{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T13:25:49Z","timestamp":1792416349856,"version":"4.4.0"},"reference-count":73,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["NIHR301413"],"award-info":[{"award-number":["NIHR301413"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100023699","name":"Health Data Research UK","doi-asserted-by":"publisher","award":["218529\/Z\/19\/Z"],"award-info":[{"award-number":["218529\/Z\/19\/Z"]}],"id":[{"id":"10.13039\/501100023699","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>The global rise in prescription opioid use has contributed to an opioid epidemic, associated harms, and unintentional deaths in several western countries. Opioids however continue to be regularly prescribed for acute pain and in the chronic pain context due to limited treatment options. Currently there are no accurate tools that help predict which patients prescribed opioids may be at risk of death, which depends on the cultural context and varies across countries. Existing models do not account for statistical considerations such as censoring and competing risks. Using nationally representative data from the United Kingdom from 1,026,139 patients newly prescribed an opioid, we developed three competing risk time-to-event models: a regression model, a random forest, and a deep neural network to predict opioid-related deaths using UK primary care records. The models were externally validated in an external cohort of 337,015 patients. The models exhibited good discrimination and positive predictive value during internal validation (C-statistic for the regression model, random forest, and neural network: 84.3%, 84.4% and 82.1% respectively), and external validation (C-statistic for the regression model, random forest, and neural network: 81.8%, 81.5% and 81.5% respectively). Prior substance abuse, lung and liver comorbidities, morphine, fentanyl, or oxycodone at initiation and co-prescription of gabapentinoids were some of candidate predictors associated with a higher risk of opioid-related mortality within the models. These results demonstrate how routinely collected data from a nationally representative dataset may be used to develop and validate opioids risk algorithms to better help clinicians and patients predict risk to this serious adverse outcome.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001190","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T18:30:51Z","timestamp":1769538651000},"page":"e0001190","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development and evaluation of machine learning algorithms for the prediction of opioid-related deaths among UK patients with non-cancer pain"],"prefix":"10.1371","volume":"5","author":[{"given":"Jose","family":"Benitez-Aurioles","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Raul Ramirez Medina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Jenkins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6393-9969","authenticated-orcid":true,"given":"Niels","family":"Peek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1487-277X","authenticated-orcid":true,"given":"Meghna","family":"Jani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"issue":"9","key":"pdig.0001190.ref001","first-page":"826","article-title":"Trends in high-dose opioid prescribing in Canada","volume":"60","author":"T Gomes","year":"2014","journal-title":"Can Fam Physician"},{"key":"pdig.0001190.ref002","article-title":"Opioid prescribing is rising in many countries","volume":"367","author":"BH Smith","year":"2019","journal-title":"BMJ"},{"issue":"10","key":"pdig.0001190.ref003","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1003270","article-title":"Time trends and prescribing patterns of opioid drugs in UK primary care patients with non-cancer pain: A retrospective cohort study","volume":"17","author":"M Jani","year":"2020","journal-title":"PLoS Med"},{"issue":"2","key":"pdig.0001190.ref004","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/S2215-0366(18)30471-1","article-title":"Opioid prescribing trends and geographical variation in England, 1998-2018: a retrospective database study","volume":"6","author":"HJ Curtis","year":"2019","journal-title":"Lancet Psychiatry"},{"issue":"11","key":"pdig.0001190.ref005","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1003829","article-title":"Opioid prescribing among new users for non-cancer pain in the USA, Canada, UK, and Taiwan: A population-based cohort study","volume":"18","author":"M Jani","year":"2021","journal-title":"PLoS Med"},{"key":"pdig.0001190.ref006","article-title":"Opioids are not just an American problem","volume":"359","author":"M Jani","year":"2017","journal-title":"BMJ"},{"key":"pdig.0001190.ref007","unstructured":"NHS England \u00bb National medicines optimisation opportunities 2024\/25. [cited 1 Jul 2025]. Available from: https:\/\/www.england.nhs.uk\/long-read\/national-medicines-optimisation-opportunities-2023-24\/#15-chronic-non-cancer-pain-management-without-opioids"},{"key":"pdig.0001190.ref008","article-title":"Drug deaths causing UK mortality to fall behind other high income countries, analysis finds","volume":"389","author":"A O\u2019Dowd","year":"2025","journal-title":"BMJ"},{"key":"pdig.0001190.ref009","unstructured":"Office for National Statistics. Deaths related to drug poisoning in England and Wales: 2024 registrations. ONS website. [cited 17 Oct 2025]. 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Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). R package version 3.2.0. 2023."},{"key":"pdig.0001190.ref042","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume":"32","author":"A Paszke","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"pdig.0001190.ref043","article-title":"PyTorchLightning\/pytorch-lightning: 0.7.6 release","author":"W Falcon"},{"key":"pdig.0001190.ref044","unstructured":"GitHub - havakv\/pycox: Survival analysis with PyTorch. [cited 10 Dec 2025]. 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Routledge"},{"issue":"8","key":"pdig.0001190.ref059","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"S Hochreiter","year":"1997","journal-title":"Neural Comput"},{"issue":"1","key":"pdig.0001190.ref060","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12559-023-10179-8","article-title":"Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence","volume":"16","author":"V Hassija","year":"2023","journal-title":"Cogn Comput"},{"issue":"8","key":"pdig.0001190.ref061","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1111\/pme.12777","article-title":"Development of a Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose in Veterans\u2019 Health Administration Patients","volume":"16","author":"B Zedler","year":"2015","journal-title":"Pain Med"},{"key":"pdig.0001190.ref062","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.drugalcdep.2019.04.016","article-title":"A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients","volume":"201","author":"H-Y Chang","year":"2019","journal-title":"Drug Alcohol Depend"},{"issue":"3","key":"pdig.0001190.ref063","article-title":"Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions","volume":"2","author":"W-H Lo-Ciganic","year":"2019","journal-title":"JAMA Netw Open"},{"key":"pdig.0001190.ref064","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13040-019-0193-0","article-title":"Predicting opioid dependence from electronic health records with machine learning","volume":"12","author":"RJ Ellis","year":"2019","journal-title":"BioData Min"},{"key":"pdig.0001190.ref065","doi-asserted-by":"crossref","first-page":"103725","DOI":"10.1016\/j.jbi.2021.103725","article-title":"Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning","volume":"116","author":"X Dong","year":"2021","journal-title":"J Biomed Inform"},{"issue":"4","key":"pdig.0001190.ref066","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1073\/pnas.1905355117","article-title":"Predicting high-risk opioid prescriptions before they are given","volume":"117","author":"JS Hastings","year":"2020","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"15","key":"pdig.0001190.ref067","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.122.026014","article-title":"Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records","volume":"11","author":"JM Ashburner","year":"2022","journal-title":"J Am Heart Assoc"},{"issue":"8","key":"pdig.0001190.ref068","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1136\/ard-2024-225544","article-title":"Development and evaluation of a text analytics algorithm for automated application of national COVID-19 shielding criteria in rheumatology patients","volume":"83","author":"M Jani","year":"2024","journal-title":"Ann Rheum Dis"},{"key":"pdig.0001190.ref069","doi-asserted-by":"crossref","first-page":"26094","DOI":"10.1038\/srep26094","article-title":"Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records","volume":"6","author":"R Miotto","year":"2016","journal-title":"Sci Rep"},{"issue":"12","key":"pdig.0001190.ref070","article-title":"Leveraging electronic health records for data science: common pitfalls and how to avoid them","volume":"4","author":"CM Sauer","year":"2022","journal-title":"Lancet Digit Health"},{"key":"pdig.0001190.ref071","article-title":"BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark. 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