基于随机森林模型的小儿重症肺炎支原体肺炎风险因素分析的应用研究

doi:10.20274/j.cnki.issn.1674-3865.2026.01.014

Abstract

Abstract:

Objective To identify risk factors for severe pediatric Mycoplasma pneumoniae pneumonia using a random forest model and to provide evidence for clinical recognition and intervention. Methods Clinical data of 198 children hospitalized for Mycoplasma pneumoniae pneumonia in our hospital between January and December 2024 were collected; 133 were classified as non-severe and 65 as severe. Clinical characteristics were compared between the two groups. Univariate Logistic regression analysis and a random forest model were employed to evaluate the impact of each variable on severe Mycoplasma pneumoniae pneumonia. The construction of random forest model included feature importance analysis and ROC curve assessment. Results No statistically significant differences were observed in age or gender distribution between the groups(P>0.05). The severe group showed significantly higher levels of fever duration, WBC, CRP, ESR, PCT, IL-6, LDH, and CK-MB than the non-severe group(P<0.05). The incidence of high fever and wheezing was also significantly higher in the severe group(P<0.05). Univariate logistic regression indicated that high fever, fever duration, wheezing, WBC, CRP, ESR, PCT, IL-6, LDH, and CK-MB were significantly associated with the risk of severe Mycoplasma pneumoniae pneumonia. The random forest model analysis revealed the lowest error rate when the number of decision trees was 45. The descending order of risk factor importance was fever duration, WBC, age, presence of high fever, CK-MB, ESR, CRP, RBC, IL-6, LDH, PCT, presence of wheezing, PLT, and gender. ROC curve analysis demonstrated an AUC of 0.882 of the random forest model, with a sensitivity of 75.68% and a specificity of 93.33%. Conclusion Through univariate Logistic regression and the random forest model, this study identifies fever duration, high fever, wheezing, and WBC as key risk factors for severe pediatric Mycoplasma pneumoniae pneumonia. These findings offer valuable reference for early clinical identification and intervention of severe Mycoplasma pneumoniae pneumonia, helping to improve the outcomes of affected children.

Key words: Severe pneumonia, Mycoplasma pneumoniae pneumonia, Risk factors, Random forest model, Logistic regression analysis, Child

CLC Number:

R725.6

Sen KONG, Liang ZHANG, Nan LIAO, Guang TU. Application research on risk factor analysis of severe Mycoplasma pneumoniae pneumonia in children based on random forest model[J]. Chinese Pediatrics of Integrated Traditional and Western Medicine, 2026, 18(1): 71-76.

Figures/Tables 5

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References 31

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变量	总计（n=198）	非重症组（n=133）	重症组（n=65）	χ²或t或Z值	P值
年龄（岁）	5.45±2.02	5.47±2.01	5.40±2.05	-0.241	0.810
性别[n（%）]				0.063	0.802
男	100（50.5）	68（51.1）	32（49.2）
女	98（49.5）	65（48.9）	33（50.8）
高热[n（%）]				32.457	<0.001
是	78（39.4）	34（25.6）	44（67.7）
否	120（60.6）	99（74.4）	21（32.3）
发热病程[M（P₂₅,P₇₅），d]	2.0（1.0,4.0）	2.0（1.0,3.0）	4.0（3.0,5.0）	-7.509	<0.001
喘息[n（%）]				5.602	0.018
是	95（48.0）	56（42.1）	39（60.0）
否	103（52.0）	77（57.9）	26（40.0）
白细胞计数（×10⁹/L）	4.14±0.87	3.94±0.84	4.56±0.81	4.935	<0.001
红细胞计数（×10¹²/L）	4.21±0.13	4.21±0.12	4.22±0.14	0.408	0.684
血小板计数（×10⁹/L）	199.41±10.89	198.89±10.81	200.49±11.07	0.947	0.331
C反应蛋白（mg/L）	11.54±5.21	10.95±5.19	12.72±5.07	2.267	0.024
红细胞沉降率（mm/h）	20.19±6.71	19.39±6.79	21.83±6.28	2.432	0.016
降钙素原（μg/L）	0.48±0.23	0.45±0.23	0.54±0.24	2.586	0.010
白细胞介素-6（ng/L）	13.57±5.74	12.88±5.71	14.98±5.58	2.455	0.015
乳酸脱氢酶（U/L）	183.78±27.80	180.32±27.35	190.86±27.59	2.539	0.012
肌酸激酶同工酶（U/L）	26.13±4.40	25.50±4.24	27.42±4.45	2.929	0.004

变量	OR值(95%CI)	P值
高热	6.10(3.19~11.68)	<0.001
发热病程	2.65(1.98~3.54)	<0.001
喘息	2.06(1.13~3.77)	0.019
白细胞计数	2.45(1.65~3.64)	<0.001
C反应蛋白	1.07(1.01~1.13)	0.026
红细胞沉降率	1.06(1.01~1.11)	0.017
降钙素原	5.10(1.44~18.12)	0.012
白细胞介素-6	1.07(1.01~1.12)	0.016
乳酸脱氢酶	1.01(1.00~1.02)	0.013
肌酸激酶同工酶	1.11(1.03~1.19)	0.005

Application research on risk factor analysis of severe Mycoplasma pneumoniae pneumonia in children based on random forest model

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