沈瑞环, 王旭, 鲁中原. 儿童法洛四联症根治术后机械通气时间延长风险的预测模型建立与内部验证[J]. 心脏杂志, 2020, 32(5): 506-512. DOI: 10.12125/j.chj.202006024
    引用本文: 沈瑞环, 王旭, 鲁中原. 儿童法洛四联症根治术后机械通气时间延长风险的预测模型建立与内部验证[J]. 心脏杂志, 2020, 32(5): 506-512. DOI: 10.12125/j.chj.202006024
    shu
    Rui-huan SHEN, Xu WANG, Zhong-yuan LU. Development and internal validation of model predicting postoperative prolonged mechanical ventilation risk in pediatric patients following corrective operation of Tetralogy of Fallot[J]. Chinese Heart Journal, 2020, 32(5): 506-512. DOI: 10.12125/j.chj.202006024
    Citation: Rui-huan SHEN, Xu WANG, Zhong-yuan LU. Development and internal validation of model predicting postoperative prolonged mechanical ventilation risk in pediatric patients following corrective operation of Tetralogy of Fallot[J]. Chinese Heart Journal, 2020, 32(5): 506-512. DOI: 10.12125/j.chj.202006024
    shu

    儿童法洛四联症根治术后机械通气时间延长风险的预测模型建立与内部验证

    Development and internal validation of model predicting postoperative prolonged mechanical ventilation risk in pediatric patients following corrective operation of Tetralogy of Fallot

      摘要
    • 摘要:
        目的  建立并内部验证预测法洛四联症(tetralogy of Fallot,TOF)根治术后机械通气时间延长(prolonged mechanical ventilation, PMV)风险的列线图模型。
        方法  连续入选2019年6月至12月在我院行TOF根治术的6月龄到6岁患儿，并回顾性分析其临床数据。PMV定义为术后机械通气持续时间超过48h。基于入选的患儿做为训练集开发预测PMV风险的列线图模型。采用最小绝对收缩与选择算子(The least absolute shrinkage and selection operator, LASSO)回归模型用于列线图模型的变量选择；应用多因素logistic回归分析来建立预测模型，该模型纳入由LASSO回归模型所选择的所有变量。采用C指数，校准图和决策曲线分析(Decision curve analysis, DCA)评估预测模型的准确性，一致性和临床实用性。采用Bootstrap重复抽样的方法对模型进行内部验证。
        结果  入选的109名患儿，分为机械通气延长组（PMV组）（n=32，占29.4%）与非机械通气延长组（非PMV组）（n=77，占70.6%）。PMV组患儿术后机械通气时间显著长于非PMV组（P<0.01）。多因素logistic回归分析显示术前McGoon比<1.5（OR=3.564，95%CI：1.078-11.782，P<0.05），术中较长的体外循环时间（OR=1.020，95%CI：1.007-1.032，P<0.01）和术后较低的左室射血分数（OR=0.885，95%CI：0.792-0.988，P<0.05）为术后PMV的独立预测因素。并且，该模型具有良好的一致性和区分能力，C指数为0.774。模型经过内部验证后，校正曲线表现良好，C指数较高，等于0.756。DCA表明，当阈概率在大于2%且小于76%的范围内，ICU医师做出改变通气策略的干预决定，列线图模型具有很好的临床效果。
        结论  我们开发并内部验证一种高精度的列线图模型，以协助ICU医生进行与术后PMV相关的临床决策。然而，在推荐用于临床实践之前，该模型需要进行外部验证。

       

      Abstract:
        AIM  To develop and internally validate a nomogram model predicting postoperative prolonged mechanical ventilation (PMV) risk in pediatric patients following corrective operation of Tetralogy of Fallot
        METHODS  Children aged from 6 months to 6 years old who underwent corrective operation of Tetralogy of Fallot in our hospital from June 2019 to December 2019 were selected and their clinical data were analyzed retrospectively. PMV was defined as the duration of postoperative mechanical ventilation exceeding 48 hours. Based on the selected children, a nomogram model predicting the risk of PMV was developed in the training set. The least absolute shrinkage and selection operator (LASSO) regression model was used to select the feature for multivariable logistic regression analysis applied to build a predicting model incorporating the feature selected in the LASSO regression model. The C-index, calibration plot and DCA were used to assess the discrimination, calibration and clinical utility of the prediction model. The bootstrapping validation was used to internally validate the model.
        RESULTS  The 109 children selected were divided into prolonged mechanical ventilation group (PMV group, 32 cases, 29.4%) and non-prolonged mechanical ventilation group (non-PMV group, 77 cases, 70.64%). The duration of postoperative mechanical ventilation in PMV group was longer than that in non-PMV group (P<0.01). Multivariate logistic regression analysis showed that the preoperative McGoon ratio < 1.5 (OR=3.564, 95% CI: 1.078-11.782, P<0.05) and the prolonged duration of cardiopulmonary bypass (CPB) (OR=1.020, 95% CI: 1.007-1.032, P<0.01) and lower postoperative left ventricular ejection fraction (OR=0.885, 95% CI: 0.792-0.988, P<0.05) were independent predictors of postoperative PMV. Moreover, the model displayed good calibration and good discrimination, with a C-index of 0.774. After the model was internally validated, the calibration plot performed well and high C-index value of 0.756 could still be reached. DCA showed that the nomogram was clinically useful when intervention was decided between the threshold probability of 2%−76%.
        CONCLUSION  We have developed and internally validated a highly accurate nomogram to assist ICU doctors in making clinical decisions regarding the postoperative prolonged mechanical ventilation. However, the model requires external validation before being recommended for clinical practice.

       

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