Multicenter Development and Validation of a Risk Stratification Tool for Ward Patients

• Published in: American journal of respiratory and critical care medicine Vol. 190; no. 6; pp. 649 - 655
• Main Authors: Churpek, Matthew M., Yuen, Trevor C., Winslow, Christopher, Robicsek, Ari A., Meltzer, David O., Gibbons, Robert D., Edelson, Dana P.
• Format: Journal Article
• Language: English
• Published: United States American Thoracic Society 15.09.2014
• Subjects: Adult; Aged; Aged, 80 and over; Blood pressure; Cardiac arrest; Clinical outcomes; Cohort Studies; Creatinine; Datasets; Dimensional Measurement Accuracy; Early Diagnosis; Electronic Health Records; Female; Heart Arrest - mortality; Hospital Rapid Response Team - statistics & numerical data; Hospitalization; Hospitals; Humans; Inpatients - statistics & numerical data; Intensive care; Intensive Care Units - statistics & numerical data; Male; Middle Aged; Models, Statistical; Original; Patient Transfer - statistics & numerical data; Patients; Risk Assessment - methods; Risk Assessment - standards; Signs; Survival Analysis; Teams; Variables; Vital signs; United States > US; early diagnosis; hospital rapid response team; heart arrest; decision support techniques; statistical models
• ISSN: 1073-449X; 1535-4970; 1535-4970
• DOI: 10.1164/rccm.201406-1022OC

Most ward risk scores were created using subjective opinion in individual hospitals and only use vital signs. To develop and validate a risk score using commonly collected electronic health record data. All patients hospitalized on the wards in five hospitals were included in this observational cohort study. Discrete-time survival analysis was used to predict the combined outcome of cardiac arrest (CA), intensive care unit (ICU) transfer, or death on the wards. Laboratory results, vital signs, and demographics were used as predictor variables. The model was developed in the first 60% of the data at each hospital and then validated in the remaining 40%. The final model was compared with the Modified Early Warning Score (MEWS) using the area under the receiver operating characteristic curve and the net reclassification index (NRI). A total of 269,999 patient admissions were included, with 424 CAs, 13,188 ICU transfers, and 2,840 deaths occurring during the study period. The derived model was more accurate than the MEWS in the validation dataset for all outcomes (area under the receiver operating characteristic curve, 0.83 vs. 0.71 for CA; 0.75 vs. 0.68 for ICU transfer; 0.93 vs. 0.88 for death; and 0.77 vs. 0.70 for the combined outcome; P value < 0.01 for all comparisons). This accuracy improvement was seen across all hospitals. The NRI for the electronic Cardiac Arrest Risk Triage compared with the MEWS was 0.28 (0.18-0.38), with a positive NRI of 0.19 (0.09-0.29) and a negative NRI of 0.09 (0.09-0.09). We developed an accurate ward risk stratification tool using commonly collected electronic health record variables in a large multicenter dataset. Further study is needed to determine whether implementation in real-time would improve patient outcomes.