European journal of obstetrics, gynecology, and reproductive biology · 2019
Coagulation parameters predictive of polycystic ovary syndrome.
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Sun Qian, Yang Yan, Peng Xuenan, Zhang Yunyan, Gao Yuan, Wang Fang, Zhang Yang, Feng Wen, Yang Wen, Kang Xiaomin
Аннотация
OBJECTIVE: To explore coagulation parameters in association with polycystic ovarian syndrome (PCOS) and establish a model for predicting the risk of PCOS.
STUDY DESIGN: This study included 181 outpatients with PCOS. A total of 301 women who attempted to seek pre-pregnancy consultation at the Department of Gynecology of our hospital were included in the control group, and six coagulation parameters were measured for all included subjects. A logistic regression model was built based on the training dataset using the purposeful selection method to select important predictors. The performance of the established model was validated on the test dataset.
RESULTS: There were statistically significant differences found among all coagulation parameters except D-Dimer (DD, P = 0.080). The purposeful selection method selected age (odds ratio [OR] = 0.89; p = 0.008), prothrombin time (PT, OR = 0.68, p < 0.0001), thrombin time (TT, OR = 3.30; p = 0.0005), and fibrin degradation products (FDP, OR = 0.24; p = 0.0002) as important predictors of PCOS risk. The receiver operating characteristic (ROC) curve analysis indicated that the area under the ROC curve (AUC) of the model was 0.81 for the training dataset with an optimal cut-off point of the predicted probability of 0.45, leading to a sensitivity of 0.71 and a specificity of 0.82. The AUC was 0.79 for the test data.
CONCLUSIONS: It was found that the coagulation parameters, including PT, TT, and FDP, are predictive of PCOS. These results highlight the potential of anti-coagulation therapies to lower the risk of adverse outcomes in women with PCOS.
STUDY DESIGN: This study included 181 outpatients with PCOS. A total of 301 women who attempted to seek pre-pregnancy consultation at the Department of Gynecology of our hospital were included in the control group, and six coagulation parameters were measured for all included subjects. A logistic regression model was built based on the training dataset using the purposeful selection method to select important predictors. The performance of the established model was validated on the test dataset.
RESULTS: There were statistically significant differences found among all coagulation parameters except D-Dimer (DD, P = 0.080). The purposeful selection method selected age (odds ratio [OR] = 0.89; p = 0.008), prothrombin time (PT, OR = 0.68, p < 0.0001), thrombin time (TT, OR = 3.30; p = 0.0005), and fibrin degradation products (FDP, OR = 0.24; p = 0.0002) as important predictors of PCOS risk. The receiver operating characteristic (ROC) curve analysis indicated that the area under the ROC curve (AUC) of the model was 0.81 for the training dataset with an optimal cut-off point of the predicted probability of 0.45, leading to a sensitivity of 0.71 and a specificity of 0.82. The AUC was 0.79 for the test data.
CONCLUSIONS: It was found that the coagulation parameters, including PT, TT, and FDP, are predictive of PCOS. These results highlight the potential of anti-coagulation therapies to lower the risk of adverse outcomes in women with PCOS.