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A Proposed Patient Selection Algorithm for Total Joint Arthroplasty Same-Day Discharge From an Ambulatory Surgery
Jonathan Liu1, Andrea Gilmore2, Mohammad Daher1
1Department of Orthopaedic Surgery, Warren Alpert Medical School of Brown University, Providence, Rhode Island.
Background:
Identifying appropriate patients for same-day discharge (SDD) total joint arthroplasty (TJA) is critical for maintaining optimal patient safety and outcomes. This study investigated patient outcomes after SDD TJA at a single ambulatory surgery center (ASC) and proposes a TJA patient-selection algorithm based on findings and existing literature.
Methods:
A retrospective chart review of 660 patients was performed between July 2019 and October 2021 for all patients who underwent primary TJA in a single ASC. Successful SDD, length of surgery, estimated blood loss (EBL), complications, and readmission events were recorded for each patient. There were 20 total complications in 331 primary total knee arthroplasties (TKAs) (6.0%) and 15 total complications in 329 primary total hip arthroplasties (THAs) (4.6%).
Results:
There was one direct admission to the hospital in TKA patients and four direct admissions in THA patients, making the successful SDD rate 99.7% in TKAs, 98.8% in THAs, and 99.2% overall. In the TKA cohort, body mass index was associated with total complications (r = -0.15, P = 0.006); comorbidities with wound complications (P = 0.006); and EBL was with readmissions (r = 0.30, P < 0.001), revision surgery (r = 0.12, P = 0.04), and total complications (r = 0.16, P = 0.03). In the THA cohort, body mass index was weakly associated with wound complications (r = -0.12, P = 0.02), EBL was with emergency department visits (r = 0.18, P = 0.002) and total complications (r = 0.14, P = 0.01). However, there was no direct association between any of the analyzed characteristics and direct admission.
Conclusions:
In our ASC cohort, patients had low rates of perioperative complications and hospital admissions, supporting the safety of SDD TJA using our proposed evidence-based algorithm to guide patient selection for SDD.
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