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Katherine E. Goodman et al
Fig. 1. A printable clinical risk score for bed- side use to predict a bacteremic patient’s likelihood of infection with an extended- spectrum β-lactamase (ESBL)–producing organism at the time of organism genus and species identification. Risk-factor points are noted in parentheses and summed among the 14 variables to produce a patient’s risk score. Possible score cutoffs for ESBL-positivebacteremia, andassociated sensitivities and specificities, are reflected in Table 2. aChronic obstructive pulmonary disease, emphysema, or chronic ventilator- dependency. bLatin America (excluding the Caribbean), the Middle East (including Egypt), South Asia, China, and the Mediterranean. *This statement reflects the positive predic- tive value of the score at a cutoff point of 7.25 and should be modified by the facility to account for local prevalence of ESBL bac- teremia. Note. MDRGN, multidrug-resistant gram-negative organism; CRE, carbapenem- resistant Enterobacteriaceae. Drug-resistant organisms were defined in accordance with the Centers for Disease Control and Prevention guidelines.9
used to predict the value of the held-out observation. This process is repeated for all observations in the dataset, and performance metrics (eg, error) can be averaged across the n fitted models (in this case, decision trees) to produce a single estimate.Weevalu- ated the discrimination of the original and cross-validated models through the generation of receiver operating characteristic (ROC) curves and calculation of C statistics. Decision tree analyses were performed using the RPART (Recursive Partitioning and Regression Trees) package in R Studio version 4.1–90.99.902 soft- ware (R Foundation for Statistical Computing, Vienna, Austria). To develop a risk score, continuous variables (eg, age and anti- biotic days) were first converted into ordinal categories to reduce
complexity, given the score’s anticipated manual application. A multivariable logistic regression model was derived using step- wise variable selection with backward elimination at an α level of 0.05. To create points, regression coefficients were rescaled by dividing by the smallest final model coefficient and rounding to the nearest integer (with the exception of antibiotic therapy, which received 0.25 points per week (up to a maximum of 1 point or ≥4 weeks), to simplify end-user calculations). Patient scores were calculated by summing their respective points (risk score model). For both the multivariable regression model and the risk score
model, discrimination was assessed withROC curves and accompa- nying C statistics (ie, area under the curve). Risk score model
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