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Prediction of Recurrent Clostridium Difficile Infection Using Comprehensive Electronic Medical Records in an Integrated Healthcare Delivery System

Published online by Cambridge University Press:  24 August 2017

Gabriel J. Escobar ,
Jennifer M. Baker ,
Patricia Kipnis ,
John D. Greene ,
T. Christopher Mast ,
Swati B. Gupta ,
Nicole Cossrow ,
Vinay Mehta ,
Vincent Liu  and
Erik R. Dubberke
Gabriel J. Escobar*
Affiliation:
Kaiser Permanente Division of Research, Oakland, California
Jennifer M. Baker
Affiliation:
Contra Costa Public Health Clinic Services, Martinez, California
Patricia Kipnis
Affiliation:
Kaiser Permanente Division of Research, Oakland, California Kaiser Permanente Northern California, Oakland, California
John D. Greene*
Affiliation:
Kaiser Permanente Division of Research, Oakland, California
T. Christopher Mast
Affiliation:
Merck Research Laboratories, North Wales, Pennsylvania
Swati B. Gupta
Affiliation:
Merck Vaccines, West Point, Pennsylvania
Nicole Cossrow
Affiliation:
Merck Research Laboratories, North Wales, Pennsylvania
Vinay Mehta
Affiliation:
Merck Research Laboratories, North Wales, Pennsylvania
Vincent Liu
Affiliation:
Kaiser Permanente Division of Research, Oakland, California Santa Clara Medical Center and Medical Offices, Kaiser Permanente Northern California, Santa Clara, California
Erik R. Dubberke
Affiliation:
Washington University School of Medicine, St Louis, Missouri
*
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).
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).
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Abstract

BACKGROUND

Predicting recurrent Clostridium difficile infection (rCDI) remains difficult. METHODS. We employed a retrospective cohort design. Granular electronic medical record (EMR) data had been collected from patients hospitalized at 21 Kaiser Permanente Northern California hospitals. The derivation dataset (2007–2013) included data from 9,386 patients who experienced incident CDI (iCDI) and 1,311 who experienced their first CDI recurrences (rCDI). The validation dataset (2014) included data from 1,865 patients who experienced incident CDI and 144 who experienced rCDI. Using multiple techniques, including machine learning, we evaluated more than 150 potential predictors. Our final analyses evaluated 3 models with varying degrees of complexity and 1 previously published model.

RESULTS

Despite having a large multicenter cohort and access to granular EMR data (eg, vital signs, and laboratory test results), none of the models discriminated well (c statistics, 0.591–0.605), had good calibration, or had good explanatory power.

CONCLUSIONS

Our ability to predict rCDI remains limited. Given currently available EMR technology, improvements in prediction will require incorporating new variables because currently available data elements lack adequate explanatory power.

Infect Control Hosp Epidemiol 2017;38:1196–1203

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 38 , Issue 10 , October 2017 , pp. 1196 - 1203
Copyright
© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved 

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