ArticlePloS one2017
Prediction of pneumonia hospitalization in adults using health checkup data.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Out-of-distribution reject option method for dataset shift problem in early disease onset prediction.Scientific reports · 2025Article
- A Broad Learning System to Predict the 28-Day Mortality of Patients Hospitalized with Community-Acquired Pneumonia: A Case-Control Study.Computational and mathematical methods in medicine · 2022Article
- The Multi-Institutional Health Screening Records Database of South Korea: Description and Evaluation of Its Characteristics.Yonsei medical journal · 2019Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesCommunity-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.
methodsWe 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.
resultsThe 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).
conclusionsOur predictive model for pneumonia hospitalization performed better than comparative models, and may be useful for supporting the development of pneumonia prevention measures.
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