PMID- 34048288 OWN - NLM STAT- MEDLINE DCOM- 20210809 LR - 20210809 IS - 1552-3365 (Electronic) IS - 0363-5465 (Linking) VI - 49 IP - 8 DP - 2021 Jul TI - Effect of Preoperative Imaging and Patient Factors on Clinically Meaningful Outcomes and Quality of Life After Osteochondral Allograft Transplantation: A Machine Learning Analysis of Cartilage Defects of the Knee. PG - 2177-2186 LID - 10.1177/03635465211015179 [doi] AB - BACKGROUND: Fresh osteochondral allograft transplantation (OCA) is an effective method of treating symptomatic cartilage defects of the knee. This restoration technique involves the single-stage implantation of viable, mature hyaline cartilage into a chondral or osteochondral lesion. The extent to which preoperative imaging and patient factors predict achieving clinically meaningful outcomes among patients undergoing OCA for cartilage lesions of the knee remains unknown. PURPOSE: To determine the predictive relationship of preoperative imaging, preoperative patient-reported outcome measures (PROMs), and patient demographics with achievement of the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) for functional and quality-of-life PROMs at 2 years after OCA for symptomatic cartilage defects of the knee. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: Data were analyzed for patients who underwent OCA before May 1, 2018, by 2 high-volume fellowship-trained cartilage surgeons. The International Knee Documentation Committee (IKDC) subjective form, Knee Outcome Survey-Activities of Daily Living (KOS-ADL), and mental and physical component summaries of the SF-36 were administered preoperatively and at 2 years postoperatively. A total of 42 predictive models were created using 7 unique architectures to detect achievement of the MCID for each of the 4 outcome measures and the SCB for the IKDC and KOS-ADL. Data inputted into the models included sex, age, body mass index, baseline PROMs, lesion size, concomitant ligamentous or meniscal tear, and presence of "bone bruise" or osseous edema. Shapley additive explanations plot analysis identified predictors of reaching the MCID and SCB. RESULTS: Of the 185 patients who underwent OCA for the knee and met eligibility criteria from an institutional cartilage registry, 153 (83%) had 2-year follow-up. Preoperative magnetic resonance imaging (MRI), baseline PROMs, and patient demographics best predicted reaching the 2-year MCID and SCB of the IKDC and KOS-ADL PROMs, with areas under the receiver operating characteristic curve of the top-performing models ranging from good (0.88) to excellent (0.91). MRI faired poorly (areas under the curve, 0.60-0.68) in predicting the MCID for the mental and physical component summaries. Higher body mass index, knee malalignment, absence of preoperative osseous edema, concomitant anterior cruciate ligament or meniscal injury, larger defect size, and the implantation of >1 OCA graft were consistent findings contributing to failure to achieve the MCID or SCB at 2 years postoperatively. CONCLUSION: Our machine learning models demonstrated that preoperative MRI, baseline PROMs, and patient demographics reliably predict the ability to reach clinically meaningful thresholds for functional knee outcomes 2 years after OCA for cartilage defects. Although clinical improvement in knee function can be reliably predicted, improvements in quality of life after OCA depend on a comprehensive preoperative assessment of the patient's perception of his or her mental and physical health. Absence of osseous edema, concomitant anterior cruciate ligament or meniscal injury, larger lesion size on MRI, knee malalignment, and elevated body mass index are predictive of failure to achieve 2-year functional benefits after OCA of the knee. FAU - Ramkumar, Prem N AU - Ramkumar PN AD - Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA. FAU - Karnuta, Jaret M AU - Karnuta JM AD - Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA. FAU - Haeberle, Heather S AU - Haeberle HS AD - Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA. AD - Sports Medicine and Shoulder Service, Institute for Cartilage Repair Hospital for Special Surgery, New York, New York, USA. FAU - Rodeo, Scott A AU - Rodeo SA AD - Sports Medicine and Shoulder Service, Institute for Cartilage Repair Hospital for Special Surgery, New York, New York, USA. FAU - Nwachukwu, Benedict U AU - Nwachukwu BU AD - Sports Medicine and Shoulder Service, Institute for Cartilage Repair Hospital for Special Surgery, New York, New York, USA. FAU - Williams, Riley J 3rd AU - Williams RJ 3rd AD - Sports Medicine and Shoulder Service, Institute for Cartilage Repair Hospital for Special Surgery, New York, New York, USA. LA - eng PT - Journal Article DEP - 20210528 PL - United States TA - Am J Sports Med JT - The American journal of sports medicine JID - 7609541 SB - IM MH - Activities of Daily Living MH - Allografts MH - Bone Transplantation MH - *Cartilage, Articular/diagnostic imaging/surgery MH - Case-Control Studies MH - Female MH - Follow-Up Studies MH - Humans MH - Knee Joint/diagnostic imaging/surgery MH - Machine Learning MH - Male MH - *Quality of Life MH - Treatment Outcome OTO - NOTNLM OT - MCID OT - MRI OT - cartilage OT - machine learning OT - osteochondral allograft EDAT- 2021/05/29 06:00 MHDA- 2021/08/10 06:00 CRDT- 2021/05/28 17:14 PHST- 2021/05/29 06:00 [pubmed] PHST- 2021/08/10 06:00 [medline] PHST- 2021/05/28 17:14 [entrez] AID - 10.1177/03635465211015179 [doi] PST - ppublish SO - Am J Sports Med. 2021 Jul;49(8):2177-2186. doi: 10.1177/03635465211015179. Epub 2021 May 28.