Pub Date : 2024-10-18DOI: 10.1213/ane.0000000000007181
Thomas Heidegger,Amina Ghulam,Markus Bischoff,Markus M Luedi
{"title":"Beyond Artificial Intelligence: A Critical Appraisal From An Airway Management Perspective.","authors":"Thomas Heidegger,Amina Ghulam,Markus Bischoff,Markus M Luedi","doi":"10.1213/ane.0000000000007181","DOIUrl":"https://doi.org/10.1213/ane.0000000000007181","url":null,"abstract":"","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BACKGROUNDHigh pain levels immediately after surgery have been associated with persistent postsurgical pain. Still, it is uncertain if analgesic treatment of immediate postsurgical pain prevents the development of persistent postsurgical pain.METHODSWe searched MEDLINE, CENTRAL, and Embase up to September 12, 2023, for randomized controlled trials investigating perioperative analgesic interventions and with reported pain levels 3 to 24 months after total hip or knee arthroplasty in patients with osteoarthritis. The primary outcome was pain score 3 to 24 months after surgery, assessed at rest and during movement separately. Two authors independently screened, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2 tool. We conducted meta-analyses and tested their robustness with trial sequential analyses and worst-best and best-worst case analyses.RESULTSWe included 49 trials with 68 intervention arms. All but 4 trials were at high risk of bias for the primary outcome. Moreover, the included trials were heterogeneous in terms of exclusion criteria, baseline pain severity, and which cointerventions the participants were offered. For pain at rest, no interventions demonstrated a statistically significant difference between intervention and control. For pain during movement, perioperative treatment with duloxetine (7 trials with 641 participants) reduced pain scores at 3 to 24 months after surgery (mean difference -4.9 mm [95% confidence interval {CI}, -6.5 to -3.4] on the 0-100 visual analog scale) compared to placebo. This difference was lower than our predefined threshold for clinical importance of 10 mm.CONCLUSIONSWe found no perioperative analgesic interventions that reduced pain 3 to 24 months after total hip or knee arthroplasty for osteoarthritis. The literature on perioperative analgesia focused little on potential long-term effects. We encourage the assessment of long-term pain outcomes.
{"title":"Perioperative Analgesic Interventions for Reduction of Persistent Postsurgical Pain After Total Hip and Knee Arthroplasty: A Systematic Review and Meta-analysis.","authors":"Jens Laigaard,Anders Karlsen,Mathias Maagaard,Troels Haxholdt Lunn,Ole Mathiesen,Søren Overgaard","doi":"10.1213/ane.0000000000007246","DOIUrl":"https://doi.org/10.1213/ane.0000000000007246","url":null,"abstract":"BACKGROUNDHigh pain levels immediately after surgery have been associated with persistent postsurgical pain. Still, it is uncertain if analgesic treatment of immediate postsurgical pain prevents the development of persistent postsurgical pain.METHODSWe searched MEDLINE, CENTRAL, and Embase up to September 12, 2023, for randomized controlled trials investigating perioperative analgesic interventions and with reported pain levels 3 to 24 months after total hip or knee arthroplasty in patients with osteoarthritis. The primary outcome was pain score 3 to 24 months after surgery, assessed at rest and during movement separately. Two authors independently screened, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2 tool. We conducted meta-analyses and tested their robustness with trial sequential analyses and worst-best and best-worst case analyses.RESULTSWe included 49 trials with 68 intervention arms. All but 4 trials were at high risk of bias for the primary outcome. Moreover, the included trials were heterogeneous in terms of exclusion criteria, baseline pain severity, and which cointerventions the participants were offered. For pain at rest, no interventions demonstrated a statistically significant difference between intervention and control. For pain during movement, perioperative treatment with duloxetine (7 trials with 641 participants) reduced pain scores at 3 to 24 months after surgery (mean difference -4.9 mm [95% confidence interval {CI}, -6.5 to -3.4] on the 0-100 visual analog scale) compared to placebo. This difference was lower than our predefined threshold for clinical importance of 10 mm.CONCLUSIONSWe found no perioperative analgesic interventions that reduced pain 3 to 24 months after total hip or knee arthroplasty for osteoarthritis. The literature on perioperative analgesia focused little on potential long-term effects. We encourage the assessment of long-term pain outcomes.","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1213/ane.0000000000007052
Cameron R Bosinski,Christopher W Connor
{"title":"Hidden Structures: Gap Junctions, the Claustrum, and Anesthesia.","authors":"Cameron R Bosinski,Christopher W Connor","doi":"10.1213/ane.0000000000007052","DOIUrl":"https://doi.org/10.1213/ane.0000000000007052","url":null,"abstract":"","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1213/ane.0000000000007288
Franklin Dexter,Randy W Loftus
{"title":"Chlorhexidine Wipes with Educational Feedback Are Effective at Reducing Axilla and Groin Bacterial Contamination at the Start of Surgery.","authors":"Franklin Dexter,Randy W Loftus","doi":"10.1213/ane.0000000000007288","DOIUrl":"https://doi.org/10.1213/ane.0000000000007288","url":null,"abstract":"","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1213/ane.0000000000007260
Pedro Luis Bravo,Francisco Gonzalez Sammarco,Daniel A Cueva Nieves,Leonardo Lorente,Jonathan Delgado,Ricardo Martinez-Ruiz
{"title":"Innovative Endotracheal Tube Design Reduces Laryngeal Injury with an Excellent Airway Seal and Minimal Cuff Pressures.","authors":"Pedro Luis Bravo,Francisco Gonzalez Sammarco,Daniel A Cueva Nieves,Leonardo Lorente,Jonathan Delgado,Ricardo Martinez-Ruiz","doi":"10.1213/ane.0000000000007260","DOIUrl":"https://doi.org/10.1213/ane.0000000000007260","url":null,"abstract":"","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BACKGROUNDEvaluating competency acquisition during residency training is crucial. The Anesthesiology Milestones have been implemented in the United States. The China Consortium of Elite Teaching Hospitals for Residency Education has also developed the Chinese Resident Core Competency Milestone Evaluation System. Despite this, Milestones tailored for anesthesiology have yet to be implemented in China. To address this gap, we have developed Chinese Anesthesiology Milestones. This study aims to assess the reliability and validity of the Chinese Anesthesiology Milestones and their correlation with objective examinations.METHODSIn this single-center cross-sectional study, we included anesthesia residents enrolled in the standardized residency training program at our hospital during the academic year 2021 to 2022. The Chinese Anesthesiology Milestones were developed based on the American version of Anesthesiology Milestones 2.0 and the Chinese Resident Core Competency Milestone Evaluation System using the Delphi method. The Delphi panel comprised a diverse group, including education administrators, faculty from teaching hospitals, and anesthesia residents. Five attending anesthesiologists independently assessed the levels achieved by each anesthesia resident based on the Chinese Anesthesiology Milestones. Subsequently, they collaboratively discussed the ratings for each resident until a consensus was reached. The interrater reliability, internal consistency, and construct validity were assessed using Kendall's coefficient, Cronbach's α coefficient/ composite reliability, and average variance extracted, respectively. Higher values indicated better reliability or validity. The correlation between Milestone ratings and objective examination scores, including written examinations and Objective Structured Clinical Examinations, were analyzed using Pearson correlation.RESULTSThe Chinese Anesthesiology Milestones encompassed 6 competencies, including professionalism, medical knowledge and technical skills, patient care, interpersonal and communication skills, teaching ability, and life-long learning. Milestone evaluation data were available and analyzed from 66 residents. The Kendall's coefficient of concordance among raters ranged from 0.799 (95% confidence interval [CI], 0.793-0.918) to 0.942 (95% CI, 0.934-0.982). The average variance extracted, composite reliability, and Cronbach's α coefficient ranged from 0.782 to 0.920, 0.935 to 0.980, and 0.916 to 0.978, respectively. Correlations between objective examination scores and related Milestone subcompetencies were as follows: written examinations: r = 0.52 (95% CI, 0.22-0.71), technical skills stations: r = 0.51 (95% CI, 0.21-0.71), the oral test station: r = 0.66 (95% CI, 0.45-0.79), and the standardized patient station: r = 0.61 (95% CI, 0.36-0.76).CONCLUSIONSThe Chinese Anesthesiology Milestones demonstrated satisfactory interrater reliability, internal consistency, construct validity, and correlation w
{"title":"Chinese Anesthesiology Milestones in Resident Evaluation: Reliability, Validity, and Correlation with Objective Examination Scores: a Cross-sectional Study.","authors":"Xia Ruan,Xiaohan Xu,Lijian Pei,Jie Yi,Chunhua Yu,Xuerong Yu,Bo Zhu,Xiang Quan,Xu Li,Hui Jv,Yuelun Zhang,Yuguang Huang","doi":"10.1213/ane.0000000000007279","DOIUrl":"https://doi.org/10.1213/ane.0000000000007279","url":null,"abstract":"BACKGROUNDEvaluating competency acquisition during residency training is crucial. The Anesthesiology Milestones have been implemented in the United States. The China Consortium of Elite Teaching Hospitals for Residency Education has also developed the Chinese Resident Core Competency Milestone Evaluation System. Despite this, Milestones tailored for anesthesiology have yet to be implemented in China. To address this gap, we have developed Chinese Anesthesiology Milestones. This study aims to assess the reliability and validity of the Chinese Anesthesiology Milestones and their correlation with objective examinations.METHODSIn this single-center cross-sectional study, we included anesthesia residents enrolled in the standardized residency training program at our hospital during the academic year 2021 to 2022. The Chinese Anesthesiology Milestones were developed based on the American version of Anesthesiology Milestones 2.0 and the Chinese Resident Core Competency Milestone Evaluation System using the Delphi method. The Delphi panel comprised a diverse group, including education administrators, faculty from teaching hospitals, and anesthesia residents. Five attending anesthesiologists independently assessed the levels achieved by each anesthesia resident based on the Chinese Anesthesiology Milestones. Subsequently, they collaboratively discussed the ratings for each resident until a consensus was reached. The interrater reliability, internal consistency, and construct validity were assessed using Kendall's coefficient, Cronbach's α coefficient/ composite reliability, and average variance extracted, respectively. Higher values indicated better reliability or validity. The correlation between Milestone ratings and objective examination scores, including written examinations and Objective Structured Clinical Examinations, were analyzed using Pearson correlation.RESULTSThe Chinese Anesthesiology Milestones encompassed 6 competencies, including professionalism, medical knowledge and technical skills, patient care, interpersonal and communication skills, teaching ability, and life-long learning. Milestone evaluation data were available and analyzed from 66 residents. The Kendall's coefficient of concordance among raters ranged from 0.799 (95% confidence interval [CI], 0.793-0.918) to 0.942 (95% CI, 0.934-0.982). The average variance extracted, composite reliability, and Cronbach's α coefficient ranged from 0.782 to 0.920, 0.935 to 0.980, and 0.916 to 0.978, respectively. Correlations between objective examination scores and related Milestone subcompetencies were as follows: written examinations: r = 0.52 (95% CI, 0.22-0.71), technical skills stations: r = 0.51 (95% CI, 0.21-0.71), the oral test station: r = 0.66 (95% CI, 0.45-0.79), and the standardized patient station: r = 0.61 (95% CI, 0.36-0.76).CONCLUSIONSThe Chinese Anesthesiology Milestones demonstrated satisfactory interrater reliability, internal consistency, construct validity, and correlation w","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BACKGROUNDAcute kidney injury (AKI) is one of the most common complications after liver transplantation (LT) and can significantly impact outcomes. The presence of hepatitis C virus (HCV) infection increases the risk of AKI development. However, the impact of HCV on AKI after LT has not been evaluated. The aim of this study was to assess the effect of HCV on AKI development in patients who underwent LT.METHODSBetween January 2008 and April 2023, 2183 patients who underwent living donor LT (LDLT) were included. Patients were divided into 2 groups based on the presence of chronic HCV infection. We compared LT recipients using the propensity score matching (PSM) method. Factors associated with AKI development were evaluated using multiple logistic regression analysis. In addition, 1-year mortality and graft failure were assessed using a Cox proportional regression model.RESULTSAmong 2183 patients, the incidence of AKI was 59.2%. After PSM, the patients with HCV showed a more frequent development of AKI (71.9% vs 63.9%, P = .026). In multivariate analysis after PSM, HCV was associated with AKI development (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.06-2.20, P = .022), 1-year mortality (Hazard ratio [HR], 1.98; 95% CI, 1.12-3.52, P = .019), and graft failure (HR, 2.12; 95% CI, 1.22-3.69, P = .008).CONCLUSIONSThe presence of HCV was associated with increased risk for the development of AKI, 1-year mortality, and graft failure after LT.
背景急性肾损伤（AKI）是肝移植（LT）后最常见的并发症之一，会严重影响治疗效果。丙型肝炎病毒（HCV）感染会增加急性肾损伤发生的风险。然而，HCV 对 LT 后 AKI 的影响尚未得到评估。方法2008年1月至2023年4月间，纳入了2183名接受活体捐献LT（LDLT）的患者。根据是否存在慢性 HCV 感染将患者分为两组。我们采用倾向得分匹配法（PSM）对LT受者进行了比较。采用多元逻辑回归分析评估了与 AKI 发生相关的因素。结果2183名患者中，AKI发生率为59.2%。PSM 后，HCV 患者发生 AKI 的频率更高（71.9% vs 63.9%，P = .026）。在 PSM 后的多变量分析中，HCV 与 AKI 发生率（几率比 [OR]，1.53；95% 置信区间 [CI]，1.06-2.20，P = .022）、1 年死亡率（危险比 [HR]，1.98；95% 置信区间 [CI]，1.12-3.52，P = .019）相关。结论HCV的存在与LT后发生AKI、1年死亡率和移植物失败的风险增加有关。
{"title":"Impact of Chronic Hepatitis C Virus on Acute Kidney Injury After Living Donor Liver Transplantation.","authors":"Jae Hwan Kim,Kyoung-Sun Kim,Hye-Mee Kwon,Sung-Hoon Kim,In-Gu Jun,Jun-Gol Song,Gyu-Sam Hwang","doi":"10.1213/ane.0000000000007253","DOIUrl":"https://doi.org/10.1213/ane.0000000000007253","url":null,"abstract":"BACKGROUNDAcute kidney injury (AKI) is one of the most common complications after liver transplantation (LT) and can significantly impact outcomes. The presence of hepatitis C virus (HCV) infection increases the risk of AKI development. However, the impact of HCV on AKI after LT has not been evaluated. The aim of this study was to assess the effect of HCV on AKI development in patients who underwent LT.METHODSBetween January 2008 and April 2023, 2183 patients who underwent living donor LT (LDLT) were included. Patients were divided into 2 groups based on the presence of chronic HCV infection. We compared LT recipients using the propensity score matching (PSM) method. Factors associated with AKI development were evaluated using multiple logistic regression analysis. In addition, 1-year mortality and graft failure were assessed using a Cox proportional regression model.RESULTSAmong 2183 patients, the incidence of AKI was 59.2%. After PSM, the patients with HCV showed a more frequent development of AKI (71.9% vs 63.9%, P = .026). In multivariate analysis after PSM, HCV was associated with AKI development (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.06-2.20, P = .022), 1-year mortality (Hazard ratio [HR], 1.98; 95% CI, 1.12-3.52, P = .019), and graft failure (HR, 2.12; 95% CI, 1.22-3.69, P = .008).CONCLUSIONSThe presence of HCV was associated with increased risk for the development of AKI, 1-year mortality, and graft failure after LT.","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Clinical Performance of a Bispectral Index Controlled Closed-Loop Administration System for Simultaneous Administration of Propofol and Remifentanil.","authors":"Michele Schiavo,Massimiliano Paltenghi,Antonio Visioli,Nicola Latronico","doi":"10.1213/ane.0000000000007289","DOIUrl":"https://doi.org/10.1213/ane.0000000000007289","url":null,"abstract":"","PeriodicalId":7799,"journal":{"name":"Anesthesia & Analgesia","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BACKGROUNDRepeated 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.METHODSUsing 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.RESULTSForty-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%.CONCLUSIONSWearable 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.
{"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":"https://doi.org/10.1213/ane.0000000000007244","url":null,"abstract":"BACKGROUNDRepeated 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.METHODSUsing 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.RESULTSForty-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%.CONCLUSIONSWearable 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":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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