A Bayesian patient-based model for detecting deterioration in vital signs using manual observations

Abstract

Deterioration in patient condition is often preceded by deterioration in the patient's vital signs. “Track-and-Trigger” systems have been adopted in many hospitals in the UK, where manual observations of the vital signs are scored according to their deviation from “normal” limits. If the score exceeds a threshold, the patient is reviewed. However, such scoring systems are typically heuristic. We propose an automated method for detection of deterioration using manual observations of the vital signs, based om Bayesian model averaging. The proposed method is compared with an existing technique - Parzen windows. The proposed method is shown to generate alerts for 79% of patients who went on to an emergency ICU admission and in 2% of patients who did not have an adverse event, as compared to 86% and 25% by the Parzen windows technique, reflecting that the proposed method has a 23% lower false alert rate than that of the existing technique.