Combined Use of Machine Learning and Metabolomics to Improve the Diagnosis and Management of Hyperandrogenism
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 800
- 主要终点
- Use of machine learning models combined with metabolomics to distinguish between different causes of hyperandrogenism
研究概览
简要总结
Hyperandrogenism is a common reason for consultation, the causes of which can range from common conditions (PCOS) to rarer conditions with major genetic implications (NC21OHD). It is characterized by elevated levels of circulating androgens, mainly testosterone. This excess of androgens usually manifests clinically as increased male-pattern hair growth and, less specifically, acne and alopecia. Its prevalence is estimated at between 6 and 12% in women of reproductive age, and its incidence is increasing.
It is also responsible for infertility. As a reminder, infertility is a major public health issue and affects more and more couples around the world.
The investigators therefore wish to develop innovative tools to improve the diagnosis and management of hyperandrogenism
详细描述
Hyperandrogenism is a common reason for consultation, the causes of which can range from common conditions (PCOS) to rarer conditions with major genetic implications (NC21OHD). It is characterized by elevated levels of circulating androgens, mainly testosterone. This excess of androgens usually manifests clinically as increased male-pattern hair growth and, less specifically, acne and alopecia. Its prevalence is estimated at between 6 and 12% in women of reproductive age, and its incidence is increasing.
It is also responsible for infertility. As a reminder, infertility is a major public health issue and affects more and more couples around the world.
The investigators therefore wish to develop innovative tools to improve the diagnosis and management of hyperandrogenism
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 16 Years 至 45 Years（Child, Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •Patients of childbearing age (16 to 45 years old)
- •Suffering from hyperandrogenism
- •Established etiological diagnosis with elimination of differential diagnoses
- •Informed and not opposed to the collection of their data for the purposes of the study
排除标准
- •Pregnancy
- •Patients under legal protection measures
结局指标
主要结局
Use of machine learning models combined with metabolomics to distinguish between different causes of hyperandrogenism
时间窗: 5 years
次要结局
- Use of metabolomics to improve the management of patients with hyperandrogenism(5 years)
- Use of metabolomics to predict CYP21A2 genotyping results(5 years)
- Study of the impact of anti-androgenic hormone therapy on the predictive capabilities of the model(5 years)