Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile.
BACKGROUND
Although many risk factors are well known, Clostridium difficile infection (CDI) continues to be a significant problem throughout the world. The purpose of this study was to develop and validate a data-driven, hospital-specific risk stratification procedure for estimating the probability that an inpatient will test positive for C difficile.


METHODS
We consider electronic medical record (EMR) data from patients admitted for ≥24 hours to a large urban hospital in the U.S. between April 2011 and April 2013. Predictive models were constructed using L2-regularized logistic regression and data from the first year. The number of observational variables considered varied from a small set of well known risk factors readily available to a physician to over 10 000 variables automatically extracted from the EMR. Each model was evaluated on holdout admission data from the following year. A total of 34 846 admissions with 372 cases of CDI was used to train the model.


RESULTS
Applied to the separate validation set of 34 722 admissions with 355 cases of CDI, the model that made use of the additional EMR data yielded an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% confidence interval [CI], .79-.83), and it significantly outperformed the model that considered only the small set of known clinical risk factors, AUROC of 0.71 (95% CI, .69-.75).


CONCLUSIONS
Automated risk stratification of patients based on the contents of their EMRs can be used to accurately identify a high-risk population of patients. The proposed method holds promise for enabling the selective allocation of interventions aimed at reducing the rate of CDI.
Department of Electrical Engineering and Computer Science , Massachusetts Institute of Technology , Cambridge.
Jenna
J
Wiens
Division of Infectious Diseases, MedStar Health , The Union Memorial Hospital , Baltimore, Maryland.
Wayne N
WN
Campbell
MedStar, National Center for Human Factors in Healthcare , Washington, District of Columbia.
Ella S
ES
Franklin
Department of Electrical Engineering and Computer Science , Massachusetts Institute of Technology , Cambridge.
John V
JV
Guttag
Microsoft Research , Redmond, Washington.
Eric
E
Horvitz
Clostridium difficile; data-driven methods; electronic medical records; machine learning; risk stratification