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Clinical Trial Summary

Cervical cancer is one of the most common cancer in women, making it the fourth largest cause of female cancer mortality worldwide. High-risk HPV types (hrHPV) continuous infection is the main cause of the premalignant and malignant lesions of the cervix. The most effective primary prevention of cervical cancer is administration HPV vaccines. There are three prophylactic HPV vaccines including bivalent HPV (2vHPV), quadrivalent HPV vaccine (4vHPV),9-valent HPV vaccine (9vHPV) currently. With the widespread of HPV vaccination, the cases related to abnormal menstrual cycle or menstrual irregularities has gradually increased in the Vaccine Adverse Event Reporting System, also the number of reports and studies were increasing. We designed this study to evaluate the statistical association of HPV vaccine and changes in menstrual cycle or menstrual irregularities、amenorrhea. And detect the signal of premature ovarian insufficiency (POI) and related events of three HPV vaccines, and to provide evidence for the safety of HPV vaccines based on the data mining and signal detection method. The research data was obtained from prospective self- developed questionnaire. Before-after study was applied in this study, investigators collect the menstrual cycle data of subjects by the questionnaire and qualitatively interview were taken. The control group is before vaccination, HPV vaccination is Intervention group.


Clinical Trial Description

High-risk HPV types (hrHPV) continuous infection is the main cause of the premalignant and malignant lesions of the cervix. The most effective primary prevention of cervical cancer is administration HPV vaccines. There are three prophylactic HPV vaccines including bivalent HPV (2vHPV), quadrivalent HPV vaccine (4vHPV),9-valent HPV vaccine (9vHPV) currently. With the widespread of HPV vaccination, the cases related to abnormal menstrual cycle or menstrual irregularities has gradually increased in the Vaccine Adverse Event Reporting System, also the number of reports and studies were increasing. Whereas all of the studies are retrospective and the data from passive surveillance system, meanwhile, HPV vaccines has been available in Chinese mainland less than 7 years. Especially, 9vHPV applicable range was broadened to 45 years shortly before. There are rarely prospective researches focus on the connection between HPV vaccines and menstrual disorder. We designed this prospective study to observe and evaluate the statistical association of HPV vaccine and menstrual disorder such as menstrual irregularities、amenorrhea、 hypomenorrhea or hypermenorrhea in Chinese women. And detect the signal of premature ovarian insufficiency (POI) and related events of three HPV vaccines, and to provide evidence for the safety of HPV vaccines based on the data mining and signal detection method. The study design used self-control, We using a self-designed questionnaire collect the menstruation data of girls and women aged 16 to 40 years who receiving HPV vaccination ( including 2vHPV、4vHPV、9vHPV) in Cancer Prevention Center, Sun Yat-sen University Cancer Center during May 10th 2023 to April 30th 2023. The self-designed questionnaire contains three parts. First part includes personal basic information such as age, profession, education Level, marital or childbearing status etc. Part two there are containing health status, menstruation conditions (include menstrual cycle, menstrual period, menstrual blood volume and dysmenorrhea etc). Part three collects menstruation conditions by qualitatively interview , covers each menstrual cycle after the first dose of HPV vaccination until 3 months after the third dose . To analyze the data, R studio statistical software was used. The general measurement data were analyzed by paired sample T test, p=0.05. Bayesian trusted Propagation neural network (BCPNN) and multi-item gamma Poisson contractor (MGPS) were used to detect the signals of POI related events and to conduct empirical Bayes data mining among ours database. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05838157
Study type Observational
Source Sun Yat-sen University
Contact Juan Xiao, Master
Phone 87345515
Email xiaojuan@sysucc.org.cn
Status Recruiting
Phase
Start date May 4, 2023
Completion date December 31, 2024

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