• Otolaryngol Head Neck Surg · Oct 2013

    Risk factors for unplanned hospital readmission in otolaryngology patients.

    • Evan M Graboyes, Tzyy-Nong Liou, Dorina Kallogjeri, Brian Nussenbaum, and Jason A Diaz.
    • Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
    • Otolaryngol Head Neck Surg. 2013 Oct 1;149(4):562-71.

    ObjectiveIdentify the risk factors that predict 30-day unplanned readmission in hospitalized otolaryngology patients.Study DesignRetrospective cohort study.SettingSingle academic hospital.Subjects And MethodsAll otolaryngology admissions for the 1-year period between January 1, 2011, and December 31, 2011, at an academic hospital were reviewed. Univariate logistic regression and multivariate logistic regression, employing a backward elimination stepwise approach, were performed to identify risk factors for unplanned readmission to the hospital within 30 days of discharge from the otolaryngology service.ResultsThere were 1058 patients that accounted for 1271 hospital admissions. The 30-day unplanned readmission rate for patients discharged from the otolaryngology service was 7.3% (93/1271). Significant predictors identified on univariate analysis were used to build a multivariable logistic regression model of risk factors for unplanned readmission. These risk factors included presence of a complication (odds ratio [OR] = 11.60, 95% confidence interval [CI], 7.11-18.93), new total laryngectomy (OR = 4.72, 95% CI, 1.58-14.10), discharge destination of skilled nursing facility (OR = 2.70, 95% CI, 1.21-6.02), severe coronary artery disease or chronic lung disease (OR = 2.33, 95% CI, 1.38-3.93), and current illicit drug use (OR = 2.60, 95% CI, 1.27-5.34). The discriminative ability of the multivariate regression model to predict unplanned readmissions, as measured by the c-statistic, was 0.85.ConclusionOtolaryngology patients have unique risk factors that predict unplanned readmission within 30 days of discharge. These data identify specific patient characteristics and care processes that can be targeted with quality improvement interventions to decrease unplanned readmissions.

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