. (2026). Individual hearts: computational models for improved management of cardiovascular disease. Heart. https://doi.org/10.1136/heartjnl-2024-324177