Classification of acute decompensated heart failure: an automated algorithm compared with a physician reviewer panel: the Atherosclerosis Risk in Communities study.

BACKGROUND: 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. METHODS AND RESULTS: 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. CONCLUSIONS: Although the automated classification improved efficiency and decreased costs, its accuracy in classifying HF hospitalizations was modest compared with a standardized physician reviewer panel.

Duke Scholars

Author Lisa Wruck Biostatistics & Bioinformatics, Division of Biostatistics