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已发表论文
传统及新型复合炎症指标对儿童重症难治性肺炎支原体肺炎的预测价值
Authors Fan GZ , Zhu YH, Hu LX, Qu ZH , Liu YB
Received 14 October 2025
Accepted for publication 15 November 2025
Published 3 December 2025 Volume 2025:18 Pages 16981—16990
DOI https://doi.org/10.2147/JIR.S561489
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Tara Strutt
Guo Zhen Fan,1 Yu Hui Zhu,1 Li Xin Hu,1 Zheng Hai Qu,1 Yin Bo Liu2
1Department of Pediatrics, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 2Department of Information Technology Management, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China
Correspondence: Zheng Hai Qu, Email quzhenghai@163.com Yin Bo Liu, Email 3113534236@qq.com
Objective: This study aimed to comprehensively evaluate the predictive efficacy of traditional single inflammatory indicators and novel composite inflammatory indicators (CLR, LMR, NLR, NPR, PIV, PLR, SII, SIRI) for severe Mycoplasma pneumoniae pneumonia (SMPP) and refractory MPP (RMPP) in children.
Methods: This study retrospectively enrolled 1791 children with MPP and collected their case data. A phased modeling strategy (univariate analysis, LASSO regression, multivariate logistic regression) was employed to construct prediction models. Model performance was evaluated using area under the curve (AUC) from receiver operating characteristic (ROC) curves, calibration curves with the Hosmer-Lemeshow test, bootstrap resampling with 1000 repetitions, and decision curve analysis (DCA).
Results: The cohort included 512 SMPP, 269 RMPP, and 1180 general MPP cases; mentiontly, 170 children met both SMPP and RMPP criteria. The SMPP prediction model identified nine independent risk factors (Hb, PLT, D-D, FIB, LMR, NPR, SII, duration of cough and fever), achieving an AUC of 0.803. The RMPP model identified seven factors (Hb, CRP, FIB, LMR, NPR, duration of cough and fever) with an AUC of 0.889. The calibration curves, Hosmer-Lemeshow test, bootstrap internal validation, and DCA curve together confirmed the robustness and clinical applicability of the models.
Conclusion: This multi-parameter integration strategy enables precise MPP risk stratification, holding significant implications for clinical treatment planning and antibiotic selection.
Keywords: composite inflammatory indicators, refractory Mycoplasma pneumoniae pneumonia, risk prediction model, Mycoplasma pneumoniae pneumonia, severe Mycoplasma pneumoniae pneumonia
1Department of Pediatrics, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 2Department of Information Technology Management, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China
Correspondence: Zheng Hai Qu, Email quzhenghai@163.com Yin Bo Liu, Email 3113534236@qq.com
Objective: This study aimed to comprehensively evaluate the predictive efficacy of traditional single inflammatory indicators and novel composite inflammatory indicators (CLR, LMR, NLR, NPR, PIV, PLR, SII, SIRI) for severe Mycoplasma pneumoniae pneumonia (SMPP) and refractory MPP (RMPP) in children.
Methods: This study retrospectively enrolled 1791 children with MPP and collected their case data. A phased modeling strategy (univariate analysis, LASSO regression, multivariate logistic regression) was employed to construct prediction models. Model performance was evaluated using area under the curve (AUC) from receiver operating characteristic (ROC) curves, calibration curves with the Hosmer-Lemeshow test, bootstrap resampling with 1000 repetitions, and decision curve analysis (DCA).
Results: The cohort included 512 SMPP, 269 RMPP, and 1180 general MPP cases; mentiontly, 170 children met both SMPP and RMPP criteria. The SMPP prediction model identified nine independent risk factors (Hb, PLT, D-D, FIB, LMR, NPR, SII, duration of cough and fever), achieving an AUC of 0.803. The RMPP model identified seven factors (Hb, CRP, FIB, LMR, NPR, duration of cough and fever) with an AUC of 0.889. The calibration curves, Hosmer-Lemeshow test, bootstrap internal validation, and DCA curve together confirmed the robustness and clinical applicability of the models.
Conclusion: This multi-parameter integration strategy enables precise MPP risk stratification, holding significant implications for clinical treatment planning and antibiotic selection.
Keywords: composite inflammatory indicators, refractory Mycoplasma pneumoniae pneumonia, risk prediction model, Mycoplasma pneumoniae pneumonia, severe Mycoplasma pneumoniae pneumonia