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Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy

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Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.

Level of evidence

III.

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Funding

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Author information

Authors and Affiliations

  1. Department of Orthopaedic Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA

    Yining Lu & Ryan R. Wilbur

  2. Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA

    Enrico Forlenza, Ophelie Lavoie-Gagne, Adam B. Yanke, Brian J. Cole, Nikhil Verma & Brian Forsythe

  3. Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA

    Michael C. Fu

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  1. Yining Lu
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Contributions

YL: study design, data acquisition, data analysis, data interpretation, manuscript drafting, and critical revision. EF: study design, data acquisition, data analysis, data interpretation, manuscript drafting, and critical revision. RRW: study design, data acquisition, data analysis, data interpretation, manuscript drafting, and critical revision. OLG: study design, data acquisition, data analysis, data interpretation, manuscript drafting, and critical revision. MCF: study design, data interpretation, manuscript drafting, and critical revision. ABY: study design, data interpretation, manuscript drafting, and critical revision. BJC: study design, data interpretation, manuscript drafting, and critical revision. NV: study design, data interpretation, manuscript drafting, and critical revision. BF: study design, data acquisition, data analysis, data interpretation, manuscript drafting, and critical revision.

Corresponding author

Correspondence to Yining Lu.

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Conflict of interest

M.C.F. reports grants from Arthrex, Inc., grants from ACUMED LLC, other from EXACTECH, INC, other from ENCORE MEDICAL, LP, other from Stryker Corporation, other from Vericel Corporation, other from Zimmer Biomet Holdings, Inc., other from DePuy Synthes Sales Inc., outside the submitted work; and Arthroscopy: Editorial or governing board, DJ Orthopaedics: Paid presenter or speaker, HSS Journal: Editorial or governing board. A.B.Y. reports personal fees and other from Joint Restoration Foundation, Inc., personal fees from Olympus America, Inc., non-financial support from Medwest Associates, other from Smith + Nephew, Inc., other from Aesculap Biologics, LLC, personal fees from CONMED, other from Arthrex, Inc, other from Organogenesis, other from Patient IQ, other from Vericel, outside the submitted work. B.J.C reports personal fees from Arthrex, Inc., personal fees from Anika Therapeutics, Inc., personal fees from DJO, LLC, other from Stryker Corporation, other from Aastrom Biosciences, Inc, other from Smith & Nephew, Inc, other from Depuy Orthopaedics Inc., personal fees and other from LifeNet Health Inc., personal fees from zimmer biomet holdings, other from Geistlich Pharma, North America, Inc., other from CyMedica Orthopedics, Inc, other from Ceterix Orthopaedics, Inc., other from Vericel Corporation, personal fees from Cartiva, Inc., other from GE Healthcare, personal fees from ACUMED LLC, personal fees from Flexion Therapeutics, Inc., other from Aesculap/B.Braun, other from Athletico, personal fees from Elsevier Publishing, other from NIH, other from Operative Techniques in Sport Medicine, other from Ossio, personal fees from Regentis, outside the submitted work; and American Journal of Orthopedics: Editorial or governing board, American Journal of Sports Medicine: Editorial or governing board, Arthroscopy Association of North America: Board or committee member, Cartilage: Editorial or governing board, International Cartilage Repair Society: Board or committee member, Journal of Shoulder and Elbow Surgery: Editor only: Editorial or governing board, Journal of the American Academy of Orthopaedic Surgeons: Editor only: Editorial or governing board. N.V. reports personal fees from Smith + Nephew, Inc, personal fees from Styker Corporation, other from Relievant Medsystems, Inc, other from DePuy Synthes Sales Inc., other from Vericel Corporation, personal fees from Medacta USA, Inc, other from Athroscopy, other from Breg, other from Cymedica, personal fees and other from Minivasive, other from Omeros, personal fees from Orthospace, other from Ossur, other from Vindico Medical-Orthopedics Hyperguide, other from Wright Medical Technology, Inc., outside the submitted work; and American Orthopaedic Society for Sports Medicine: Board or committee member, American Shoulder and Elbow Surgeons: Board or committee member, Arthroscopy Association of North America: Board or committee member, Knee: Editorial or governing board, SLACK Incorporated: Editorial or governing board. B.F. reports research support from Arthrex and Stryker; educational support from Medwest, Smith & Nephew, and Ossur; consulting fees from Arthrex, DJO, Smith & Nephew, Ossur, Sonoma Orthopedics, and Stryker; speaking fees from Arthrex; honoraria from Arthrosurface; and royalties from Elsevier and Arthrex; and has stock options in Jace Medical.

Ethical approval

This study utilized previously collected data from an institutional database. The present study was granted IRB exemption by the IRB at Rush University and was adherent to all ethical standards put forth by IRB at Rush University.

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Lu, Y., Forlenza, E., Wilbur, R.R. et al. Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy. Knee Surg Sports Traumatol Arthrosc 30, 762–772 (2022). https://doi.org/10.1007/s00167-020-06421-7

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  • DOI: https://doi.org/10.1007/s00167-020-06421-7

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