Advances in Critical Care Engineering

Current algorithms identifying hemodynamically unstable intensive care unit patients typically are limited to detecting existing dangerous conditions and suffer from high false alert rates. Our objective was to predict hemodynamic instability at least two hours before patient deterioration while maintaining a low false alert rate, using minute-by-minute heart rate (HR) and blood pressure (BP) data. We identified 66 stable and 104 unstable patients meeting our stabilityinstability criteria from the MIMIC II database, and developed multi-parameter measures using HR and BP. An instability index combining measures of BP, shock index, rate pressure product, and HR variation was developed from a multivariate regression model to predict hemodynamic instability (ROC of 0.82±0.03, sensitivity of 0.57±0.07 when the specificity was targeted at 0.90; the alert rate ratio of unstable to stable patients was 7.62). We conclude that these algorithms could form the basis for reliable predictive clinical alerts which identify patients likely to become hemodynamically unstable within the next few hours so that the clinicians can proactively manage these patients and provide necessary care.