Patient-Reported Data Augment Health Record Data for Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery.

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Citation: Plastic & Reconstructive Surgery. 2023 Feb 14PMID: 36780362Institution: Curtis National Hand Center | MedStar Health Research Institute | MedStar Health Research Institute, VirginiaForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: IN PROCESS -- NOT YET INDEXEDYear: 2023ISSN:
  • 0032-1052
Name of journal: Plastic and reconstructive surgeryAbstract: BACKGROUND: Opioids play a role in pain management after surgery, but prolonged use contributes to developing opioid use disorder. Identifying patients at risk of prolonged use is critical for deploying interventions that reduce or avoid opioids; however, available predictive models do not incorporate patient-reported data (PRD) and it remains unclear if PRD can predict post-operative use behavior. We used a machine learning (ML) approach leveraging preoperative PRD and electronic health record (EHR) data to predict persistent opioid use after upper extremity (UE) surgery.CONCLUSION: This opioid use prediction model using pre-intervention data had good discriminative performance. PRD variables augmented EHR-based ML algorithms in predicting post-surgical use behaviors and were some of the strongest predictors. PRD should be used in future efforts to guide proper opioid stewardship. Copyright © 2023 by the American Society of Plastic Surgeons.METHODS: Included patients underwent UE surgery, completed preoperative PRD questionnaires, and were prescribed opioids after surgery. We trained models using a 2018 cohort and tested in a 2019 cohort. Opioid use was determined by patient report and filled prescriptions up to 6 months after surgery. We assessed model performance using AUROC, sensitivity, specificity, and Brier score.RESULTS: Among 1656 patients, 19% still used opioids at 6 weeks, 11% at 3 months, and 9% at 6 months. The XGBoost model trained on PRD plus EHR data achieved AUROC 0.73 at 6 months. Factors predictive of prolonged opioid use included income, education, tobacco, drug or alcohol abuse, cancer, depression, and race. Protective factors included preoperative PROMIS Global Physical Health and preoperative PROMIS Upper Extremity scores.All authors: Giladi AM, Shipp MM, Sanghavi KK, Zhang G, Gupta S, Miller KE, Belouali A, Madhavan SFiscal year: FY2023Digital Object Identifier: Date added to catalog: 2023-04-11
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BACKGROUND: Opioids play a role in pain management after surgery, but prolonged use contributes to developing opioid use disorder. Identifying patients at risk of prolonged use is critical for deploying interventions that reduce or avoid opioids; however, available predictive models do not incorporate patient-reported data (PRD) and it remains unclear if PRD can predict post-operative use behavior. We used a machine learning (ML) approach leveraging preoperative PRD and electronic health record (EHR) data to predict persistent opioid use after upper extremity (UE) surgery.

CONCLUSION: This opioid use prediction model using pre-intervention data had good discriminative performance. PRD variables augmented EHR-based ML algorithms in predicting post-surgical use behaviors and were some of the strongest predictors. PRD should be used in future efforts to guide proper opioid stewardship. Copyright © 2023 by the American Society of Plastic Surgeons.

METHODS: Included patients underwent UE surgery, completed preoperative PRD questionnaires, and were prescribed opioids after surgery. We trained models using a 2018 cohort and tested in a 2019 cohort. Opioid use was determined by patient report and filled prescriptions up to 6 months after surgery. We assessed model performance using AUROC, sensitivity, specificity, and Brier score.

RESULTS: Among 1656 patients, 19% still used opioids at 6 weeks, 11% at 3 months, and 9% at 6 months. The XGBoost model trained on PRD plus EHR data achieved AUROC 0.73 at 6 months. Factors predictive of prolonged opioid use included income, education, tobacco, drug or alcohol abuse, cancer, depression, and race. Protective factors included preoperative PROMIS Global Physical Health and preoperative PROMIS Upper Extremity scores.

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