Patient-centered Precision Medicine Lab Result Communication for Older Adults - Validation and Refinement of an Existing Chronic Kidney Disease (CKD) Risk Model
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 18,000
- 试验地点
- 1
- 主要终点
- Performance of the Risk Prediction Model
研究概览
简要总结
This study aims to improve how lab results are communicated to older adults by refining a predictive model that uses electronic health record (EHR) data. The model was originally developed to estimate the risk of chronic kidney disease (CKD) progression. Researchers will use existing health data to test and improve the accuracy of the model and explore how it might be adapted for use in other health conditions. The study does not involve direct interaction with patients and is conducted entirely using de-identified data in a secure environment.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 65 Years 至 —（Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •include, but are not limited to:
- •being over the age of 65; having at least 5 years of clinical follow up; and having a serum creatinine lab test conducted
排除标准
- •Patients younger than 65 years old
- •Patients with less than 5 years of clinical follow-up
- •Patients from health systems outside of the UC Health network.
结局指标
主要结局
Performance of the Risk Prediction Model
时间窗: Up to 5 years of retrospective follow up
Evaluate the predictive performance of a machine learning-based risk model using retrospective Electronic Health Records (EHR) data. The model estimates the likelihood of disease progression in older adults. The model should be designed to be adaptable to various clinical conditions. Metrics include Area Under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, and specificity.
次要结局
未报告次要终点
研究者
Catherine A. Sarkisian
Professor of Medicine
University of California, Los Angeles