• Patricia Kipnis, Benjamin J Turk, David A Wulf, Juan Carlos LaGuardia, Vincent Liu, Matthew M Churpek, Santiago Romero-Brufau, and Gabriel J Escobar.
• Kaiser Foundation Health Plan, Inc., 1950 Franklin St., 17th Floor, Oakland, CA 94612, United States; Kaiser Permanente Northern California, Division of Research, 2000 Broadway Avenue, 032 R01, Oakland, CA 94612, United States. Electronic address: patricia.kipnis@kp.org.
• J Biomed Inform. 2016 Dec 1; 64: 10-19.

BackgroundPatients in general medical-surgical wards who experience unplanned transfer to the intensive care unit (ICU) show evidence of physiologic derangement 6-24h prior to their deterioration. With increasing availability of electronic medical records (EMRs), automated early warning scores (EWSs) are becoming feasible.ObjectiveTo describe the development and performance of an automated EWS based on EMR data.Materials And MethodsWe used a discrete-time logistic regression model to obtain an hourly risk score to predict unplanned transfer to the ICU within the next 12h. The model was based on hospitalization episodes from all adult patients (18years) admitted to 21 Kaiser Permanente Northern California (KPNC) hospitals from 1/1/2010 to 12/31/2013. Eligible patients met these entry criteria: initial hospitalization occurred at a KPNC hospital; the hospitalization was not for childbirth; and the EMR had been operational at the hospital for at least 3months. We evaluated the performance of this risk score, called Advanced Alert Monitor (AAM) and compared it against two other EWSs (eCART and NEWS) in terms of their sensitivity, specificity, negative predictive value, positive predictive value, and area under the receiver operator characteristic curve (c statistic).ResultsA total of 649,418 hospitalization episodes involving 374,838 patients met inclusion criteria, with 19,153 of the episodes experiencing at least one outcome. The analysis data set had 48,723,248 hourly observations. Predictors included physiologic data (laboratory tests and vital signs); neurological status; severity of illness and longitudinal comorbidity indices; care directives; and health services indicators (e.g. elapsed length of stay). AAM showed better performance compared to NEWS and eCART in all the metrics and prediction intervals. The AAM AUC was 0.82 compared to 0.79 and 0.76 for eCART and NEWS, respectively. Using a threshold that generated 1 alert per day in a unit with a patient census of 35, the sensitivity of AAM was 49% (95% CI: 47.6-50.3%) compared to the sensitivities of eCART and NEWS scores of 44% (42.3-45.1) and 40% (38.2-40.9), respectively. For all three scores, about half of alerts occurred within 12h of the event, and almost two thirds within 24h of the event.ConclusionThe AAM score is an example of a score that takes advantage of multiple data streams now available in modern EMRs. It highlights the ability to harness complex algorithms to maximize signal extraction. The main challenge in the future is to develop detection approaches for patients in whom data are sparser because their baseline risk is lower.Copyright © 2016 Elsevier Inc. All rights reserved.

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