胎龄≤34周早产儿重度视网膜病变风险预测模型的构建及验证

Construction and verification of risk prediction model for severe retinopathy in premature infants with gestational age≤34 weeks

背景重度早产儿视网膜病变(ROP)可造成不可逆的视力受损,目前缺乏适合中国早产儿人群的识别重度ROP的风险预测模型。目的 基于中国新生儿人群,构建及验证胎龄≤34周早产儿发生重度ROP的早期风险预测模型。设计回顾性队列研究。方法 纳入2020年1月至2022年3月在NICU住院、胎龄≤34周、完成眼底筛查的早产儿,以2021年6月的入院时间为截点划分为训练集和验证集,采集出生后首次眼底筛查时点的临床信息,根据ROP筛查指南进行定期筛查和随访,重度ROP包括3期及以上病变、附加病变阳性、接受激光和冷凝手术治疗;余为轻度ROP。在训练集中根据Pearson相关系数排除存在共线性的临床变量,通过Lasso回归和Logistic回归分析确定最终的预测因子,建立预测模型并绘制列线图,在验证集病例中采用受试者工作特征曲线(ROC)的曲线下面积(AUC)评估区分度并确定预测模型的最佳界值,计算模型的敏感度和特异度。基于总人群,采用决策曲线分析评估模型的临床有效性。主要结局指标模型对早产儿进展为重度ROP的预测性能。结果 训练集1 380例(76例重度ROP),验证集592例(36例重度ROP),训练集和验证集早产儿的基线信息的分布差异均无统计学意义。最终纳入胎龄、出生体重、产前应用皮质激素、有创机械通气、晚发型败血症5个预测因子建立列线图预测模型,在训练集和验证集病例中AUC分别为0.87(95%CI:0.83~0.90)和0.88(95%CI:0.82~0.94),当预测界值取0.04时,模型在验证集病例中敏感度为83.3%(95%CI:71.2%~95.5%),特异度为78.6%(95%CI:75.2%~82.0%)。临床决策曲线显示模型的临床适用度好,在阈值概率为5%~40%时具有较高的净收益。结论 建立的重度ROP预测模型对筛查胎龄≤34周早产儿发生重度ROP的风险具有一定的参考价值。

Background Retinopathy of prematurity(ROP)can cause irreversible visual impairment in severe cases,and there is a lack of appropriate risk prediction model for identifying severe ROP specifically tailored to the Chinese preterm population.Objective To construct and validate an early risk prediction model for severe ROP in premature infants with gestational age≤34 weeks based on the Chinese neonatal population.Design Retrospective cohort study.Methods From January 2020 to March 2022,premature infants admitted to the Neonatal Intensive Care Unit(NICU)with a gestational age of≤34 weeks and who underwent a complete eye examination were included.The cohort was divided into training and validation sets using the June 2021 admission time as the cutoff point.Clinical information was collected at the time of the first postnatal eye examination,and regular screenings and follow-ups were conducted according to the ROP screening guidelines.Severe ROP is defined as having stage 3 or higher lesions,positive for plus disease,or requiring laser and cryotherapy treatment.All other cases are categorized as mild ROP.In the training set,clinical variables with collinearity were excluded based on the Pearson correlation coefficient and the final predictive factors were determined through Lasso regression and logistic regression analysis,then the prediction model was constructed and presented as a nomogram.In the validation set,the area under the curve(AUC)of receiver operating characteristic(ROC)was used to evaluate the discrimination and determine the optimal cut-off value of the prediction model,in which the sensitivity and specificity of the model were calculated.Based on the total population,clinical efficacy of the model was finally assessed by decision curve analysis(DCA).Main outcome measures The predictive performance of the model for identifying premature infants who will progress to severe ROP.Results There were 1,380 cases in the training set(76 cases with severe ROP)and 592 cases in the validation set(36 cases with severe ROP).The differences in the distribution of baseline information between these two groups were not statistically significant.The nomogram prediction model established by including five predictors of gestational age,birth weight,prenatal corticosteroids,invasive mechanical ventilation,and late-onset sepsis had an AUC of 0.87(95%CI:0.83 to 0.90)and 0.88(95%CI:0.82 to 0.94)in the training and validation sets,respectively.When the predictive threshold was set at 0.04,the model had a sensitivity of 0.833(95%CI:0.712-0.955)and a specificity of 0.786(95%CI:0.752-0.820)in the validation set.The clinical decision curve demonstrates that the model has good clinical utility and provides higher net benefits when the threshold probability ranges from 5%to 40%.Conclusions The severe ROP prediction model established has a certain reference value for evaluating the risk of severe ROP in neonates with gestational age≤34 weeks.