Department of Surgery, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA; Computer Science Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, MA.
Department of Surgery, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA. Electronic address: https://twitter.com/Jayson_Marwaha.
Surgery. 2022 Aug;172(2):655-662. doi: 10.1016/j.surg.2022.03.027. Epub 2022 May 5.
Many U.S. institutions have adopted postsurgical opioid-prescribing guidelines to standardize prescribing practices, and yet there is inherent variability in patients' opioid consumption after surgery. The utility of these guidelines is limited by the fact that some patients' needs will inevitably exceed them, and yet there are no evidence-based tools to help providers identify these patients. In this study we aimed to maximize the value of these guidelines by training machine learning models to predict patients whose needs will be met by these smaller recommended prescriptions, and patients who may require an additional degree of personalization. The aim of the present study was to develop predictive models for determining whether a surgical patient's postdischarge opioid requirement will fall above or below common opioid prescribing guidelines.
We conducted a retrospective cohort study of surgical patients at one institution from 2017 to 2018. Patients were called after discharge to collect opioid consumption data. Machine learning models were used to identify outlier opioid consumers (ie, exceeding our institutional prescribing guidelines) using diagnosis codes, medical history, in-hospital opioid use, and perioperative factors as predictors. External validation was performed on opioid consumption data collected at a second institution from 2020 to 2021, and sensitivity analysis was performed using a third institution's prescribing guidelines.
The development and external validation cohorts included 1,867 and 498 patients, respectively. Age, body mass index, tobacco use, preoperative opioid exposure, and in-hospital opioid consumption were the strongest predictors of postdischarge consumption. A lasso regression model exhibited an area under the receiver operating characteristic curve of 0.74 (95% confidence interval 0.67-0.81) in predicting postdischarge opioid consumption. External validation of a limited lasso model yielded an area under the receiver operating characteristic curve of 0.67 (0.60-0.74). Performance was preserved when evaluated on another institution's guidelines (area under the receiver operating characteristic curve 0.76 [0.72-0.80]).
Patient characteristics reliably predict postdischarge opioid consumption in relation to prescribing guidelines for both opioid-naive and exposed populations. This model may be used to help providers confidently follow prescribing guidelines for patients with typical opioid responsiveness and correctly pursue more personalized prescribing for others.
许多美国机构已经采用了术后阿片类药物处方指南来规范处方实践，但患者术后的阿片类药物消耗仍存在固有差异。这些指南的实用性受到限制，因为一些患者的需求不可避免地会超过这些指南，而且没有基于证据的工具来帮助提供者识别这些患者。在这项研究中，我们旨在通过训练机器学习模型来预测那些需要通过较小推荐剂量就能满足的患者，以及那些可能需要更大程度个性化治疗的患者，从而最大限度地发挥这些指南的价值。本研究的目的是开发预测模型，以确定手术患者出院后的阿片类药物需求是否高于或低于常见的阿片类药物处方指南。
我们对一家机构 2017 年至 2018 年的外科患者进行了回顾性队列研究。患者在出院后接受电话随访，以收集阿片类药物消耗数据。使用诊断代码、病史、住院期间阿片类药物使用情况和围手术期因素作为预测因子的机器学习模型来识别阿片类药物消耗的异常值（即超过我们机构的处方指南）。在 2020 年至 2021 年从第二家机构收集的阿片类药物消耗数据上进行了外部验证，并使用第三家机构的处方指南进行了敏感性分析。
开发和外部验证队列分别包括 1867 名和 498 名患者。年龄、体重指数、吸烟、术前阿片类药物暴露和住院期间阿片类药物使用是预测出院后消耗的最强预测因子。套索回归模型预测出院后阿片类药物消耗的受试者工作特征曲线下面积为 0.74（95%置信区间 0.67-0.81）。在另一家机构的指南上评估时，有限套索模型的受试者工作特征曲线下面积为 0.67（0.60-0.74）。当在另一家机构的指南上评估时，性能保持不变（受试者工作特征曲线下面积 0.76 [0.72-0.80]）。
患者特征可可靠地预测阿片类药物处方指南与阿片类药物初治和暴露人群的关系。该模型可用于帮助提供者自信地遵循对典型阿片类药物反应患者的处方指南，并正确地为其他患者提供更个性化的处方。
