A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort.
Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights. Purpose: To...
| Publicado en: | Orthopaedic Journal of Sports Medicine Vol. 12; no. 11; pp. 1 - 15 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Nov2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181276594&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181276594 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23259671 FUGT jtl: Orthopaedic Journal of Sports Medicine issn: 23259671 maglogo: Y pubinfo: dt: Nov2024 vid: 12 iid: 11 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 181276594 181276594 181276594 10.1177/23259671241291920 181276594 ppf: 1 ppct: 14 formats: tig: atl: A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort. aug: au: Vasavada, Kinjal Vasavada, Vrinda Moran, Jay Devana, Sai Lee, Changhee Hame, Sharon L. Jazrawi, Laith M. Sherman, Orrin H. Huston, Laura J. Haas, Amanda K. Allen, Christina R. Cooper, Daniel E. DeBerardino, Thomas M. Spindler, Kurt P. Stuart, Michael J. Amendola, Annunziato Annunziata, Christopher C. Arciero, Robert A. Bach Jr, Bernard R. Baker III, Champ L. affil: Yale University, New Haven, Connecticut, USA sug: subj: Machine Learning Anterior Cruciate Ligament Reconstruction Reoperation Treatment Failure Human Prediction Models Prospective Studies Patient-Reported Outcomes Logistic Regression ROC Curve Allografts Sports Participation Scales Short Form-36 Health Survey (SF-36) Patient Selection ab: Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights. Purpose: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure. Study Design: Cohort study; Level of evidence, 3. Methods: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patient-reported outcomes, additional surgeries, and graft failure. Machine learning models including logistic regression (LR), XGBoost, gradient boosting (GB), random forest (RF), and a validated ensemble algorithm (AutoPrognosis) were built to predict graft failure by 6 years postoperatively. Validated performance metrics and feature importance measures were used to evaluate model performance. Results: The cohort included 960 patients who completed 6-year follow-up, with 5.7% (n = 55) experiencing graft failure. AutoPrognosis demonstrated the highest discriminative power (model area under the receiver operating characteristic curve: AutoPrognosis, 0.703; RF, 0.618; GB, 0.660; XGBoost, 0.680; LR, 0.592), with well-calibrated scores (model Brier score: AutoPrognosis, 0.053; RF, 0.054; GB, 0.057; XGBoost, 0.058; LR, 0.111). The most important features for AutoPrognosis model performance were prior compromised femoral and tibial tunnels (placement and size) and allograft graft type used in current rACLR. Conclusion: The present study demonstrated the ability of the novel AutoPrognosis machine learning model to best predict the risk of graft failure in patients undergoing rACLR at 6 years postoperatively with moderate predictive ability. Femoral and tibial tunnel size and position in prior ACLR and allograft use in current rACLR were all risk factors for rACLR failure in the context of the AutoPrognosis model. This study describes a unique model that can be externally validated with larger data sets and contribute toward the creation of a robust rACLR bedside risk calculator in future studies. Registration: NCT00625885 (ClinicalTrials.gov identifier). pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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