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(eg, isolation rooms, who is roomed where, other family members exposure), if such data become available. One alternative that we did not explore because it is cur-


rently not feasible with existingEMRs, was to develop dynamic models. In contrast to the static approach we and others have employed (ie, providing a single probability estimate based on a discrete set of predictors available at some T0), such models adjust posterior probabilities based on new information. In the case of rCDI, having additional information on both antibiotic treatment as well as other exposures (eg, proton pump inhibitors) could have dramatic effects on our ability to predict recurrence.37,38 The development of such models would require EMRs with greater capabilities than those currently available. Our study had several additional limitations. Due to


resource limitations and sparse data, we limited our cases to inpatient iCDI. During this study, KPNC implemented aggressive efforts to reduceCDI. As a result, our data show that the incidences of iCDI and rCDI were decreasing in our study cohort. Despite these limitations, models to predict recurrence have value. They do permit identification of patient subsets with elevated or very low risk. In some scenarios, and in the context of discrete interventions, the use of these models might improve outcomes and decrease costs. In addition, existing models point to predictors that can be assessed in the future, such as the aforementioned ecological ones. Compared to our ability to predict other outcomes (eg,


death, unplanned transfer to intensive care),16,20,22 our ability to predict rCDI is limited and contrasts with much better ability to predict iCDI.39,40 Given the major consequences of rCDI on patient outcomes, our results support the need to expand research on the prevention and treatment of recur- rence. Such research may also result in the identification of novel predictors that are currently unavailable even in the most comprehensive EMRs.


acknowledgments


This project was funded by a grant from Merck Sharp & Dohme Corporation, Whitehouse Station, New Jersey. The authors wish to thank Juan Carlos LaGuardia for help assembling the dataset, Dr Tracy Lieu for reviewing the manuscript, Vanessa Rodriguez for formatting the text for publication, Anna Cardellino for her assistance in drafting the protocol, and Mary Beth Dorr for her review and guidance in the analysis. Financial support: Dr Vincent Liu was funded by a National Institutes of


Health award (grant no. K23GM112018). Potential conflicts of interest: The Kaiser Permanente authors Escobar, Kip-


nis, Liu, Greene, and Baker have no conflicts of interest to report. Dr Erik Dubberke has received grant support from Rebiotix, Merck, and Sanofi Pas- teur; he also has consulting and advisory board relationships with Rebiotix, Summit, GSK, Valenva, Sanofi Pasteur. The remaining coauthors Cossrow, Gupta, Mast, and Mehta are or were employees of Merck Sharp & Dohme Corporation, a subsidiary of Merck & Co., Kenilworth, New Jersey, and potentially own stock and/or hold stock options in the company.


Address correspondence to Gabriel J. Escobar, MD, Systems Research Initiative, Kaiser Permanente Division of Research, 2000 Broadway Ave


(032 R01), Oakland, CA 94612-2304 (gabriel.escobar@kp.org) or John Greene, MA, Systems Research Initiative, Kaiser Permanente Northern California Division of Research, 2000 Broadway Ave, Oakland, CA 94612 (john.d.greene@kp.org).


supplementary material


To view supplementary material for this article, please visit https://doi.org/10.1017/ice.2017.176


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