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克服缺失的数据:准确预测2型糖尿病的心血管风险,系统性审查
Wenhui Ren1, Keyu Fan2, Zheng Liu1
1Department of Clinical Epidemiology and Biostatistics, Peking University People's Hospital, Beijing, China.
Journal of diabetes
|January 22, 2025
概括
对于处理心血管疾病 (CVD) 预测模型中缺少的数据,对2型糖尿病 (T2DM) 的理解有限. 研究越来越多地报告缺失的数据,归算是最常见的策略,但报告的清晰度仍然是一个挑战.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 心血管疾病 (CVD) 是2型糖尿病 (T2DM) 的主要并发症.
- 准确的预测模型对于管理T2DM患者心血管疾病风险至关重要.
- 缺少数据在这些模型的开发和验证中构成了重大挑战.
研究的目的:
- 调查T2DM心血管疾病预测模型中缺失数据的流行率和处理策略.
- 评估相关研究中缺少数据方法的报告质量.
主要方法:
- 在2024年6月30日之前,对MEDLINE的英语语言研究进行系统的文献搜索.
- 提取和总结缺失数据的百分比,机制和处理策略.
- 分析了51篇专注于预测模型开发和验证的文章.
主要成果:
- 在大多数发展 (40/51) 和外部验证 (12/16) 研究中报告了缺失的数据.
- 在开发 (74.5%) 和验证 (68.8%) 阶段,计算是处理缺失数据的主要方法.
- 在2016年之后,报告缺失数据的数量显著增加,但对方法的清晰度仍然不足.
结论:
- 虽然在T2DM心血管疾病预测模型中经常遇到和解决缺失数据,但报告实践往往是不充分的.
- 提高透明度和应用强大的缺失数据处理技术对于提高预测模型质量和可靠性至关重要.
- 进一步的研究应集中在临床预测模型中缺少数据的标准化报告准则上.
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