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Objective <p>To construct a nomogram model for analyzing risk factors of wheezing in infants with Mycoplasma pneumoniae pneumonia (MPP), so as to provide evidence for clinical intervention.</p> Methods <p>A retrospective analysis was performed on clinical data of 569 infants with MPP hospitalized in the Department of Respiratory Medicine and Department of Infectious Diseases, Children’s Hospital of Soochow University. They were divided into a wheezing group (204 cases) and a non-wheezing group (365 cases) according to symptoms. Demographic characteristics, clinical manifestations, laboratory examinations, bronchoscopy frequency and chest imaging findings were collected. Differences in clinical and laboratory indicators between the two groups were compared. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for preliminary screening, followed by Logistic regression to identify independent risk factors for wheezing in infants with MPP. Finally, a nomogram prediction model was established, and its performance was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA).</p> Results <p>1. Univariate and multivariate analysis of predictors for wheezing in MPP: Univariate Logistic regression screening revealed 16 potential risk factors: fever, cough, history of wheezing, history of eczema, WBC, N#, L#, PLT, CRP, IgG, IgM, CKMB, D-D (ug/L), HRV, HRSV, HMPV (<i>P</i> &lt; 0.05). Multivariate Logistic regression showed that history of wheezing (OR = 5.894, 95%CI: 3.541–9.812), history of eczema (OR = 1.951, 95%CI: 1.295–2.939), elevated IgG (OR = 1.166, 95%CI: 1.074–1.266), elevated CKMB (OR = 1.083, 95%CI: 1.007–1.164), and combined HRSV infection (OR = 3.430, 95%CI: 1.819–6.468) were risk factors, while fever (OR = 0.389, 95%CI: 0.252–0.599) was a protective factor against wheezing in infants with MPP.</p> <p>2. Establishment and validation of the nomogram model: Based on multivariate Logistic regression, six indicators were included to construct the nomogram for predicting wheezing in MPP: history of wheezing, history of eczema, elevated IgG, elevated CKMB, combined HRSV infection, and fever. Each predictor was scored independently, and the total score corresponded to the predicted probability of wheezing caused by MPP. Model evaluation: Training set: AUC = 0.7862, sensitivity = 72.4%, specificity = 78.1%; Internal validation set: AUC = 0.6863, sensitivity = 53.7%, specificity = 79.5%, indicating favorable discrimination. DCA suggested potential clinical applicability.To further evaluate the robustness of the nomogram and to correct for potential optimism, we performed 200 bootstrap resampling iterations within the development cohort. The optimism-corrected area under the receiver operating characteristic curve (AUC) was calculated by subtracting the average optimism (estimated from the difference between apparent performance in each bootstrap sample and the performance applied to the original dataset) from the training-set AUC. The corrected AUC was 0.705 (95% CI: 0.662–0.748), which is consistent with the internal validation result (AUC = 0.686) and indicates moderate discriminative ability. Although the corrected AUC remained above 0.70, the decline from the training-set AUC (0.786) suggests some degree of overfitting, likely attributable to the single-center retrospective design and limited sample size. These bootstrap-corrected estimates support the potential clinical utility of the nomogram.</p> Conclusion <p>Atopic constitution and mixed viral infection are risk factors for wheezing in infants with MPP. The nomogram model is clinically valuable for predicting wheezing in infants with MPP.</p>

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Development of a nomogram for risk factors of wheezing in infants with Mycoplasma pneumoniae pneumonia

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  • 发表日期：

    2026年7月23日

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  • Chen Jianlong,
  • Ye Yingying,
  • Zhang Zhiling,
  • Zhou Weifang,
  • Ji Wei,
  • Chu Chu

摘要

Objective

To construct a nomogram model for analyzing risk factors of wheezing in infants with Mycoplasma pneumoniae pneumonia (MPP), so as to provide evidence for clinical intervention.

Methods

A retrospective analysis was performed on clinical data of 569 infants with MPP hospitalized in the Department of Respiratory Medicine and Department of Infectious Diseases, Children’s Hospital of Soochow University. They were divided into a wheezing group (204 cases) and a non-wheezing group (365 cases) according to symptoms. Demographic characteristics, clinical manifestations, laboratory examinations, bronchoscopy frequency and chest imaging findings were collected. Differences in clinical and laboratory indicators between the two groups were compared. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for preliminary screening, followed by Logistic regression to identify independent risk factors for wheezing in infants with MPP. Finally, a nomogram prediction model was established, and its performance was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA).

Results

1. Univariate and multivariate analysis of predictors for wheezing in MPP: Univariate Logistic regression screening revealed 16 potential risk factors: fever, cough, history of wheezing, history of eczema, WBC, N#, L#, PLT, CRP, IgG, IgM, CKMB, D-D (ug/L), HRV, HRSV, HMPV (P < 0.05). Multivariate Logistic regression showed that history of wheezing (OR = 5.894, 95%CI: 3.541–9.812), history of eczema (OR = 1.951, 95%CI: 1.295–2.939), elevated IgG (OR = 1.166, 95%CI: 1.074–1.266), elevated CKMB (OR = 1.083, 95%CI: 1.007–1.164), and combined HRSV infection (OR = 3.430, 95%CI: 1.819–6.468) were risk factors, while fever (OR = 0.389, 95%CI: 0.252–0.599) was a protective factor against wheezing in infants with MPP.

2. Establishment and validation of the nomogram model: Based on multivariate Logistic regression, six indicators were included to construct the nomogram for predicting wheezing in MPP: history of wheezing, history of eczema, elevated IgG, elevated CKMB, combined HRSV infection, and fever. Each predictor was scored independently, and the total score corresponded to the predicted probability of wheezing caused by MPP. Model evaluation: Training set: AUC = 0.7862, sensitivity = 72.4%, specificity = 78.1%; Internal validation set: AUC = 0.6863, sensitivity = 53.7%, specificity = 79.5%, indicating favorable discrimination. DCA suggested potential clinical applicability.To further evaluate the robustness of the nomogram and to correct for potential optimism, we performed 200 bootstrap resampling iterations within the development cohort. The optimism-corrected area under the receiver operating characteristic curve (AUC) was calculated by subtracting the average optimism (estimated from the difference between apparent performance in each bootstrap sample and the performance applied to the original dataset) from the training-set AUC. The corrected AUC was 0.705 (95% CI: 0.662–0.748), which is consistent with the internal validation result (AUC = 0.686) and indicates moderate discriminative ability. Although the corrected AUC remained above 0.70, the decline from the training-set AUC (0.786) suggests some degree of overfitting, likely attributable to the single-center retrospective design and limited sample size. These bootstrap-corrected estimates support the potential clinical utility of the nomogram.

Conclusion

Atopic constitution and mixed viral infection are risk factors for wheezing in infants with MPP. The nomogram model is clinically valuable for predicting wheezing in infants with MPP.