Published in:
01-12-2012 | Economic Aspects
Cross-sectional Study of Variables Associated with Length of Stay and ICU Need in Open Roux-En-Y Gastric Bypass Surgery for Morbid Obese Patients: An Exploratory Analysis Based on the Public Health System Administrative Database (Datasus) in Brazil
Published in: Obesity Surgery | Issue 12/2012
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Background
This is an exploratory analysis of potential variables associated with open Roux-en-Y gastric bypass (RYGB) surgery hospitalization resource use pattern.
Methods
Cross-sectional study based on an administrative database (DATASUS) records. Inclusion criteria were adult patients undergoing RYGB between Jan/2008 and Jun/2011. Dependent variables were length of stay (LoS) and ICU need. Independent variables were: gender, age, region, hospital volume, surgery at certified center of excellence (CoE) by the Surgical Review Corporation (SRC), teaching hospital, and year of hospitalization. Univariate and multivariate analysis (logistic regression for ICU need and linear regression for length of stay) were performed.
Results
Data from 13,069 surgeries were analyzed. In crude analysis, hospital volume was the most impactful variable associated with log-transformed LoS (1.312 ± 0.302 high volume vs. 1.670 ± 0.581 low volume, p < 0.001), whereas for ICU need it was certified CoE (odds ratio (OR), 0.016; 95 % confidence interval (CI), 0.010–0.026). After adjustment by logistic regression, certified CoE remained as the strongest predictor of ICU need (OR, 0.011; 95 % CI, 0.007–0.018), followed by hospital volume (OR, 3.096; 95 % CI, 2.861–3.350). Age group, male gender, and teaching hospital were also significantly associated (p < 0.001). For log-transformed LoS, final model includes hospital volume (coefficient, −0.223; 95 % CI, −0.250 to −0.196) and teaching hospital (coefficient, 0.375; 95 % CI, 0.351–0.398). Region of Brazil was not associated with any of the outcomes.
Conclusions
High-volume hospital was the strongest predictor for shorter LoS, whereas SRC certification was the strongest predictor of lower ICU need. Public health policies targeting an increase of efficiency and patient access to the procedure should take into account these results.