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

Machine-Learning Models Reliably Predict Clinical Outcomes in Medial Patellofemoral Ligament Reconstruction

PURPOSE

To develop a machine-learning model to predict clinical outcomes after medial patellofemoral ligament reconstruction (MPFLR) and identify the important predictive indicators.

METHODS

This study included patients who underwent MPFLR from January 2018 to December 2022. The exclusion criteria were as follows: (1) concurrent bony procedures, (2) history of other knee surgeries, and (3) follow-up period of less than 12 months. Forty-two predictive models were constructed for 7 clinical outcomes (failure to achieve minimum clinically important difference of clinical scores, return to preinjury sports, pivoting sports, and recurrent instability) using 6 machine-learning algorithms (random forest, logistic regression, support vector machine, decision tree, implemented multilayer perceptron, and K-nearest neighbor). The performance of the model was evaluated using metrics such as the area under the receiver operating characteristic curve, accuracy, specificity, and sensitivity. In addition, SHapley Additive exPlanation summary plot was employed to identify the important predictive factors of the best-performing model.

RESULTS

A total of 218 patients met criteria. For the best-performing models in predicting failure to achieve the minimum clinically important difference for Lysholm, International Knee Documentation Committee, Kujala, and Tegner scores, the area under the receiver operating characteristic curves and accuracies were 0.884 (good) and 87.3%, 0.859 (good) and 86.2%, 0.969 (excellent) and 97.0%, and 0.760 (fair) and 76.8%, respectively; 0.952 (excellent) and 95.2% for return to preinjury sports; 0.756 (fair) and 75.4% for return to pivoting sports; and 0.943 (excellent) and 94.9% for recurrent instability. Low preoperative Tegner score, shorter time to surgery, and absence of severe trochlear dysplasia were significant predictors for return to preinjury sports, whereas the absence of severe trochlear dysplasia and patellar alta were significant predictors for return to pivoting sports. Older age, female sex, and low preoperative Lysholm score were highly predictive of recurrent instability.

CONCLUSIONS

The predictive models developed using machine-learning algorithms can reliably forecast the clinical outcomes of MPFLR, particularly demonstrating excellent performance in predicting recurrent instability.

LEVEL OF EVIDENCE

Level III, case-control study.

Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations

PURPOSE OF REVIEW

Patient-reported outcome measures (PROM) play a critical role in evaluating the success of treatment interventions for musculoskeletal conditions. However, predicting which patients will benefit from treatment interventions is complex and influenced by a multitude of factors. Artificial intelligence (AI) may better anticipate the propensity to achieve clinically meaningful outcomes through leveraging complex predictive analytics that allow for personalized medicine. This article provides a contemporary review of current applications of AI developed to predict clinically significant outcome (CSO) achievement after musculoskeletal treatment interventions.

RECENT FINDINGS

The highest volume of literature exists in the subspecialties of total joint arthroplasty, spine, and sports medicine, with only three studies identified in the remaining orthopedic subspecialties combined. Performance is widely variable across models, with most studies only reporting discrimination as a performance metric. Given the complexity inherent in predictive modeling for this task, including data availability, data handling, model architecture, and outcome selection, studies vary widely in their methodology and results. Importantly, the majority of studies have not been externally validated or demonstrate important methodological limitations, precluding their implementation into clinical settings. A substantial body of literature has accumulated demonstrating variable internal validity, limited scope, and low potential for clinical deployment. The majority of studies attempt to predict the MCID-the lowest bar of clinical achievement. Though a small proportion of models demonstrate promise and highlight the utility of AI, important methodological limitations need to be addressed moving forward to leverage AI-based applications for clinical deployment.

The Future of Artificial Intelligence in Sports Medicine and Return to Play

Artificial intelligence (AI) has shown tremendous growth over the last decade, with the more recent development of clinical applications in health care. The ability of AI to synthesize large amounts of complex data automatically allows health care providers to access previously unavailable metrics and thus enhance and personalize patient care. These innovations include AI-assisted diagnostic tools, prediction models for each treatment pathway, and various tools for workflow optimization. The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play.

Characteristics and Clinical Outcomes After Osteochondral Allograft Transplantation for Treating Articular Cartilage Defects: Systematic Review and Single-Arm Meta-analysis of Studies From 2001 to 2020

Background

Osteochondral allograft transplantation (OCA) treats symptomatic focal cartilage defects with satisfactory clinical results.

Purpose

To comprehensively analyze the characteristics and clinical outcomes of OCA for treating articular cartilage defects.

Study Design

Systematic review; Level of evidence, 4.

Methods

We searched Embase, PubMed, Cochrane Database, and Web of Science for studies published between January 1, 2001, and December 31, 2020, on OCA for treating articular cartilage defects. Publication information, patient data, osteochondral allograft storage details, and clinical outcomes were extracted to conduct a comprehensive summative analysis.

Results

In total, 105 studies involving 5952 patients were included. The annual reported number of patients treated with OCA increased from 69 in 2001 to 1065 in 2020, peaking at 1504 cases in 2018. Most studies (90.1%) were performed in the United States. The mean age at surgery was 34.2 years, and 60.8% of patients were male and had a mean body mass index of 26.7 kg/m2. The mean lesion area was 5.05 cm2, the mean follow-up duration was 54.39 months, the mean graft size was 6.85 cm2, and the number of grafts per patient was 54.7. The failure rate after OCA was 18.8%, and 83.1% of patients reported satisfactory results. Allograft survival rates at 2, 5, 10, 15, 20, and 25 years were 94%, 87.9%, 80%, 73%, 55%, and 59.4%, respectively. OCA was mainly performed on the knee (88.9%). The most common diagnosis in the knee was osteochondritis dissecans (37.9%), and the most common defect location was the medial femoral condyle (52%). The most common concomitant procedures were high tibial osteotomy (28.4%) and meniscal allograft transplantation (24.7%). After OCA failure, 54.7% of patients underwent revision with primary total knee arthroplasty.

Conclusion

The annual reported number of patients who underwent OCA showed a significant upward trend, especially from 2016 to 2020. Patients receiving OCA were predominantly young male adults with a high body mass index. OCA was more established for knee cartilage than an injury at other sites, and its best indication was osteochondritis dissecans. This analysis demonstrated satisfactory long-term postoperative outcomes.

Development and internal validation of machine learning algorithms for predicting complications after primary total hip arthroplasty

INTRODUCTION

Complications after total hip arthroplasty (THA) may result in readmission or reoperation and impose a significant cost on the healthcare system. Understanding which patients are at-risk for complications can potentially allow for targeted interventions to decrease complication rates through pursuing preoperative health optimization. The purpose of the current was to develop and internally validate machine learning (ML) algorithms capable of performing patient-specific predictions of all-cause complications within two years of primary THA.

METHODS

This was a retrospective case-control study of clinical registry data from 616 primary THA patients from one large academic and two community hospitals. The primary outcome was all-cause complications at a minimum of 2-years after primary THA. Recursive feature elimination was applied to identify preoperative variables with the greatest predictive value. Five ML algorithms were developed on the training set using tenfold cross-validation and internally validated on the independent testing set of patients. Algorithms were assessed by discrimination, calibration, Brier score, and decision curve analysis to quantify performance.

RESULTS

The observed complication rate was 16.6%. The stochastic gradient boosting model achieved the best performance with an AUC = 0.88, calibration intercept = 0.1, calibration slope = 1.22, and Brier score = 0.09. The most important factors for predicting complications were age, drug allergies, prior hip surgery, smoking, and opioid use. Individual patient-level explanations were provided for the algorithm predictions and incorporated into an open access digital application: https://sorg-apps.shinyapps.io/tha_complication/ CONCLUSIONS: The stochastic boosting gradient algorithm demonstrated good discriminatory capacity for identifying patients at high-risk of experiencing a postoperative complication and proof-of-concept for creating office-based applications from ML that can perform real-time prediction. However, this clinical utility of the current algorithm is unknown and definitions of complications broad. Further investigation on larger data sets and rigorous external validation is necessary prior to the assessment of clinical utility with respect to risk-stratification of patients undergoing primary THA.

LEVEL OF EVIDENCE

III, therapeutic study.

Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention

PURPOSE

This study sought to develop and internally validate a machine learning model to identify risk factors and quantify overall risk of secondary meniscus injury in a longitudinal cohort after primary ACL reconstruction (ACLR).

METHODS

Patients with new ACL injury between 1990 and 2016 with minimum 2-year follow-up were identified. Records were extensively reviewed to extract demographic, treatment, and diagnosis of new meniscus injury following ACLR. Four candidate machine learning algorithms were evaluated to predict secondary meniscus tears. Performance was assessed through discrimination using area under the receiver operating characteristics curve (AUROC), calibration, and decision curve analysis; interpretability was enhanced utilizing global variable importance plots and partial dependence curves.

RESULTS

A total of 1187 patients underwent ACLR; 139 (11.7%) experienced a secondary meniscus tear at a mean time of 65 months post-op. The best performing model for predicting secondary meniscus tear was the random forest (AUROC = 0.790, 95% CI: 0.785-0.795; calibration intercept = 0.006, 95% CI: 0.005-0.007, calibration slope = 0.961 95% CI: 0.956-0.965, Brier's score = 0.10 95% CI: 0.09-0.12), and all four machine learning algorithms outperformed traditional logistic regression. The following risk factors were identified: shorter time to return to sport (RTS), lower VAS at injury, increased time from injury to surgery, older age at injury, and proximal ACL tear.

CONCLUSION

Machine learning models outperformed traditional prediction models and identified multiple risk factors for secondary meniscus tears after ACLR. Following careful external validation, these models can be deployed to provide real-time quantifiable risk for counseling and timely intervention to help guide patient expectations and possibly improve clinical outcomes.

LEVEL OF EVIDENCE

III.

Predicting the Objective and Subjective Clinical Outcomes of Anterior Cruciate Ligament Reconstruction: A Machine Learning Analysis of 432 Patients

BACKGROUND

Sports levels, baseline patient-reported outcome measures (PROMs), and surgical procedures are correlated with the outcomes of anterior cruciate ligament reconstruction (ACLR). Machine learning may be superior to conventional statistical methods in making repeatable and accurate predictions.

PURPOSE

To identify the best-performing machine learning models for predicting the objective and subjective clinical outcomes of ACLR and to determine the most important predictors.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

A total of 432 patients who underwent anatomic double-bundle ACLR with hamstring tendon autograft between January 2010 and February 2019 were included in the machine learning analysis. A total of 15 predictive variables and 6 outcome variables were selected to validate the logistic regression, Gaussian naïve Bayes machine, random forest, Extreme Gradient Boosting (XGBoost), isotonically calibrated XGBoost, and sigmoid calibrated XGBoost models. For each clinical outcome, the best-performing model was determined using the area under the receiver operating characteristic curve (AUC), whereas the importance and direction of each predictive variable were demonstrated in a Shapley Additive Explanations summary plot.

RESULTS

The AUC and accuracy of the best-performing model, respectively, were 0.944 (excellent) and 98.6% for graft failure; 0.920 (excellent) and 91.4% for residual laxity; 0.930 (excellent) and 91.0% for failure to achieve the minimal clinically important difference (MCID) of the Lysholm score; 0.942 (excellent) and 95.1% for failure to achieve the MCID of the International Knee Documentation Committee (IKDC) score; 0.773 (fair) and 70.5% for return to preinjury sports; and 0.777 (fair) and 69.2% for return to pivoting sports. Medial meniscal resection, participation in competitive sports, and steep posterior tibial slope were top predictors of graft failure, whereas high-grade preoperative knee laxity, long follow-up period, and participation in competitive sports were top predictors of residual laxity. High preoperative Lysholm and IKDC scores were highly predictive of not achieving the MCIDs of PROMs. Young age, male sex, high preoperative IKDC score, and large graft diameter were important predictors of return to preinjury or pivoting sports.

CONCLUSION

Machine learning analysis can provide reliable predictions for the objective and subjective clinical outcomes (graft failure, residual laxity, PROMs, and return to sports) of ACLR. Patient-specific evaluation and decision making are recommended before and after surgery.

Isolated Lateral Tibiofemoral Compartment Osteoarthritis: Survivorship and Patient Acceptable Symptom State After Lateral Fixed-Bearing Unicompartmental Knee Arthroplasty at Mean 10-Year Follow-up

BACKGROUND

Lateral unicompartmental knee arthroplasty (UKA) is an excellent option to alleviate disability and restore function in patients with lateral compartment knee osteoarthritis (OA). The purpose of the present study was to determine the survivorship and long-term outcomes in both younger/middle-aged and older patients with lateral compartment OA following non-robotically-assisted, fixed-bearing lateral UKA and to determine if an acceptable symptom state can be achieved.

METHODS

All patients were managed with fixed-bearing lateral UKA by a single surgeon utilizing a lateral parapatellar approach without robotic assistance. The primary outcome variables were the Knee injury and Osteoarthritis Outcome Score (KOOS) Activities of Daily Living (ADL) and Sport subscale scores. In addition, the other KOOS subscores, the Lysholm score, the achievement of the Patient Acceptable Symptom State (PASS), and the Veterans RAND (VR-12) Physical Component Summary score (PCS) and Mental Component Summary score (MCS) were collected. Failure was defined as conversion to total knee arthroplasty (TKA). Patients were divided into 2 cohorts: younger/middle-aged patients (<60 years of age) and older patients (≥60 years of age).

RESULTS

A cohort of 256 patients underwent medial (n = 193) or lateral (n = 63) UKA. Sixty-one patients met the inclusion criteria. At mean of 10 years (range, 4 to 17 years) of follow-up, there were no significant differences between the groups in terms of any patient-reported outcome measures (p > 0.05). The percentage of patients in whom PASS was achieved on the KOOS ADL and Sport subscores was 82% and 88%, respectively, in the younger cohort and 80% and 80%, respectively, in the older cohort. The mean survival estimate of the prothesis was 15.3 years (95% confidence interval [CI], 14.5 to 16.2 years) for the entire cohort. The estimated rate of implant survival in the younger cohort was 100% at 5 and 10 years, and the estimated rate of implant survival in the older cohort was 98% at 5 years and 96% at 10 years.

CONCLUSIONS

Lateral fixed-bearing, non-robotic UKA for the treatment of isolated lateral compartment OA resulted in >80% of patients reaching an acceptable symptom state in terms of both activities of daily living and sporting activities. UKA provides an excellent option that provides longevity with high PASS rates and return to activities with a low risk of complications and failure.

LEVEL OF EVIDENCE

Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Implementation of Photosensitive, Injectable, Interpenetrating, and Kartogenin-Modified GELMA/PEDGA Biomimetic Scaffolds to Restore Cartilage Integrity in a Full-Thickness Osteochondral Defect Model

Cartilage defects caused by mechanical tear and wear are challenging clinical problems. Articular cartilage has unique load-bearing properties and limited self-repair ability. The current treatment methods, such as microfractures and autogenous cartilage transplantation to repair full-thickness cartilage defects, have apparent limitations. Tissue engineering technology has the potential to repair cartilage defects and directs current research development. To enhance the regenerative capacities of cartilage in weight-bearing areas, we attempted to develop a biomimetic scaffold loaded with a chondroprotective factor that can recreate structure, restore mechanical properties, and facilitate anabolic metabolism in larger joint defects. For enhanced spatial control over both bone and cartilage layers, it is envisioned that biomaterials that meet the needs of both tissue components are required for successful osteochondral repair. We used gelatin methacrylate (GELMA) and polyethylene glycol diacrylate (PEGDA) light-cured dual-network cross-linking modes that can significantly increase the mechanical properties of scaffolds and are capable of restoring function and prolonging the degradation time. Once the hydrogel complex was injected into the osteochondral defect, in situ UV light curing was applied to seamlessly connect the defect repair tissue with the surrounding normal cartilage tissue. The small molecule active substance kartogenin (KGN) can promote cartilage repair. We encapsulated KGN in biomimetic scaffolds so that, as the scaffold degrades, scaffold-loaded KGN was slowly released to induce endogenous mesenchymal stem cells to home and differentiate into chondrocytes to repair defective cartilage tissue. Our experiments have proven that, compared with the control group, GELMA/PEGDA + KGN repaired cartilage defects and restored cartilage to hyaline cartilage. Our study suggests that implementing photosensitive, injectable, interpenetrating, and kartogenin-modified GELMA/PEDGA biomimetic scaffolds may be a novel approach to restore cartilage integrity in full-thickness osteochondral defects.

Meaningless Applications and Misguided Methodologies in Artificial Intelligence-Related Orthopaedic Research Propagates Hype Over Hope

There exists great hope and hype in the literature surrounding applications of artificial intelligence (AI) to orthopaedic surgery. Between 2018 and 2021, a total of 178 AI-related articles were published in orthopaedics. However, for every 2 original research papers that apply AI to orthopaedics, a commentary or review is published (30.3%). AI-related research in orthopaedics frequently fails to provide use cases that offer the uninitiated an opportunity to appraise the importance of AI by studying meaningful questions, evaluating unknown hypotheses, or analyzing quality data. The hype perpetuates a feed-forward cycle that relegates AI to a meaningless buzzword by rewarding those with nascent understanding and rudimentary technical knowhow into committing several basic errors: (1) inappropriately conflating vernacular ("AI/machine learning"), (2) repackaging registry data, (3) prematurely releasing internally validated algorithms, (4) overstating the "black box phenomenon" by failing to provide weighted analysis, (5) claiming to evaluate AI rather than the data itself, and (6) withholding full model architecture code. Relevant AI-specific guidelines are forthcoming, but forced application of the original Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines designed for regression analyses is irrelevant and misleading. To safeguard meaningful use, AI-related research efforts in orthopaedics should be (1) directed toward administrative support over clinical evaluation and management, (2) require the use of the advanced model, and (3) answer a question that was previously unknown, unanswered, or unquantifiable.

The aging athlete

Aging athletes, those 60 years and older, are a growing population of mature, active individuals who value sports and exercise participation throughout their lifespan. Although recommendations for younger and masters athletes have been extrapolated to this population, there remains a paucity of specific guidelines, treatment algorithms, and considerations for aging athletes. The benefits of living an active lifestyle must be weighed against the risks for unique cardiovascular, metabolic, and musculoskeletal injuries requiring diagnostic and therapeutic interventions. In this article, we review the unique cardiovascular and muscular physiology of aging athletes and how it influences the risk of specific medical conditions. We also discuss general prevention and treatment strategies. Finally, we identify areas of future research priorities and emerging treatments.

Association Between Preoperative Patient Factors and Clinically Meaningful Outcomes After Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis

BACKGROUND

The International Hip Outcome Tool 12-Item Questionnaire (IHOT-12) has been proposed as a more appropriate outcome assessment for hip arthroscopy populations. The extent to which preoperative patient factors predict achieving clinically meaningful outcomes among patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (FAIS) remains poorly understood.

PURPOSE

To determine the predictive relationship of preoperative imaging, patient-reported outcome measures, and patient demographics with achievement of the minimal clinically important difference (MCID), Patient Acceptable Symptom State (PASS), and substantial clinical benefit (SCB) for the IHOT-12 at a minimum of 2 years postoperatively.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

Data were analyzed for consecutive patients who underwent hip arthroscopy for FAIS between 2012 and 2018 and completed the IHOT-12 preoperatively and at a minimum of 2 years postoperatively. Fifteen novel machine learning algorithms were developed using 47 potential demographic, clinical, and radiographic predictors. Model performance was evaluated with discrimination, calibration, decision-curve analysis and the brier score.

RESULTS

A total of 859 patients were identified, with 685 (79.7%) achieving the MCID, 535 (62.3%) achieving the PASS, and 498 (58.0%) achieving the SCB. For predicting the MCID, discrimination for the best-performing models ranged from fair to excellent (area under the curve [AUC], 0.69-0.89), although calibration was excellent (calibration intercept and slopes: -0.06 to 0.02 and 0.24 to 0.85, respectively). For predicting the PASS, discrimination for the best-performing models ranged from fair to excellent (AUC, 0.63-0.81), with excellent calibration (calibration intercept and slopes: 0.03-0.18 and 0.52-0.90, respectively). For predicting the SCB, discrimination for the best-performing models ranged from fair to good (AUC, 0.61-0.77), with excellent calibration (calibration intercept and slopes: -0.08 to 0.00 and 0.56 to 1.02, respectively). Thematic predictors for failing to achieve the MCID, PASS, and SCB were presence of back pain, anxiety/depression, chronic symptom duration, preoperative hip injections, and increasing body mass index (BMI). Specifically, thresholds associated with lower likelihood to achieve a clinically meaningful outcome were preoperative Hip Outcome Score-Activities of Daily Living <55, preoperative Hip Outcome Score-Sports Subscale >55.6, preoperative IHOT-12 score ≥48.5, preoperative modified Harris Hip Score ≤51.7, age >41 years, BMI ≥27, and preoperative α angle >76.6°.

CONCLUSION

We developed novel machine learning algorithms that leveraged preoperative demographic, clinical, and imaging-based features to reliably predict clinically meaningful improvement after hip arthroscopy for FAIS. Despite consistent improvements after hip arthroscopy, meaningful improvements are negatively influenced by greater BMI, back pain, chronic symptom duration, preoperative mental health, and use of hip corticosteroid injections.

Artificial intelligence and machine learning: an introduction for orthopaedic surgeons

The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care.Level of evidence VI.

Risk Factors for Failure After Osteochondral Allograft Transplantation of the Knee: A Systematic Review and Exploratory Meta-analysis

BACKGROUND

Graft failure after osteochondral allograft transplantation (OCA) of the knee is a devastating outcome, often necessitating subsequent interventions. A comprehensive understanding of the risk factors for failure after OCA of the knee may provide enhanced prognostic data for the knee surgeon and facilitate more informed shared decision-making discussions before surgery.

PURPOSE

To perform a systematic review and meta-analysis of risk factors associated with graft failure after OCA of the knee.

STUDY DESIGN

Systematic review and meta-analysis; Level of evidence, 4.

METHODS

The PubMed, Ovid/MEDLINE, and Cochrane databases were queried in April 2021. Data pertaining to study characteristics and risk factors associated with failure after OCA were recorded. DerSimonian-Laird binary random-effects models were constructed to quantitatively evaluate the association between risk factors and graft failure by generating effect estimates in the form of odds ratios (ORs) with 95% CIs, while mean differences (MDs) were calculated for continuous data. Qualitative analysis was performed to describe risk factors that were variably reported.

RESULTS

A total of 16 studies consisting of 1401 patients were included. The overall pooled prevalence of failure was 18.9% (range, 10%-46%). There were 44 risk factors identified, of which 9 were explored quantitatively. There was strong evidence to support that the presence of bipolar chondral defects (OR, 4.20 [95% CI, 1.17-15.08]; P = .028) and male sex (OR, 2.04 [95% CI, 1.17-3.55]; P = .012) were significant risk factors for failure after OCA. Older age (MD, 5.06 years [95% CI, 1.44-8.70]; P = .006) and greater body mass index (MD, 1.75 kg/m² [95% CI, 0.48-3.03]; P = .007) at the time of surgery were also significant risk factors for failure after OCA. There was no statistically significant evidence to incontrovertibly support that concomitant procedures, chondral defect size, and defect location were associated with an increased risk of failure after OCA.

CONCLUSION

Bipolar chondral defects, male sex, older age, and greater body mass index were significantly associated with an increased failure rate after OCA of the knee. No statistically significant evidence presently exists to support that chondral defect size and location or concomitant procedures are associated with an increased graft failure rate after OCA of the knee. Additional studies are needed to evaluate these associations.

Estimating Health-Related Quality of Life Based on Demographic Characteristics, Questionnaires, Gait Ability, and Physical Fitness in Korean Elderly Adults

The elderly population in South Korea accounted for 15.5% of the total population in 2019. Thus, it is important to study the various elements governing the process of healthy aging. Therefore, this study investigated multiple prediction models to determine the health-related quality of life (HRQoL) in elderly adults based on the demographics, questionnaires, gait ability, and physical fitness. We performed eight physical fitness tests on 775 participants wearing shoe-type inertial measurement units and completing walking tasks at slower, preferred, and faster speeds. The HRQoL for physical and mental components was evaluated using a 36-item, short-form health survey. The prediction models based on multiple linear regression with feature importance were analyzed considering the best physical and mental components. We used 11 variables and 5 variables to form the best subset of features underlying the physical and mental components, respectively. We laid particular emphasis on evaluating the functional endurance, muscle strength, stress level, and falling risk. Furthermore, stress, insomnia severity, number of diseases, lower body strength, and fear of falling were taken into consideration in addition to mental-health-related variables. Thus, the study findings provide reliable and objective results to improve the understanding of HRQoL in elderly adults.