Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.

Stephanie Carreiro,Pravitha Ramanand,Washim Akram,Joshua Stapp,Brittany Chapman,David Smelson,Premananda Indic
{"title":"Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.","authors":"Stephanie Carreiro,Pravitha Ramanand,Washim Akram,Joshua Stapp,Brittany Chapman,David Smelson,Premananda Indic","doi":"10.1213/ane.0000000000007244","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nRepeated 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.\r\n\r\nMETHODS\r\nUsing 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.\r\n\r\nRESULTS\r\nForty-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%.\r\n\r\nCONCLUSIONS\r\nWearable 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.","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"68 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anesthesia & Analgesia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1213/ane.0000000000007244","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

BACKGROUND 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. METHODS 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. RESULTS 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%. CONCLUSIONS 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.
开发基于可穿戴传感器的阿片类药物依赖性数字生物标记。
背景反复暴露于阿片类药物会导致各种生理适应,这些适应以不同的速度发展,并可能预示着阿片类药物使用障碍(OUD)的风险,包括依赖性和戒断。使用非侵入性传感器来识别阿片类药物使用的生理适应性的数字药物警戒策略是一种促进更安全地开具阿片类药物处方的新策略。本研究旨在通过比较阿片类药物过敏者和患有急性疼痛的阿片类药物慢性使用者,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类人群。方法采用纵向观察研究设计,收集患有急性疼痛并接受阿片类药物镇痛的参与者的连续生理数据。监测贯穿整个住院期间和出院后的 7 天。阿片类药物给药数据来自电子健康记录(EHR)和参与者的自我报告。根据阿片类药物使用史,参与者被分为 3 类:新手、偶尔使用或长期使用。30 个特征来自传感器数据,另外 9 个特征来自参与者的人口统计和治疗特征。使用阿片类药物后立即出现的生理特征行为在新手和长期参与者之间进行了比较,随后特征被用于生成机器学习模型,并通过交叉验证和保留数据进行了验证。研究人员利用 14 种基于传感器的特征和 5 种临床特征开发了四种可解释的基于决策树的机器学习模型,用于预测给定观测值(剂量)和参与者级别的类别成员。结论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的使用状态(天真与慢性),其差异化特征可用于检测阿片类药物依赖性。今后的工作应旨在进一步界定这些模型中发现的现象(包括阿片依赖和/或戒断),并确定个体在重复接触阿片类药物后从一种特征转变为另一种特征的过渡状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信