AIM:To evaluate the predicting efficacy of severe retinopathy of prematurity(ROP)by the WINROP algorithm(http://winrop.com)in Southern China.METHODS:All preterm infants with the gestational age(GA)less than 32 wk were...AIM:To evaluate the predicting efficacy of severe retinopathy of prematurity(ROP)by the WINROP algorithm(http://winrop.com)in Southern China.METHODS:All preterm infants with the gestational age(GA)less than 32 wk were included.Their ROP screening results and serial postnatal body weight were analysed retrospectively.Weekly body weight was entered into and measured by the WINROP system.The outcomes were analysed,and the sensitivity,specificity,positive predictive value and negative predictive value(NPV)were calculated.RESULTS:Totally 432 infants with a median GA of 30.0(24.0-31.9)wk,and a median birth weight(BW)of 1360(540-2700)g were included.Among these 432 infants,50 were diagnosed as type 1 ROP but only 28 were identified by the WINROP algorithm.The sensitivity was 56%(28/50)and the NPV was 92%(252/274).However,for infants with BW<1000g or GA<28 wk,the sensitivity was 93.8%(15/16)and 93.3%(14/15),respectively.Meanwhile,with several postnatal complications added as additional risk factors,the sensitivity was increased to 96%(48/50).CONCLUSION:The sensitivity of the WINROP algorithm from the Southern Chinese cohort is not as high as that reported in developed countries.This algorithm is effective for detecting severe ROP from extremely small or preterm infants.Modification of the algorithm with additional risk factors could improve the predictive value for infants with a GA>28 wk in China.展开更多
目的:评估WINROP、ROPScore和PW-ROP模型检测早产儿视网膜病变(ROP)的预测效能,为传统筛查标准提供参考。方法:选择2019年1月至2021年12月于温州医科大学附属第二医院育英儿童医院新生儿科住院,符合孕周≤32周和(或)出生体质量≤1500 g ...目的:评估WINROP、ROPScore和PW-ROP模型检测早产儿视网膜病变(ROP)的预测效能,为传统筛查标准提供参考。方法:选择2019年1月至2021年12月于温州医科大学附属第二医院育英儿童医院新生儿科住院,符合孕周≤32周和(或)出生体质量≤1500 g ROP标准,进行过ROP眼底筛查且结果明确的患儿共352例。收集患儿出生体质量、孕周、出生后每周体质量、吸氧、输血等相关信息。分别将符合算法要求的患儿进行算法计算,得出WINROP、ROPScore和PW-ROP模型的预测结果。结果:282例患儿纳入WINROP模型,352例纳入PW-ROP模型,155例纳入ROPScore模型。在ROP患儿的筛查中,WINROP模型的灵敏度和特异度均较低;PW-ROP模型的灵敏度与特异度均较高;ROPScore模型的灵敏度较高,但特异度相对较低。各模型AUC的成对DeLong检验显示,严重ROP患儿中,PW-ROP模型(AUC=0.830,95%CI=0.755~0.905)和ROPScore模型(AUC=0.717,95%CI=0.606~0.827)的预测价值高于WINROP模型(AUC=0.582,95%CI=0.473~0.691),差异有统计学意义(P<0.001);任何阶段ROP患儿中,PW-ROP模型(AUC=0.853,95%CI=0.811~0.895)和ROPScore模型(AUC=0.861,95%CI=0.617~1.000)的预测价值也高于WINROP(AUC=0.705,95%CI=0.653~0.758),差异有统计学意义(P<0.001);而PW-ROP模型与ROPScore模型的AUC差异无统计学意义(P>0.05)。结论:PW-ROP模型与ROPScore模型在新生儿中对所有ROP具有较高的预测效能,且其预测性能优于WINROP模型。PW-ROP模型与ROPScore模型的预测性能无显著差异。展开更多
基金Supported by the National Nature Science Foundation of China(No.81500722)。
文摘AIM:To evaluate the predicting efficacy of severe retinopathy of prematurity(ROP)by the WINROP algorithm(http://winrop.com)in Southern China.METHODS:All preterm infants with the gestational age(GA)less than 32 wk were included.Their ROP screening results and serial postnatal body weight were analysed retrospectively.Weekly body weight was entered into and measured by the WINROP system.The outcomes were analysed,and the sensitivity,specificity,positive predictive value and negative predictive value(NPV)were calculated.RESULTS:Totally 432 infants with a median GA of 30.0(24.0-31.9)wk,and a median birth weight(BW)of 1360(540-2700)g were included.Among these 432 infants,50 were diagnosed as type 1 ROP but only 28 were identified by the WINROP algorithm.The sensitivity was 56%(28/50)and the NPV was 92%(252/274).However,for infants with BW<1000g or GA<28 wk,the sensitivity was 93.8%(15/16)and 93.3%(14/15),respectively.Meanwhile,with several postnatal complications added as additional risk factors,the sensitivity was increased to 96%(48/50).CONCLUSION:The sensitivity of the WINROP algorithm from the Southern Chinese cohort is not as high as that reported in developed countries.This algorithm is effective for detecting severe ROP from extremely small or preterm infants.Modification of the algorithm with additional risk factors could improve the predictive value for infants with a GA>28 wk in China.
文摘目的:评估WINROP、ROPScore和PW-ROP模型检测早产儿视网膜病变(ROP)的预测效能,为传统筛查标准提供参考。方法:选择2019年1月至2021年12月于温州医科大学附属第二医院育英儿童医院新生儿科住院,符合孕周≤32周和(或)出生体质量≤1500 g ROP标准,进行过ROP眼底筛查且结果明确的患儿共352例。收集患儿出生体质量、孕周、出生后每周体质量、吸氧、输血等相关信息。分别将符合算法要求的患儿进行算法计算,得出WINROP、ROPScore和PW-ROP模型的预测结果。结果:282例患儿纳入WINROP模型,352例纳入PW-ROP模型,155例纳入ROPScore模型。在ROP患儿的筛查中,WINROP模型的灵敏度和特异度均较低;PW-ROP模型的灵敏度与特异度均较高;ROPScore模型的灵敏度较高,但特异度相对较低。各模型AUC的成对DeLong检验显示,严重ROP患儿中,PW-ROP模型(AUC=0.830,95%CI=0.755~0.905)和ROPScore模型(AUC=0.717,95%CI=0.606~0.827)的预测价值高于WINROP模型(AUC=0.582,95%CI=0.473~0.691),差异有统计学意义(P<0.001);任何阶段ROP患儿中,PW-ROP模型(AUC=0.853,95%CI=0.811~0.895)和ROPScore模型(AUC=0.861,95%CI=0.617~1.000)的预测价值也高于WINROP(AUC=0.705,95%CI=0.653~0.758),差异有统计学意义(P<0.001);而PW-ROP模型与ROPScore模型的AUC差异无统计学意义(P>0.05)。结论:PW-ROP模型与ROPScore模型在新生儿中对所有ROP具有较高的预测效能,且其预测性能优于WINROP模型。PW-ROP模型与ROPScore模型的预测性能无显著差异。
