Multidimensional Analysis of Preoperative Patient-Reported Outcomes Identifies Distinct Phenotypes in Total Knee Arthroplasty: Secondary Analysis of the SHARKS Registry in a Public Hospital Department.

Abstract

Traditional research on total knee arthroplasty (TKA) relies on preoperative patient-reported outcome measures (PROMs) to predict postoperative satisfaction. We aim to identify distinct patient phenotypes among TKA candidates, and investigate their correlations with patient characteristics. Between 2017 and 2021, patients with primary knee cases at a metropolitan public hospital were enrolled in a clinical quality registry. Demographics, clinical data, and the Veterans Rand 12 and Oxford Knee Score were collected. Imputed data were utilized for the primary analysis, employing <i>k</i>-means clustering to identify four phenotypes. Analysis of variance assessed differences in scores between clusters, and nominal logistic regression determined relationships between phenotypes and patient age, sex, body mass index (BMI), and laterality. The sample comprised 389 patients with 450 primary knees. Phenotype 4 (mild symptoms with good mental health) exhibited superior physical function and overall health. In contrast, patients in phenotype 2 (severe symptoms with poor mental health) experienced the most knee pain and health issues. Phenotype 1 (moderate symptoms with good mental health) reported high mental health scores despite knee pain and physical impairment. Patient characteristics significantly correlated with phenotypes; those in the severe symptoms with poor mental health phenotype were more likely to be younger, female, have a higher BMI, and bilateral osteoarthritis (<i>p</i> < 0.05). This multidimensional analysis identified TKA patient phenotypes based on common PROMs, revealing associations with patient demographics. This approach has the potential to inform prognostic models, enhancing clinical decision-making and patient outcomes in joint replacement.