Heart Failure Diagnosis and Validation Techniques

Heart failure encompasses a spectrum of syndromes defined by the heart’s inability to meet circulatory demands, typically classified by left ventricular ejection fraction. Accurate diagnosis relies on a combination of clinical evaluation, imaging and biochemical markers. Echocardiography remains the cornerstone for quantifying ejection fraction and characterising structural changes, while measurement of natriuretic peptides aids in distinguishing cardiac from non-cardiac dyspnoea. Administrative and electronic health records have emerged as valuable sources for large-scale case identification, employing International Classification of Diseases codes, natural language processing and rule-based algorithms. Validation of these approaches against expert-adjudicated or chart-review gold standards is essential to ensure sensitivity and specificity, minimise misclassification and support reliable epidemiological and outcomes research. Recent advances include the use of iterative algorithm refinement and integration of multiple data modalities—imaging reports, laboratory results and prescription data—to establish computable phenotypes that can identify acute decompensated heart failure, preserved and reduced ejection fraction subtypes, and to optimise population-level dashboards for service planning and quality improvement.

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A systematic review and meta-analysis of routinely collected healthcare data found that algorithms based on diagnostic codes and free‐text searches achieve high specificity (over 95%) but only moderate sensitivity (around 64%) for identifying acute and prevalent heart failure. Heterogeneity across studies highlighted the need to balance broader code definitions with false-positive risk and to combine multiple data sources to improve case detection in clinical trial settings.

An optimisation of the Veterans Affairs national heart failure dashboard described stepwise improvements in case definitions, incorporation of a hierarchy of imaging quality and the application of natural language processing to clinical narratives. These refinements raised accuracy for heart failure with reduced ejection fraction from 54% to 89%, and preserved ejection fraction from 54% to 88%, demonstrating the value of multimodal algorithms for population health management.

Development of a computable phenotype for acute decompensated heart failure demonstrated that a rule-based electronic search of clinical notes can reach sensitivity and specificity above 97%, substantially outperforming International Classification of Diseases codes alone. This approach underscores the feasibility of leveraging unstructured text within electronic medical records to create reliable case‐finding tools for research and clinical surveillance.

Heart Failure Diagnosis and Validation Techniques publication trend

The graph below shows the total number of articles in heart failure diagnosis and validation techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Ejection fraction: The percentage of blood ejected from the left ventricle with each contraction, used to classify heart failure subtype.

Heart failure with reduced ejection fraction (HFrEF): A form of heart failure defined by an ejection fraction below established thresholds, indicating impaired systolic function.

Heart failure with preserved ejection fraction (HFpEF): A subtype characterised by an ejection fraction within normal limits but with clinical and structural evidence of diastolic dysfunction.

Computable phenotype: A reproducible algorithm combining coded and free-text data elements to identify patients with a specific condition from electronic health records.

International Classification of Diseases (ICD) codes: A standardised coding system used in administrative data to record diagnoses and procedures.

Natural language processing (NLP): A set of computational methods for extracting structured information from unstructured clinical text.

References

1. Development and Optimization of the Veterans Affairs’ National Heart Failure Dashboard for Population Health Management. Journal of Cardiac Failure (2023).
2. Accuracy of heart failure ascertainment using routinely collected healthcare data: a systematic review and meta-analysis. Systematic Reviews (2024).
3. Derivation and validation of a computable phenotype for acute decompensated heart failure in hospitalized patients. BMC Medical Informatics and Decision Making (2020).