Cureus. 2026 Sep 30;18(9):e117234. doi: 10.7759/cureus.117234. eCollection 2026 Sep.
ABSTRACT
Mechanical circulatory support (MCS) is not a single intervention but a sequence of decisions: whether to initiate support, which device to use, how to titrate it while other therapies change, when to test for recovery, and whether to wean, escalate, bridge, or withdraw. Each decision has its own eligible population, time origin, action set, and cost of error. Interest is growing in patient-specific digital twins and offline reinforcement learning to support these decisions, and the first device-specific probabilistic twins and safety-regularized weaning policies have now been published. This narrative review examines what that literature currently establishes. Cardiac digital twins have produced their most convincing results in electrophysiology and resynchronization, where the modeled substrate is anatomically stable, and the intervention is discrete; circulatory support is harder because it involves a continuously titrated device acting on a rapidly changing patient under unrecorded co-interventions. The published MCS work is small, largely single-device, restricted to patients already receiving support, and evaluated against hemodynamic surrogate rewards inside simulators estimated from the same trajectories used for training. Three problems recur and appear to be the limiting factors rather than model capacity: predictive accuracy for an endpoint does not establish that an action is beneficial; a simulator cannot independently certify a policy optimized against it; and information available to a retrospective analyst is not information that was available at the decision time. The defensible near-term claim is improved state estimation and forecasting with calibrated uncertainty and explicit abstention. The initiation question requires target-trial emulation rather than a post-implant cohort, and public critical-care datasets can validate chronology and transport but cannot validate device titration or weaning policy.
PMID:42819577 | PMC:PMC13626182 | DOI:10.7759/cureus.117234