Prognostic factors for early identification of asthma: Analysis of the Korean Health Panel Survey (2014–2018)

This study aimed to develop and validate a predictive nomogram for asthma incidence using longitudinal panel data from South Korea. The goal was to support clinical decision-making and enhance early intervention for primary healthcare providers. Although asthma is manageable through outpatient care, its high hospitalization rate in Korea presents a significant healthcare burden. Therefore, early screening and targeted interventions are essential to improve patient outcomes. We analyzed data from 16,630 adults in the Korea Health Panel Survey (2014-2018), including 155 with asthma (J45-J46). We randomly split the data into training (70%) and validation (30%) sets. Using multivariable logistic regression, we identified significant predictors of asthma incidence. We then validated the nomogram using the concordance index (C-index), calibration plots, receiver operating characteristic analysis, and bootstrapping with 100 resamples. Our analysis identified male sex, age over 65, lower educational attainment, medical aid, and comorbidities as significant predictors of asthma. The model demonstrated good discriminatory power in the training set, with an area under the curve of 0.786 (95% confidence interval: 0.753-0.818) and a C-index of 0.798. The validated nomogram serves as a practical tool for healthcare providers to identify patients at high risk for asthma. This tool enables rapid risk assessment, facilitates targeted patient education, and supports multidisciplinary collaboration, potentially improving the quality and efficiency of asthma care.