Jue Zhen-Zhen, Song Juan, Zhou Zhu-Ye, Li Wen-Dong, Yue Yu-Yang, Xu Fa-Lin
Department of Neonatology, Third Affiliated Hospital of Zhengzhou University/Henan Provincial Key Laboratory of Pediatric Brain Injury/Henan Provincial Clinical Research Center of Pediatric Diseases/Advanced Medical Research Center of Zhengzhou University, Zhengzhou 450052, China.
Zhongguo Dang Dai Er Ke Za Zhi. 2022 Jun 15;24(6):654-661. doi: 10.7499/j.issn.1008-8830.2202027.
To establish a nomogram model for predicting the risk of death of very preterm infants during hospitalization.
A retrospective analysis was performed on the medical data of 1 714 very preterm infants who were admitted to the Department of Neonatology, the Third Affiliated Hospital of Zhengzhou University, from January 2015 to December 2019. These infants were randomly divided into a training cohort (1 179 infants) and a validation cohort (535 infants) at a ratio of 7∶3. The logistic regression analysis was used to screen out independent predictive factors and establish a nomogram model, and the feasibility of the nomogram model was assessed by the validation set. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the discriminatory ability, accuracy, and clinical applicability of the model.
Among the 1 714 very preterm infants, 260 died and 1 454 survived during hospitalization. By the multivariate logistic regression analysis of the training set, 8 variables including gestational age <28 weeks, birth weight <1 000 g, severe asphyxia, severe intraventricular hemorrhage (IVH), grade III-IV respiratory distress syndrome (RDS), and sepsis, cesarean section, and use of prenatal glucocorticoids were selected and a nomogram model for predicting the risk of death during hospitalization was established. In the training cohort, the nomogram model had an AUC of 0.790 (95%: 0.751-0.828) in predicting the death of very preterm infants during hospitalization, while in the validation cohort, it had an AUC of 0.808 (95%: 0.754-0.861). The Hosmer-Lemeshow goodness-of-fit test showed a good fit (>0.05). DCA results showed a high net benefit of clinical intervention in very preterm infants when the threshold probability was 10%-60% for the training cohort and 10%-70% for the validation cohort.
A nomogram model for predicting the risk of death during hospitalization has been established and validated in very preterm infants, which can help clinicians predict the probability of death during hospitalization in these infants.
建立预测极早产儿住院期间死亡风险的列线图模型。
对2015年1月至2019年12月在郑州大学第三附属医院新生儿科住院的1714例极早产儿的医疗数据进行回顾性分析。这些婴儿按7∶3的比例随机分为训练队列（1179例婴儿）和验证队列（535例婴儿）。采用逻辑回归分析筛选出独立预测因素并建立列线图模型，通过验证集评估列线图模型的可行性。采用受试者操作特征曲线（AUC）下面积、校准曲线和决策曲线分析（DCA）评估模型的鉴别能力、准确性和临床适用性。
1714例极早产儿中，260例住院期间死亡，1454例存活。通过对训练集进行多因素逻辑回归分析，选择了包括胎龄<28周、出生体重<1000g、重度窒息、重度脑室内出血（IVH）、III-IV级呼吸窘迫综合征（RDS）、败血症、剖宫产和产前使用糖皮质激素在内的8个变量，建立了预测住院期间死亡风险的列线图模型。在训练队列中，列线图模型预测极早产儿住院期间死亡的AUC为0.790（95%：0.751-0.828），而在验证队列中，AUC为0.808（95%：0.754-0.861）。Hosmer-Lemeshow拟合优度检验显示拟合良好（>0.05）。DCA结果显示，当训练队列的阈值概率为10%-60%、验证队列的阈值概率为10%-70%时，极早产儿临床干预的净效益较高。
已建立并验证了预测极早产儿住院期间死亡风险的列线图模型，该模型可帮助临床医生预测这些婴儿住院期间的死亡概率。
