Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data()

AIMS OF STUDY: To develop and validate a centile-based early warning score using manually-recorded data (mCEWS). To compare mCEWS performance with a centile-based early warning score derived from continuously-acquired data (from bedside monitors, cCEWS), and with other published early warning scores. MATERIALS AND METHODS: We used an unsupervised approach to investigate the statistical properties of vital signs in an in-hospital patient population and construct an early-warning score from a “development” dataset. We evaluated scoring systems on a separate “validation” dataset. We assessed the ability of scores to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission, or death, each within 24 h of a given vital-sign observation, using metrics including the area under the receiver-operating characteristic curve (AUC). RESULTS: The development dataset contained 301,644 vital sign observations from 12,153 admissions (median age (IQR): 63 (49–73); 49.2% females) March 2014–September 2015. The validation dataset contained 1,459,422 vital-sign observations from 53,395 admissions (median age (IQR): 68 (48–81), 51.4% females) October 2015–May 2017. The AUC (95% CI) for the mCEWS was 0.868 (0.864–0.872), comparable with the National EWS, 0.867 (0.863–0.871), and other recently proposed scores. The AUC for cCEWS was 0.808 (95% CI, 0.804–0.812). The improvement in performance in comparison to the continuous CEWS was mainly explained by respiratory rate threshold differences. CONCLUSIONS: Performance of an EWS is highly dependent on the database from which itis derived. Our unsupervised statistical approach provides a straightforward, reproducible method to enable the rapid development of candidate EWS systems.