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. 2023 Jul 12;23(1):351.
doi: 10.1186/s12887-023-04160-5.

Congenital heart diseases with airway stenosis: a predictive nomogram to risk-stratify patients without airway intervention

Qiyu He #  1 , Yuze Liu #  1 , Zheng Dou  1 , Kai Ma  1 , Shoujun Li  2
Affiliations

Congenital heart diseases with airway stenosis: a predictive nomogram to risk-stratify patients without airway intervention

Qiyu He et al. BMC Pediatr. .

Abstract

Background: This study focused on congenital heart disease (CHD) patients complicated with airway stenosis (AS) without airway intervention and aimed to identify the patients with potential risks.

Methods: Patients diagnosed with CHD and AS were enrolled in this retrospective study. The primary outcome was defined as a postoperative mechanical ventilation duration of more than two weeks. We constructed a prediction model to predict the risk of prolonged mechanical ventilation (PMV).

Results: A total of 185 patients diagnosed with CHD and AS in Fuwai Hospital from July 2009 to December 2022 were included in the study. Weight at CHD surgery, cardiopulmonary bypass (CPB) duration, complex CHD and comorbid tracheobronchomalacia were identified as risk factors and included in the model. The ROC curve showed a good distinguishing ability, with an AUC of 0.847 (95% CI: 0.786-0.908). According to the optimal cut-off value of the ROC curve, patients were divided into high- and low-risk groups, and the subsequent analysis showed significant differences in peri-operative characteristics and in-hospital deaths.

Conclusions: With the predictive model, several factors could be used to assess the risky patients with PMV. More attention should be paid to these patients by early identification and routine surveillance.

Keywords: Airway stenosis; Congenital heart diseases; Mechanical ventilation; Nomogram; Predictive model.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Factors associated with PMV. (A) Univariate logistic regression. Factors with predictive potential were included in the univariate logistic regression, and the result was presented with a forest plot. (B) Multivariate logistic regression. Four factors were included in the predictive model and presented with a forest plot, including weight at CHD surgery, CPB duration, complex CHD, and tracheobronchomalacia. P < 0.05 was considered to be statistically significant
Fig. 2
Fig. 2
The diagnosis of the predictive model. (A-C) Calibration curves for logistic regression models. The predicted probability of PMV was presented on the x-axis, and the actual probability was presented on the y-axis. All three models passed the Hosmer-Lemeshow test (P > 0.05); (D-F) ROC curves for logistic regression models. The ROC curves and optimal cut-off values for Model 1 (grey), Model 2 (red), and Model 3 (blue) were plotted; (G) Decision curve analysis for PMV prediction. We preferred the prediction model with higher net benefit values over a larger threshold probability range. All three prediction models had higher net benefits than the control line (treat all and treat none) over the full range of threshold probabilities (H) Diagnosis of multicollinearity of Model 1. The result showed no multicollinearity (Variance Inflation Factor < 5). Variables: Model 1: weight at CHD surgery, CPB duration, complex CHD and comorbid tracheobronchomalacia; Model 2: weight at CHD surgery, complex CHD and comorbid tracheobronchomalacia; Model 3: weight at CHD surgery, CPB duration and comorbid tracheobronchomalacia
Fig. 3
Fig. 3
The nomogram for predicting the risk of PMV. The corresponding total score was calculated based on the value of each variable. The corresponding risk of PMV was based on the total score
See this image and copyright information in PMC

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