• R Andrew Taylor, Joseph R Pare, Arjun K Venkatesh, Hani Mowafi, Edward R Melnick, William Fleischman, and M Kennedy Hall.
• Department of Emergency Medicine, Yale University, Yale-New Haven Hospital, New Haven, CT.
• Acad Emerg Med. 2015 Dec 17.

ObjectivesPredictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDR) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a pre-selected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case.MethodsThis was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome (SIRS) with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80/20 percent split for training and validation. A random forest model (machine-learning approach) was constructed using over 500 clinical variables from data available within the EHR of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and χ(2) statistics.ResultsThere were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUC with 95% confidence intervals (CI) for the different models were: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p-value ≤ 0.003 for all comparisons).ConclusionsIn this proof-of-concept study, a local big data driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.

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