Infection Control & Hospital Epidemiology
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dependency) (2 points); self-identification as Asian race (2 points).
4. Healthcare exposure within the previous 6 months. Post-acute care facility (2 points); ≥1 night of international hospitali- zation in an ESBL high-burden region (5 points).
5. MDRGN colonization or infection within the previous 6 months. ESBL (6 points); carbapenem-resistant Entero- bacteriaceae (CRE) (6 points); MDR Pseudomonas spp (−4 points).
6. Antibiotic exposure within the previous 6 months. Weeks of therapy with gram-negative activity (0.25 points per week, up to a maximum of 1 point).
Patient scores ranged from−3 to 18.75, with a median score of 2
Fig. 2. Discrimination and calibration metrics for the multivariable logistic regres- sion model and resulting risk score model. (A) Receiver operating characteristic (ROC) curve for the logistic regression model, prior to risk score transformation. The area under the curve (AUC) was 0.87 which, after rounding, was unchanged fol- lowing conversion to a point-based risk-score model. See Table 2 for exact sensi- tivity and specificity values at different score cutoff points. (B) Calibration plot of observed proportion versus ESBL probabilities predicted by the risk score model, by decile groups.
calibration was evaluated using Hosmer-Lemeshow (HL) goodness- of-fit tests and graphical plots of observed proportion versus model- predicted ESBL probabilities by decile groups. Discrimination was internally validated with leave-one-out cross-validation. Risk score analyses were performed in Stata version 13.0 software (StataCorp, College Station, TX) and R Studio.
Results
Spanning the 2008 to 2015 time period, a total of 1,288 bacteremic patients met inclusion criteria, of whom 194 (15%) were ESBL positive. Patient and microbial characteristics have been reported previously.8
Risk score
The multivariable model and resulting risk score included 14 variables (Table 1), which were broadly categorizable into 6 groups (Fig. 1):
1. Indwelling hardware on day of culture. Orthopedic hardware (2 points); chronic indwelling vascular hardware (1 point); nephrostomy tube or Foley catheter (2 points); gastrointesti- nal feeding tube (2 points).
2. Presumptive source of bloodstream infection. central vascular catheter (2 points); pneumonia (2 points).
3.
Patient characteristics. Structural lung disease (chronic obstructive pulmonary disease, emphysema, or tracheostomy
points (interquartile range: 0–3.25). The C statistic for the clinical risk score was 0.87 and 0.89 following cross-validation. The C statistic for the multivariable logistic regression model was also 0.87 (Fig. 2). The multivariable logistic regression model provided evidence of acceptable calibration (HL goodness-of-fit test P = .13). Following point conversion, however, the risk score model over- or underestimated the probability of ESBL infection at different points along the risk continuum, with the exception of very high-risk deciles (HL goodness-of-fit test P < .001) (Fig. 2). An ESBL-positive cutoff point of ≥7.25 maximized overall ESBL classification accuracy (92%). At this cutoff point, the risk score had a sensitivity of 49.5% and a specificity of 99.5%, and its positive and negative predictive values were 94.6% and 91.8%, respectively. Table 2 provides the risk score’s sensitivity and specificity at each possible ESBL-positive cutoff point.
Decision tree
The final decision tree8 included 5 predictors: central vascular catheter, age ≥43 years, and in the prior 6 months: history of ESBL colonization/infection, ≥1 night hospitalization in an ESBL high-burden region, and/or ≥1 week of gram-negative active antibiotic therapy (Fig. 3). The C statistic of the decision tree was 0.77 (unchanged in cross-validation); the sensitivity and specificity were 51.0% and 99.1%, and the positive and negative predictive val- ues were 90.8% and 91.9%, respectively. Table 3 presents a com- parison of the performance metrics of the risk score versus the decision tree.
Discussion
Despite advances in rapid diagnostics, timely identification of MDRGNs remains a clinical and epidemiological challenge. Diagnostic delays can prolong the period of ineffective antibiotic therapy and can increase the risk of nosocomial transmissions.3,4 Statistical models for predicting drug resistance can play an impor- tant role in settings where rapid diagnostic tests are unavailable or are resource-impractical. This case study of ESBL bloodstream infections explores 2 approaches for developing predictive models: traditional logistic regression-derived risk scores and machine learning-derived decision trees. The risk score included 14 independent predictors, broadly classifiable into 6 categories: indwelling hardware, bloodstream infection source, patient characteristics, recent gram-negative antibiotic exposure, healthcare exposure, and MDRO history. Many of these variables (eg, antibiotic use, prior ESBL colonization or infection) were retained in the decision tree. They are also consistent with other studies examining risk factors for MDRGN bloodstream infections14 and recent scores for identifying
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