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Clinical Application of Phase Angle and BIVA Z-Score Analyses in Patients Admitted to an Emergency Department with Acute Heart Failure
04:05

Clinical Application of Phase Angle and BIVA Z-Score Analyses in Patients Admitted to an Emergency Department with Acute Heart Failure

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Classification of acute decompensated heart failure: an automated algorithm compared with a physician reviewer panel:

Laura R Loehr1, Sunil K Agarwal, Chris Baggett

  • 1Department of Epidemiology, University of North Carolina, 137 E Franklin St, Suite 306, Chapel Hill, NC, USA. lloehr@email.unc.edu

Circulation. Heart Failure
|May 8, 2013
PubMed

Insights

An automated algorithm for classifying heart failure (HF) hospitalizations showed moderate agreement with physician reviewers. While efficient, its accuracy for acute decompensated HF diagnosis needs improvement.

Area of Science:

  • Cardiology
  • Health Informatics
  • Clinical Research

Background:

  • A new algorithm for classifying heart failure (HF) endpoints using biomarkers and echocardiography was proposed.
  • This study evaluated the agreement of this algorithm with a physician reviewer panel.
  • Data was abstracted from community-based hospital records.

Purpose of the Study:

  • To assess the agreement between an automated HF classification algorithm and a standardized physician reviewer panel.
  • To evaluate the accuracy of contemporary HF classification methods in real-world clinical data.

Main Methods:

  • Utilized hospitalization data from 4 US communities (2005-2007) from the Atherosclerosis Risk in Communities (ARIC) study.
  • An automated HF classification algorithm was applied to 2729 hospitalizations with biomarker or ejection fraction data.
  • Compared automated algorithm results against a standardized physician reviewer panel (ARIC reviewers).

Main Results:

  • The automated algorithm classified 54% of hospitalizations as acute decompensated HF, versus 68% by the physician panel.
  • Chance-corrected agreement (κ=0.39) between the automated algorithm and physician panel was moderate.
  • The algorithm demonstrated a sensitivity of 0.68 and specificity of 0.75 when compared to the physician panel.

Conclusions:

  • The automated algorithm offers improved efficiency and reduced costs for HF hospitalization classification.
  • However, the algorithm's accuracy in classifying heart failure hospitalizations was modest compared to expert physician review.
  • Further refinement of automated algorithms is needed for reliable clinical application.
Abstract

Keywords:
ARICBNPclassificationejection fractionheart failure

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