TY - JOUR
T1 - A pragmatic machine learning model to predict carbapenem resistance
AU - McGuire, Ryan J.
AU - Yu, Sean C.
AU - Payne, Philip R.O.
AU - Lai, Albert M.
AU - Vazquez-Guillamet, M. Cristina
AU - Kollef, Marin H.
AU - Michelson, Andrew P.
N1 - Publisher Copyright:
Copyright © 2021 American Society for Microbiology. All Rights Reserved.
PY - 2021/7
Y1 - 2021/7
N2 - 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 .18years of age admitted to a tertiary-care academic medical center between 1 January 2012 and 10 October 2017 with $1 bacterial culture were eligible for inclusion. All demographic, medication, vital sign, procedure, laboratory, and culture/sensitivity data were extracted from the electronic health record. Organisms were considered CR if a single isolate was reported as intermediate or resistant. Patients with CR and non-CR organisms were temporally matched to maintain the positive/negative case ratio. Extreme gradient boosting was used for model development. In total, 68,472 patients met inclusion criteria, with 1,088 patients identified as having CR organisms. 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.
AB - 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 .18years of age admitted to a tertiary-care academic medical center between 1 January 2012 and 10 October 2017 with $1 bacterial culture were eligible for inclusion. All demographic, medication, vital sign, procedure, laboratory, and culture/sensitivity data were extracted from the electronic health record. Organisms were considered CR if a single isolate was reported as intermediate or resistant. Patients with CR and non-CR organisms were temporally matched to maintain the positive/negative case ratio. Extreme gradient boosting was used for model development. In total, 68,472 patients met inclusion criteria, with 1,088 patients identified as having CR organisms. 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.
KW - Antibiotic resistance
KW - Carbapenem
KW - Electronic health record
KW - Machine learning
KW - Predictive modeling
UR - http://www.scopus.com/inward/record.url?scp=85108279215&partnerID=8YFLogxK
U2 - 10.1128/AAC.00063-21
DO - 10.1128/AAC.00063-21
M3 - Article
C2 - 33972243
AN - SCOPUS:85108279215
SN - 0066-4804
VL - 65
JO - Antimicrobial agents and chemotherapy
JF - Antimicrobial agents and chemotherapy
IS - 7
M1 - e00063-21
ER -