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A Pragmatic Machine Learning Model To Predict Carbapenem Resistance
- Source :
- Antimicrob Agents Chemother
- Publication Year :
- 2021
-
Abstract
- 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 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.
- Subjects :
- Carbapenem
Microbiological culture
medicine.drug_class
medicine.medical_treatment
Antibiotics
Machine learning
computer.software_genre
Risk Assessment
Epidemiology and Surveillance
Machine Learning
03 medical and health sciences
0302 clinical medicine
Text mining
Antibiotic resistance
Predictive Value of Tests
Medicine
Humans
Pharmacology (medical)
030212 general & internal medicine
Carbapenem resistance
Retrospective Studies
Pharmacology
0303 health sciences
Receiver operating characteristic
030306 microbiology
business.industry
Infectious Diseases
Carbapenems
Artificial intelligence
business
computer
Central venous catheter
medicine.drug
Subjects
Details
- ISSN :
- 10986596
- Volume :
- 65
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- Antimicrobial agents and chemotherapy
- Accession number :
- edsair.doi.dedup.....350938b539310677946b0b550855c5f6