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BACKGROUND: Weekend admission is associated with increased mortality across a range of patient populations and health-care systems. The aim of this study was to determine whether weekend admission is independently associated with serious adverse events (SAEs), in-hospital mortality, or failure to rescue (FTR) in emergency general surgery (EGS). METHODS: An observational study was performed using the National Inpatient Sample in 2012-2013; the largest all-payer inpatient database in the United States, which represents a 20% stratified sample of hospital discharges. The inclusion criteria were all inpatients with a primary EGS diagnosis. Outcomes were SAE, in-hospital mortality, and FTR (in-hospital mortality in the population of patients that developed an SAE). Multivariable logistic regression were used to adjust for patient- (age, sex, race, payer status, and Charlson comorbidity index) and hospital-level (trauma designation and hospital bed size) characteristics. RESULTS: There were 1,344,828 individual patient records (6.7 million weighted admissions). The overall rate of SAE was 15.1% (15.1% weekend, 14.9% weekday, P < 0.001), FTR 5.9% (6.2% weekend, 5.9% weekday, P = 0.010), and in-hospital mortality 1.4% (1.5% weekend, 1.3% weekday, P < 0.001). Within logistic regression models, weekend admission was an independent risk factor for development of SAE (adjusted odds ratio 1.08, 1.07-1.09), FTR (1.05, 1.01-1.10), and in-hospital mortality (1.14, 1.10-1.18). CONCLUSIONS: This study found evidence that outcomes coded in an administrative data set are marginally worse for EGS patients admitted at weekends. This justifies further work using clinical data sets that can be used to better control for differences in case mix.

Original publication

DOI

10.1016/j.jss.2017.10.019

Type

Journal article

Journal

J surg res

Publication Date

02/2018

Volume

222

Pages

219 - 224

Keywords

Acute care surgery, Emergency general surgery, Weekend effect, Emergency Service, Hospital, Female, General Surgery, Hospital Mortality, Humans, Male, Middle Aged, Time Factors, United States