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Introduction Anterior cruciate ligament (ACL) tears occur frequently in young athletes, and ligament repair and reconstruction are surgical treatments. Although there are suggested benefits for both approaches, there is a lack of direct comparisons between ACL repair and reconstruction.This study aims to compare the mid-term functional outcomes and quality of life measures between patients that have undergone ACL repair versus reconstruction. Methods A retrospective review was conducted for demographic and operative report data of patients who underwent an ACL repair or reconstruction between 2012 and 2018. Patients were contacted over the phone and underwent a Patient-Reported Outcomes Measurement Information System (PROMIS) survey evaluating pain interference, mobility, and function. Patients were excluded from the study if there was an incomplete operative note, missing contact information, or failure to answer phone calls. Results A total of 74 eligible patients were included, with n = 54 in the ACL reconstruction group (73.0%) and n = 20 in the ACL repair group (27.0%). Reconstruction patients had a PROMIS (median (IQR)) physical function score of 22.50 (16.00-59.00), as compared to repair patients' physical function score of 60.00 (21.50-60.00). There was a significant difference favoring repair (p = 0.040). In addition, ACL reconstruction patients had a significantly higher rate of additional procedures, with 63.0% of reconstruction patients receiving an additional operation as compared to 30.0% of repair patients (p = 0.017). The surgery type did not show a significant effect on physical function scores, while additional procedures remained significant in the linear regression analysis. Conclusion Although ACL repair is associated with improved physical function scores as compared to reconstruction in the univariate analysis, surgery type did not show significance when controlling for other variables. Further studies are necessary to compare patients with similar injuries to account for differences in additional procedures, but the results remain promising in assisting with patient-driven treatment decisions.
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BACKGROUND: Supervised machine learning techniques have been increasingly applied to predict patient outcomes after hip and knee arthroplasty procedures. The purpose of this study was to systematically review the applications of supervised machine learning techniques to predict patient outcomes after primary total hip and knee arthroplasty. METHODS: A comprehensive literature search using the electronic databases MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews was conducted in July of 2021. The inclusion criteria were studies that utilized supervised machine learning techniques to predict patient outcomes after primary total hip or knee arthroplasty. RESULTS: Search criteria yielded n = 30 relevant studies. Topics of study included patient complications (n = 6), readmissions (n = 1), revision (n = 2), patient-reported outcome measures (n = 4), patient satisfaction (n = 4), inpatient status and length of stay (LOS) (n = 9), opioid usage (n = 3), and patient function (n = 1). Studies involved TKA (n = 12), THA (n = 11), or a combination (n = 7). Less than 35% of predictive outcomes had an area under the receiver operating characteristic curve (AUC) in the excellent or outstanding range. Additionally, only 9 of the studies found improvement over logistic regression, and only 9 studies were externally validated. CONCLUSION: Supervised machine learning algorithms are powerful tools that have been increasingly applied to predict patient outcomes after total hip and knee arthroplasty. However, these algorithms should be evaluated in the context of prognostic accuracy, comparison to traditional statistical techniques for outcome prediction, and application to populations outside the training set. While machine learning algorithms have been received with considerable interest, they should be critically assessed and validated prior to clinical adoption.