1️⃣ Why Study Readmissions in NRD?

The National Readmissions Database (NRD) is specifically designed to track hospital readmissions across the same calendar year. It allows researchers to study 30-day readmission rates and identify risk factors associated with early rehospitalization. Because readmission (yes/no) is a binary outcome, logistic regression is commonly used to evaluate independent predictors.

2️⃣ Model Construction and Variable Selection

In NRD regression analysis, the primary outcome is 30-day readmission, while exposures may include factors such as chronic kidney disease, heart failure, or discharge disposition. Covariates like age, sex, insurance type, hospital size, and comorbidity burden are included to adjust for confounding. Applying discharge weights ensures nationally representative estimates.

3️⃣ Adjusted Outcomes and Clinical Interpretation

Multivariable logistic regression provides Adjusted Odds Ratios (aORs), which reflect the independent association between each predictor and readmission risk. An aOR above 1 indicates increased odds of readmission, while below 1 suggests protective factors. Clinical interpretation is crucial — statistical significance must align with real-world impact.

4️⃣ Example for Better Understanding

Suppose we analyze patients hospitalized with heart failure using the NRD and examine whether chronic kidney disease predicts 30-day readmission. After adjustment for demographics, comorbidities, and hospital characteristics, we find an aOR of 1.40 (95% CI: 1.28–1.53, p < 0.001). This suggests that patients with chronic kidney disease have 40% higher odds of being readmitted within 30 days compared to those without it, independent of other factors.

This demonstrates how regression modeling in NRD helps identify high-risk populations and guide targeted interventions.