基于早期新型炎症复合指标的儿童重症肺炎支原体肺炎预测模型的构建与评价OA

Objective To develop a clinical prediction model based on early novel inflammatory composite indicators for assessing the risk of progression to severe Mycoplasma pneumoniae pneumonia(SMPP)in children with Mycoplasma pneumoniae pneumonia(MPP).Methods A total of 135 children with Mycoplasma pneumoniae pneumonia(MPP)admitted to The Affiliated Guangdong Second Provincial General Hospital of Jinan University from January 1 to December 31,2024 were retrospectively enrolled,including 36 severe cases and 99 non-severe cases.Demographic characteristics,clinical data,and laboratory indicators were analyzed,including inflammatory composite indicators such as the C-reactive protein-to-albumin ratio(CAR),neutrophil-to-lymphocyte ratio(NLR),lymphocyte-to-monocyte ratio(LMR),platelet-to-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),and hemoglobin-to-red cell distribution width ratio(HRR).Multivariate logistic regression analysis was performed to identify independent predictors,and a nomogram prediction model was constructed.The discriminative ability of the model was evaluated using the receiver operating characteristic(ROC)curve.Calibration was assessed using the calibration curve and the Hosmer-Lemeshow test.Clinical utility was evaluated through decision curve analysis(DCA).Results Compared with the non-SMPP group,the SMPP group had significantly lower lymphocyte count and LMR,and significantly higher levels of CRP,NLR,HRR,CAR,as well as higher incidences of pulmonary consolidation and atelectasis(Z/χ2 values ranged from-3.17 to 96.36,respectively,P<0.05).The SMPP group also had a longer hospital stay and higher proportions of oxygen use,doxycycline administration,and bronchoalveolar lavage(Z/χ2 values ranged from-4.08 to 15.46,respectively,P<0.05).Multivariate logistic regression identified CAR as an independent risk factor for SMPP(OR=3.91,95％CI:1.22-12.55)and lymphocyte count as an independent protective factor(OR=0.54,95％CI:0.32-0.89),respectively,P<0.05.The prediction model based on these two variables showed acceptable discrimination with an AUC of 0.72(95％CI:0.62-0.81).At the optimal cutoff value of 0.264,the model achieved a sensitivity of 78％,specificity of 64％,and negative predictive value of 89％.The calibration curve demonstrated good agreement between predicted and observed risks,and the Hosmer-Lemeshow test yielded P>0.05.Decision curve analysis revealed clinical net benefit within a wide threshold range(0.07-0.50).Conclusion We developed a nomogram prediction model incorporating lymphocyte count and CAR.This model exhibits acceptable predictive ability for SMPP,and its high negative predictive value shows potential for clinical application in ruling out the risk of severe disease.