Can machine learning models predict failure of revision total hip arthroplasty?

INTRODUCTION: Revision total hip arthroplasty (THA) represents a technically demanding surgical procedure which is associated with significant morbidity and mortality. Understanding risk factors for failure of revision THA is of clinical importance to identify at-risk patients. This study aimed to develop and validate novel machine learning algorithms for the prediction of re-revision surgery for patients following revision total hip arthroplasty. METHODS: A total of 2588 consecutive patients that underwent revision THA was evaluated, including 408 patients (15.7%) with confirmed re-revision THA. Electronic patient records were manually reviewed to identify patient demographics, implant characteristics and surgical variables that may be associated with re-revision THA. Machine learning algorithms were developed to predict re-revision THA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for re-revision THA as predicted by the four validated machine learning models were the American Society of Anaesthesiology score, obesity (> 35 kg/m2) and indication for revision THA. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.80), calibration and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION: This study developed four machine learning models for the prediction of re-revision surgery for patients following revision total hip arthroplasty. The study findings show excellent model performance, highlighting the potential of these computational models to assist in preoperative patient optimization and counselling to improve revision THA patient outcomes. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

Artificial neural networks for the prediction of transfusion rates in primary total hip arthroplasty.

BACKGROUND: Despite advancements in total hip arthroplasty (THA) and the increased utilization of tranexamic acid, acute blood loss anemia necessitating allogeneic blood transfusion persists as a post-operative complication. The prevalence of allogeneic blood transfusion in primary THA has been reported to be as high as 9%. Therefore, this study aimed to develop and validate novel machine learning models for the prediction of transfusion rates following primary total hip arthroplasty. METHODS: A total of 7265 consecutive patients who underwent primary total hip arthroplasty were evaluated using a single tertiary referral institution database. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with transfusion rates. Four state-of-the-art machine learning algorithms were developed to predict transfusion rates following primary THA, and these models were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The factors most significantly associated with transfusion rates include tranexamic acid usage, bleeding disorders, and pre-operative hematocrit (< 33%). The four machine learning models all achieved excellent performance across discrimination (AUC > 0.78), calibration, and decision curve analysis. CONCLUSION: This study developed machine learning models for the prediction of patient-specific transfusion rates following primary total hip arthroplasty. The results represent a novel application of machine learning, and has the potential to improve outcomes and pre-operative planning. LEVEL OF EVIDENCE: III, case-control retrospective analysis.

The Same Day Discharges following Primary Total Knee Arthroplasty: A Single Surgeon, Propensity Score-Matched Cohort Analysis.

This is a retrospective study. As new surgical techniques and improved perioperative care approaches have become available, the same-day discharge in selected total knee arthroplasty (TKA) patients was introduced to decrease health care costs without compromising outcomes. This study aimed to compare clinical and functional outcomes between same-day discharge TKA patients and inpatient-discharge TKA patients. A retrospective review of 100 consecutive patients with same-day discharge matched to a cohort of 300 patients with inpatient discharge that underwent TKA by a single surgeon at a tertiary referral center was conducted. Propensity-score matching was performed to adjust for baseline differences in preoperative patient demographics, medical comorbidities, and patient-reported outcome measures (PROMs) between both cohorts. All patients had a minimum of 1-year follow-up (range: 1.2-2.8 years). In terms of clinical outcomes for the propensity score-matched cohorts, there was no significant difference in terms of revision rates (1.0 vs. 1.3%, p = 0.76), 90-day emergency department visits (3.0 vs. 3.3%, p = 0.35), 30-day readmission rates (1.0 vs. 1.3%, p = 0.45), and 90-day readmission rates (3.0 vs. 3.6%, p = 0.69). Patients with same-day discharge demonstrated significantly higher postoperative PROM scores, at both 3-month and 1-year follow-up, for PROMIS-10 Physical Score (50 vs. 46, p = 0.028), PROMIS-10 Mental Score (56 vs. 53, p = 0.039), and Physical SF10A (57 vs. 52, p = 0.013). This study showed that patients with same-day discharge had similar clinical outcomes and superior functional outcomes, when compared with patients that had a standard inpatient protocol. This suggests that same-day discharge following TKA may be a safe, viable option in selected total knee joint arthroplasty patients.

Artificial Neural Networks Can Predict Early Failure of Cementless Total Hip Arthroplasty in Patients With Osteoporosis.

BACKGROUND: Total hip arthroplasty (THA) done in the aging population is associated with osteoporosis-related complications. The altered bone density in osteoporotic patients is a risk factor for revision surgery. This study aimed to develop and validate machine learning (ML) models to predict revision surgery in patients with osteoporosis after primary noncemented THA. METHODS: We retrospectively reviewed a consecutive series of 350 patients with osteoporosis (T-score less than or equal to -2.5) who underwent primary noncemented THA at a tertiary referral center. All patients had a minimum 2-year follow-up (range: 2.1 to 5.6). Four ML algorithms were developed to predict the probability of revision surgery, and these were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The overall incidence of revision surgery was 5.2% at a mean follow-up of 3.7 years after primary noncemented THA in osteoporotic patients. Revision THA was done because of periprosthetic fracture in nine patients (50%), aseptic loosening/subsidence in five patients (28%), periprosthetic joint infection in two patients (11%) and dislocation in two patients (11%). The strongest predictors for revision surgery in patients after primary noncemented THA were female sex, BMI (>35 kg/m2), age (>70 years), American Society of Anesthesiology score (≥3), and T-score. All four ML models demonstrated good model performance across discrimination (AUC range: 0.78 to 0.81), calibration, and decision curve analysis. CONCLUSION: The ML models presented in this study demonstrated high accuracy for the prediction of revision surgery in osteoporotic patients after primary noncemented THA. The presented ML models have the potential to be used by orthopaedic surgeons for preoperative patient counseling and optimization to improve the outcomes of primary noncemented THA in osteoporotic patients.

The Ability of Deep Learning Models to Identify Total Hip and Knee Arthroplasty Implant Design From Plain Radiographs.

INTRODUCTION: The surgical management of patients with failed total hip or knee arthroplasty (THA and TKA) necessitates the identification of the implant manufacturer and model. Failure to accurately identify implant design leads to delays in care, increased morbidity, and healthcare costs. The automated identification of implant designs has the potential to assist in the surgical management of patients with failed arthroplasty. This study aimed to develop and validate a convolutional neural network deep learning model for the identification of primary and revision hip and knee total joint arthroplasty designs from plain radiographs. METHODS: This study trained a convolutional neural network deep learning model to automatically identify 24 THA designs and 14 TKA designs from 11,204 anterior-posterior radiographs obtained from 8,763 patients. From these radiographs, 8,963 radiographs (80%) were used for model training and 2,241 radiographs (20%) were used for model validation. Model performance was assessed through receiver operating curve characteristics. RESULTS: After 1,000 training epochs by the convolutional neural network deep learning model, the computational model discriminated 17 primary THA designs with an area under the receiver operating curve (AUC) of 0.98, sensitivity of 95.8%, and specificity of 98.6%. The deep learning model discriminated eight primary TKA designs with an AUC of 0.97, sensitivity of 94.9%, and specificity of 97.8%. The deep learning model demonstrated an AUC of 0.98 and 0.96 for the identification of seven revision THA and six revision TKA designs, respectively. DISCUSSION: This study developed and validated a convolutional neural network deep learning model for the identification of hip and knee total joint arthroplasty designs from plain radiographs. The study findings demonstrate excellent accuracy of the deep learning model for the identification of 24 THA and 14 TKA designs, illustrating the great potential of the deep learning model to assist in preoperative surgical planning of failed arthroplasty patients.

Artificial intelligence algorithms accurately predict prolonged length of stay following revision total knee arthroplasty.

PURPOSE: Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA. METHODS: A total of 2512 consecutive patients who underwent revision TKA were evaluated. Those patients with a length of stay greater than 75th percentile for all length of stays were defined as patients with prolonged LOS. Three artificial intelligence algorithms were developed to predict prolonged LOS following revision TKA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for prolonged length of stay following revision TKA were age (> 75 years; p < 0.001), Charlson Comorbidity Index (> 6; p < 0.001) and body mass index (> 35 kg/m2; p < 0.001). The three artificial intelligence algorithms all achieved excellent performance across discrimination (AUC > 0.84) and decision curve analysis (p < 0.01). CONCLUSION: The study findings demonstrate excellent performance on discrimination, calibration and decision curve analysis for all three candidate algorithms. This highlights the potential of these artificial intelligence algorithms to assist in the preoperative identification of patients with an increased risk of prolonged LOS following revision TKA, which may aid in strategic discharge planning. LEVEL OF EVIDENCE: IV.