EXPRESS: Analysis of Risk Factors for Uterine Fibroids and Construction of Prediction Model.
Due to the unclear mechanism of uterine fibroids, more risk factors need to be clarified. This study explored associated factors of uterine fibroids and established a prediction model using clinical data from our institution.
Logistic and Least Absolute Shrinkage and Selection Operator (LASSO) analyses were used to screen the key associated factors of uterine fibroids. The receiver operating characteristic (ROC) curve and DeLong test were used to analyze the prediction performance of indicators. XGBoost classification and random forest were used to rank the feature importance. A prediction model based on the key factors was established. Decision Curve Analysis (DCA), ROC, and nomogram analysis were used to assess the performance of model for predicting uterine fibroids.
Of the 303 patients enrolled, 201 had uterine fibroids. Logistic and LASSO regression analyses identified 5 core risk indicators, including age, thyroid-stimulating hormone index (TSHI), number of deliveries, abnormal menstruation, and polycystic syndrome. Both ROC and feature importance ranking analyses consistently implied the importance of age and TSHI on uterine fibroid risk. With the increase of age and TSHI, the uterine fibroids risk was significantly increased (all P for trend<0.05). The combination of age and TSHI achieved favorable performance and clinical net benefit in fibroids risk prediction, and its favorable performance was also validated in the external National Health and Nutrition Examination Surveys database.
Age and TSHI were the key associated factors of uterine fibroids, and their combination had promising clinical value for predicting uterine fibroids risk.
Logistic and Least Absolute Shrinkage and Selection Operator (LASSO) analyses were used to screen the key associated factors of uterine fibroids. The receiver operating characteristic (ROC) curve and DeLong test were used to analyze the prediction performance of indicators. XGBoost classification and random forest were used to rank the feature importance. A prediction model based on the key factors was established. Decision Curve Analysis (DCA), ROC, and nomogram analysis were used to assess the performance of model for predicting uterine fibroids.
Of the 303 patients enrolled, 201 had uterine fibroids. Logistic and LASSO regression analyses identified 5 core risk indicators, including age, thyroid-stimulating hormone index (TSHI), number of deliveries, abnormal menstruation, and polycystic syndrome. Both ROC and feature importance ranking analyses consistently implied the importance of age and TSHI on uterine fibroid risk. With the increase of age and TSHI, the uterine fibroids risk was significantly increased (all P for trend<0.05). The combination of age and TSHI achieved favorable performance and clinical net benefit in fibroids risk prediction, and its favorable performance was also validated in the external National Health and Nutrition Examination Surveys database.
Age and TSHI were the key associated factors of uterine fibroids, and their combination had promising clinical value for predicting uterine fibroids risk.