A Pragmatic Machine Learning Model to Predict Carbapenem Resistance.

Infection caused by carbapenem resistant (CR) organisms is a rising problem in the United States. While the risk factors for antibiotic resistance are well known, there remains a large need for the early identification of antibiotic resistant infections. Using machine learning (ML), we sought to develop a prediction model for carbapenem resistance. All patients >18 years of age admitted to a tertiary-care academic medical center between Jan 1, 2012 and Oct 10, 2017 with ≥1 bacterial culture were eligible for inclusion. All demographic, medication, vital sign, procedure, laboratory, and culture/sensitivity data was extracted from the electronic health record. Organisms were considered CR if a single isolate was reported as intermediate or resistant. CR and non-CR patients were temporally matched to maintain positive/negative case ratio. Extreme gradient boosting was used for model development. In total, 68,472 patients met inclusion criteria with 1,088 CR patients identified. Sixty-seven features were used for predictive modeling. The most important features were number of prior antibiotic days, recent central venous catheter placement, and inpatient surgery. After model training, the area under the receiver operating characteristic curve was 0.846. The sensitivity of the model was 30%, with a positive predictive value (PPV) of 30% and a negative predictive value of 99%. Using readily available clinical data, we were able to create a ML model capable of predicting CR infections at the time of culture collection with a high PPV.