Digital Twin Heart Models for Personalized Cardiac Care and Predictive Simulation: A Systematic Review and Meta Analysis

Background: Digital twin heart models capable of predicting and planning care more effectively and recreating the cardiac physiology at a patient scale is the new cardiology industry technology. Their success against traditional practices has never been adequately evaluated even though they are evolving very rapidly. The objective of this systematic review and meta-analysis was to integrate evidence on the safety, predictive, and efficacy of digital twin heart models when used in personal heart care. Methods: It was searched in compliance with the PRISMA 2020 protocol (a systematic search of PubMed, Embase, Web of Science and Cochrane Library, January 2010 - May 2025). Randomized controlled trials, cohort or casecontrol trials that evaluated adult patients (at least 18 years of age) were included only because they were eligible when they evaluated the digital twin heart models versus usual clinical care methods. Findings on predictive accuracy, patient-specific concordance, major adverse cardiac events (MACE) and discontinuation rates were retrieved. The Cochrane Risk of Bias tool and Newcastle-Ottawa Scale were used to determine critical to quality. The meta-analysis and subgrouping based on age and simulation type was performed using a random-effects model and the presence of publication bias was assessed using the Egger test. Results: They used 15 studies (n ≈ 4,500 patients). The results of the combined analysis revealed that digital twins' models were superior to traditional care in predictive accuracy (OR 1.32-1.81), as well as in personalized outcome concordance (increase of +20%). MACE went down by 18 to 24% on average; old patients (>65 years) had the greatest improvement. Statistical or hybrid designs were not as successful in prediction as mechanistic twin models. The level of heterogeneity was medium (I² = 42-63%) and could be easily explained by random-effects models. Publication bias was low but some of the studies were borderline. Few negative cases (simulation errors, model drift) and discontinuations (approximately 6% to 12%). Strength of results verified via sensitivity. Conclusion: This meta-analysis and systematic review indicate that digital twin heart models are associated with superior predictive validity, concordance of outcomes and safety in comparison to conventional cardiac care. The senior sample population and the simulation of mechanistic twins have most of what renders it important particularly precision cardiology. The practice of digital twins can have a positive impact on patient-centered plans and decision making. The results of the long-term, the hybrid AI-mechanism model and the long-term clinical-use approaches is the foundation of future research.

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