Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation

Jia'an Qin; Yuhe Yang; Chao Ai; Zhaoshuai Ji; Wei Chen; Yingchang Song; Jiayu Zeng; Meili Duan; Wenjie Qi; Shutian Zhang; Zhuoling An; Yang Lin; Sha Xu; Kejun Deng; Hao Lin; Dan Yan

doi:10.1016/j.ijantimicag.2024.107122

Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation

Int J Antimicrob Agents. 2024 May;63(5):107122. doi: 10.1016/j.ijantimicag.2024.107122. Epub 2024 Feb 29.

Authors

Jia'an Qin¹, Yuhe Yang², Chao Ai³, Zhaoshuai Ji³, Wei Chen⁴, Yingchang Song¹, Jiayu Zeng¹, Meili Duan¹, Wenjie Qi¹, Shutian Zhang¹, Zhuoling An⁵, Yang Lin⁶, Sha Xu⁶, Kejun Deng⁷, Hao Lin⁸, Dan Yan⁹

Affiliations

¹ Beijing Institute of Clinical Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
² College of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
³ Department of Clinical Pharmacy, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
⁴ Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
⁵ Department of Pharmacy, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
⁶ Department of Pharmacy, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
⁷ College of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. Electronic address: dengkj@uestc.edu.cn.
⁸ College of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. Electronic address: hlin@uestc.edu.cn.
⁹ Beijing Institute of Clinical Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China. Electronic address: danyan@ccmu.edu.cn.

PMID: 38431108
DOI: 10.1016/j.ijantimicag.2024.107122

Abstract

Background: With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is unclear.

Methods: We conducted statistical descriptions on 16 101 antibiotic coprescriptions for inpatients with bacterial infections from 2015 to 2023. By integrating the frequency and effectiveness of prescriptions, we formulated recommendations for the feasibility of antibiotic combinations. Initially, a machine learning algorithm was utilised to optimise grading thresholds and habits for antibiotic combinations. A feedforward neural network (FNN) algorithm was employed to develop antibiotic combination recommendation model (ACRM). To enhance interpretability, we combined sequential methods and DrugBank to explore the correlation between antibiotic combinations and drug interactions.

Results: A total of 55 antibiotics, covering 657 empirical clinical antibiotic combinations were used for ACRM construction. Model performance on the test dataset showed AUROCs of 0.589-0.895 for various antibiotic recommendation classes. The ACRM showed satisfactory clinical relevance with 61.54-73.33% prediction accuracy in a new independent retrospective cohort. Antibiotic interaction detection showed that the risk of drug interactions was 29.2% for strongly recommended and 43.5% for not recommended. A positive correlation was identified between the level of clinical recommendation and the risk of drug interactions.

Conclusions: Machine learning modelling of retrospective antibiotic prescriptions habits has the potential to predict antibiotic combination recommendations. The ACRM plays a supporting role in reducing the incidence of drug interactions. Clinicians are encouraged to adopt such systems to improve the management of antibiotic usage and medication safety.

Keywords: Antibiotic combination; Bacterial infection; Drug interaction; Machine learning; Recommendation model.

Publication types

Multicenter Study

MeSH terms

Algorithms
Anti-Bacterial Agents* / therapeutic use
Bacterial Infections* / drug therapy
Drug Interactions*
Drug Therapy, Combination
Humans
Machine Learning*
Retrospective Studies

Substances

Anti-Bacterial Agents