Qian Sun1, Yan Yang2, Xuenan Peng3, Yunyan Zhang2, Yuan Gao1, Fang Wang1, Yang Zhang1, Wen Feng1, Wen Yang4, Xiaomin Kang5. 1. Department of Gynecology, The First People's Hospital of Lianyungang, Lianyung, Jiangsu, China. 2. Department of Laboratory, The First People's Hospital of Lianyungang, Lianyung, Jiangsu, China. 3. Department of Clinical Medicine, Medical College of Soochow University, Soochow, Jiangsu, China. 4. Department of Gynecology, The First People's Hospital of Lianyungang, Lianyung, Jiangsu, China. Electronic address: 1138514130@qq.com. 5. Department of Reproductive Medical Centre, The First People's Hospital of Yunnan Province, Kunming, Yunnan, China. Electronic address: 13401058138@163.com.
Abstract
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.
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.
Authors: Abu Saleh Md Moin; Thozhukat Sathyapalan; Ilhame Diboun; Mohamed A Elrayess; Alexandra E Butler; Stephen L Atkin Journal: Sci Rep Date: 2021-03-05 Impact factor: 4.379