Purpose: This study aimed to investigate the association of metabolic factors with endometrial atypical hyperplasia and endometrial cancer, and to develop a nomogram model to predict the risk of developing endometrial cancer.
Patients and Methods: We collected data of patients with endometrial atypical hyperplasia and endometrial cancer as the case group and then selected patients with simple hyperplasia or polypoid hyperplasia of the endometrium during the same period as the control group using the age-matched method. Laboratory results of metabolic factors were retrieved from the clinical data of the two groups of patients. Multivariable logistic regression analysis was used to determine the risk factors associated with endometrial malignant hyperplasia and to develop a nomogram prediction model of risk factors associated with endometrial malignant hyperplasia. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using the C-index, calibration plot, and decision curve analysis.
Results: Predictors included in the column line graph model were age, hypertension, diabetes, BMI, uric acid, and hyperlipidemia. We calculated the C-index of the model and performed bootstrap validation. Our nomogram model had good discriminatory power and was well calibrated. Decision curve analysis was also used to guide the practical application of this column line graph model.
Conclusion: The development of endometrial malignant hyperplasia is significantly associated with metabolic factors: BMI > 25, hyperuricemia, and hyperlipidemia are the main risk factors. Hypertension, hyperglycemia and elevated CA199 were also associated with the development of endometrial malignant hyperplasia. The nomogram prediction model based on physical examination and laboratory testing developed in this study can be used as a rapid method for predicting the risk of endometrial malignancy development and screening for risk factors in a population of women with metabolism-related high-risk factors.
Keywords: risk factors, gynecologic oncology, predictors, nomogram