Construction of a risk prediction model for severe community-acquired pneumonia in children in Qinghai region

In this study, we successfully constructed a model for predicting the occurrence of severe CAP in children in Qinghai based on 10 clinical indicators: number of days of coughing, site of lung solid involvement, percentage of lymphocytes, procalcitonin, CRP, ALT, AST, IL-6, IL-8 and LDH, which had a good predictive efficacy and was useful for the intuitive clinical assessment. The model has good predictive efficacy. This study developed a nomogram model for severe CAP by integrating multidimensional clinical, radiological and laboratory indicators. The model not only provides a reliable tool for early identification of severe CAP in paediatric populations from the Qinghai region but also demonstrates promising clinical application potential with its high sensitivity and specificity. Future multicentre collaborative studies are warranted to further optimise the model and explore its potential value in dynamic monitoring and precision therapy, thereby advancing personalised and precision management of paediatric CAP.