Evidence map›Paper›PMID 40865987›Full record

ArticleKorean journal of anesthesiology2025

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

Jonghae Kim, Hyun-Lim Yang, Eugene Kim, Hyung-Chul Lee, Hyun-Kyu Yoon, Yun Jin Kim, Kyu-Nam Kim, Ji-Yoon Kim, Jeong Min Sung, Tagkeun Lee

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Article in Korean journal of anesthesiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jonghae KimDepartment of Anesthesiology and Pain Medicine, Daegu Catholic University Medical Center, Daegu Catholic University School of Medicine, Daegu, Korea.
Hyun-Lim YangOffice of Hospital Information, Seoul National University Hospital, Seoul, Korea.
Eugene KimDepartment of Anesthesiology and Pain Medicine, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Korea.
Hyung-Chul LeeDepartment of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Hyun-Kyu YoonDepartment of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Yun Jin KimBiostatistical Consulting and Research Lab, Medical Research Collaborating Center, Hanyang University, Seoul, Korea.
Kyu-Nam KimDepartment of Anesthesiology and Pain Medicine, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Korea.
Ji-Yoon KimDepartment of Anesthesiology and Pain Medicine, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Korea.
Jeong Min SungDepartment of Anesthesiology and Pain Medicine, Hanyang University Medical Center, Seoul, Korea.
Tagkeun LeeDepartment of Anesthesiology and Pain Medicine, Hanyang University Medical Center, Seoul, Korea.

Funding

Ministry of Science and ICT 2022R1F1A1066482National Research Foundation of Korea
6 · The paper itself

Abstract

backgroundChronic 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).

methodsThis 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.

resultsPCOU 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.

conclusionsAccording 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.

Indexed as

Analgesics, OpioidOpioid-Related DisordersPostoperative PainAdolescentAdultAgedChildCohort StudiesDatabases, FactualFemaleHumansMachine LearningMaleMiddle AgedRepublic of KoreaRetrospective StudiesAnalgesics, OpioidDependence, opioidGradient boosting algorithmsMachine learningPostoperative periodPrediction methods, machineSurgical procedures, operative

Identifiers

PMID40865987
PMCPMC12489590

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