Carreiro Stephanie, Ramanand Pravitha, Akram Washim, Stapp Joshua, Chapman Brittany, Smelson David, Indic Premananda
From the Department of Emergency Medicine, Division of Medical Toxicology, University of Massachusetts Chan Medical School, Worcester, MA.
Department of Electrical and Computer Engineering, The University of Texas at Tyler, Tyler, TX.
Anesth Analg. 2025 Aug 1;141(2):393-402. doi: 10.1213/ANE.0000000000007244. Epub 2024 Oct 16.
Repeated opioid exposure leads to a variety of physiologic adaptations that develop at different rates and may foreshadow risk of opioid-use disorder (OUD), including dependence and withdrawal. Digital pharmacovigilance strategies that use noninvasive sensors to identify physiologic adaptations to opioid use represent a novel strategy to facilitate safer opioid prescribing. This study aims to identify wearable sensor-derived features associated with opioid dependence by comparing opioid-naïve individuals to chronic opioid users with acute pain and developing a machine-learning model to distinguish between the 2 groups.
Using a longitudinal observational study design, continuous physiologic data were collected on participants with acute pain receiving opioid analgesia. Monitoring continued throughout hospitalization and for up to 7 days posthospital discharge. Opioid administration data were obtained from electronic health record (EHR) and participant self-report. Participants were classified as belonging to 1 of 3 categories based on opioid use history: naïve, occasional, or chronic use. Thirty features were derived from sensor data, and an additional 9 features were derived from participant demographic and treatment characteristics. Physiologic feature behavior immediately postopioid use was compared among naïve and chronic participants, and subsequently features were used to generate machine learning models which were validated using cross-validation and holdout data.
Forty-one participants with a combined total of 169 opioid administrations were ultimately included in the final analysis. Four interpretable decision tree-based machine learning models with 14 sensor-based and 5 clinical features were developed to predict class membership on the level of a given observation (dose) and on the participant level. Ranges for model metrics on the participant level were as follows: accuracy 70% to 90%, sensitivity 67% to 100%, and specificity 67% to 100%.
Wearable sensor-derived digital biomarkers can be used to predict opioid use status (naïve versus chronic) and the differentiating features may be detecting opioid dependence. Future work should be aimed at further delineating the phenomenon identified in these models (including opioid dependence and/or withdrawal) and at identifying transition states where an individual changes from 1 profile to another with repetitive opioid exposure.
反复接触阿片类药物会导致多种生理适应性变化，这些变化以不同速率发展，可能预示着阿片类药物使用障碍（OUD）的风险，包括依赖和戒断。利用非侵入性传感器识别对阿片类药物使用的生理适应性变化的数字药物警戒策略是一种促进更安全阿片类药物处方的新策略。本研究旨在通过比较未使用过阿片类药物的个体与患有急性疼痛的慢性阿片类药物使用者，识别与阿片类药物依赖相关的可穿戴传感器衍生特征，并开发一种机器学习模型来区分这两组人群。
采用纵向观察性研究设计，收集接受阿片类镇痛的急性疼痛参与者的连续生理数据。监测持续整个住院期间以及出院后长达7天。阿片类药物给药数据来自电子健康记录（EHR）和参与者自我报告。根据阿片类药物使用史，参与者被分类为以下3类之一：未使用过、偶尔使用或慢性使用。从传感器数据中提取30个特征，另外9个特征从参与者人口统计学和治疗特征中提取。比较未使用过和慢性参与者在使用阿片类药物后立即出现的生理特征行为，随后使用这些特征生成机器学习模型，并使用交叉验证和留存数据进行验证。
最终共有41名参与者，总计169次阿片类药物给药被纳入最终分析。开发了四个基于可解释决策树的机器学习模型，具有14个基于传感器的特征和5个临床特征，用于预测给定观察（剂量）水平和参与者水平上的类别归属。参与者水平上模型指标的范围如下：准确率70%至90%，灵敏度67%至100%，特异性67%至100%。
可穿戴传感器衍生的数字生物标志物可用于预测阿片类药物使用状态（未使用过与慢性使用），且区分特征可能是检测阿片类药物依赖。未来的工作应旨在进一步阐明这些模型中识别出的现象（包括阿片类药物依赖和/或戒断），并识别个体在反复接触阿片类药物后从一种状态转变为另一种状态的过渡状态。
