infection control & hospital epidemiology october 2017, vol. 38, no. 10 original article


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


Gabriel J. Escobar, MD;1 Jennifer M. Baker, MPH, CHES;2 Patricia Kipnis, PhD;1,3 John D. Greene, MA;1


T. Christopher Mast, PhD, MSc;4 Swati B. Gupta, DrPH, MPH;5 Nicole Cossrow, MPH, PhD;4 Vinay Mehta, PhD;4 Vincent Liu, MD, MS;1,6 Erik R. Dubberke, MD7


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


Clostridium difficile infection (CDI) is a serious illness whose presentation can range from loose stools to profuse watery diarrhea, leading to dehydration, life-threatening complica- tions, and sometimes death. This illness is associated with substantial morbidity, mortality, excess health services utiliza- tion, and increased cost.1–3 The Centers for Disease Control and prevention estimated that there were 453,000 cases of incident CDI (iCDI) in 2011, with 29,000 associated deaths and 83,000 first recurrences (rCDI).1 Recurrences are com- mon due to persistent or newly acquired bacterial spores.4 After initial treatment and resolution of diarrhea, up to 35% of CDI patients experience rCDI.1,5,6 Of those with a primary recurrence, 40% will have another CDI episode, and after 2 recurrences, the likelihood of an additional episode increases to as high as 65%.7 However, due to recent advances, this estimate may be overstated.8,9 Prevention of rCDI remains a critical unmet medical need,


and it is desirable to predict which patients are at highest risk of recurrence. A number of research teams have developed


predictive models for rCDI.10–13 These models have had lim- ited sample size, have been restricted to data from a single center, have employed imprecise proxies for measures of dis- ease severity, and have made limited use of electronic medical record (EMR) data. A need exists for risk prediction models to address these


gaps. As more healthcare systems in the United States transi- tion to fully automatedEMRs, it is important to take advantage of the increased granular clinical data that are becoming available. Although health systems are beginning to experi- ment with predictive models embedded in EMRs,14–16 access to such capability remains limited. The overall incidence of CDI is affected by local factors such as antimicrobial stew- ardship efforts, patient case mix, varying antibiotic utilization patterns, C. difficile strain epidemiology, and prevention. Thus, models may not be completely generalizable and may need periodic updating. Although considerable interest in predict- ing rCDI exists, descriptions of the performance characteristics of existing models have been limited, and few have been


Affiliations: 1. Kaiser Permanente Division of Research, Oakland, California; 2. Contra Costa Public Health Clinic Services, Martinez, California; 3. Kaiser


Permanente Northern California, Oakland, California; 4. Merck Research Laboratories, North Wales, Pennsylvania; 5. Merck Vaccines, West Point, Pennsylvania; 6. Santa ClaraMedical Center andMedical Offices, Kaiser Permanente Northern California, Santa Clara, California; 7. Washington University School of Medicine, St Louis, Missouri.


© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved. 0899-823X/2017/3810-0009. DOI: 10.1017/ice.2017.176 Received February 23, 2017; accepted July 14, 2017; electronically published August 24, 2017


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116  |  Page 117  |  Page 118  |  Page 119  |  Page 120  |  Page 121  |  Page 122  |  Page 123  |  Page 124  |  Page 125  |  Page 126  |  Page 127  |  Page 128  |  Page 129  |  Page 130  |  Page 131  |  Page 132  |  Page 133  |  Page 134  |  Page 135  |  Page 136  |  Page 137  |  Page 138  |  Page 139  |  Page 140  |  Page 141  |  Page 142  |  Page 143  |  Page 144