External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults-A Prospective, Observational, Multicentre Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 1,000
- 主要终点
- Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model
研究概览
简要总结
Colorectal adenomas are precursors to colorectal cancer (CRC). Accurate pre-procedure risk stratification could optimize colonoscopy yield and resource allocation in India, where adenoma prevalence varies by age, sex, and lifestyle/metabolic factors. ML models can integrate multiple predictors to estimate individualized risk.
Existing risk scores are largely Western; performance and calibration may not be appropriate in Indian populations with different socio-demographic and metabolic profiles. External, prospective, multicentre validation is essential before clinical implementation.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Adults ≥18 years undergoing diagnostic colonoscopy.
- •Adequate bowel preparation (Boston Bowel Preparation Scale total ≥6 with each segment ≥2).
- •Complete examination (cecal intubation; withdrawal time ≥6 min when no therapy).
- •Availability of all model predictors per CRF.
排除标准
- •• Known CRC or polyp, prior colectomy, polyposis syndromes, known IBD, or strong hereditary CRC syndromes (e.g., Lynch) if excluded in derivation.
- •Inadequate prep, incomplete colonoscopy, obstructing lesions preventing optical diagnosis beyond obstruction.
- •Emergency colonoscopies, therapeutic-only procedures without diagnostic intent.
研究组 & 干预措施
Single prospective observational cohort
Participants undergo standard-of-care colonoscopy
No allocation into treatment or comparison arms
干预措施: Not Applicable / Observational study (Procedure)
结局指标
主要结局
Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model
时间窗: 1 YEAR
Area under the receiver operating characteristic curve (AUROC) of the machine learning-based prediction model for identifying the presence of histologically proven colonic adenoma
次要结局
- Validation Performance of the Machine Learning Prediction Model(1 YEAR)
研究者
Mohan Ramchandani
Consultant Gastroenterology
Asian Institute of Gastroenterology, India