Infection Control & Hospital Epidemiology


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Table 1. Regression Model and Corresponding Points Scoring Systema for Predicting Extended-Spectrumβ-Lactamase (ESBL) Status in a Cohort of Adult Patients with Escherichia coli and Klebsiella spp Bacteremia


Variable Intercept


Orthopedic hardware (day of culture)


Chronic indwelling vascular hardware (day of culture) Nephrostomy tube or Foley catheter (day of culture) Gastrointestinal feeding tube (day of culture)


Presumptive infection source: central venous catheter Presumptive infection source: pneumonia Structural lung diseaseb Self-identifies as Asian race


Post-acute care facility stay (prior 6 mo)


≥1 night of hospitalization in an international ESBL high-burden regionc (prior 6 mo) ESBL colonization or infection (prior 6 mo)


Carbapenem-resistant Enterobacteriaceae colonization or infection (prior 6 mo) Multidrug-resistant Pseudomonas spp (prior 6 mo)


Weeks of active gram-negative therapy (per week, up to a maximum of 4, in prior 6 mo)


β Coefficient Odds Ratio (95% CI) −3.81 1.30 0.60 1.17 0.97 0.98 1.12 1.15 1.07 1.04 3.21 3.92 3.45


3.68 (1.21–11.17) 1.82 (1.13–2.94) 3.22 (1.87–5.57) 2.65 (1.35–5.18) 2.68 (1.56–4.60) 2.98 (1.37–6.49) 3.17 (1.43–7.00) 2.93 (1.23–6.94) 2.84 (1.12–7.27)


−2.42 0.15


were calculated by summing their respective points (risk score model). bChronic obstructive pulmonary disease, emphysema, or chronic ventilator dependency. cLatin America (excluding the Caribbean), the Middle East (including Egypt), South Asia, China, and the Mediterranean.


24.86 (10.99–56.24) 50.68 (25.97–98.92) 31.47 (2.52–393.30) 0.09 (0.01–0.83) 1.17 (1.02–1.34)


Points ::: 2 1 2 2 2 2 2 2 2 5 6 6


−4 0.25/week; max of 1 pt


aTo create points, the smallest model coefficient (0.15, per week of antibiotic therapy) was identified. To simplify end-user calculations, antibiotic therapy was scaled to receive 0.25 points per week, up to a maximum of 1 point or ≥4 weeks, by dividing by 0.60 (0.15/0.60 = 0.25). All other coefficients were also divided by 0.60 and rounded to the nearest whole integer. Patient scores


compare the predictive accuracy of these 2 methods and to illus- trate the advantages and disadvantages of logistic regression risk scores versus CART decision trees. Our objective is to offer general guiding principles for epidemiologists and researchers for when they might consider one prediction approach versus the other.


Methods Cohort


The full description of the cohort has been previously reported.8 Briefly, the study included adults hospitalized at the Johns Hopkins Hospital with bacteremia due to Escherichia coli or Klebsiella spp, from 2008 to 2015. Only the first episode of bacteremia per patient was included. Escherichia coli or Klebsiella spp with ceftriaxone mini- muminhibitory concentrations (MICs) ≥2 μg/mL underwent testing for ESBL production.Adecrease of≥3 doubling dilutions in theMIC for a third-generation cephalosporin tested in combination with 4 μg/ mL of clavulanic acid, versus its MIC when tested alone, was used to confirm ESBL status. Patient data were collected via manual chart review from all


available inpatient and outpatient medical records from facilities within the Johns Hopkins Health System, as well as from medical records for patients who previously receivedmedical care at institu- tions in the Epic Care Everywhere Network (www.epic.com/ CareEverywhere/). Patient data collected, which was based on the time period prior to day 1 of bacteremia (defined as the date the initial blood culture was collected), included the following: (1) dem- ographic data; (2) preexisting medical conditions; (3) presumptive source of bacteremia (eg, catheter, pneumonia); (4) indwelling hard- ware; (5) multidrug-resistant organism (MDRO) colonization or infection (MDR Pseudomonas aeruginosa,MDR Acinetobacter bau- mannii, ESBL-producing Enterobacteriaceae, carbapenem-resistant


Enterobacteriaceae, vancomycin-resistant Enterococcus species, and methicillin-resistant Staphylococcus aureus)9 in the prior 6 months; (6) days of antibiotic therapy with gram-negative activity in the prior 6 months; (7) length of stay in any healthcare facility in the prior 6 months; (8) post-acute care facility stay in the prior 6 months; and (9) hospitalization in another country in the prior 6months (assessed by standard nursing intake questionnaire upon Johns Hopkins Hospital admission). International hospitalizations in the following regions were classified as ESBL “high-burden”: Latin America (excluding the Caribbean), the Middle East (including Egypt), South Asia, China, and the Mediterranean.10,11


Statistical methods


Descriptive statistics, univariable analyses, and decision tree deri- vation and validation have been described previously.8 Briefly, a tree was derived using the following process: (1) identification of the single variable that, when used to split the dataset into 2 groups (“nodes”), best separated ESBL-positive from ESBL- negative patients, according to the Gini impurity criterion12,13; (2) repetition of this partitioning process in each daughter node and subsequent generations of nodes (“branching”); and (3) termi- nation at “terminal” nodes (“leaves”) when no additional variables in the data sufficiently distinguished patients by their ESBL status. Terminal nodes in binary recursive partitioning trees predict ESBL status categorically, but by evaluating the node impurity (eg, the mixture of ESBL-positive and ESBL-negative patients), they also offer associated probabilities. We internally validated the performance of our tree using the leave-one-out cross-validation method,12 in which a single obser- vation is held out and a new model is derived from a dataset con- taining the remaining n − 1 observations. The resulting model is


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