Clinical Characteristics and Risk Factor Analysis of Children with Severe Mycoplasma pneumoniae Pneumonia Complicated by Plastic Bronchitis

Background Since 2023, Mycoplasma pneumoniae (MP) has become increasingly prevalent in Asia, with some cases progressing to Severe Mycoplasma pneumoniae pneumonia (SMPP), which can lead to plastic bronchitis (PB) development. PB poses a significant threat to children's health and imposes a considerable burden on families and society. Therefore, early detection and timely intervention for PB are crucial. In this study, we aimed to identify the clinical characteristics and risk factors associated with SMPP complicated with PB, thereby aiding in the early recognition of PB in clinical practice. Methods This retrospective cohort study involved children diagnosed with SMPP who were admitted to Chengdu Women’s and Children’s Central Hospital between January 2023 and March 2024. We analyzed anonymized data, including general information, clinical manifestations, laboratory tests, imaging examinations, and pulmonary function test outcomes. Binary logistic regression analysis was conducted to determine independent risk factors, and receiver operating characteristic (ROC) curve analysis was used to assess the predictive value. Results Overall, data on 115 pediatric patients were obtained from the hospital's electronic medical record system. Univariate analysis followed by binary logistic regression analysis employing a stepwise selection was used to identify three independent risk factors for PB: fever duration (odds ratio [OR] = 1.342, 95% confidence interval [CI]: 1.083–1.663; P < 0.01), lactate dehydrogenase (LDH) (OR = 1.006, 95% CI: 1.000–1.011, P < 0.05), and infection affecting either the unilateral or bilateral lung lobes (OR = 0.263; 95% CI, 0.083–0.827; P < 0.05). The area under the curve (AUC) for predicting PB occurrence based on these three factors was 0.864. Conclusion We confirmed that fever duration, LDH level, and involvement of the unilateral or bilateral lung lobes serve as independent risk factors for the occurrence of PB in children with SMPP. Furthermore, our established predictive model based on these three indicators can accurately forecast PB onset, thus providing valuable assistance for early identification in clinical settings.