发生慢性病的风险预测方程的开发
Robert G Nelson1, Morgan E Grams2, Shoshana H Ballew2
1Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona.
JAMA
|November 9, 2019
概括
新的风险方程可以识别患慢性病 (CKD) 的高风险人群. 这些工具使用了500多万人的数据, 预测CKD发展的准确度很高.
科学领域:
- 肝脏病学
- 流行病学
- 生物统计学
背景情况:
- 早期识别慢性病 (CKD) 风险对于及时干预和改善患者结果至关重要.
- 目前的评估工具可能无法充分识别患有CKD风险较高的个体,因此需要开发更精确的预测模型.
研究的目的:
- 开发和验证风险评估工具,以识别患有慢性脏病风险增加的个体,以减少估计的膜过率 (eGFR) 来定义.
主要方法:
- 分析了34个跨国队列 (CKD预测联盟) 的个人数据,其中包括超过520万名参与者.
- 进行了两阶段的分析,分别为患有糖尿病和没有糖尿病的个体开发了模型,并纳入了人口和临床因素.
- 评估模型的性能是使用差异化 (C统计) 和校准,并对9个外部队列进行验证.
主要成果:
- 开发了风险方程,包括年龄,性别,种族/民族,eGFR,心血管疾病史,吸烟状况,高血压,BMI和白血病.
- 对于糖尿病患者,模型还包括糖尿病药物和血红蛋白A1c. 在5年风险预测中,C的中位数为0. 845 (没有糖尿病) 和0. 801 (有糖尿病).
- 校准分析表明不同种群的性能可接受,大多数研究种群的观察到预测风险的斜率在0. 80到1. 25之间.
结论:
- 从大量多样化的患者群体中开发的发生性结核病的风险预测方程显示出强有力的歧视.
- 虽然校准在不同人群中是可变的,但开发的方程对识别患有CKD风险的个体有希望.
- 需要进一步的研究来确认这些风险方程的临床应用是否能改善患者的护理和结果.
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