Development and validation of a nomogram for predicting Mycoplasmapneumoniae pneumonia in adults

• Published in: Scientific reports Vol. 12; no. 1; p. 21859
• Main Authors: Ren, Yuan, Wang, Yan, Liang, Ruifeng, Hao, Binwei, Wang, Hongxia, Yuan, Jianwei, Wang, Lin, Guo, Zhizun, Zhang, Jianwei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.12.2022; Nature Publishing Group
• Subjects: 692/308; 692/499; 692/53; 692/699; 692/700; Body temperature; Calibration; Cough; Humanities and Social Sciences; multidisciplinary; Nomograms; Pneumonia; Prediction models; Risk groups; Science; Science (multidisciplinary)
• ISSN: 2045-2322
• DOI: 10.1038/s41598-022-26565-5

The study aimed to explore predictors of Mycoplasma pneumoniae pneumonia (MPP) in adults and develop a nomogram predictive model in order to identify high-risk patients early. We retrospectively analysed the clinical data of a total of 337 adult patients with community-acquired pneumonia (CAP) and divided them into MPP and non-MPP groups according to whether they were infected with MP. Univariate and multivariate logistic regression analyses were used to screen independent predictors of MPP in adults and to developed a nomogram model. Receiver operating characteristic (ROC) curve, calibration curve, concordance index (C-index), and decision curve analysis (DCA) were used for the validation of the evaluation model. Finally, the nomogram was further evaluated by internal verification. Age, body temperature, dry cough, dizziness, CRP and tree-in-bud sign were independent predictors of MPP in adults (P  <  0.05 ). The nomogram showed high accuracy with C-index of 0.836 and well-fitted calibration curves in both the training and validation sets. The area under the receiver operating curve (AUROC) was 0.829 (95% CI 0.774–0.883) for the training set and 0.847 (95% CI 0.768–0.925) for the validation set. This nomogram prediction model can accurately predict the risk of MPP occurrence in adults, which helps clinicians identify high-risk patients at an early stage and make drug selection and clinical decisions.