Challener Douglas W, Fida Madiha, Martin Peter, Rivera Christina G, Virk Abinash, Walker Lorne W
Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, USA.
Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
J Antimicrob Chemother. 2024 Dec 2;79(12):3055-3062. doi: 10.1093/jac/dkae340.
This study aimed to conduct a scoping review of machine learning (ML) techniques in outpatient parenteral antimicrobial therapy (OPAT) for predicting adverse outcomes and to evaluate their validation, implementation and potential barriers to adoption.
This scoping review included studies applying ML algorithms to adult OPAT patients, covering techniques from logistic regression to neural networks. Outcomes considered were medication intolerance, toxicity, catheter complications, hospital readmission and patient deterioration. A comprehensive search was conducted across databases including Cochrane Central, Cochrane Reviews, Embase, Ovid MEDLINE and Scopus, from 1 January 2000 to 1 January 2024.
Thirty-two studies met the inclusion criteria, with the majority being single-centre experiences primarily from North America. Most studies focused on developing new ML models to predict outcomes such as hospital readmissions and medication-related complications. However, there was very little reporting on the performance characteristics of these models, such as specificity, sensitivity and C-statistics. There was a lack of multi-centre or cross-centre validation, limiting generalizability. Few studies advanced beyond traditional logistic regression models, and integration into clinical practice remains limited.
ML shows promise for enhancing OPAT outcomes by predicting adverse events and enabling pre-emptive interventions. Despite this potential, significant gaps exist in development, validation and practical implementation. Barriers include the need for representative data sets and broadly applicable, validated models.
Future research should address these barriers to fully leverage ML's potential in optimizing OPAT care and patient safety. Models must deliver timely, accurate and actionable insights to improve adverse event prediction and prevention in OPAT settings.
本研究旨在对门诊胃肠外抗菌治疗（OPAT）中用于预测不良结局的机器学习（ML）技术进行范围综述，并评估其验证、实施情况以及采用的潜在障碍。
本范围综述纳入了将ML算法应用于成年OPAT患者的研究，涵盖从逻辑回归到神经网络的技术。所考虑的结局包括药物不耐受、毒性、导管并发症、医院再入院和患者病情恶化。对2000年1月1日至2024年1月1日期间包括Cochrane Central、Cochrane Reviews、Embase、Ovid MEDLINE和Scopus在内的数据库进行了全面检索。
32项研究符合纳入标准，其中大多数是主要来自北美的单中心经验。大多数研究专注于开发新的ML模型以预测医院再入院和药物相关并发症等结局。然而，关于这些模型的性能特征，如特异性、敏感性和C统计量的报告非常少。缺乏多中心或跨中心验证，限制了可推广性。很少有研究超越传统逻辑回归模型，并且在临床实践中的整合仍然有限。
ML有望通过预测不良事件并进行预防性干预来改善OPAT结局。尽管有这种潜力，但在开发、验证和实际实施方面仍存在重大差距。障碍包括需要代表性数据集和广泛适用、经过验证的模型。
未来的研究应克服这些障碍，以充分利用ML在优化OPAT护理和患者安全方面的潜力。模型必须提供及时、准确且可操作的见解，以改善OPAT环境中的不良事件预测和预防。
