JOURNAL ARTICLE

Prediction of pneumonia 30-day readmissions: a single-center attempt to increase model performance

Jeffrey F Mather, Gilbert J Fortunato, Jenifer L Ash, Michael J Davis, Ajay Kumar
Respiratory Care 2014, 59 (2): 199-208
23942750

BACKGROUND: Existing models developed to predict 30 days readmissions for pneumonia lack discriminative ability. We attempted to increase model performance with the addition of variables found to be of benefit in other studies.

METHODS: From 133,368 admissions to a tertiary-care hospital from January 2009 to March 2012, the study cohort consisted of 956 index admissions for pneumonia, using the Centers for Medicare and Medicaid Services definition. We collected variables previously reported to be associated with 30-day all-cause readmission, including vital signs, comorbidities, laboratory values, demographics, socioeconomic 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.

RESULTS: Of the 965 index admissions for pneumonia, 148 (15.5%) subjects were readmitted within 30 days. The variables in the multivariate-model that were significantly associated with 30-day all-cause readmission were male sex (odds ratio 1.59, 95% CI 1.03-2.45), 3 or more previous admissions (odds ratio 1.84, 95% CI 1.22-2.78), chronic lung disease (odds ratio 1.63, 95% CI 1.07-2.48), cancer (odds ratio 2.18, 95% CI 1.24-3.84), median income < $43,000 (odds ratio 1.82, 95% CI 1.18-2.81), history of anxiety or depression (odds ratio 1.62, 95% CI 1.04-2.52), and hematocrit < 30% (odds ratio 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 socioeconomic status and healthcare utilization variables significantly improved model performance, compared to the model using only the Centers for Medicare and Medicaid Services variables.

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