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Validity of heart failure diagnoses in administrative databases: a systematic review and meta-analysis

Natalie McCormick1, Diane Lacaille2, Vidula Bhole3

  • 1Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Arthritis Research Centre of Canada, Richmond, British Columbia, Canada.

Plos One
|August 16, 2014
PubMed

Insights

Administrative data accurately identifies most heart failure (HF) cases, but misses about a quarter. Improving search parameters and incorporating lab data can enhance HF case identification in research.

Area of Science:

  • Cardiology
  • Health Informatics
  • Epidemiology

Background:

  • Heart failure (HF) is a critical factor in elderly and cardiovascular studies.
  • Administrative data is increasingly utilized for long-term clinical research.
  • Validating HF diagnostic codes in administrative data is crucial for research integrity.

Purpose of the Study:

  • To systematically review and meta-analyze studies on the validity of diagnostic codes for identifying HF in administrative data.
  • To assess the accuracy of administrative data in capturing heart failure cases.

Main Methods:

  • Systematic review and meta-analysis of 19 studies (1999-2009) using MEDLINE and EMBASE.
  • Quality assessment using the Quality Assessment of Diagnostic Accuracy Studies tool.
  • Pooled analysis of sensitivity, specificity, positive predictive value (PPV), and likelihood ratios (LR+, LR-) using a random-effects model.

Main Results:

  • Pooled sensitivity for HF codes was 75.3% (95% CI: 74.7-75.9), with specificity at 96.8% (95% CI: 96.8-96.9).
  • Positive likelihood ratio (LR+) was 51.9, negative likelihood ratio (LR-) was 0.27, and diagnostic odds ratio (DOR) was 186.5.
  • While specificity and PPV were high, sensitivity indicated that approximately 25% of HF cases were missed.

Conclusions:

  • Most HF diagnoses in administrative databases reflect true cases, but a significant proportion of HF cases are missed.
  • Broader search parameters and integration of laboratory and prescription data may improve HF case ascertainment.
  • Findings highlight the need for careful interpretation of HF data derived from administrative sources.
Abstract

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