Early Deterioration Indicator: Data-driven approach to detecting deterioration in general ward

摘要

Abstract Introduction Early detection of deterioration could facilitate more timely interventions which are instrumental in reducing transfer to higher levels of care such as Intensive Care Unit (ICU) and mortality . Methods and results We developed the Early Deterioration Indicator (EDI) which uses log likelihood risk of vital signs to calculate continuous risk scores. EDI was developed using data from 11,864 general ward admissions. To validate EDI, we calculated EDI scores on an additional 2418 general ward stays and compared it to the Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS). EDI was trained using the most significant variables in predicting deterioration by leveraging the knowledge from a large dataset through data mining. It was implemented electronically for continuous automatic computation. The discriminative performance of EDI, MEWS, and NEWS was calculated before deterioration using the area under the receiver operating characteristic curve (AUROC). Additionally, the performance of the 3 scores for 24h prior to deterioration were computed. EDI was a better discriminator of deterioration than MEWS or NEWS; AUROC values for the validation dataset were: EDI – 0.7655, NEWS – 0.6569, MEWS – 0.6487. EDI also identified more patients likely to deteriorate for the same specificity as NEWS or MEWS. EDI had the best performance among the 3 scores for the last 24h of the patient stay. Conclusion EDI detects more deteriorations for the same specificity as the other two scores. Our results show that EDI performs better at predicting deterioration than commonly used NEWS and MEWS.