目的 探讨湖北恩施土家族苗族自治州新生儿窒息发生的危险因素，并构建预测新生儿窒息发生风险的列线图模型。 方法 回顾性纳入湖北恩施土家族苗族自治州20家协作医院2019年1—12月收治的613例新生儿窒息患儿作为窒息组，随机抽取同期在这些协作医院出生并入住新生儿科的988例非窒息患儿作为对照组。对新生儿窒息的危险因素进行单因素及多因素分析。采用R软件（4.2.2）构建预测新生儿窒息发生风险的列线图模型，采用受试者操作特征曲线、校正曲线和决策曲线分析分别评估模型的区分度、校准度和临床实用价值。 结果 多因素logistic回归分析显示：新生儿为土家族、男婴、早产儿、先天畸形、胎位异常、宫内窘迫、母亲职业为农民、母亲高中以下文化程度、产前检查<9次、先兆流产、脐带异常、羊水异常、前置胎盘、胎盘早剥、急诊剖宫产、助产是新生儿窒息发生的独立危险因素（P<0.05）。基于这些危险因素建立的列线图模型预测新生儿窒息发生的曲线下面积为0.748（95%CI：0.723~0.772）；校正曲线提示该模型预测新生儿窒息发生的准确性较高；决策曲线分析显示，使用该模型预测新生儿窒息发生风险可使患儿获得较高的净获利。 结论 湖北恩施土家族苗族自治州新生儿窒息发生的危险因素是多因素的，基于这些因素构建的新生儿窒息发生的预测模型具有良好的价值，有利于临床医生早期识别窒息高危新生儿，降低新生儿窒息的发生率。
Abstract
Objective To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia. Methods A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively. Results Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia. Conclusions The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.
关键词
新生儿窒息 /
危险因素 /
列线图 /
预测模型 /
多中心研究 /
新生儿
Key words
Neonatal asphyxia /
Risk factor /
Nomogram /
Prediction model /
Multicenter study /
Neonate
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