The post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay - using only pre-operatively identified factors - and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set. A case resequencing exercise was then performed on the test set, in which historic cases were re-sequenced based on the predicted risk for prolonged PACU length of stay. The frequency of patients remaining in the PACU after-hours (≥ 7:00 pm) were compared between the simulated operating days versus actual operating room days. There were 10,928 ambulatory surgical patients included in the analysis, of which 580 (5.31%) had a PACU length of stay ≥ 3 hours. XGBoost with SMOTE performed the best (AUC = 0.712). The case resequencing exercise utilizing the XGBoost model resulted in an over three-fold improvement in the number of days in which patients would be in the PACU past 7pm as compared with historic performance (41% versus 12%, P<0.0001). Predictive models using preoperative patient characteristics may allow for optimized case sequencing, which may mitigate the effects of prolonged PACU lengths of stay on after-hours staffing utilization.
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http://dx.doi.org/10.1007/s10916-023-01966-9 | DOI Listing |
Minerva Anestesiol
December 2024
Department of Anesthesiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
Background: Frail elderly patients have a higher risk of postoperative morbidity and mortality. Prehabilitation is a potential intervention for optimizing postoperative outcomes in frail patients. We studied the impact of a prehabilitation program on length of stay (LOS) in frail elderly patients undergoing elective surgery.
View Article and Find Full Text PDFPediatr Crit Care Med
January 2025
Department of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA.
Objectives: To report the feasibility of a fluid management practice bundle and describe the pre- vs. post-implementation prevalence and odds of cumulative fluid balance greater than 10% in critically ill pediatric patients with respiratory failure.
Design: Retrospective cohort from May 2022 to December 2022.
Am J Respir Crit Care Med
January 2025
Radbound Univeristy Medical Center, Nijmegen, Netherlands;
Rationale: In critically ill patients receiving invasive mechanical ventilation, switching from controlled to assisted ventilation is a crucial milestone towards ventilator liberation. The optimal timing for switching to assisted ventilation has not been studied.
Objectives: Our objective was to determine whether a strategy of early as compared to delayed switching affects the duration of invasive mechanical ventilation, ICU length of stay, and mortality.
J Trauma Acute Care Surg
January 2025
From the Department of Surgery, Westchester Medical Center, New York Medical College, Valhalla, NY.
Background: Extracorporeal membrane oxygenation (ECMO) has emerged as a critical intervention in the management of patients with trauma-induced cardiorespiratory failure. This study aims to compare outcomes in patients with severe thoracic injuries with and without venovenous extracorporeal membrane oxygenation (VV-ECMO).
Methods: We performed a retrospective cohort study on Trauma Quality Improvement Program (2017-2021) and included all patients with isolated blunt thoracic injuries with Abbreviated Injury Scale score of ≥4 who required intubation.
Popul Health Manag
January 2025
Department of Anesthesiology, New York Presbyterian/Weill Cornell Hospital, New York, USA.
Total hip arthroplasty (THA) is a widely performed surgical procedure in the United States, but disparities in THA outcomes related to hospital-level factors, such as safety-net burden, are underexplored. This study expands on previous research by analyzing multicenter, multistate data from 2015 to 2020 to investigate the impact of hospital safety-net burden-defined as the proportion of services billed to Medicaid and uninsured patients-on THA outcomes. This study is a retrospective analysis using data from the State Inpatient Databases for Florida, Kentucky, Maryland, New York, Washington, New Jersey, and North Carolina.
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