Improving Diagnostic Accuracy for Acute Heart Failure — INDICATE-HF  

Dyspnea Clinical Trial

Official title: Improving Diagnostic Accuracy for Acute Heart Failure

Status Active, not recruiting

Clinical Trial Summary

Acute heart failure is a common reason for emergency department visits and hospitalization, but the diagnosis can be challenging because of non-specific symptoms and signs. The current diagnostic approach to acute heart failure has modest accuracy, leading to delayed diagnosis and treatment, which associate with worse prognosis. Prior work suggests diagnostic accuracy can be improved with the addition of multiple circulating biomarkers discovered through proteomics, and this study will derive and validate a multi-marker model to improve diagnostic accuracy for acute heart failure in the emergency department.

Clinical Trial Description

Acute heart failure (HF) is highly morbid, lethal, and costly. It is a difficult diagnosis to make given its symptoms and signs overlap with other cardiac and non-cardiac conditions. In the emergency department (ED), misdiagnosis of acute HF is common and associated with adverse outcomes. Biomarker testing can facilitate accurate diagnosis; however, natriuretic peptides (NP) are the only guideline recommend biomarker of HF for diagnostic testing, and are better for ruling-out, rather than ruling-in, acute HF. Even with NP testing, in contemporary clinical practice misdiagnosis of acute HF still occurs in 10 to 45% of patients presenting to the ED with dyspnea. Clinical prediction models including multiple biomarkers hold promise for improving diagnostic accuracy. The few prior studies investigating a multiple biomarker approach for diagnosing acute HF were limited by constraint to highly correlated markers from known biologic pathways, relatively small sample sizes, lack of inclusion of all a priori selected biomarkers into a single model, and absence of validation cohorts. The current study is designed to address these limitations. Recent advances in "omics" enable novel biomarker discovery on a larger scale and investigations less "biased" by existing knowledge. The overarching hypothesis of this study is that a multi-marker model incorporating novel proteins discovered with plasma proteomics improves diagnostic accuracy for acute HF. In a preliminary proof of concept study plasma proteomics was utilized to discover a multi-marker panel of 21 biomarkers which improved diagnostic accuracy for acute HF beyond current clinical practice using clinical data and NP levels. These promising preliminary data motivate broader discovery in a larger sample size with subsequent derivation and validation of a multi-marker model for diagnosing acute HF in independent samples of adequate size. The specific aims of this study are to: 1) discover a multi-marker panel of 21 biomarkers to improve diagnostic accuracy for acute HF, 2) derive a model for diagnosing acute HF incorporating the 21-biomarker panel, and 3) test performance of the multi-marker model in a prospective validation cohort. In aim 1, existing plasma samples from ~900 patients will be used to assay 925 proteins to discover a smaller set of novel biomarkers most strongly associated with an adjudicated acute HF diagnosis. In aim 2, an existing prospective observational cohort, EMROC-AHF, will be utilized to derive the multi-marker model in ~900 patients who presented to the ED with acute dyspnea. In aim 3, from four EDs in Detroit, MI and Nashville, TN a new sample will prospectively recruit ~1,000 patients presenting with acute dyspnea and adjudicate the presence of acute HF by cardiologist panel review. Given the burden of HF, the frequency of inaccurate diagnosis and its adverse consequences, this study will address a significant unmet need by improving diagnostic accuracy for acute HF. ;

Related Conditions & MeSH terms

• Acute Heart Failure
• Dyspnea
• Dyspnea; Cardiac
• Heart Failure

• NCT number: NCT04886128
• Study type: Observational
• Source: Vanderbilt University Medical Center
• Status: Active, not recruiting
• Start date: August 31, 2021
• Completion date: June 30, 2025