在一般人群中使用例行收集的健康数据来诊断心力衰竭病例识别算法的诊断准确性:系统性审查
Anita Andreano1, Vito Lepore2, Pietro Magnoni3
1SC Unità Di Epidemiologia, Agenzia Di Tutela Della Salute Della Città Metropolitana Di Milano, Via Conca del Naviglio 45, Milan, 20123, Italy.
Systematic reviews
|December 24, 2024
概括
对心力衰竭 (HF) 检测的行政健康数据 (HAD) 算法显示了高度可变的准确性. 研究中的偏差和异质性高风险阻止了对一般成年人群的综合诊断准确性估计.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 心力衰竭 (HF) 是一个重要的公共卫生问题,影响工业化国家1-4%的成人.
- 行政健康数据 (HAD) 算法用于经济高效,及时检测HF患者进行人口级别研究.
- 现有的基于HAD的算法在报告的诊断准确性方面表现出相当大的变化.
研究的目的:
- 评估验证的基于HAD的算法用于检测充血性心力衰竭 (HF) 的诊断准确性.
- 为了比较HAD算法的准确性与临床诊断作为参考标准.
- 确定导致算法性能观察到异质性的因素.
主要方法:
- 在MEDLINE和Embase (1946-2023) 的系统文献搜索中,对基于HAD的高频算法进行诊断精度研究.
- 包括使用临床检查或图表审查作为阻塞性HF诊断的参考标准的研究.
- 使用QUADAS-2工具评估偏差风险和适用性问题;由于研究异质性而进行叙事综合.
主要成果:
- 包括24项研究,包括161524名患者和36个算法.
- 灵敏度和特异性在各个算法中差异很大,取决于人口子组 (门诊患者和住院患者).
- 14项研究表明,至少在一个领域存在偏差的高风险,9项研究提出了适用性问题.
结论:
- 在研究中存在重大偏差风险和实质性的临床异质性,阻止了统一的诊断准确性估计.
- 基于HAD的算法在未经选择的一般成年人群中检测HF的可靠性仍然不确定.
- 需要进一步的高质量研究来改进和验证HAD算法,以准确检测HF.
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