Pre-operative Mental Health Predicts Clinically Meaningful Outcomes after Osteochondral Allograft for Cartilage Defects of the Knee: A Machine Learning Analysis. Issue 7 (30th July 2021)

• Record Type: Journal Article
• Authors: Karnuta, Jaret; Haeberle, Heather; Owusu-Akyaw, Kwadwo; Nkrumah, Kwame; Rodeo, Scott; Nwachukwu, Benedict; Williams, Riley; Ramkumar, Prem
• Is Part Of: Orthopaedic journal of sports medicine. Volume 9:Issue 7(2021)Supplement 4
• Publication Date: 2021-07-30
• Subjects: Sports medicine -- Periodicals; Orthopedics -- Periodicals; Arthroscopy -- Periodicals; Arthroplasty -- Periodicals; Knee -- Surgery -- Periodicals; 616.7
• DOI: 10.1177/2325967121S00217
• Languages: English
• ISSNs: 2325-9671

Abstract

Objectives: Fresh osteochondral allograft transplantation (OCA) is an effective method of treating symptomatic cartilage defects of the knee. This cartilage restoration technique involves the single-stage implantation of viable, mature hyaline cartilage into chondral or osteochondral lesion. Predictive models for reaching the clinically meaningful outcome among patients undergoing OCA for cartilage lesions of the knee remain under investigation. The purpose of the study was to apply machine learning to determine which preoperative variables are predictive for achieving the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) at one and two years after OCA for cartilage lesions of the knee. Methods: Data were analyzed for patients who underwent OCA of the knee by a two high-volume fellowship-trained cartilage surgeons before May 1, 2018. The International Knee Documentation Committee (IKDC) subjective form, Knee Outcome Survey-Activities of Daily Living (KOS-ADL), and Mental Component (MCS) and Physical Component (PCS) Summaries of the SF-36 were administered preoperatively and at one and two-years postoperatively. A total of 84 predictive models were created using 7 unique architectures to detect achievement of the MCID for each of the four outcome measures and the SCB for the IKDC and KOS-ADL at both timepoints. Data inputted into the models included prior and concomitant surgical history, laterality, sex, age, body mass index (BMI), intraoperative findings, and patient-reported outcome measures (PROMs). Shapley additive explanations (SHAP) 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, 135 (73%) patients were available for one-year follow-up and 153 (83%) patients with two-year follow-up. In predicting outcomes after OCA in terms of the IKDC, KOS-ADL, MCS, and PCS at one and two years, AUCs of the top performing models ranged from fair (0.72) to excellent (0.94). Lower baseline mental health (MCS), higher baseline physical health (PCS) and knee function scores (KOS-ADL, IKDC subjective), lower baseline activity demand (Marx, Cincinnati Sports), worse pain symptoms (Cincinnati pain, SF36 pain), and higher BMI were thematic predictors contributing to failure to achieve the MCID or SCB at one and two-years postoperatively. Conclusions: Our machine learning models were effective in predicting outcomes and elucidating the relationships between baseline factors contributing to achieving the MICID for osteochondral allograft transplantation of the knee. Patients who preoperatively report poor mental health, catastrophize pain symptoms, compensate with higher physical health and knee function, and exhibit lower activity demands are at risk for failing to reach clinically meaningful outcomes after OCA of the knee. :