Prediction of Pneumonia 30-Day Readmissions: A Single-Center Attempt to Increase Model Performance.

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 Januar...

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Publicado en:Respiratory Care Vol. 59; no. 2; pp. 199 - 209
Autores principales: Mather, Jeffrey F., Fortunato, Gilbert J., Ash, Jenifer L., Davis, Michael J., Kumar, Ajay
Formato: research tables/charts Journal Article
Publicado: Mary Ann Liebert, Inc. Feb2014
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Prediction of Pneumonia 30-Day Readmissions: A Single-Center Attempt to Increase Model Performance.
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        au:
          Mather, Jeffrey F.
          Fortunato, Gilbert J.
          Ash, Jenifer L.
          Davis, Michael J.
          Kumar, Ajay
        affil: Department of Research Administration, Hartford Hospital, Hartford, Connecticut
      sug:
        subj:
          Pneumonia Complications
          Hospitalization
          Readmission
          Risk Assessment
          Socioeconomic Factors
          Health Resource Utilization
          Depression
          Human
          Prospective Studies
          Record Review
          Logistic Regression
          Female
          Male
          Confidence Intervals
          Odds Ratio
          United States Centers for Medicare and Medicaid Services
          Retrospective Design
          Nonexperimental Studies
          Academic Medical Centers
          Connecticut
          Aged
          Aged, 80 and Over
          Descriptive Statistics
          Chi Square Test
          T-Tests
          Multiple Logistic Regression
          Data Analysis Software
          Kaplan-Meier Estimator
          Multivariate Analysis
          Goodness of Fit Chi Square Test
          ROC Curve
          Aged: 65+ years
          Aged, 80 & over
          Female
          Male
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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