Baseline Analysis of Patients Presenting for Surgical Review of Anterior Cruciate Ligament Rupture Reveals Heterogeneity in Patient-Reported Outcome Measures

Abstract

<jats:title>Abstract</jats:title><jats:p>Despite the establishment of successful surgical techniques and rehabilitation protocols for anterior cruciate ligament (ACL) reconstruction, published return to sport rates are less than satisfactory. This has led orthopaedic surgeons and researchers to develop more robust patient selection methods, and investigate prognostic patient characteristics. No previous studies have integrated baseline characteristics and responses to patient-reported outcome measures (PROMs) of patients with ACL rupture presenting for surgical review. Patients electing to undergo ACL reconstruction under the care of a single orthopaedic surgeon at a metropolitan public hospital were enrolled in a clinical quality registry. Patients completed Veterans RAND 12-item Health Survey (VR-12) Physical Component Summary and Mental Component Summary scores, Tegner activity scale, and International Knee Documentation Committee (IKDC) questionnaires at presentation. Total scores were extracted from the electronic registry, and a machine learning approach (k-means) was used to identify subgroups based on similarity of questionnaire responses. The average scores in each cluster were compared using analysis of variance (ANOVA; Kruskal–Wallis) and nominal logistic regression was performed to determine relationships between cluster membership and patient age, gender, body mass index (BMI), and injury-to-examination delay. A sample of 107 patients with primary ACL rupture were extracted, with 97 (91%) available for analysis with complete datasets. Four clusters were identified with distinct patterns of PROMs responses. These ranged from lowest (Cluster 1) to highest scores for VR-12 and IKDC (Cluster 4). In particular, Cluster 4 returned median scores within 6 points of the patient acceptable symptom state for the IKDC score for ACL reconstruction (70.1, interquartile range: 59–78). Significant (p &lt; 0.05) differences in PROMs between clusters were observed using ANOVA, with variance explained ranging from 40 to 69%. However, cluster membership was not significantly associated with patient age, gender, BMI, or injury-to-examination delay. Patients electing to undergo ACL reconstruction do not conform to a homogenous group but represent a spectrum of knee function, general physical and mental health, and preinjury activity levels, which may not lend itself to uniform treatment and rehabilitation protocols. The factors driving these distinct responses to PROMs remain unknown but are unrelated to common demographic variables.</jats:p>

Multidimensional analysis of preoperative patient-reported outcomes identifies distinct phenotypes in patients booked for total knee arthroplasty: Secondary analysis of the SHARKS registry in a metropolitan hospital department

“…An approach called cluster analysis is an unsupervised machine learning technique that can simultaneously analyse and combine scores from various PROMs. The advantage of this statistical method is the potential to identify more nuanced patterns in patient responses that may not be apparent when considering every variation of scores to generate phenotypes.This technique has been previously used to identify phenotypes in cohorts of orthopaedic patients (Ting et al 2022; George et al 2021; Kung et al 2023) . A recent study by Kung et al (2023) used cluster analysis to identify phenotypes based on preoperative PROMs and found associations with TKA outcomes.…”