RESUMEN
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.
Asunto(s)
Artroplastia de Reemplazo de Rodilla , Cirujanos , Humanos , Artroplastia de Reemplazo de Rodilla/métodos , Estudios Retrospectivos , Puntaje de Propensión , Alta del Paciente , Estudios de CohortesRESUMEN
BACKGROUND: Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty. METHODS: A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN). RESULTS: We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m2) were the strongest predictors associated with surgical operative time. CONCLUSIONS: This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time. LEVEL OF EVIDENCE: Level III, case control retrospective analysis.