RT Journal Article
SR Electronic
T1 Prediction of Pneumonia 30-Day Readmissions: A Single-Center Attempt to Increase Model Performance.
JF Respiratory Care
FD American Association for Respiratory Care
SP respcare.02563
DO 10.4187/respcare.02563
A1 Jeffrey F. Mather
A1 Gilbert J. Fortunato
A1 Jenifer L. Ash
A1 Michael J. Davis
A1 Ajay Kumar
YR 2013
UL http://rc.rcjournal.com/content/early/2013/08/13/respcare.02563.abstract
AB Background: Existing models developed to predict 30 days readmissions for pneumonia lack discriminative ability. This study is an attempt to increase model performance with the addition of variables found of benefit in other studies. Methods: From 133,368 admissions to a tertiary-care hospital from Jan 2009 to March 2012, the study cohort consisted of 956 index admissions for pneumonia using the CMS definition. We collected variables previously reported to be associated with 30-day all-cause readmission, including vital signs, comorbidities, laboratory values, demographics, socio-economic indicators, and indicators of hospital utilization. Separate logistic regression models were developed to identify the predictors of all-cause hospital readmission 30 days after discharge from the index pneumonia admission for pneumonia-related readmissions, and for pneumonia-unrelated readmissions. Result: Of the 965 index admissions for pneumonia, 148 (15.5%) patients were readmitted within 30 days. Variables in the multivariate-model significantly associated with 30-day all cause readmission include male gender (odds ratio (OR)=1.59, 95% confidence interval(CI) = 1.03-2.45), 3 or more previous admissions (OR=1.84, 95% CI = 1.22-2.78), chronic lung disease (OR=1.63, 95% CI = 1.07-2.48), cancer (OR= 2.18, 95% CI = 1.24-3.84), median income <= $43,000 (OR=1.82, 95% CI = 1.18-2.81), history of anxiety or depression (OR=1.62, 95% CI = 1.04-2.52) and hematocrit < 30% (OR=1.86, 95% CI = 1.07-3.22). The model performance as measured by the c-statistic was 0.71 (0.66-0.75), with minimal optimism according to bootstrap re-sampling (optimism corrected c-statistic = 0.67). Conclusions: The addition of variables to measure socio-economic status (SES), health-care utilization significantly improved model performance when compared to the model using CMS variables alone.