RESUMEN
Background: Discharge disposition and length of stay (LOS) are widely recognized markers of healthcare utilization patterns of total hip and knee joint arthroplasty (TJA). These markers are commonly associated with increased postoperative complications, patient dissatisfaction, and higher costs. Area deprivation index (ADI) has been validated as a composite metric of neighborhood-level disadvantage. This study aims to determine the potential association between ADI and discharge disposition or extended LOS following revision TJA. Methods: This study conducted a retrospective analysis of a consecutive series of revision hip and knee TJA patients from a single tertiary institution. Univariate and multivariate regression analysis was used to determine the association between ADI and discharge disposition or LOS, adjusting for patient demographics and comorbidities. Results: 1047 consecutive revision TJA patients were identified across 463 different neighborhoods. 193 (18.4 %) had an extended LOS, and 334 (31.9 %) were discharged to non-home facilities. Compared with Q1 (least deprived cohort), Q2 (odds ratio [OR] = 1.63; p = 0.030) and Q4 (most deprived cohort: OR = 2.04; p = 0.002) cohorts demonstrated higher odds of non-home discharge. Patients in the highest ADI quartile (most deprived cohort) were associated with increased odds of prolonged LOS following revision TJA compared to those in the lowest ADI quartile (OR = 2.63; p < 0.001). Conclusion: This study suggests that higher levels of neighborhood-level disadvantage may be associated with higher odds of non-home discharge and prolonged LOS following revision TJA. Development of interventions based on the area deprivation index may improve discharge planning and reduce unnecessary non-home discharges in patients living in areas of socioeconomic deprivation.
RESUMEN
BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (TJA), machine learning (ML) based predictive tools have emerged as a promising alternative for improved risk stratification. This study aimed to compare the predictive ability of ML models for 30-day mortality following revision TJA to that of traditional risk-assessment indices such as the CARDE-B score (congestive heart failure, albumin (<3.5 mg/dL), renal failure on dialysis, dependence for daily living, elderly (>65 years of age), and body mass index of <25 kg/m2), 5-item (5MFI), and 6-item modified frailty index (6MFI). METHODS: Adult patients undergoing revision TJA between 2013 and 2020 were selected from the ACS-NSQIP database and randomly split 80:20 to compose the training and validation cohorts. There were three ML models - extreme gradient boosting (XGB), random forest (RF), and elastic-net penalized logistic regression (NEPLR) - that were developed and evaluated using discrimination, calibration metrics, and accuracy. The discrimination of CARDE-B, 5MFI, and 6MFI scores was assessed individually and compared to that of ML models. RESULTS: All models were equally accurate (Brier score = 0.005) and demonstrated outstanding discrimination with similar areas (AUC) under the receiver operating characteristic curve (XGB = 0.94, RF = NEPLR = 0.93). The NEPLR was the best-calibrated model overall (slope = 0.54, intercept = -0.004). The CARDE-B had the highest discrimination among the scores (AUC = 0.89), followed by 6MFI (AUC = 0.80), and 5MFI (AUC = 0.68). Albumin < 3.5 mg/dL and BMI (< 30.15) were the most important predictors of 30-day mortality following revision TJA. CONCLUSIONS: The ML models outperform traditional risk-assessment indices in predicting postoperative 30-day mortality after revision TJA. Our findings highlight the utility of ML for risk stratification in a clinical setting. The identification of hypoalbuminemia and BMI as prognostic markers may allow patient-specific perioperative optimization strategies to improve outcomes following revision TJA.
