predicting recurrent c.diff infection with emr data 1199
our population. The 4 models, arranged according to increasing complexity, are summarized in Table 1.
Statistical Methods
We divided cohort data into derivation (patients with iCDI between 2007 and 2013) and validation (iCDI in 2014) datasets. All analyses during model development were performed using the derivation dataset, with final coefficients applied once to the validation dataset. As a further precaution against overfitting, we divided derivation data intoDerivation 1 (iCDI dates 2007–2012) and Derivation 2 (2013) datasets.28 Within the Derivation 1 dataset, we identified a set of candidate predictors by first per- forming univariate and bivariate analyses and then applying a randomforest algorithm.28,29Weevaluated the performance and robustness of allmodels on the Derivation 2 data set using 5-fold cross-validation.30 We excluded multiple models because, although they performed well in the derivation dataset, perfor- mance deteriorated dramatically following cross-validation. This was particularly true with respect to models that incorporated multiple interaction terms. We fit a simple logistic regression, excluding deaths prior
to rCDI for the basic, the enhanced, and the automated models. However, because patients with CDI have a substantial mortality risk and might die prior to developing rCDI, we eval- uated several models (based on the enhancedmodel predictors) to address the possible impact of mortality on rCDI prediction. These included competing risk discrete survival models29 and Cox competing risk survival regression.31 We conducted sensitivity analyses in which we first assigned a probability of rCDI to all patients in a randomly selected portion of the derivation dataset. We then tested various models using the remaining records in which the dependent variable was not dichotomous but continuous (ie, patients who died were assigned a probability of rCDI, and then we modeled for rCDI as a continuous outcome), and we incorporated the conditional probability of mortality into the analyses. Additional details are provided in the Appendix. We compared the discrimination of each model using
the c statistic (area under the receiver operator characteristic curve),32 calibration through calibration plots,33 the incremental contribution of additional predictors using integrated discrimina- tion improvement (IDI), and net reclassification improvement as recommended by Cook34 andPencina et al.35 As recommended by Cook,34 we also included the Nagelkerke pseudo-R2 in our assessments of model performance. In standard linear regression models, the ratio of the mean-squared error to the variance of the dependent variable can be subtracted from 1 to define anR2 that is always between 0 and 1. In a validation sample, however, the mean-squared error may exceed the variance of the dependent variable, and the resulting R2 may be negative. A negative R2 indicates a very poor fitwiththe validation sample.36 We also conducted sensitivity analyses in which we
employed a 30-day (as opposed to an 84-day) period for outcome ascertainment.
results
We scanned KPNC databases from 2007 to 2014 and identified a total of 41,499 positive tests for Clostridium difficile. A total of 11,251 patients who experienced iCDI. In the derivation
dataset, a total of 9,386 patients with iCDI experienced 1,311 first recurrences (14.0%); 2,197 (23.4%) patients died prior to the end of the follow-up period; and 260 (2.8%) died following a recurrence. The corresponding numbers in the validation dataset were 1,865 iCDIs, 144 (7.7%) rCDIs, 376 (20.2%) deaths prior to the end of the follow-up period, and 27 (1.4%) deaths following rCDI. The Appendix provides a flow chart describing the cohort assembly. Excluding patients who died prior to the end of the follow-up period, Table 2 summarizes our cohort characteristics, which are fairly similar to the cohort described by Zilberberg et al.25 However, in general, the KPNC cohort was older but healthier (eg, the proportion with Charlson scores <3 was 80%, while that in the Zilberberg et al cohort was ~55%). Furthermore, the KPNC cohort generally had lower risk (eg, only 24% were receiving high-risk anti- biotics, compared to 40% in the Zilberberg cohort). Expanded versions of this table are provided in the Appendix. We compared performance of the discrete time survival
and competing risk Cox regression models against the simple logistic regression algorithm where we excluded patients who died prior to an rCDI. The simple logistic regression basic, enhanced, and automated models showed performance com- parable to that of the competing risk survival models. Table 3 summarizes performance characteristics of our
models in the validation dataset. All models demonstrated modest discrimination, as shown by their areas under the receiver-operator characteristic curve, or c statistics (range, 0.591–0.605) and poor explanatory power, with negative Nagelkerke pseudo-R2s(−0.1033 to −0.0875). At a predicted risk of ≥15% the positive predictive value ranged from 11.0%
to 12.1%; sensitivity ranged from 69.4% to 79.2%; and speci- ficity ranged from 32.0% to 43.6% across the models. With this threshold, the number of patients needed to evaluate (NNE) to detect 1 case of rCDI ranged from 8.3 to 9.0 across the models. Figure 2 shows calibration of the Zilberberg model and the enhanced model; neither model was well calibrated. Sensitivity analyses of the possible impact of mortality
indicate that consideration of this issue (eg, by assigning a weighted probability of rCDI to patients who died and then modeling for rCDI as a continuous outcome) did not improve prediction. Sensitivity analyses using a 30-day (instead of 84- day) follow-up period resulted in worse model performance. Additional results are provided in the Appendix.
discussion
Using a large recent cohort, we developed and validated 3 rCDI predictive models using contemporary modeling techniques and EMR data. We also validated a previously published model25 in a different population. However, despite including
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
