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Al Faysal J, Noor-E-Alam M, Young GJ, Lo-Ciganic WH, Goodin AJ, Huang JL, Wilson DL, Park TW, Hasan MM. An explainable machine learning framework for predicting the risk of buprenorphine treatment discontinuation for opioid use disorder among commercially insured individuals. Comput Biol Med 2024; 177:108493. [PMID: 38833799 DOI: 10.1016/j.compbiomed.2024.108493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 02/22/2024] [Accepted: 04/17/2024] [Indexed: 06/06/2024]
Abstract
OBJECTIVES Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation. METHODS This retrospective study used United States (US) 2018-2021 MarketScan commercial claims data of insured individuals aged 18-64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within 12 months of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, extreme gradient boosting, Adaboost, and random forest-extreme gradient boosting ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. For model validation, we used several statistics to evaluate performance, such as C-statistics and precision-recall curves. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models. RESULTS A total of 30,373 patients initiated buprenorphine and 14.98% (4551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured after one month and three months following treatment initiation significantly increased the models' discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-months PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply. CONCLUSION ML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.
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Affiliation(s)
- Jabed Al Faysal
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA
| | - Md Noor-E-Alam
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA
| | - Gary J Young
- Center for Health Policy and Healthcare Research, Northeastern University, Boston, MA, USA; Bouve College of Health Sciences, Northeastern University, Boston, MA, USA; D'Amore-McKim School of Business, Northeastern University, Boston, MA, USA
| | - Wei-Hsuan Lo-Ciganic
- Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Center for Pharmaceutical Policy & Prescribing, University of Pittsburgh, Pittsburgh, PA, USA; North Florida/South Georgia Veterans Health System; Geriatric Research Education and Clinical Center, Gainesville, FL, USA
| | - Amie J Goodin
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA
| | - James L Huang
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA
| | - Debbie L Wilson
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA
| | - Tae Woo Park
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Md Mahmudul Hasan
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA; Department of Information Systems and Operations Management, University of Florida, Gainesville, FL, USA; Center for Drug Evaluation and Safety, University of Florida, Gainesville, FL, USA.
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Song SL, Dandapani HG, Estrada RS, Jones NW, Samuels EA, Ranney ML. Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose. J Addict Med 2024; 18:218-239. [PMID: 38591783 PMCID: PMC11150108 DOI: 10.1097/adm.0000000000001276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
BACKGROUND This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. METHODS In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). RESULTS The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. CONCLUSIONS Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.
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Affiliation(s)
- Sophia L Song
- From the Warren Alpert Medical School of Brown University, Providence, RI (SLS, HGD, RSE, EAS); Brown University School of Public Health, Providence, RI (NWJ, EAS); Department of Emergency Medicine, Warren Alpert Medical School of Brown University, Providence, RI (EAS); Department of Emergency Medicine, University of California, Los Angeles, CA (EAS); and Yale Univeristy School of Public Health, New Haven, CT (MLR)
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Chang CY, Jones BL, Hincapie-Castillo JM, Park H, Heldermon CD, Diaby V, Wilson DL, Lo-Ciganic WH. Association between trajectories of prescription opioid use and risk of opioid use disorder and overdose among US nonmetastatic breast cancer survivors. Breast Cancer Res Treat 2024; 204:561-577. [PMID: 38191684 DOI: 10.1007/s10549-023-07205-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 11/29/2023] [Indexed: 01/10/2024]
Abstract
PURPOSE To examine the association between prescription opioid use trajectories and risk of opioid use disorder (OUD) or overdose among nonmetastatic breast cancer survivors by treatment type. METHODS This retrospective cohort study included female nonmetastatic breast cancer survivors with at least 1 opioid prescription fill in 2010-2019 Surveillance, Epidemiology and End Results linked Medicare data. Opioid mean daily morphine milligram equivalents (MME) calculated within 1.5 years after initiating active breast cancer therapy. Group-based trajectory models identified distinct opioid use trajectory patterns. Risk of time to first OUD/overdose event within 1 year after the trajectory period was calculated for distinct trajectory groups using Cox proportional hazards models. Analyses were stratified by treatment type. RESULTS Four opioid use trajectories were identified for each treatment group. For 38,030 survivors with systemic endocrine therapy, 3 trajectories were associated with increased OUD/overdose risk compared with early discontinuation: minimal dose (< 5 MME; adjusted hazard ratio [aHR] = 1.73 [95% CI 1.43-2.09]), very low dose (5-25 MME; 2.67 [2.05-3.48]), and moderate dose (51-90 MME; 6.20 [4.69-8.19]). For 9477 survivors with adjuvant chemotherapy, low-dose opioid use was associated with higher OUD/overdose risk (aHR = 7.33 [95% CI 2.52-21.31]) compared with early discontinuation. For 3513 survivors with neoadjuvant chemotherapy, the differences in OUD/OD risks across the 4 trajectories were not significant. CONCLUSIONS Among Medicare nonmetastatic breast cancer survivors receiving systemic endocrine therapy or adjuvant chemotherapy, compared with early discontinuation, low-dose or moderate-dose opioid use were associated with six- to sevenfold higher OUD/overdose risk. Breast cancer survivors at high-risk of OUD/overdose may benefit from targeted interventions (e.g., pain clinic referral).
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Affiliation(s)
- Ching-Yuan Chang
- Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL, 32611, USA
| | - Bobby L Jones
- Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL, 32611, USA
| | | | - Haesuk Park
- Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL, 32611, USA
- Center for Drug Evaluation and Safety, College of Pharmacy, University of Florida, Gainesville, FL, 32611, USA
| | - Coy D Heldermon
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Vakaramoko Diaby
- Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL, 32611, USA
- Center for Drug Evaluation and Safety, College of Pharmacy, University of Florida, Gainesville, FL, 32611, USA
| | - Debbie L Wilson
- Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, FL, 32611, USA
| | - Wei-Hsuan Lo-Ciganic
- Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, USA.
- Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, USA.
- Geriatric Research Education and Clinical Center, North Florida/South Georgia Veterans Health System, Gainesville, USA.
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Nowels MA, Duberstein PR, Crystal S, Treitler P, Miles J, Olfson M, Samples H. Suicide within 1 year of non-fatal overdose: Risk factors and risk reduction with medications for opioid use disorder. Gen Hosp Psychiatry 2024; 86:24-32. [PMID: 38061284 PMCID: PMC10880030 DOI: 10.1016/j.genhosppsych.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/15/2023] [Accepted: 11/03/2023] [Indexed: 01/27/2024]
Abstract
OBJECTIVE Individuals with substance use disorders and overdoses have high risk of suicide death, but evidence is limited on the relationship between interventions following the initial overdose and subsequent suicide death. METHODS National Medicare data were used to identify Medicare disability beneficiaries (MDBs) with inpatient or emergency care for non-fatal opioid overdoses from 2008 to 2016. Data were linked with National Death Index (NDI) to obtain dates and causes of death for the sample. Cox proportional hazards models estimated the associations between exposure to interventions (mechanical ventilation, MOUD) and suicide death. RESULTS The sample (n = 81,654) had a suicide rate in the year following a non-fatal overdose of 566 per 100,000 person-years. Post-overdose MOUD was associated with an adjusted hazard ratio of 0.20 (95%CI: 0.05,0.85). Risk of suicide was elevated for those whose initial overdoses required mechanical ventilation as part of the treatment (aHR: 1.86, 95%CI:[1.48,2.34]). CONCLUSIONS The year following a non-fatal opioid overdose is a very high-risk period for suicide among MDBs. Those receiving MOUD had an 80% reduction in the hazards of suicide, while those whose overdose treatment involved mechanical ventilation had 86% higher hazards of death by suicide. Our findings highlight the importance of psychiatric intervention in this high-risk population. Efforts are needed to initiate and retain more patients in MOUD.
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Affiliation(s)
- Molly A Nowels
- Department of Health Behavior, Society, and Policy, Rutgers School of Public Health, 683 Hoes Lane West, Piscataway, NJ 08854, USA; Institute for Health, Health Care Policy, and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ 08901, USA.
| | - Paul R Duberstein
- Department of Health Behavior, Society, and Policy, Rutgers School of Public Health, 683 Hoes Lane West, Piscataway, NJ 08854, USA
| | - Stephen Crystal
- Institute for Health, Health Care Policy, and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ 08901, USA
| | - Peter Treitler
- School of Social Work, Boston University, 264 Bay State Road, Boston, MA 02215, USA
| | - Jennifer Miles
- Institute for Health, Health Care Policy, and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ 08901, USA
| | - Mark Olfson
- New York Psychiatric Institute, Columbia University, 1051 Riverside Drive, New York, NY 10032, USA
| | - Hillary Samples
- Department of Health Behavior, Society, and Policy, Rutgers School of Public Health, 683 Hoes Lane West, Piscataway, NJ 08854, USA; Institute for Health, Health Care Policy, and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ 08901, USA
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Miles J, Treitler P, Hermida R, Nyaku AN, Simon K, Gupta S, Crystal S, Samples H. Racial/ethnic disparities in timely receipt of buprenorphine among Medicare disability beneficiaries. Drug Alcohol Depend 2023; 252:110963. [PMID: 37748421 PMCID: PMC10615876 DOI: 10.1016/j.drugalcdep.2023.110963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/31/2023] [Accepted: 09/06/2023] [Indexed: 09/27/2023]
Abstract
BACKGROUND Medicare disability beneficiaries (MDBs) have disproportionately high risk of opioid use disorder (OUD) and related harms given high rates of comorbidities and high-dose opioid prescribing. Despite this increased risk, little is known about timely receipt of medication for opioid use disorder (MOUD), including potential disparities by patient race/ethnicity or moderation by county-level characteristics. METHODS National Medicare claims for a sample of MDBs with incident OUD diagnosis between March 2016 and June 2019 were linked with county-level data. Multivariable mixed effects Cox proportional hazards models estimated time (in days) to buprenorphine receipt within 180 days of incident OUD diagnosis. Primary exposures included individual-level race/ethnicity and county-level buprenorphine prescriber availability, percent non-Hispanic white (NHW) residents, and Social Deprivation Index (SDI) score. RESULTS The sample (n=233,079) was predominantly White (72.3%), ≥45 years old (76.3%), and male (54.8%). Black (adjusted hazard ratio [aHR]=0.50; 95% CI, 0.47-0.54), Asian/Pacific Islander (aHR=0.54; 95% CI, 0.41-0.72), Hispanic/Latinx (aHR=0.81; 95% CI, 0.76-0.87), and Other racial/ethnic groups (aHR=0.75; 95% CI, 0.58-0.97) had a lower likelihood of timely buprenorphine than non-Hispanic white beneficiaries after adjusting for individual and county-level confounders. Timely buprenorphine receipt was positively associated with county-level buprenorphine prescriber availability (aHR=1.05; 95% CI, 1.04-1.07), percent non-Hispanic white residents (aHR=1.01; 95% CI, 1.00-1.01), and SDI (aHR=1.06; 95% CI, 1.01-1.10). CONCLUSIONS Racial/ethnic disparities highlight the need to improve access to care for underserved groups. Implementing equity-focused quality and performance measures and developing interventions to increase office-based buprenorphine prescribing in predominantly minority race/ethnicity counties may reduce disparities in timely access to medication for OUD.
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Affiliation(s)
- Jennifer Miles
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA.
| | - Peter Treitler
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA; School of Social Work, Rutgers University, New Brunswick, NJ, USA
| | - Richard Hermida
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA
| | - Amesika N Nyaku
- Department of Medicine, Division of Infectious Diseases, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Kosali Simon
- O'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN, USA; National Bureau of Economic Research, Cambridge, MA, USA
| | - Sumedha Gupta
- Department of Economics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
| | - Stephen Crystal
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA; School of Social Work, Rutgers University, New Brunswick, NJ, USA; School of Public Health, Rutgers University, Piscataway, NJ, USA
| | - Hillary Samples
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA; Department of Health Behavior, Society and Policy, Rutgers School of Public Health, Piscataway, NJ, USA
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Cartus AR, Samuels EA, Cerdá M, Marshall BD. Outcome class imbalance and rare events: An underappreciated complication for overdose risk prediction modeling. Addiction 2023; 118:1167-1176. [PMID: 36683137 PMCID: PMC10175167 DOI: 10.1111/add.16133] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 12/22/2022] [Indexed: 01/24/2023]
Abstract
BACKGROUND AND AIMS Low outcome prevalence, often observed with opioid-related outcomes, poses an underappreciated challenge to accurate predictive modeling. Outcome class imbalance, where non-events (i.e. negative class observations) outnumber events (i.e. positive class observations) by a moderate to extreme degree, can distort measures of predictive accuracy in misleading ways, and make the overall predictive accuracy and the discriminatory ability of a predictive model appear spuriously high. We conducted a simulation study to measure the impact of outcome class imbalance on predictive performance of a simple SuperLearner ensemble model and suggest strategies for reducing that impact. DESIGN, SETTING, PARTICIPANTS Using a Monte Carlo design with 250 repetitions, we trained and evaluated these models on four simulated data sets with 100 000 observations each: one with perfect balance between events and non-events, and three where non-events outnumbered events by an approximate factor of 10:1, 100:1, and 1000:1, respectively. MEASUREMENTS We evaluated the performance of these models using a comprehensive suite of measures, including measures that are more appropriate for imbalanced data. FINDINGS Increasing imbalance tended to spuriously improve overall accuracy (using a high threshold to classify events vs non-events, overall accuracy improved from 0.45 with perfect balance to 0.99 with the most severe outcome class imbalance), but diminished predictive performance was evident using other metrics (corresponding positive predictive value decreased from 0.99 to 0.14). CONCLUSION Increasing reliance on algorithmic risk scores in consequential decision-making processes raises critical fairness and ethical concerns. This paper provides broad guidance for analytic strategies that clinical investigators can use to remedy the impacts of outcome class imbalance on risk prediction tools.
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Affiliation(s)
- Abigail R. Cartus
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island
| | - Elizabeth A. Samuels
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island
- Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, Rhode Island
| | - Magdalena Cerdá
- Division of Epidemiology, Department of Population Health, Center for Opioid Epidemiology and Policy, School of Medicine, New York University, New York
| | - Brandon D.L. Marshall
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island
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Garbin C, Marques N, Marques O. Machine learning for predicting opioid use disorder from healthcare data: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107573. [PMID: 37148670 DOI: 10.1016/j.cmpb.2023.107573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/16/2023] [Accepted: 04/26/2023] [Indexed: 05/08/2023]
Abstract
INTRODUCTION The US opioid epidemic has been one of the leading causes of injury-related deaths according to the CDC Injury Center. The increasing availability of data and tools for machine learning (ML) resulted in more researchers creating datasets and models to help analyze and mitigate the crisis. This review investigates peer-reviewed journal papers that applied ML models to predict opioid use disorder (OUD). The review is split into two parts. The first part summarizes the current research in OUD prediction with ML. The second part evaluates how ML techniques and processes were used to achieve these results and suggests improvements to refine further attempts to use ML for OUD prediction. METHODS The review includes peer-reviewed journal papers published on or after 2012 that use healthcare data to predict OUD. We searched Google Scholar, Semantic Scholar, PubMed, IEEE Xplore, and Science.gov in September of 2022. Data extracted includes the study's goal, dataset used, cohort selected, types of ML models created, model evaluation metrics, and the details of the ML tools and techniques used to create the models. RESULTS The review analyzed 16 papers. Three papers created their dataset, five used a publicly available dataset, and the remaining eight used a private dataset. Cohort size ranged from the low hundreds to over half a million. Six papers used one type of ML model, and the remaining ten used up to five different ML models. The reported ROC AUC was higher than 0.8 for all but one of the papers. Five papers used only non-interpretable models, and the other 11 used interpretable models exclusively or in combination with non-interpretable ones. The interpretable models were the highest or second-highest ROC AUC values. Most papers did not sufficiently describe the ML techniques and tools used to produce their results. Only three papers published their source code. CONCLUSIONS We found that while there are indications that ML methods applied to OUD prediction may be valuable, the lack of details and transparency in creating the ML models limits their usefulness. We end the review with recommendations to improve studies on this critical healthcare subject.
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Affiliation(s)
- Christian Garbin
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA.
| | - Nicholas Marques
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA
| | - Oge Marques
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA
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Using Machine Learning to Predict Treatment Adherence in Patients on Medication for Opioid Use Disorder. J Addict Med 2023; 17:28-34. [PMID: 35914118 DOI: 10.1097/adm.0000000000001019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Patients receiving medication for opioid use disorder (MOUD) may continue using nonprescribed drugs or have trouble with medication adherence, and it is difficult to predict which patients will continue to do so. In this study, we develop and validate an automated risk-modeling framework to predict opioid abstinence and medication adherence at a patient's next attended appointment and evaluate the predictive performance of machine-learning algorithms versus logistic regression. METHODS Urine drug screen and attendance records from 40,005 appointments drawn from 2742 patients at a multilocation office-based MOUD program were used to train logistic regression, logistic ridge regression, and XGBoost models to predict a composite indicator of treatment adherence (opioid-negative and norbuprenorphine-positive urine, no evidence of urine adulteration) at next attended appointment. RESULTS The XGBoost model had similar accuracy and discriminative ability (accuracy, 88%; area under the receiver operating curve, 0.87) to the two logistic regression models (accuracy, 88%; area under the receiver operating curve, 0.87). The XGBoost model had nearly perfect calibration in independent validation data; the logistic and ridge regression models slightly overestimated adherence likelihood. Historical treatment adherence, attendance rate, and fentanyl-positive urine at current appointment were the strongest contributors to treatment adherence at next attended appointment. DISCUSSION There is a need for risk prediction tools to improve delivery of MOUD. This study presents an automated and portable risk-modeling framework to predict treatment adherence at each patient's next attended appointment. The XGBoost algorithm appears to provide similar classification accuracy to logistic regression models; however, XGBoost may offer improved calibration of risk estimates compared with logistic regression.
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Liu YS, Kiyang L, Hayward J, Zhang Y, Metes D, Wang M, Svenson LW, Talarico F, Chue P, Li XM, Greiner R, Greenshaw AJ, Cao B. Individualized Prospective Prediction of Opioid Use Disorder. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2023; 68:54-63. [PMID: 35892186 PMCID: PMC9720482 DOI: 10.1177/07067437221114094] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases. In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data. METHOD We present an ensemble machine-learning model trained on a cross-linked Canadian administrative health data set from 2014 to 2018 (n = 699,164), with validation of model-predicted OUD cases on a hold-out sample from 2014 to 2018 (n = 174,791) and prospective prediction of OUD cases on a non-overlapping sample from 2019 (n = 316,039). We used administrative records of OUD diagnosis for each subject based on International Classification of Diseases (ICD) codes. RESULTS With 6409 OUD cases in 2019 (mean [SD], 45.34 [14.28], 3400 males), our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders. CONCLUSION Our study presents an individualized prospective prediction of OUD cases by applying ML to large administrative health datasets. Such prospective predictions based on ML would be essential for potential future clinical applications in the early detection of OUD.
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Affiliation(s)
- Yang S Liu
- Department of Psychiatry, 3158University of Alberta, Edmonton, Alberta, Canada.,Analytics and Performance Reporting Branch, Ministry of Health, 151965Government of Alberta, Edmonton, Alberta, Canada
| | - Lawrence Kiyang
- Analytics and Performance Reporting Branch, Ministry of Health, 151965Government of Alberta, Edmonton, Alberta, Canada
| | - Jake Hayward
- Department of Emergency Medicine, 3158University of Alberta, Edmonton, Alberta, Canada
| | - Yanbo Zhang
- Department of Psychiatry, 3158University of Alberta, Edmonton, Alberta, Canada
| | - Dan Metes
- Analytics and Performance Reporting Branch, Ministry of Health, 151965Government of Alberta, Edmonton, Alberta, Canada
| | - Mengzhe Wang
- Analytics and Performance Reporting Branch, Ministry of Health, 151965Government of Alberta, Edmonton, Alberta, Canada
| | - Lawrence W Svenson
- Analytics and Performance Reporting Branch, Ministry of Health, 151965Government of Alberta, Edmonton, Alberta, Canada.,School of Public Health, 3158University of Alberta, Edmonton, Alberta, Canada.,Division of Preventive Medicine, 3158University of Alberta, Edmonton, Alberta, Canada.,Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Fernanda Talarico
- Department of Psychiatry, 3158University of Alberta, Edmonton, Alberta, Canada
| | - Pierre Chue
- Department of Psychiatry, 3158University of Alberta, Edmonton, Alberta, Canada
| | - Xin-Min Li
- Department of Psychiatry, 3158University of Alberta, Edmonton, Alberta, Canada
| | - Russell Greiner
- Department of Psychiatry, 3158University of Alberta, Edmonton, Alberta, Canada.,Department of Computing Science, 3158University of Alberta, Edmonton, Alberta, Canada.,Alberta Machine Intelligence Institute (Amii), Edmonton, Alberta, Canada
| | - Andrew J Greenshaw
- Department of Psychiatry, 3158University of Alberta, Edmonton, Alberta, Canada
| | - Bo Cao
- Department of Psychiatry, 3158University of Alberta, Edmonton, Alberta, Canada.,Analytics and Performance Reporting Branch, Ministry of Health, 151965Government of Alberta, Edmonton, Alberta, Canada
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Warren D, Marashi A, Siddiqui A, Eijaz AA, Pradhan P, Lim D, Call G, Dras M. Using machine learning to study the effect of medication adherence in Opioid Use Disorder. PLoS One 2022; 17:e0278988. [PMID: 36520864 PMCID: PMC9754174 DOI: 10.1371/journal.pone.0278988] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient's adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment. METHODS We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making. RESULTS Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%. CONCLUSIONS The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk.
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Affiliation(s)
| | - Amir Marashi
- Macquarie University, Sydney, NSW, Australia
- Digital Health Cooperative Research Centre, Sydney, NSW, Australia
| | | | | | - Pooja Pradhan
- Western Sydney University, Campbelltown, NSW, Australia
| | - David Lim
- Western Sydney University, Campbelltown, NSW, Australia
| | - Gary Call
- Gainwell Technologies, Tysons, VA, United States of America
| | - Mark Dras
- Macquarie University, Sydney, NSW, Australia
- * E-mail:
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11
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Falise AM, Sharma V, Hoeflich CC, Lopez-Quintero C, Striley CW. Screening the "Invisible Population" of Older Adult Patients for Prescription Pain Reliever Non-Medical Use and Use Disorders. Subst Use Misuse 2022; 58:153-159. [PMID: 36519790 PMCID: PMC10104763 DOI: 10.1080/10826084.2022.2148483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background: In the United States, the number of older adults reporting non-medical use of prescription pain relievers (NMUPPR) between 2015 and 2019 has remained constant, while those meeting criteria for opioid use disorders (OUDs) between 2013 and 2018 increased three-fold. These rates are expected to increase due to increased life expectancy among this population coupled with higher rates of substance use. However, they have consistently lower screening rates for problematic prescription pain reliever use, compared to younger cohorts. Objectives: This commentary reviewed trends in older adult NMUPPR and OUDs and reviewed several available screening tools. We then considered reasons why providers may not be screening their patients, with a focus on older adults, for NMUPPR and OUDs. Finally, we provided recommendations to increase screenings in healthcare settings. Results: Low screening rates in older adult patients may be due to several contributing factors, such as providers' implicit biases and lack of training, time constraints, and comorbid conditions that mask NMUPPR and OUD-related symptoms. Recommendations include incorporating more addiction-related curricula in medical schools, encouraging participation in CME training focused on substance use, attending implicit bias training, and breaking down the silos between pharmacy and geriatric, addiction, and family medicine. Conclusions: There is a growing need for older adult drug screenings, and we have provided several recommendations for improvement. By increasing screenings among older populations, providers will assist in the identification and referral of patients to appropriate and timely substance use treatment and resources to ultimately ameliorate the health of older adult patients.
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Affiliation(s)
- Alyssa M. Falise
- Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Vinita Sharma
- Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
- School of Public and Population Health, Boise State University, Boise, ID, USA
| | - Carolin C. Hoeflich
- Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Catalina Lopez-Quintero
- Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Catherine W. Striley
- Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
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12
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Guo J, Gellad WF, Yang Q, Weiss JC, Donohue JM, Cochran G, Gordon AJ, Malone DC, Kwoh CK, Kuza CC, Wilson DL, Lo-Ciganic WH. Changes in predicted opioid overdose risk over time in a state Medicaid program: a group-based trajectory modeling analysis. Addiction 2022; 117:2254-2263. [PMID: 35315173 PMCID: PMC10184496 DOI: 10.1111/add.15878] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 03/01/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND AIMS The time lag encountered when accessing health-care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one's opioid overdose risk changes over time. We aimed to identify longitudinal patterns of individual predicted overdose risks among Medicaid beneficiaries after initiation of opioid prescriptions. DESIGN, SETTING AND PARTICIPANTS A retrospective cohort study in Pennsylvania, USA among Pennsylvania Medicaid beneficiaries aged 18-64 years who initiated opioid prescriptions between July 2017 and September 2018 (318 585 eligible beneficiaries (mean age = 39 ± 12 years, female = 65.7%, White = 62.2% and Black = 24.9%). MEASUREMENTS We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency room or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores over time. FINDINGS Among eligible beneficiaries, 0.61% had one or more occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time, including consistent low-risk (63%), consistent medium-risk (25%) and consistent high-risk (4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high- to medium-risk (3%) and another group that increased from medium- to high-risk over time (5%). CONCLUSIONS More than 90% of Medicaid beneficiaries in Pennsylvania USA with one or more opioid prescriptions had consistent, predicted opioid overdose risks over 15 months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.
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Affiliation(s)
- Jingchuan Guo
- Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.,Center for Drug Evaluation and Safety (CoDES), College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Walid F Gellad
- Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA, USA.,Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, USA.,Center for Health Equity Research Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Qingnan Yang
- Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jeremy C Weiss
- Carnegie Mellon University, Heinz College, Pittsburgh, PA, USA
| | - Julie M Donohue
- Department of Health Policy and Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gerald Cochran
- Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Adam J Gordon
- Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA.,Informatics, Decision-Enhancement, and Analytic Sciences Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Daniel C Malone
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - C Kent Kwoh
- Division of Rheumatology, Department of Medicine, University of Arizona Arthritis Center, University of Arizona, Tucson, AZ, USA
| | - Courtney C Kuza
- Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Debbie L Wilson
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Wei-Hsuan Lo-Ciganic
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.,Center for Drug Evaluation and Safety (CoDES), College of Pharmacy, University of Florida, Gainesville, FL, USA
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13
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Allen B, Cerdá M. Opportunities for opioid overdose prediction: building a population health approach. Lancet Digit Health 2022; 4:e403-e404. [PMID: 35623796 PMCID: PMC9897051 DOI: 10.1016/s2589-7500(22)00097-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 05/04/2022] [Indexed: 02/06/2023]
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14
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Lo-Ciganic WH, Donohue JM, Yang Q, Huang JL, Chang CY, Weiss JC, Guo J, Zhang HH, Cochran G, Gordon AJ, Malone DC, Kwoh CK, Wilson DL, Kuza CC, Gellad WF. Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study. Lancet Digit Health 2022; 4:e455-e465. [PMID: 35623798 PMCID: PMC9236281 DOI: 10.1016/s2589-7500(22)00062-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 02/21/2022] [Accepted: 03/16/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state). METHODS This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use. FINDINGS A total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose. INTERPRETATION A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries. FUNDING National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.
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Affiliation(s)
- Wei-Hsuan Lo-Ciganic
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA; Center for Drug Evaluation and Safety, College of Pharmacy, University of Florida, Gainesville, FL, USA.
| | - Julie M Donohue
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA; Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Qingnan Yang
- Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - James L Huang
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Ching-Yuan Chang
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Jeremy C Weiss
- Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jingchuan Guo
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA; Center for Drug Evaluation and Safety, College of Pharmacy, University of Florida, Gainesville, FL, USA; Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hao H Zhang
- Department of Mathematics, University of Arizona, Tucson, AZ, USA
| | - Gerald Cochran
- Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Adam J Gordon
- Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA; Informatics, Decision-Enhancement, and Analytic Sciences Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Daniel C Malone
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Chian K Kwoh
- Division of Rheumatology, Department of Medicine, and the University of Arizona Arthritis Center, University of Arizona, Tucson, AZ, USA
| | - Debbie L Wilson
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Courtney C Kuza
- Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Walid F Gellad
- Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, PA, USA; Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Center for Health Equity Research Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
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15
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Abstract
This paper is the forty-third consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2020 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).
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Affiliation(s)
- Richard J Bodnar
- Department of Psychology and Neuropsychology Doctoral Sub-Program, Queens College, City University of New York, Flushing, NY, 11367, United States.
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16
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Zhao S, Browning J, Cui Y, Wang J. Using machine learning to classify patients on opioid use. JOURNAL OF PHARMACEUTICAL HEALTH SERVICES RESEARCH 2022; 12:502-508. [PMID: 35003334 DOI: 10.1093/jphsr/rmab055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022]
Abstract
Objectives High-frequent opioid use tends to increase an individual's risk of opioid use disorder, overdose and death. Thus, it is important to predict an individuals' opioid use frequency to improve opioid prescription utilization outcomes. Methods Individuals receiving at least one opioid prescription from 2016 to 2018 in the national representative data, Medical Expenditure Panel Survey, were included. This study applied five machine learning (ML) techniques, including support vector machine, random forest, neural network, gradient boosting and XGBoost (extreme gradient boosting), to predict opioid use frequency. This study compared the performance of these ML models with penalized logistic regression. The study outcome was whether an individual lied in the upper 10% of the opioid prescription distribution. Predictors were selected based on Gelberg-Andersen's Behavioral Model of Health Services Utilization. The prediction performance was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) in the test data. Patient characteristics as predictors for high-frequency use of opioids were ranked by the relative importance in prediction in the test data. Key findings Random forest and gradient boosting achieved the top values of both AUROC and AUPRC, outperforming logistic regression and three other ML methods. In the best performing model, the random forest, the following characteristics had high predictive power in the frequency of opioid use: age, number of chronic conditions, public insurance and self-perceived health status. Conclusions The results of this study demonstrate that ML techniques can be a promising and powerful technique in predicting the frequency of opioid use and health outcomes.
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Affiliation(s)
- Shirong Zhao
- Department of Investment, School of Finance, Dongbei University of Finance and Economics, Dalian, Liaoning, China
| | - Jamie Browning
- Department of Clinical Pharmacy and Translational Science, University of Tennessee Health Science Center College of Pharmacy, Memphis, TN, USA
| | - Yan Cui
- Department of Genetics, Genomics & Informatics, University of Tennessee Health Science Center, Memphis, TNUSA
| | - Junling Wang
- Department of Clinical Pharmacy and Translational Science, University of Tennessee Health Science Center College of Pharmacy, Memphis, TN, USA
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17
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Edrees H, Song W, Syrowatka A, Simona A, Amato MG, Bates DW. Intelligent Telehealth in Pharmacovigilance: A Future Perspective. Drug Saf 2022; 45:449-458. [PMID: 35579810 PMCID: PMC9112241 DOI: 10.1007/s40264-022-01172-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2022] [Indexed: 01/28/2023]
Abstract
Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices.
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Affiliation(s)
- Heba Edrees
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA USA ,Department of Pharmacy Practice, MCPHS University, Boston, MA USA ,Harvard Medical School, 1620 Tremont St., 3rd Floor, Boston, MA 02120 USA
| | - Wenyu Song
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA USA ,Harvard Medical School, 1620 Tremont St., 3rd Floor, Boston, MA 02120 USA
| | - Ania Syrowatka
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA USA ,Harvard Medical School, 1620 Tremont St., 3rd Floor, Boston, MA 02120 USA
| | - Aurélien Simona
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA USA ,Harvard Medical School, 1620 Tremont St., 3rd Floor, Boston, MA 02120 USA
| | - Mary G. Amato
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA USA
| | - David W. Bates
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA USA ,Harvard Medical School, 1620 Tremont St., 3rd Floor, Boston, MA 02120 USA ,Department of Health Policy and Management, Harvard School of Public Health, Boston, MA USA
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18
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Syrowatka A, Song W, Amato MG, Foer D, Edrees H, Co Z, Kuznetsova M, Dulgarian S, Seger DL, Simona A, Bain PA, Purcell Jackson G, Rhee K, Bates DW. Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. Lancet Digit Health 2021; 4:e137-e148. [PMID: 34836823 DOI: 10.1016/s2589-7500(21)00229-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 08/13/2021] [Accepted: 09/10/2021] [Indexed: 12/31/2022]
Abstract
Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.
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Affiliation(s)
- Ania Syrowatka
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Wenyu Song
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mary G Amato
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
| | - Dinah Foer
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Heba Edrees
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
| | - Zoe Co
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Sevan Dulgarian
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Diane L Seger
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aurélien Simona
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Paul A Bain
- Countway Library of Medicine, Harvard Medical School, Boston, MA, USA
| | - Gretchen Purcell Jackson
- IBM Watson Health, Cambridge, MA, USA; Department of Pediatric Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyu Rhee
- IBM Watson Health, Cambridge, MA, USA; CVS Health, Wellesley Hills, MA, USA
| | - David W Bates
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Harvard T H Chan School of Public Health, Boston, MA, USA
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19
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Schirle L, Jeffery A, Yaqoob A, Sanchez-Roige S, Samuels DC. Two data-driven approaches to identifying the spectrum of problematic opioid use: A pilot study within a chronic pain cohort. Int J Med Inform 2021; 156:104621. [PMID: 34673309 PMCID: PMC8609775 DOI: 10.1016/j.ijmedinf.2021.104621] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/29/2021] [Accepted: 10/09/2021] [Indexed: 01/04/2023]
Abstract
BACKGROUND Although electronic health records (EHR) have significant potential for the study of opioid use disorders (OUD), detecting OUD in clinical data is challenging. Models using EHR data to predict OUD often rely on case/control classifications focused on extreme opioid use. There is a need to expand this work to characterize the spectrum of problematic opioid use. METHODS Using a large academic medical center database, we developed 2 data-driven methods of OUD detection: (1) a Comorbidity Score developed from a Phenome-Wide Association Study of phenotypes associated with OUD and (2) a Text-based Score using natural language processing to identify OUD-related concepts in clinical notes. We evaluated the performance of both scores against a manual review with correlation coefficients, Wilcoxon rank sum tests, and area-under the receiver operating characteristic curves. Records with the highest Comorbidity and Text-based scores were re-evaluated by manual review to explore discrepancies. RESULTS Both the Comorbidity and Text-based OUD risk scores were significantly elevated in the patients judged as High Evidence for OUD in the manual review compared to those with No Evidence (p = 1.3E-5 and 1.3E-6, respectively). The risk scores were positively correlated with each other (rho = 0.52, p < 0.001). AUCs for the Comorbidity and Text-based scores were high (0.79 and 0.76, respectively). Follow-up manual review of discrepant findings revealed strengths of data-driven methods over manual review, and opportunities for improvement in risk assessment. CONCLUSION Risk scores comprising comorbidities and text offer differing but synergistic insights into characterizing problematic opioid use. This pilot project establishes a foundation for more robust work in the future.
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Affiliation(s)
- Lori Schirle
- Vanderbilt University School of Nursing, 461 21st Avenue South, Nashville, TN 37240, USA.
| | - Alvin Jeffery
- Vanderbilt University School of Nursing, 461 21st Avenue South, Nashville, TN 37240, USA; Vanderbilt University, Department of Biomedical Informatics, 2525 West End Ave #1475, Nashville, TN 37203, USA.
| | - Ali Yaqoob
- Vanderbilt University, Department of Biomedical Informatics, 2525 West End Ave #1475, Nashville, TN 37203, USA; Vanderbilt University, Data Science Institute, Sony Building, # 2000, 1400 18th Avenue South, Nashville, TN 37212, USA.
| | - Sandra Sanchez-Roige
- Vanderbilt University Medical Center, Division of Genetic Medicine, Robinson Research Building #536, 220 Pierce Avenue, Nashville, TN 37232, USA; University of California, Department of Psychiatry, 9500 Gilman Dr., LaJolla, CA 92093, USA.
| | - David C Samuels
- Vanderbilt University School of Medicine, Light Hall #507B, Department of Molecular Physiology and Biophysics, Vanderbilt Genetics Institute, 2215 Garland Avenue, Nashville, TN 37232, USA.
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Dong X, Deng J, Rashidian S, Abell-Hart K, Hou W, Rosenthal RN, Saltz M, Saltz JH, Wang F. Identifying risk of opioid use disorder for patients taking opioid medications with deep learning. J Am Med Inform Assoc 2021; 28:1683-1693. [PMID: 33930132 DOI: 10.1093/jamia/ocab043] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 02/02/2020] [Accepted: 03/01/2021] [Indexed: 01/16/2023] Open
Abstract
OBJECTIVE The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions. METHODS Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner's Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve. RESULTS The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN). CONCLUSIONS LSTM-based sequential deep learning models can accurately predict OUD using a patient's history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.
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Affiliation(s)
- Xinyu Dong
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Jianyuan Deng
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Sina Rashidian
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Kayley Abell-Hart
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Wei Hou
- Department of Family, Population and Preventive Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Richard N Rosenthal
- Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Mary Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Joel H Saltz
- Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.,Department of Biomedical Informatics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
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