Prediction of vancomycin plasma concentration in elderly patients based on multi-algorithm mining combined with population pharmacokinetics

• Published in: Scientific reports 2024-11, Vol.14 (1), p.27165-15, Article 27165
• Main Authors: Ma, Pan, Ma, Huan, Liu, Ruixiang, Wen, Haini, Li, Haisheng, Huang, Yifan, Li, Ying, Xiong, Lirong, Xie, Linli, Wang, Qian
• Format: Article
• Language: English
• Subjects: 631/154; 631/154/436; 631/154/436/108; 631/154/436/1729; 631/154/436/2388; Accuracy; Aged; Aged, 80 and over; Algorithms; Anti-Bacterial Agents - blood; Anti-Bacterial Agents - pharmacokinetics; Antibiotics; Elderly; Feature selection; Female; Humanities and Social Sciences; Humans; Machine Learning; Male; Models, Biological; multidisciplinary; Pharmacokinetics; Population pharmacokinetics; Population studies; Precision medicine; Prediction models; Retrospective Studies; Science; Science (multidisciplinary); Vancomycin; Vancomycin - blood; Vancomycin - pharmacokinetics
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-78558-1

The pharmacokinetics of vancomycin exhibit significant inter-individual variability, particularly among elderly patients. This study aims to develop a predictive model that integrates machine learning with population pharmacokinetics (popPK) to facilitate personalized medication management for this demographic. A retrospective analysis incorporating 33 features, including popPK parameters such as clearance and volume of distribution. A combination of multiple algorithms and Shapley Additive Explanations was utilized for feature selection to identify the most influential factors affecting drug concentrations. The performance of each algorithm with popPK parameters was superior to that without popPK parameters. Our final ensemble model, composed of support vector regression, light gradient boosting machine, and categorical boosting in a 6:3:1 ratio, included 16 optimized features. This model demonstrated superior predictive accuracy compared to models utilizing all features, with testing group metrics including an R 2 of 0.656, mean absolute error of 3.458, mean square error of 28.103, absolute accuracy within ± 5 mg/L of 81.82%, and relative accuracy within ± 30% of 76.62%. This study presents a rapid and cost-effective predictive model for estimating vancomycin plasma concentrations in elderly patients. The model offers a valuable tool for clinicians to accurately determine effective plasma concentration ranges and tailor individualized dosing regimens, thereby enhancing therapeutic outcomes and safety.