Beyond static risk scores: dynamic world models simulating patient-specific trajectories to inform preoperative risk mitigation strategies.

Static scores are used in preoperative risk assessment and provide an estimate of a patient's risk status at a specific time. They do not explicitly represent short-term changes in physiology or recorded care activity. The Dynamic Patient World Model (DPWM) is an alternative method of preoperative risk assessment that departs from static predictions of risk to simulation of the evolution of the patient's health status based on three components: (1) a patient state encoder to account for irregular multimodal clinical data; (2) an intervention-conditioned dynamic model to predict the change in state; and, (3) a counterfactual outcome evaluator to assess potential outcomes based on the current intervention(s) and/or other potential intervention alternatives. DPWM was evaluated against 14 baselines using 4 architectural types across 3 publicly available surgical databases (VitalDB, MIMIC-IV, eICU-CRD), with complication AUROC gains of 1.4-2.2 percentage points over the strongest temporal/world-model baseline; the 1.4-point VitalDB comparison was not statistically significant. Furthermore, in an outcome-conditioned retrospective analysis, 76.2% of DPWM-suggested interventions were concordant with clinical decisions associated with favorable outcomes; this does not estimate clinical benefit. Relative to DNN, DPWM achieved an NRI of 0.128 (95% CI: 0.062-0.194); ASA-PS subgroup comparisons were descriptive and did not isolate temporal information.