The Rapid Prediction of Carbapenem Resistance in Patients With Bacteremia Using Electronic Medical Record Data.

BACKGROUND

The administration of active antibiotics is often delayed in cases of carbapenem-resistant gram-negative bacteremia. Using electronic medical record (EMR) data to rapidly predict carbapenem resistance in patients with bacteremia could help reduce the time to active therapy.

METHODS

All cases of bacteremia at Mount Sinai Hospital from September 2012 through September 2016 were included. Cases were randomly divided into a "training set" and a "testing set." EMR data from the training set cases were reviewed, and significant risk factors for carbapenem resistance were entered into a multiple logistic regression model. Performance was assessed by repeated K-fold cross-validation and by applying the training set model to the testing set. All cases were also reviewed to determine the time to effective antibiotic therapy.

RESULTS

A total of 613 cases of bacteremia were included, 61 (10%) of which were carbapenem-resistant. The training and testing sets consisted of 460 and 153 cases, respectively. The regression model derived from the training set correctly predicted 73% of carbapenem-resistant cases and 59% of carbapenem-susceptible cases in the testing set (sensitivity, 73%; specificity, 59%; positive predictive value, 16%; negative predictive value, 95%). The mean area under the receiver operator characteristic curve of the K-fold cross-validation repeats was 0.731. Patients with carbapenem-resistant infections received active antibiotics significantly later than those with susceptible infections (40.4 hours vs 9.6 hours, < .0001).

CONCLUSIONS

A multiple logistic regression model using EMR data can generate rapid, sensitive predictions of carbapenem resistance in patients with bacteremia, which could help shorten the time to effective therapy in these cases.