在健康检查中心开发,验证和可视化基于网络的诺米图,用于预测慢性病发病率
Chuxuan Luo1, Lanjun Fu2, Lin Liu3
1Department of Nephrology, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Taizhou, Zhejiang, China.
Renal failure
|October 8, 2024
概括
一个新的基于网络的诺米图准确地预测了中国成年人使用常规健康数据的慢性病 (CKD) 风险. 这种工具有助于临床医生在早期CKD检测和预防战略.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 预防医学 预防医学
- 医疗信息学 医疗信息学
背景情况:
- 慢性病 (CKD) 构成了全球健康的重大负担.
- 早期检测和风险分层对于有效的CKD管理至关重要.
- 预测工具可以帮助在例行健康检查期间识别有风险的个人.
研究的目的:
- 在4年内开发和验证一个基于网络的诺姆图,用于预测发生慢性病 (CKD) 的情况.
- 在接受例行体检的队列中确定新发性CKD的关键预测因子.
- 创建一个用户友好的工具,用于临床应用在风险评估.
主要方法:
- 使用后勤回归模型开发了一个预测CKD风险的名图.
- 来自两个中心的9667名健康个体的数据被用于培训,内部和外部验证.
- 使用接收器运行特征曲线 (AUC) 下的面积,校准图和决策曲线分析来评估性能.
主要成果:
- 纳米图包括年龄,糖尿病史,缩血压,血清肌素,白蛋白和甘油三水平.
- 该模型表现出了很好的区别:AUC为0.8806 (培训),0.8506 (内部验证) 和0.9183 (外部验证).
- 一个基于网络的计算器被开发用于实际的临床使用.
结论:
- 开发的基于网络的诺米图是预测中国成年人CKD风险的可靠工具.
- 命名图显示了不同验证队列的强有力的预测性能.
- 这种工具可以很容易地集成到临床实践中,用于早期CKD风险评估.
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