A new dynamic nomogram for predicting the risk of severe Mycoplasma pneumoniae pneumonia in children - Xue Zhang; Ruiyang Sun; Wanyu Jia; Peng Li; Chunlan Song (Scientific Reports, 2024)
Abstract Mycoplasma pneumoniae pneumonia (MPP) is usually mild and self-limiting, but still about 12% of them will progress to severe Mycoplasma pneumoniae pneumonia (SMPP), which have poor survival rates and often require intensive medical resource utilization. We retrospectively collected clinical data from 526 children with MPP admitted to the Children’s Hospital Affiliated to Zhengzhou University from June 2018 to February 2023 and randomly divided the data into a training cohort and a validation cohort at a ratio of 4:1. Univariate and multivariate logistic regressions were used to identify independent risk factors for SMPP. Age, AGR, NLR, CRP, ESR, MPV, coinfection, pleural effusion, primary disease, fever days ≥ 7 and wheeze are independent risk factors for SMPP in children. Then, we built an online dynamic nomogram ( https://ertongyiyuanliexiantu.shinyapps.io/SMPP/ ) based on the 11 independent risk factors. The C-index, ROC curve, DCA curve and calibration curve were used to assess the performance of the nomogram, which all showed that the dynamic nomogram has excellent clinical value. Based on age, AGR, NLR, CRP, ESR, MPV, coinfection, pleural effusion, primary disease, fever days ≥ 7 and wheeze, the first dynamic nomogram for accurately predicting SMPP was successfully established.