{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(11)"],"submitter":["MARS Group"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Purpose</h4>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.<h4>Study design</h4>Cohort study; Level of evidence, 3.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.<h4>Registration</h4>NCT00625885 (ClinicalTrials.gov identifier)."],"journal":["Orthopaedic journal of sports medicine"],"pagination":["23259671241291920"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11565622"],"repository":["biostudies-literature"],"pubmed_title":["A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort."],"pmcid":["PMC11565622"],"pubmed_authors":["Carey JL","Huston LJ","Wright RW","Cole BJ","Cooper JM","Lindsay Harris N","Haas AK","Arciero RA","Nissen CW","Kaeding CC","Annunziata CC","Spindler KP","David TS","Mathien GM","Moran J","Butler V JB","Jazrawi LM","Hame SL","MARS Group","Ma CB","Dunn WR","Jones MH","McAllister DR","Vidal AF","Levy BA","Berg JH","Ramappa AJ","Parker RD","Brockmeier SF","Kamath GV","Baumgarten KM","Purnell ML","Bartolozzi AR","Gatt CJ","Rauh MA","Slauterbeck JR","Tingstad EM","Devana S","Brophy RH","Cox CL","Giffin JR","Allen CR","Hannafin JA","McCarty EC","Sherman OH","Cooper DE","Miller BS","Bernas GA","Sekiya JK","Smith MV","Rettig AC","Lee C","Shea KG","Bush-Joseph CA","Hershman EB","Klootwyk TE","McCormack RG","Tenuta JJ","Hoellrich RG","Svoboda SJ","Marx RG","Matava MJ","Owens BD","Frederick RW","Johnson DC","Johnson TS","Ganley TJ","Williams JS","Viskontas DG","Vasavada V","Bach BR","Ned Amendola A","White RA","Flanigan DC","O'Neill DF","Maiers GP","Vasavada K","DeBerardino TM","Creighton RA","Spang JT","Baker CL","Wolcott ML","Wolf BR","Hechtman KS","Carpenter JE","Taft TN","Stuart MJ","York JJ","Gecha SR"],"additional_accession":[]},"is_claimable":false,"name":"A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort.","description":"<h4>Background</h4>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.<h4>Purpose</h4>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.<h4>Study design</h4>Cohort study; Level of evidence, 3.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.<h4>Registration</h4>NCT00625885 (ClinicalTrials.gov identifier).","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-06-02T03:35:05.665Z","creation":"2025-04-04T12:01:03.869Z"},"accession":"S-EPMC11565622","cross_references":{"pubmed":["39555321"],"doi":["10.1177/23259671241291920"]}}