RESUMO
BACKGROUND: Robotic-arm assistance continues to gain popularity in total hip arthroplasty (THA) for its potential to improve component placement accuracy and patient outcomes. Nonetheless, there is limited data on the impact of robotic-assisted THA (RA-THA) on hospital length of stay (LOS) and discharge location. This study thus aimed to compare LOS, discharge location, and readmission rate for propensity-matched cohorts of RA-THA versus manual THA (M-THA). METHODS: A retrospective review of a multi-hospital database was performed to identify patients who underwent THA between January 2016 and December 2021 from surgeons who performed both RA-THA and M-THA at 77 geographically diverse hospitals. The RA-THA and M-THA cohorts were 1-to-1 matched based on patient sex, age, and body mass index, resulting in 8,536 patients per cohort. Insurance type, LOS, same-day discharge, discharge disposition, and 90-day all-cause readmission rate were compared using Mann-Whitney U and Chi-square tests. RESULTS: Average LOS was significantly shorter for RA-THA patients (1.39 ± 0.85 days) than for M-THA patients (1.48 ± 0.91 days, P < .001). Compared to 5.6% of M-THA patients, 5.3% of RA-THA patients underwent same-day discharge (P = .38). There were statistically significant differences in discharge disposition between cohorts, with more RA-THA cases discharged home without home healthcare compared to M-THA (47.9 versus 45.5%, P = .001) and fewer RA-THA cases discharged to a skilled nursing facility compared to M-THA (5.6 versus 6.9%, P = .001). The 90-day all-cause readmission rate for RA-THA cases was 3.0%, compared to 3.4% for M-THA cases (P = .26). CONCLUSIONS: Compared to M-THA, RA-THA had a shorter average LOS, a similar percentage of patients with same-day discharge, fewer patients who had skilled nursing facility discharge, and a similar all-cause 90-day readmission rate. These results may be of interest to surgeons participating in bundled payment programs and engaging in cost savings.
RESUMO
PURPOSE: Increased operative time can be due to patient, surgeon and surgical factors, and may be predicted by machine learning (ML) modeling to potentially improve staff utilization and operating room efficiency. The purposes of our study were to: (1) determine how demographic, surgeon, and surgical factors affected operative times, and (2) train a ML model to estimate operative time for robotic-assisted primary total knee arthroplasty (TKA). METHODS: A retrospective study from 2007 to 2020 was conducted including 300,000 unilateral primary TKA cases. Demographic and surgical variables were evaluated using Wilcoxon/Kruskal-Wallis tests to determine significant factors of operative time as predictors in the ML models. For the ML analysis of robotic-assisted TKAs (> 18,000), two algorithms were used to learn the relationship between selected predictors and operative time. Predictive model performance was subsequently assessed on a test data set comparing predicted and actual operative time. Root mean square error (RMSE), R2 and percentage of predictions with an error < 5/10/15 min were computed. RESULTS: Males, BMI > 40 kg/m2 and cemented implants were associated with increased operative time, while age > 65yo, cementless, and high surgeon case volume had reduced operative time. Robotic-assisted TKA increased operative time for low-volume surgeons and decreased operative time for high-volume surgeons. Both ML models provided more accurate operative time predictions than standard time estimates based on surgeon historical averages. CONCLUSIONS: This study demonstrated that greater surgeon case volume, cementless fixation, manual TKA, female, older and non-obese patients reduced operative time. ML prediction of operative time can be more accurate than historical averages, which may lead to optimized operating room utilization. LEVEL OF EVIDENCE: III.
