predicting recurrent c.diff infection with emr data 1197
sufficiently validated outside the populations in which they were developed. Now that treatments are available to prevent the recurrence of CDI (eg, fidaxomicin,17,18 bezlotoxumab19), it is advantageous to patients and healthcare providers to identify those at greatest risk for recurrence who may benefit from the most appropriate treatments. To address these gaps, we developed and validated rCDI pre-
dictive models in a large and representative sample of adults. For our defined population, cared for by a singlemedical groupwithin an integrated delivery system, Kaiser Permanente Northern California (KPNC), comprehensive EMR data were available. Our modeling process included comparing different models and externally validating a previously published model.
materials and methods
viously.20–22 Under a mutual exclusivity arrangement, salaried physicians of The PermanenteMedical Group care for 4.2million Kaiser Foundation Health Plan members at facilities owned by Kaiser FoundationHospitals. All KPNC facilities (21 hospitals and an additional 60 clinics) employ the same information systems with a commonmedical record number.23 ComprehensiveKPNC information systems permit tracking of patient information across the continuum of care, including some aspects of care outside KPNC.22,23 Deployment of the Epic EMR system (www.epicsys-
tems.com), known internally as KP HealthConnect (KPHC), beganin2006and was completed in2010. Theeligiblepopulation(denominator) included adults ≥18
This project was approved by the KPNC Institutional Review Board for the Protection of Human Subjects, which has jurisdic- tion over all the hospitals and clinics described in this report. Our setting consisted of 21 KPNC hospitals described pre-
years of age with at least 1 positive test (the index test) for C. difficile toxins or DNA associated with a hospitalization between 2007 and 2014. The date-time stamp of the physician order for the index test was time zero (T0) for all study mea- surements. Details on KPNC assays and testing procedures are provided in the Appendix.
Measures Primary study outcome. The dependent variable was rCDI,
demographic databases and publicly available files of deceased patients provided by the Social Security Administration, as described previously.22 Model development. We assessed more than 150 potential predictors, including age, sex, and different configurations of historical variables (eg, antibiotic exposure, recent hos- pitalizations, and surgery). The final set of 23 predictors incorporated in the 3 models was based on clinical grounds, statistical performance, data abstraction burden in settings without EMRs, and (for the fully automated models) current KPNC data availability.15,16 Predictors fell into the following categories: demographic
which could occur either in the inpatient or outpatient setting. To ensure that we distinguished between incident and recurrent episodes, T0 had to be preceded by an 84-day period with no evidence of CDI (Figure 1). A patient’s treatment period extended from the first known instance of antibiotic treatment to 48 hours after conclusion of such treatment. A positive test defining a patient as having rCDI had to occur within 84 days after the end of the treatment period. Tests that occurred within the treatment period were not included. Figure 1 also shows that predictors were included if available up to 4 days after T0, a clinically reasonable period for acquisition of information following a CDI testing order. Mortality. We ascertained mortality using KPNC patient
(age, sex), location of iCDI onset (either the inpatient setting or a skilled nursing facility), medication exposure (antibiotics, proton pump inhibitors), comorbidities (both as individual predictors as well as composite indices such as the Charlson comorbidity index24 and the 12-month longitudinal COmorbidity Point Score, version 2, or COPS222), medical history (eg, recent surgery involving the gastrointestinal tract), and physiologic markers (ie, laboratory tests, vital signs, and a severity of illness score, the Laboratory-based Acute Physiology Score, version 2 (LAPS2).22 The LAPS2 employs 16 laboratory tests, vital signs, pulse oximetry, and neurological status checks. We categorized 24 antibiotics as
figure 1. Time periods employed to define patient inclusion in cohort and patient data in predictive models. The T0 is defined by the date/time stamp of the physician order for the index test. In order for the patient to be included in the cohort, the T0 had to be preceded by 84 days with no positive test for Clostridium difficile (“clean” period). To be considered an outcome, an infection had to occur during the Recurrence period. This meant that a positive test result occurred within 84 days following the end of a variable treatment period (time between the T0 and completion of antibiotic treatment, ABXEND within 4 days from the T0 (Predictor period). See text for additional details.
). Patient data included in the predictive models had to be available
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