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BACKGROUND: As the number of revision total knee arthroplasty (TKA) continues to rise, close attention has been paid to factors influencing postoperative length of stay (LOS). The aim of this study is to develop generalizable machine learning (ML) algorithms to predict extended LOS following revision TKA using data from a national database. METHODS: 23,656 patients undergoing revision TKA between 2013 and 2020 were identified using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Patients with missing data and those undergoing re-revision or conversion from unicompartmental knee arthroplasty were excluded. Four ML algorithms were applied and evaluated based on their (1) ability to distinguish between at-risk and not-at-risk patients, (2) accuracy, (3) calibration, and (4) clinical utility. RESULTS: All four ML predictive algorithms demonstrated good accuracy, calibration, clinical utility, and discrimination, with all models achieving a similar area under the curve (AUC) (AUCLR=AUCRF=AUCHGB=0.75, AUCANN=0.74). The most important predictors of prolonged LOS were found to be operative time, preoperative diagnosis of sepsis, and body mass index (BMI). CONCLUSIONS: ML models developed in this study demonstrated good performance in predicting extended LOS in patients undergoing revision TKA. Our findings highlight the importance of utilizing nationally representative patient data for model development. Prolonged operative time, preoperative sepsis, BMI, and elevated preoperative serum creatinine and BUN were noted to be significant predictors of prolonged LOS. Knowledge of these associations may aid with patient-specific preoperative planning, discharge planning, patient counseling, and cost containment with revision TKA.

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BACKGROUND: Total joint arthroplasty (TJA) is the most common procedure associated with malpractice claims within orthopaedic surgery. Although prior research has assessed prevalent causes and outcomes of TJA-related lawsuits before 2018, the dynamic healthcare environment demands regular re-evaluations. This study aimed to provide an updated analysis of the predominant causes and outcomes of TJA-related malpractice lawsuits and analyze the outcomes of subsequent appeals following initial jury verdicts. METHODS: A legal database was queried for cases between 2018 and 2022 involving primary hip and knee TJA in the United States. Cases were listed as original rulings or appeals and reviewed for the alleged negligence, damages incurred, demographics, and verdicts. Appeals were further assessed for appellant details, preliminary judgment, and outcomes. The findings were compared to previous litigation data using descriptive statistics. RESULTS: The final cohort comprised 59 cases: 33 (56%) total knee arthroplasty (TKA) and 26 (44%) total hip arthroplasty (THA) from 2018 to 2022. The TKA cases primarily cited pain (24%), while the THA cases cited nerve injuries (31%). Negligence largely stemmed from procedural error (47%), postsurgical error (27%), and failure to inform (14%). Case outcomes were in favor of the defense in 66% of cases. Overall, 90% of primary verdicts led to appeals, with 71% by the plaintiff. Initial rulings were upheld in 87% of plaintiff appeals, whereas only 53% of defendant appeals retained the initial judgment. CONCLUSION: The primary causative factor of litigation shifted from infection to ongoing/worsening pain postoperatively in TKA cases over time. While nerve injury TKA cases have decreased, it remains the most cited damage in THA cases. Defense verdicts are common, but there is an increasing number of verdicts against defendants. Plaintiffs are more likely to appeal, but are less successful in appellate courts. These findings allow surgeons and policymakers to address emerging litigation trends in TJA to enhance patient care, mitigate risks, and improve the overall quality of TJA.

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INTRODUCTION: Prolonged length of stay (LOS) following revision total hip arthroplasty (THA) can lead to increased healthcare costs, higher rates of readmission, and lower patient satisfaction. In this study, we investigated the predictive power of machine learning (ML) models for prolonged LOS after revision THA using patient data from a national-scale patient repository. MATERIALS AND METHODS: We identified 11,737 revision THA cases from the American College of Surgeons National Surgical Quality Improvement Program database from 2013 to 2020. Prolonged LOS was defined as exceeding the 75th value of all LOSs in the study cohort. We developed four ML models: artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor, to predict prolonged LOS after revision THA. Each model's performance was assessed during training and testing sessions in terms of discrimination, calibration, and clinical utility. RESULTS: The ANN model was the most accurate with an AUC of 0.82, calibration slope of 0.90, calibration intercept of 0.02, and Brier score of 0.140 during testing, indicating the model's competency in distinguishing patients subject to prolonged LOS with minimal prediction error. All models showed clinical utility by producing net benefits in the decision curve analyses. The most significant predictors of prolonged LOS were preoperative blood tests (hematocrit, platelet count, and leukocyte count), preoperative transfusion, operation time, indications for revision THA (infection), and age. CONCLUSIONS: Our study demonstrated that the ML model accurately predicted prolonged LOS after revision THA. The results highlighted the importance of the indications for revision surgery in determining the risk of prolonged LOS. With the model's aid, clinicians can stratify individual patients based on key factors, improve care coordination and discharge planning for those at risk of prolonged LOS, and increase cost efficiency.

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Introduction: Revision hip and knee total joint arthroplasty (TJA) carries a high burden of postoperative complications, including surgical site infections (SSI), venous thromboembolism (VTE), reoperation, and readmission, which negatively affect postoperative outcomes and patient satisfaction. Socioeconomic area-level composite indices such as the area deprivation index (ADI) are increasingly important measures of social determinants of health (SDoH). This study aims to determine the potential association between ADI and SSI, VTE, reoperation, and readmission occurrence 90 days following revision TJA. Methods: 1047 consecutive revision TJA patients were retrospectively reviewed. Complications, including SSI, VTE, reoperation, and readmission, were combined into one dependent variable. ADI rankings were extracted using residential zip codes and categorized into quartiles. Univariate and multivariate logistic regressions were performed to analyze the association of ADI as an independent factor for complication following revision TJA. Results: Depression (p = 0.034) and high ASA score (p < 0.001) were associated with higher odds of a combined complication postoperatively on univariate logistic regression. ADI was not associated with the occurrence of any of the complications recorded following surgery (p = 0.092). ASA remained an independent risk factor for developing postoperative complications on multivariate analysis. Conclusion: An ASA score of 3 or higher was significantly associated with higher odds of developing postoperative complications. Our findings suggest that ADI alone may not be a sufficient tool for predicting postoperative outcomes following revision TJA, and other area-level indices should be further investigated as potential markers of social determinants of health.

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Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA. Individual model performance was assessed and compared based on discrimination, accuracy, calibration, and clinical utility. Length of stay (> 2.5 days), body mass index (BMI) (> 33.21 kg/m2), and operation time (> 93 min) were important determinants of 30-day readmission. All ML models demonstrated equally good accuracy, calibration, and discriminatory ability (Brier score, ANN = RF = HGB = NEPLR = 0.03; ANN, slope = 0.90, intercept = - 0.11; RF, slope = 0.93, intercept = - 0.12; HGB, slope = 0.90, intercept = - 0.12; NEPLR, slope = 0.77, intercept = 0.01; AUCANN = AUCRF = AUCHGB = AUCNEPLR = 0.78). This study validates the generalizability of four previously developed ML algorithms in predicting readmission risk in patients undergoing TKA and offers surgeons an opportunity to reduce readmissions by optimizing discharge planning, BMI, and surgical efficiency.

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BACKGROUND: Chronic renal failure is a progressive and irreversible loss of kidney function, and the hemodialysis (HD) is one of the most common modalities in this regard. Oxidative stresses [like interleukin-8 (IL-8) and tumor necrosis factor-alpha (TNF-α)] and inflammation are the main risk factors associated with cardiovascular diseases and other complications in many organs in hemodialysis patients; meanwhile, antioxidants like alpha lipoic acid (ALA) may reduce the oxidative stress markers and the levels of inflammatory cytokines, so can improve of the patient's quality of life. METHODS: In this randomized clinical trial study, 60 HD patients were randomly categorized in two case and control groups. Case group received a daily capsule of 600 mg of ALA supplementation for 8 weeks, and the control group received placebo capsules daily. The serum level of IL-8 and TNF-α was measured in both groups before and after the intervention. RESULTS: There were no significant differences in age, gender, duration of dialysis, and causative factor for dialysis between both groups (P > 0.05). The mean of IL-8 and TNF-α after the intervention in case group was 26.20 ± 15.34 and 21.25 ± 9.61, respectively; the difference between both groups was not statistically significant (P > 0.05). CONCLUSION: Based on the better feeling and other beneficial effects of ALA were found in our study; we can conclude that it is a beneficial and recommended supplement, especially, for diabetic and dialysis patients.

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