Why the study?
What are the predictors of unplanned readmission following outpatient otolaryngological surgery?
What are the predictors of unplanned readmission following outpatient otolaryngological surgery?
Outpatient otolaryngological surgery has a low unplanned readmission rate of 2.01%, with superficial surgical site infection being a significant positive predictor.
Superficial SSI may flag higher readmission risk after outpatient otolaryngologic surgery; hypothesis-generating and should not yet change practice.
OBJECTIVES/HYPOTHESIS: Hospital readmissions increase costs to hospitals and patients. There is a paucity of data on benchmark rates of readmission for otolaryngological surgery. Understanding the risk factors that increase readmission rates may help enhance patient education and set system-wide expectations. We aimed to provide benchmark data on causes and predictors of readmission following outpatient otolaryngological surgery. STUDY DESIGN: This study is a retrospective analysis of the 2011 National Surgical Quality Improvement Program (NSQIP) dataset. METHODS: NSQIP was reviewed for outpatients with "Otolaryngology (ENT)" as their recorded surgical specialty. Readmission was tracked through the "Unplanned Readmission" variable. Patient characteristics and outcomes were compared using chi-square analysis and student t tests for categorical and continuous variables, respectively. Multivariate regression analysis investigated predictors of readmission. RESULTS: A total of 6,788 outpatient otolaryngological surgery patients were isolated. The unplanned readmission rate was 2.01%. Multivariate regression analysis revealed superficial surgical site infection (odds ratio [OR] 2.672, confidence interval [CI] 1.133-6.304, P = .025) and work relative value units (RVU) (OR .972, CI .944-1, P = .049) to be significant predictors of readmission. CONCLUSION: Outpatient otolaryngological surgery has an associated 2.01% unplanned readmission rate. Superficial surgical site infection and work RVUs proved to be significant positive and negative risk factors, respectively, for readmission. These findings will help to benchmark outpatient readmission rates and manage patient and hospital system expectations.
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Jain et al. (2013) studied this question.
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