Development and evaluation of a clinical prediction model for macrolide-unresponsive Mycoplasma pneumoniae pneumonia in children
10.3969/j.issn.1673-4130.2024.19.016
- VernacularTitle:儿童大环内酯类药物无反应性肺炎支原体肺炎临床预测模型的建立与评估
- Author:
Jie LIU
1
;
Jie SUN
;
Yeqiong LIU
;
Weixin XU
Author Information
1. 上海市嘉定区中心医院检验科,上海 201899
- Keywords:
macrolide-unresponsive Mycoplasma pneumoniae pneumonia;
novel inflammatory markers;
nomogram;
prediction model
- From:
International Journal of Laboratory Medicine
2024;45(19):2386-2391
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the early predictors of macrolide-unresponsive Mycoplasma pneumoniae pneumonia(MUMPP)and construct a nomogram prediction model.Methods The clinical data of 159 chil-dren with Mycoplasma pneumoniae pneumonia(MPP)admitted to the hospital from January 2023 to Februar-y 2024 were retrospectively collected.According to the time of admission,they were divided into modeling group(112 cases)and validation group(47 cases).The modeling group was further divided into MUMPP group(51 cases)and MPP group(61 cases)according to the drug response.The clinical data and laboratory indexes of each group were compared.The independent predictors of MUMPP were analyzed by univariate and multivariate analysis,and a nomogram prediction model was constructed.Receiver operating characteristic(ROC)curve,area under the curve(AUC),decision curve,calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the discrimination,clinical practicability and calibration of the model.Results The systemic immune inflammation index(SII),C-reactive protein(CRP)/albumin(ALB)and D-dimer were independent influencing factors of MUMPP(P＜0.05).The AUC was 0.938(95％CI 0.890-0.986)in the modeling group and 0.912(95％CI 0.832-0.992)in the validation group.x2 was 3.768 and P was 0.877 in Hosmer-Lemeshow goodness-of-fit test,threshold probability between 5％and 99％had high net clinical benefit.Conclusion The nomogram prediction model established by SII,CRP/ALB and D-dimer has good prediction accuracy and high clinical application value for children with MUMPP.
