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已发表论文
儿童肺炎支原体肺炎临床诊断预测模型
Authors Liu X, Du H, Zhang L, Yu Y, Wang G, Zhong F, Zhang M, Huang H, Xu Y
Received 23 June 2025
Accepted for publication 8 October 2025
Published 17 October 2025 Volume 2025:18 Pages 5365—5378
DOI https://doi.org/10.2147/IDR.S541396
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Hemant Joshi
Xiaohua Liu,1,* Hongfei Du,2,* Linyan Zhang,1,* Yang Yu,1 Guixiang Wang,1 Fan Zhong,1 Mingjun Zhang,3 Huacui Huang,1 Ying Xu2
1Department of Medical Laboratory, Xindu District People’s Hospital of Chengdu, Chengdu, 610500, People’s Republic of China; 2Department of Clinical Laboratory, the First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, People’s Republic of China; 3Department of Medical Laboratory, Jiulongpo District People’s Hospital of Chongqing, Chongqing, 400050, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Ying Xu, Department of Clinical Laboratory, the First Affiliated Hospital of Chengdu Medical College, No. 278, Baoguang Road, Xindu District, Chengdu, Sichuan, 610500, People’s Republic of China, Email yingxu825@126.com Huacui Huang, Department of Clinical Laboratory, Xindu District People’s Hospital of Chengdu, No. 199, Yuying Road South, Xindu District, Chengdu, Sichuan, 610500, People’s Republic of China, Email 839373273@qq.com
Background: The aim of this research is to ascertain the risk determinants associated with Mycoplasma pneumoniae pneumonia (MPP) in pediatric patients diagnosed with community-acquired pneumonia (CAP), as well as to construct predictive models to forecast the incidence of MPP.
Methods: This study was conducted at Xindu District People’s Hospital of Chengdu from August 2023 to March 2024. A total of 1030 children aged 0 to 14 years with CAP were enrolled and divided into MPP (n=414) and non-MPP (NMPP, n=616) groups based on diagnostic criteria including MP antibody and MP RNA. Data were collected within 24 hours of admission, including peripheral blood counts, inflammatory markers, and other biochemical parameters. The Logistic+Stepwise, Logistic+Lasso, Logistic+Elastic-net, and Logistic+Ridge were employed to identify risk factors, and were used for variable selection with penalization algorithms. Model performance was evaluated using C-index, sensitivity, specificity, accuracy, recall, and F1 score.
Results: The results of prediction model showed that four models had good performance. The area under the ROC curve revealed good predictive ability (AUC > 0.8 in both Logistic model and Experience model), the results of calibration curves indicated a good consistency consistency. Logistic+Lasso model selected 9 key variables for further analysis.
Conclusion: We have developed and validated a clinical prediction model in children with Mycoplasma pneumoniae pneumonia (MPP). The model identifies NEUT%, EOS%, HSCRP, ADA, Crea, Urea, HDL, P, and ESR as significant independent predictors. It demonstrated robust discriminative ability and good calibration, offering a practical tool for clinicians to stratify risk and guide early intervention in pediatric patients.
Keywords: mycoplasma pneumoniae pneumonia, prediction model, clinical characteristics, children
1Department of Medical Laboratory, Xindu District People’s Hospital of Chengdu, Chengdu, 610500, People’s Republic of China; 2Department of Clinical Laboratory, the First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, People’s Republic of China; 3Department of Medical Laboratory, Jiulongpo District People’s Hospital of Chongqing, Chongqing, 400050, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Ying Xu, Department of Clinical Laboratory, the First Affiliated Hospital of Chengdu Medical College, No. 278, Baoguang Road, Xindu District, Chengdu, Sichuan, 610500, People’s Republic of China, Email yingxu825@126.com Huacui Huang, Department of Clinical Laboratory, Xindu District People’s Hospital of Chengdu, No. 199, Yuying Road South, Xindu District, Chengdu, Sichuan, 610500, People’s Republic of China, Email 839373273@qq.com
Background: The aim of this research is to ascertain the risk determinants associated with Mycoplasma pneumoniae pneumonia (MPP) in pediatric patients diagnosed with community-acquired pneumonia (CAP), as well as to construct predictive models to forecast the incidence of MPP.
Methods: This study was conducted at Xindu District People’s Hospital of Chengdu from August 2023 to March 2024. A total of 1030 children aged 0 to 14 years with CAP were enrolled and divided into MPP (n=414) and non-MPP (NMPP, n=616) groups based on diagnostic criteria including MP antibody and MP RNA. Data were collected within 24 hours of admission, including peripheral blood counts, inflammatory markers, and other biochemical parameters. The Logistic+Stepwise, Logistic+Lasso, Logistic+Elastic-net, and Logistic+Ridge were employed to identify risk factors, and were used for variable selection with penalization algorithms. Model performance was evaluated using C-index, sensitivity, specificity, accuracy, recall, and F1 score.
Results: The results of prediction model showed that four models had good performance. The area under the ROC curve revealed good predictive ability (AUC > 0.8 in both Logistic model and Experience model), the results of calibration curves indicated a good consistency consistency. Logistic+Lasso model selected 9 key variables for further analysis.
Conclusion: We have developed and validated a clinical prediction model in children with Mycoplasma pneumoniae pneumonia (MPP). The model identifies NEUT%, EOS%, HSCRP, ADA, Crea, Urea, HDL, P, and ESR as significant independent predictors. It demonstrated robust discriminative ability and good calibration, offering a practical tool for clinicians to stratify risk and guide early intervention in pediatric patients.
Keywords: mycoplasma pneumoniae pneumonia, prediction model, clinical characteristics, children