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...

Full description

Saved in:

Bibliographic Details
Published in:	Korean journal of anesthesiology Vol. 78; no. 5; pp. 429 - 442
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	Korea (South) Korean Society of Anesthesiologists 01.10.2025 대한마취통증의학회
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	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.
AbstractList	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. 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). 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. 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. 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. 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).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).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.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.PCOU occurred in 9 308 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.ResultsPCOU occurred in 9 308 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.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.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. 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. KCI Citation Count: 0
Author	Lee, Hyung-Chul Kim, Ji-Yoon Lee, Tagkeun Kim, Eugene Sung, Jeong Min Kim, Jonghae Yoon, Hyun-Kyu Kim, Yun Jin Yang, Hyun-Lim Kim, Kyu-Nam
Author_xml	– sequence: 1 givenname: Jonghae orcidid: 0000-0003-1222-0054 surname: Kim fullname: Kim, Jonghae – sequence: 2 givenname: Hyun-Lim orcidid: 0000-0003-2221-3042 surname: Yang fullname: Yang, Hyun-Lim – sequence: 3 givenname: Eugene orcidid: 0000-0002-1926-4191 surname: Kim fullname: Kim, Eugene – sequence: 4 givenname: Hyung-Chul orcidid: 0000-0003-0048-7958 surname: Lee fullname: Lee, Hyung-Chul – sequence: 5 givenname: Hyun-Kyu orcidid: 0000-0001-5424-3559 surname: Yoon fullname: Yoon, Hyun-Kyu – sequence: 6 givenname: Yun Jin orcidid: 0000-0003-3202-2696 surname: Kim fullname: Kim, Yun Jin – sequence: 7 givenname: Kyu-Nam orcidid: 0000-0003-2526-8348 surname: Kim fullname: Kim, Kyu-Nam – sequence: 8 givenname: Ji-Yoon orcidid: 0000-0001-7676-0346 surname: Kim fullname: Kim, Ji-Yoon – sequence: 9 givenname: Jeong Min orcidid: 0000-0003-0641-1145 surname: Sung fullname: Sung, Jeong Min – sequence: 10 givenname: Tagkeun orcidid: 0000-0002-1942-8838 surname: Lee fullname: Lee, Tagkeun
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40865987$$D View this record in MEDLINE/PubMed https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003247758$$DAccess content in National Research Foundation of Korea (NRF)
BookMark	eNo9ks9u1DAQhyNUREvpgRdAPgLSFv-NE26risJKlZBQOVsTe7Lr3cRe7KRoH4D3brK71JexRt98c5jf2-IixIBF8Z7RW0lr_WW3hVsuK8FeFVecUrXQqhQX538pWX1Z3OS8pdMTgtKSvykuJa1KVVf6qvi3DNAdss8EgiMOn7CL-x7DQGJLks87sk_ovB18DKSPDrtM2piI3aQYvCVx76N3ZMxIoB0wkTymNabDVwLExk1MA8nD6A4T4cOaDBskAWbZX--QOBiggYzvitctdBlvzvW6-H3_7fHux-Lh5_fV3fJhYUUlhoVoWk0RNNaaU4lSImOyEdYpyplTVQPOVmAnZaUVasE4Y3RuoJCssY24Lj6fvCG1Zme9ieCPdR3NLpnlr8eVYVSXTHI9wasT7CJszT75HtLhOHFsxLQ2kAZvOzSKC11VNXUCtWxlWzdQS3AcGgWysbPr48m1T_HPiHkwvc8Wuw4CxjEbwWUpqVJ0Rj-c0bHp0b0s_n-zCfh0AmyKOSdsXxBGzRwJM0XCHCMhngHVKamp
Cites_doi	10.1136/bmj.g1251 10.1002/sim.5525 10.3346/jkms.2024.39.e265 10.1111/j.1526-4637.2010.00983.x 10.1001/archinternmed.2011.1827 10.4093/dmj.2016.40.1.79 10.1097/jto.0b013e31822918bd 10.1097/01.ccm.0000275267.64078.b0 10.1001/jama.2013.278344 10.1177/0310057x1704500609 10.1016/s0885-3924(01)00294-9 10.1007/s00167-021-06511-0 10.1001/jamasurg.2017.0504 10.1001/jamainternmed.2016.3298 10.1001/jamanetworkopen.2020.7367 10.1213/01.ane.0000181289.09675.7d 10.1161/circoutcomes.110.959023 10.1016/j.soard.2023.06.005 10.1016/j.athoracsur.2020.01.056 10.1016/j.jopan.2022.09.004 10.3346/jkms.2021.36.e127 10.1007/s00268-019-05016-9 10.1200/jco.2011.37.3068 10.2105/ajph.2009.180646 10.1371/journal.pone.0155705 10.1177/2049463712438493 10.5414/cp202974 10.1016/j.bja.2018.02.022 10.1111/j.1533-2500.2009.00335.x 10.3410/f.731220250.793569875 10.1001/jama.2016.1464 10.1097/j.pain.0000000000000892 10.1016/j.artd.2023.101275 10.1097/brs.0000000000002734 10.1213/ane.0000000000002458 10.2106/jbjs.17.01239 10.5414/cp203042 10.1016/j.jamda.2022.01.081 10.1016/s0140-6736(19)30352-6 10.1001/jamanetworkopen.2019.0968 10.1097/mlr.0000000000000405 10.1097/00002508-200309000-00002 10.3389/fnbot.2013.00021 10.1016/j.bja.2018.10.058 10.1038/s41572-019-0137-5 10.2106/jbjs.j.01473 10.1002/ejp.567
ContentType	Journal Article
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 DOA ACYCR
DOI	10.4097/kja.24831
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic DOAJ Directory of Open Access Journals Korean Citation Index
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	CrossRef MEDLINE MEDLINE - Academic
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
EISSN	2005-7563
EndPage	442
ExternalDocumentID	oai_kci_go_kr_ARTI_10761427 oai_doaj_org_article_52378890d3e74f4f9ba94ad2ab5a4bc7 40865987 10_4097_kja_24831
Genre	Journal Article
GeographicLocations	Republic of Korea
GeographicLocations_xml	– name: Republic of Korea
GrantInformation_xml	– fundername: Ministry of Science and ICT grantid: 2022R1F1A1066482 – fundername: National Research Foundation of Korea
GroupedDBID	5-W 53G 8JR 8XY AAYXX ABDBF ACUHS ALMA_UNASSIGNED_HOLDINGS CITATION DIK E3Z EBD EF. ESX F5P GROUPED_DOAJ HYE KQ8 M48 O5R O5S OK1 PGMZT RPM TUS 9ZL ADRAZ CGR CUY CVF ECM EIF NPM 7X8 ACYCR
ID	FETCH-LOGICAL-c383t-3bf70ea7e97204e44e114b3cd5021d58badc8acaba875e73121108acae341bcb3
IEDL.DBID	DOA
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001586997600004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2005-6419 2005-7563
IngestDate	Thu Oct 02 04:45:38 EDT 2025 Fri Oct 03 12:39:19 EDT 2025 Thu Sep 04 12:31:17 EDT 2025 Sun Oct 05 01:50:24 EDT 2025 Sat Nov 29 07:25:44 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	5
Keywords	Gradient boosting algorithms Dependence, opioid Prediction methods, machine Surgical procedures, operative Postoperative period Machine learning
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c383t-3bf70ea7e97204e44e114b3cd5021d58badc8acaba875e73121108acae341bcb3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 https://doi.org/10.4097/kja.24831
ORCID	0000-0002-1926-4191 0000-0001-5424-3559 0000-0001-7676-0346 0000-0003-1222-0054 0000-0003-3202-2696 0000-0003-2526-8348 0000-0003-2221-3042 0000-0003-0048-7958 0000-0003-0641-1145 0000-0002-1942-8838
OpenAccessLink	https://doaj.org/article/52378890d3e74f4f9ba94ad2ab5a4bc7
PMID	40865987
PQID	3246405507
PQPubID	23479
PageCount	14
ParticipantIDs	nrf_kci_oai_kci_go_kr_ARTI_10761427 doaj_primary_oai_doaj_org_article_52378890d3e74f4f9ba94ad2ab5a4bc7 proquest_miscellaneous_3246405507 pubmed_primary_40865987 crossref_primary_10_4097_kja_24831
PublicationCentury	2000
PublicationDate	2025-10-01
PublicationDateYYYYMMDD	2025-10-01
PublicationDate_xml	– month: 10 year: 2025 text: 2025-10-01 day: 01
PublicationDecade	2020
PublicationPlace	Korea (South)
PublicationPlace_xml	– name: Korea (South)
PublicationTitle	Korean journal of anesthesiology
PublicationTitleAlternate	Korean J Anesthesiol
PublicationYear	2025
Publisher	Korean Society of Anesthesiologists 대한마취통증의학회
Publisher_xml	– name: Korean Society of Anesthesiologists – name: 대한마취통증의학회
References	ref13 ref12 ref15 ref14 ref11 ref10 ref17 ref16 ref19 ref18 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 Scott (ref26) ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref25 ref20 ref22 ref21 ref28 ref27 ref29
References_xml	– ident: ref8 doi: 10.1136/bmj.g1251 – ident: ref37 doi: 10.1002/sim.5525 – ident: ref42 doi: 10.3346/jkms.2024.39.e265 – ident: ref12 doi: 10.1111/j.1526-4637.2010.00983.x – ident: ref13 doi: 10.1001/archinternmed.2011.1827 – ident: ref26 – ident: ref16 doi: 10.4093/dmj.2016.40.1.79 – ident: ref17 doi: 10.1097/jto.0b013e31822918bd – ident: ref38 doi: 10.1097/01.ccm.0000275267.64078.b0 – ident: ref22 doi: 10.1001/jama.2013.278344 – ident: ref27 doi: 10.1177/0310057x1704500609 – ident: ref25 doi: 10.1016/s0885-3924(01)00294-9 – ident: ref34 – ident: ref29 doi: 10.1007/s00167-021-06511-0 – ident: ref10 doi: 10.1001/jamasurg.2017.0504 – ident: ref9 doi: 10.1001/jamainternmed.2016.3298 – ident: ref15 doi: 10.1001/jamanetworkopen.2020.7367 – ident: ref41 doi: 10.1213/01.ane.0000181289.09675.7d – ident: ref19 doi: 10.1161/circoutcomes.110.959023 – ident: ref32 doi: 10.1016/j.soard.2023.06.005 – ident: ref46 doi: 10.1016/j.athoracsur.2020.01.056 – ident: ref33 doi: 10.1016/j.jopan.2022.09.004 – ident: ref43 doi: 10.3346/jkms.2021.36.e127 – ident: ref30 doi: 10.1007/s00268-019-05016-9 – ident: ref2 doi: 10.1200/jco.2011.37.3068 – ident: ref28 doi: 10.2105/ajph.2009.180646 – ident: ref20 doi: 10.1371/journal.pone.0155705 – ident: ref1 doi: 10.1177/2049463712438493 – ident: ref45 doi: 10.5414/cp202974 – ident: ref3 doi: 10.1016/j.bja.2018.02.022 – ident: ref23 doi: 10.1111/j.1533-2500.2009.00335.x – ident: ref6 doi: 10.3410/f.731220250.793569875 – ident: ref14 doi: 10.1001/jama.2016.1464 – ident: ref5 doi: 10.1097/j.pain.0000000000000892 – ident: ref48 doi: 10.1016/j.artd.2023.101275 – ident: ref47 doi: 10.1097/brs.0000000000002734 – ident: ref40 doi: 10.1213/ane.0000000000002458 – ident: ref35 doi: 10.2106/jbjs.17.01239 – ident: ref44 doi: 10.5414/cp203042 – ident: ref31 doi: 10.1016/j.jamda.2022.01.081 – ident: ref11 doi: 10.1016/s0140-6736(19)30352-6 – ident: ref21 doi: 10.1001/jamanetworkopen.2019.0968 – ident: ref18 doi: 10.1097/mlr.0000000000000405 – ident: ref24 doi: 10.1097/00002508-200309000-00002 – ident: ref36 doi: 10.3389/fnbot.2013.00021 – ident: ref4 doi: 10.1016/j.bja.2018.10.058 – ident: ref7 doi: 10.1038/s41572-019-0137-5 – ident: ref49 doi: 10.2106/jbjs.j.01473 – ident: ref39 doi: 10.1002/ejp.567
SSID	ssj0000330062
Score	2.3407178
Snippet	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... 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... 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...
SourceID	nrf doaj proquest pubmed crossref
SourceType	Open Website Aggregation Database Index Database
StartPage	429
SubjectTerms	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
Title	Analysis and development of risk prediction models for chronic opioid use after surgery: a cohort study using the nationwide database
URI	https://www.ncbi.nlm.nih.gov/pubmed/40865987 https://www.proquest.com/docview/3246405507 https://doaj.org/article/52378890d3e74f4f9ba94ad2ab5a4bc7 https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003247758
Volume	78
WOSCitedRecordID	wos001586997600004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
ispartofPNX	Korean Journal of Anesthesiology, 2025, 78(5), , pp.429-442
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2005-7563 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000330062 issn: 2005-6419 databaseCode: DOA dateStart: 19680101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwELZgxYELAvHqsqyGxzWsG7t1vDdArOCy4gBSb5Yfk1WolFRJC7-A_82M3S7LAXHhFCnKw5pJMt8Xj79PiNdKxmi0T3mtU6WtVFVIra5YOy2hUl7ZNptNmMvLZrWyn29YfXFPWJEHLoE7I6JELM3KpNDoVrc2eEvXrn1YeB1iXkdOqOcGmcrfYKLpMruJZqnNpZ7bIivE8k5n62_-Ta0bNf-jGGXNfiox_dj-HW7msnNxX9zb40V4W8b5QNzC_qH4eZASAd8nSL_7fmBogZvFYTPyBAwHHbLXzQQETiEWJVwYNt3QJdhNCNkjHKayOPocPLBj7riFLDsL3BV_BYQRofw0_NElBG4q5eL3SHy9-PDl_cdq76dQReKh20qF1kj0Bi0706DWSGQoqJgWVOjTogk-xcZHugSRGDSK1d8k70AqdSEG9Vgc9UOPTwUoE4LEpOMyWG1ish69lgnrMG-J0MiZeHkIrNsU2QxHdIOj7yj6Lkd_Jt5xyK8PYKXrvIPy7_b5d__K_0y8ooS5dezy-by9Gtx6dMQHPtE9DeGPmo56cUioo_eHJ0V8j8NucgQolwRaCRbPxJOS6esBaeJ7C9uY4_8x0Gfibs3ewbkR8EQcbccdPhd34vdtN42n4rZZNaf5If4F94f2_w
linkProvider	Directory of Open Access Journals
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Analysis+and+development+of+risk+prediction+models+for+chronic+opioid+use+after+surgery%3A+a+cohort+study+using+the+nationwide+database&rft.jtitle=Korean+journal+of+anesthesiology&rft.au=%EA%B9%80%EC%A2%85%ED%95%B4&rft.au=Yang+Hyun-Lim&rft.au=Kim+Eugene&rft.au=Lee+Hyung-Chul&rft.date=2025-10-01&rft.pub=%EB%8C%80%ED%95%9C%EB%A7%88%EC%B7%A8%ED%86%B5%EC%A6%9D%EC%9D%98%ED%95%99%ED%9A%8C&rft.issn=2005-6419&rft.eissn=2005-7563&rft.spage=429&rft.epage=442&rft_id=info:doi/10.4097%2Fkja.24831&rft.externalDBID=n%2Fa&rft.externalDocID=oai_kci_go_kr_ARTI_10761427
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2005-6419&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2005-6419&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2005-6419&client=summon