女性围绝经期综合征患病影响因素及患病概率预测研究

Influencing factors and probability prediction of perimenopausal syndrome

目的 本研究旨在分析女性围绝经期综合征(PMS)的社会、生物学影响因素,预测围绝经期女性罹患PMS的概率,为预防、诊断、管理PMS和预测女性患病概率提供相应依据。方法 选取2019年10月—2020年6月于杭州市妇产科医院更年期保健专科门诊就诊的围绝经期女性212人作为研究对象。根据研究对象改良Kupperman量表(KMI)得分将其分为2组,其中KMI得分<15分者分入健康组(n=111),KMI得分≥15分者分入PMS组(n=101)。采用健康问卷(一般信息问卷+KMI)对研究对象进行问卷调查,并在完成问卷调查1周内完成实验室辅助检查。通过多因素Logistic回归分析PMS患病的影响因素;依据回归分析结果构建PMS患病风险预测模型,并通过诺莫(Nomogram)图将模型可视化。结果通过单因素分析和多因素分析得出:γ-谷氨酰转肽酶(γ-GT)、钙、文化水平、痛经情况、每日水果摄入量、适量运动情况为研究对象PMS患病的影响因素(P<0.05)。根据回归分析结果构建PMS患病风险预测模型,其受试者工作特征(ROC)曲线下面积(AUC)为0.783(P<0.001,95%CI:0.721~0.846),提示此模型可用于围绝经期女性罹患PMS的概率预测。通过Nomogram图将PMS患病风险预测模型进行可视化,发现γ-GT对PMS患病的影响最大,其次为钙,此后依次为文化水平、每日水果摄入量、痛经和适量运动情况,以上各项相加总得分为21~59分时所对应围绝经期女性罹患PMS的概率为0.1~0.9。结论 围绝经期女性PMS患病的影响因素包括γ-GT、钙、文化水平、痛经情况、每日水果摄入量、适量运动情况,根据PMS患病影响因素的多因素Logistic回归分析结果构建的PMS患病风险预测模型可用于围绝经期女性罹患PMS的概率预测,且可通过Nomogram图将其可视化。

Objective This study aimed to analyze the social and biological influencing factors for the perimenopausal syndrome(PMS)in women and probabilistic prediction of PMS to provide corresponding evidence for prevention,diagnosis,management and prediction.Methods From October 2019 to June 2020,212 perimenopausal women who visited the Menopausal Health Specialized Clinic of Hangzhou Obstetrics and Gynecology Hospital were selected as the participants.They were assigned to two groups following their modified Kupperman Menopausal Index(KMI)score,those with a KMI score less than 15 points allocated to the healthy group(n=111)and those with a KMI score of 15 points or above to the PMS group(n=101).A health questionnaire(General Information Questionnaire+KMI)was performed on participants and laboratory-assisted examinations were conducted within 1 week of completing the questionnaire.The influencing factors of PMS were analyzed using multivariate logistic regression,and the risk prediction model of PMS was constructed according to the results of regression analysis.The model was visualized by the Nomogram plot.Results Univariate analysis and multivariate analysis showed thatγ-glutamyl transpeptidase(γ-GT),calcium,educational level,dysmenorrhea,daily fruit intake and moderate exercise were the influencing factors of PMS(P<0.05).According to the results of regression analysis,a prediction model for the risk of PMS was constructed,and the area under the receiver operating characteristic(ROC)curve(AUC)was 0.783(95%CI:0.721~0.846;P<0.001),suggesting that this model can be used to predict PMS in perimenopausal women.The risk prediction model of PMS was visualized by the Nomogram plot.It was found thatγ-GT had the greatest effect on the risk of PMS,followed by calcium,and then education level,daily fruit intake,dysmenorrhea and moderate exercise.The probability of PMS in perimenopausal women corresponding to the total score of 21 to 59 points was 0.1 and 0.9.Conclusion The influencing factors of PMS in perimenopausal women includeγ-GT,calcium,education level,dysmenorrhea,daily fruit intake and moderate exercise.The risk prediction model of PMS constructed according to the results of multivariate logistic regression analysis of the influencing factors of PMS can be used to predict PMS in perimenopausal women,and it can be visualized via the Nomogram diagram.