Prediction of pneumonia hospitalization in adults using health checkup data

OBJECTIVES: Community-acquired pneumonia is a common cause of hospitalization, and pneumococcal vaccinations are recommended for high-risk individuals. Although risk factors for pneumonia have been identified, there are currently no pneumonia hospitalization prediction models based on the risk profiles of healthy subjects. This study aimed to develop a predictive model for pneumonia hospitalization in adults to accurately identify high-risk individuals to facilitate the efficient prevention of pneumonia. METHODS: We conducted a retrospective database analysis using health checkup data and health insurance claims data for residents of Kyoto prefecture, Japan, between April 2010 and March 2015. We chose adults who had undergone health checkups in the first year of the study period, and tracked pneumonia hospitalizations over the next 5 years. Subjects were randomly divided into training and test sets. The outcome measure was pneumonia hospitalization, and candidate predictors were obtained from the health checkup data. The prediction model was developed and internally validated using a LASSO logistic regression analysis. Lastly, we compared the new model with comparative models. RESULTS: The study sample comprised 54,907 people who had undergone health checkups. Among these, 921 were hospitalized for pneumonia during the study period. The c-statistic for the prediction model in the test set was 0.71 (95% confidence interval: 0.69–0.73). In contrast, a comparative model with only age and comorbidities as predictors had a lower c-statistic of 0.55 (95% confidence interval: 0.54–0.56). CONCLUSIONS: Our predictive model for pneumonia hospitalization performed better than comparative models, and may be useful for supporting the development of pneumonia prevention measures.