Analysis and development of risk prediction models for chronic opioid use after surgery: a cohort study using the nationwide database

Background: Chronic opioid use has become a socioeconomic as well as a medical problem. This study aimed to identify risk factors and develop prediction models for postoperative chronic opioid use (PCOU).Methods: This retrospective cohort study used data from the Korean National Health Insurance Ser...

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Vydáno v:Korean journal of anesthesiology Ročník 78; číslo 5; s. 429 - 442
Hlavní autoři: Kim, Jonghae, Yang, Hyun-Lim, Kim, Eugene, Lee, Hyung-Chul, Yoon, Hyun-Kyu, Kim, Yun Jin, Kim, Kyu-Nam, Kim, Ji-Yoon, Sung, Jeong Min, Lee, Tagkeun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Korea (South) Korean Society of Anesthesiologists 01.10.2025
대한마취통증의학회
Témata:
Adolescent
Adult
Aged
Analgesics, Opioid - administration & dosage
Analgesics, Opioid - adverse effects
Child
Cohort Studies
Databases, Factual - trends
dependence, opioid
Female
gradient boosting algorithms
Humans
Machine Learning
Male
Middle Aged
Opioid-Related Disorders - diagnosis
Opioid-Related Disorders - epidemiology
Opioid-Related Disorders - etiology
Pain, Postoperative - diagnosis
Pain, Postoperative - drug therapy
Pain, Postoperative - epidemiology
postoperative period
prediction methods, machine
Republic of Korea - epidemiology
Retrospective Studies
Risk Assessment - methods
Risk Factors
surgical procedures, operative
Young Adult
마취과학
Republic of Korea
Gradient boosting algorithms
Dependence, opioid
Prediction methods, machine
Surgical procedures, operative
Postoperative period
Machine learning
ISSN:2005-6419, 2005-7563, 2005-7563
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Shrnutí:Background: Chronic opioid use has become a socioeconomic as well as a medical problem. This study aimed to identify risk factors and develop prediction models for postoperative chronic opioid use (PCOU).Methods: This retrospective cohort study used data from the Korean National Health Insurance Service (NHIS) between January 2008 and December 2018. Of 2 077 825 patients aged seven years or older who underwent surgery, survived at least one year, and had no additional surgeries, 1 108 119 were randomly selected. Logistic regression (LR) and machine learning models were developed to identify risk factors for PCOU. PCOU was defined as having filled 10 or more prescriptions or receiving more than 120 days’ supply between postoperative days 91 and 365. Age, sex, medical comorbidities (systemic diseases, psychological disorders, and substance use disorders), preoperative medications (antidepressants, antipsychotics, anticonvulsants, benzodiazepines, opioids, and nonopioid analgesics), and type of surgery were assessed as potential risk factors.Results: PCOU occurred in 9308 patients (0.84%). Older age, preoperative history of opioid use, and high in-hospital opioid doses were the three most important predictors. Among the 28 most commonly performed surgical procedures in Korea, lung surgery, general spinal surgery, and total knee arthroplasty were most strongly associated with chronic opioid use.Conclusions: According to the best-performing gradient boosting model, older age, longer hospital stay, high in-hospital opioid consumption, and preoperative opioid use were the most important risk factors for PCOU.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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https://doi.org/10.4097/kja.24831
ISSN:2005-6419
2005-7563
2005-7563
DOI:10.4097/kja.24831