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Abstract：
OBJECTIVE: To establish an early clinical diagnosis model based on the retinal vascular features associated with POI, supplying a non-invasive way for accurately and early predicted the risk of POI.
METHODS: A total of 78 women with spontaneous POI and 48 healthy women were recruited from the Affiliated Shenzhen Maternity & Child Healthcare Hospital in the study. Retinal characteristics were analyzed using an automated retinal image analysis system. Binary logistic regression was used to identify POI cases and develop predictive models.
RESULTS: Compared to the normal group, the POI group had larger central retinal artery equivalent (CRAE) (P = 0.006), central retinal vein equivalent (CRVE) (P = 0.001), index of venules asymmetry (Vasym) (P = 0.000); larger bifurcation angles of arterioles (Aangle) (P = 0.001), bifurcation coefficient of venule (BCV) (P = 0.001) and more obvious arteriovenous nipping (Nipping) (P = 0.005), but lower arteriole-to-venule ratio (AVR) (P = 0.012). In the POI group, the odds ratio (OR) of Vasym was 6.72e-32 (95% C.I. 4.62e-49-9.79e-15, P = 0.000), the OR of BCV was 5.66e-20 (95% C.I. 1.93e-34-.0000, P = 5.66e-20) and the OR of Nipping was 6.65e-06 (95% C.I. 6.33e-10-.0698, P = 0.012). Moreover, the area under the ROC curve for the binary logistic regression with retinal characteristics was 0.8582, and the fitting degree of regression models was 60.48% (Prob > chi-square = 0.6048).
CONCLUSIONS: This study demonstrated that retinal image analysis can provide useful information for POI identification and certain characteristics may help with early clinical diagnosis of POI.
METHODS: A total of 78 women with spontaneous POI and 48 healthy women were recruited from the Affiliated Shenzhen Maternity & Child Healthcare Hospital in the study. Retinal characteristics were analyzed using an automated retinal image analysis system. Binary logistic regression was used to identify POI cases and develop predictive models.
RESULTS: Compared to the normal group, the POI group had larger central retinal artery equivalent (CRAE) (P = 0.006), central retinal vein equivalent (CRVE) (P = 0.001), index of venules asymmetry (Vasym) (P = 0.000); larger bifurcation angles of arterioles (Aangle) (P = 0.001), bifurcation coefficient of venule (BCV) (P = 0.001) and more obvious arteriovenous nipping (Nipping) (P = 0.005), but lower arteriole-to-venule ratio (AVR) (P = 0.012). In the POI group, the odds ratio (OR) of Vasym was 6.72e-32 (95% C.I. 4.62e-49-9.79e-15, P = 0.000), the OR of BCV was 5.66e-20 (95% C.I. 1.93e-34-.0000, P = 5.66e-20) and the OR of Nipping was 6.65e-06 (95% C.I. 6.33e-10-.0698, P = 0.012). Moreover, the area under the ROC curve for the binary logistic regression with retinal characteristics was 0.8582, and the fitting degree of regression models was 60.48% (Prob > chi-square = 0.6048).
CONCLUSIONS: This study demonstrated that retinal image analysis can provide useful information for POI identification and certain characteristics may help with early clinical diagnosis of POI.
摘要：
目的：建立基于POI相关视网膜血管特征的早期临床诊断模型。为准确、早期预测POI风险提供非侵入性方法。
方法：从深圳市附属妇幼保健院抽取78例自发性POI妇女和48例健康妇女。使用自动视网膜图像分析系统分析视网膜特征。二元逻辑回归用于识别POI病例并建立预测模型。
结果：与正常组相比，POI组视网膜中央动脉当量(CRAE)较大(P=0.006),视网膜中央静脉当量（CRVE）（P=0.001），小静脉不对称指数（Vasym）（P=0.000）；小动脉分叉角较大（Aangle）（P=0.001），小静脉分叉系数（BCV）（P=0.001）和更明显的动静脉挤压（Npping）（P=0.005），但小动脉与小静脉比（AVR）较低（P=0.012）。在POI组中，Vasym的比值比(OR)为6.72e-32(95%C.I.4.62e-49-9.79e-15,P=0.000),BCV的OR为5.66e-20（95％C.I.1.93e-34-.0000，P=5.66e-20），Npping的OR为6.65e-06（95％C.I.6.33e-10-.0698，P=0.012）。此外，具有视网膜特征的二元逻辑回归的ROC曲线下面积为0.8582，回归模型的拟合度为60.48％（Prob>卡方=0.6048）。
结论：这项研究表明，视网膜图像分析可以为POI识别提供有用的信息，某些特征可能有助于POI的早期临床诊断。
方法：从深圳市附属妇幼保健院抽取78例自发性POI妇女和48例健康妇女。使用自动视网膜图像分析系统分析视网膜特征。二元逻辑回归用于识别POI病例并建立预测模型。
结果：与正常组相比，POI组视网膜中央动脉当量(CRAE)较大(P=0.006),视网膜中央静脉当量（CRVE）（P=0.001），小静脉不对称指数（Vasym）（P=0.000）；小动脉分叉角较大（Aangle）（P=0.001），小静脉分叉系数（BCV）（P=0.001）和更明显的动静脉挤压（Npping）（P=0.005），但小动脉与小静脉比（AVR）较低（P=0.012）。在POI组中，Vasym的比值比(OR)为6.72e-32(95%C.I.4.62e-49-9.79e-15,P=0.000),BCV的OR为5.66e-20（95％C.I.1.93e-34-.0000，P=5.66e-20），Npping的OR为6.65e-06（95％C.I.6.33e-10-.0698，P=0.012）。此外，具有视网膜特征的二元逻辑回归的ROC曲线下面积为0.8582，回归模型的拟合度为60.48％（Prob>卡方=0.6048）。
结论：这项研究表明，视网膜图像分析可以为POI识别提供有用的信息，某些特征可能有助于POI的早期临床诊断。
