Aims: International early warning scores (EWS) including the additive National Early Warning Score (NEWS) and logistic EWS currently utilise physiological snapshots to predict clinical deterioration. We hypothesised that a dynamic score including vital sign trajectory would improve discriminatory power. Methods: Multicentre retrospective analysis of electronic health record data from postoperative patients admitted to cardiac surgical wards in four UK hospitals. Least absolute shrinkage and selection operator-type regression (LASSO) was used to develop a dynamic model (DyniEWS) to predict a composite adverse event of cardiac arrest, unplanned intensive care re-admission or in-hospital death within 24 h. Results: A total of 13,319 postoperative adult cardiac patients contributed 442,461 observations of which 4234 (0.96%) adverse events in 24 h were recorded. The new dynamic model (AUC = 0.80 [95% CI 0.78À0.83], AUPRC = 0.12 [0.10À0.14]) outperforms both an updated snapshot logistic model (AUC = 0.76 [0.73À0.79], AUPRC = 0.08 [0.60À0.10]) and the additive National Early Warning Score (AUC = 0.73 [0.70À0.76], AUPRC = 0.05 [0.02 À0.08]). Controlling for the false alarm rates to be at current levels using NEWS cutoffs of 5 and 7, DyniEWS delivers a 7% improvement in balanced accuracy and increased sensitivities from 41% to 54% at NEWS 5 and 18%À30% at NEWS 7. Conclusions: Using an advanced statistical approach, we created a model that can detect dynamic changes in risk of unplanned readmission to intensive care, cardiac arrest or in-hospital mortality and can be used in real time to risk-prioritise clinical workload.