Department of Epidemiology, University of North Carolina, 137 E Franklin St, Suite 306, Chapel Hill, NC, USA.
Circ Heart Fail. 2013 Jul;6(4):719-26. doi: 10.1161/CIRCHEARTFAILURE.112.000195. Epub 2013 May 6.
An algorithm to classify heart failure (HF) end points inclusive of contemporary measures of biomarkers and echocardiography was recently proposed by an international expert panel. Our objective was to assess agreement of HF classification by this contemporaneous algorithm with that by a standardized physician reviewer panel, when applied to data abstracted from community-based hospital records.
During 2005-2007, all hospitalizations were identified from 4 US communities under surveillance as part of the Atherosclerosis Risk in Communities (ARIC) study. Potential HF hospitalizations were sampled by International Classification of Diseases discharge codes and demographics from men and women aged ≥ 55 years. The HF classification algorithm was automated and applied to 2729 (n=13854 weighted hospitalizations) hospitalizations in which either brain natriuretic peptide measures or ejection fraction were documented (mean age, 75 years). There were 1403 (54%; n=7534 weighted) events classified as acute decompensated HF by the automated algorithm, and 1748 (68%; n=9276 weighted) such events by the ARIC reviewer panel. The chance-corrected agreement between acute decompensated HF by physician reviewer panel and the automated algorithm was moderate (κ=0.39). Sensitivity and specificity of the automated algorithm with ARIC reviewer panel as the referent standard were 0.68 (95% confidence interval, 0.67-0.69) and 0.75 (95% confidence interval, 0.74-0.76), respectively.
Although the automated classification improved efficiency and decreased costs, its accuracy in classifying HF hospitalizations was modest compared with a standardized physician reviewer panel.
最近，一个国际专家小组提出了一种用于分类心力衰竭（HF）终点的算法，该算法包括生物标志物和超声心动图的当代测量方法。我们的目的是评估当应用于基于社区的医院记录中提取的数据时，该同期算法对 HF 分类与标准化医师审查员小组的分类的一致性。
在 2005-2007 年期间，作为 Atherosclerosis Risk in Communities（ARIC）研究的一部分，从美国 4 个社区监测中确定了所有住院情况。通过国际疾病分类出院代码和人口统计学数据从年龄≥55 岁的男性和女性中抽取潜在的 HF 住院患者。HF 分类算法是自动化的，并应用于 2729 例（n=13854 例加权住院）中记录了脑钠肽测量或射血分数的住院患者（平均年龄 75 岁）。有 1403 例（54%；n=7534 例加权）由自动化算法分类为急性失代偿性 HF，有 1748 例（68%；n=9276 例加权）由 ARIC 审查员小组分类为急性失代偿性 HF。医师审查员小组和自动化算法对急性失代偿性 HF 的机会校正一致性为中度（κ=0.39）。以 ARIC 审查员小组为参考标准，自动化算法的敏感性和特异性分别为 0.68（95%置信区间，0.67-0.69）和 0.75（95%置信区间，0.74-0.76）。
尽管自动化分类提高了效率并降低了成本，但与标准化医师审查员小组相比，其对 HF 住院分类的准确性仅为中等水平。
