Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy.
Ensemble
Knee arthroscopy
Knee surgery
Machine learning
Opioids
Postoperative opioids
Journal
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
ISSN: 1433-7347
Titre abrégé: Knee Surg Sports Traumatol Arthrosc
Pays: Germany
ID NLM: 9314730
Informations de publication
Date de publication:
Mar 2022
Mar 2022
Historique:
received:
16
10
2020
accepted:
14
12
2020
pubmed:
10
1
2021
medline:
11
3
2022
entrez:
9
1
2021
Statut:
ppublish
Résumé
Recovery following elective knee arthroscopy can be compromised by prolonged postoperative opioid utilization, yet an effective and validated risk calculator for this outcome remains elusive. The purpose of this study is to develop and validate a machine-learning algorithm that can reliably and effectively predict prolonged opioid consumption in patients following elective knee arthroscopy. A retrospective review of an institutional outcome database was performed at a tertiary academic medical centre to identify adult patients who underwent knee arthroscopy between 2016 and 2018. Extended postoperative opioid consumption was defined as opioid consumption at least 150 days following surgery. Five machine-learning algorithms were assessed for the ability to predict this outcome. Performances of the algorithms were assessed through discrimination, calibration, and decision curve analysis. Overall, of the 381 patients included, 60 (20.3%) demonstrated sustained postoperative opioid consumption. The factors determined for prediction of prolonged postoperative opioid prescriptions were reduced preoperative scores on the following patient-reported outcomes: the IKDC, KOOS ADL, VR12 MCS, KOOS pain, and KOOS Sport and Activities. The ensemble model achieved the best performance based on discrimination (AUC = 0.74), calibration, and decision curve analysis. This model was integrated into a web-based open-access application able to provide both predictions and explanations. Following appropriate external validation, the algorithm developed presently could augment timely identification of patients who are at risk of extended opioid use. Reduced scores on preoperative patient-reported outcomes, symptom duration and perioperative oral morphine equivalents were identified as novel predictors of prolonged postoperative opioid use. The predictive model can be easily deployed in the clinical setting to identify at risk patients thus allowing providers to optimize modifiable risk factors and appropriately counsel patients preoperatively. III.
Identifiants
pubmed: 33420807
doi: 10.1007/s00167-020-06421-7
pii: 10.1007/s00167-020-06421-7
doi:
Substances chimiques
Analgesics, Opioid
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
762-772Informations de copyright
© 2021. European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).
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