Lack of Evidence Regarding Markers Identifying Acute Heart Failure in Patients with COPD: An AI-Supported Systematic Review

Abstract

Background

Due to shared symptoms, acute heart failure (AHF) is difficult to differentiate from an acute exacerbation of COPD (AECOPD). This systematic review aimed to identify markers that can diagnose AHF underlying acute dyspnea in patients with COPD presenting at the hospital.

Methods

All types of observational studies and clinical trials that investigated any marker’s ability to diagnose AHF in acutely dyspneic COPD patients were considered eligible for inclusion. An AI tool (ASReview) supported the title and abstract screening of the articles obtained from PubMed, Scopus, Web of Science, the Cochrane Library, Embase, and CINAHL until April 2023. Full text screening was independently performed by two reviewers. Twenty percent of the data extraction was checked by a second reviewer and the risk of bias was assessed in duplicate using the QUADAS-2 tool. Markers’ discriminative abilities were evaluated in terms of sensitivity, specificity, positive and negative predictive values, and the area under the curve when available.

Results

The search identified 10,366 articles. After deduplication, title and abstract screening was performed on 5,386 articles, leaving 153 relevant, of which 82 could be screened full text. Ten distinct studies (reported in 16 articles) were included, of which 9 had a high risk of bias. Overall, these studies evaluated 12 distinct laboratory and 7 non-laboratory markers. BNP, NT-proBNP, MR-proANP, and inspiratory inferior vena cava diameter showed the highest diagnostic discrimination.

Conclusion

There is not much evidence for the use of markers to diagnose AHF in acutely dyspneic COPD patients in the hospital setting. BNPs seem most promising, but should be interpreted alongside imaging and clinical signs, as this may lead to improved diagnostic accuracy. Future validation studies are urgently needed before any AHF marker can be incorporated into treatment decision-making algorithms for patients with COPD.

Protocol Registration

CRD42022283952.

Keywords:

• COPD
• chronic heart failure
• biomarkers
• systematic review
• differential diagnosis

Abbreviations

AECOPD, acute exacerbation of chronic obstructive pulmonary disease; AHF, acute heart failure, AI, artificial intelligence; AUC, area under the receiver operating characteristic curve; BNP, b-type natriuretic peptide; CHF, chronic heart failure; COPD, chronic obstructive pulmonary disease; ED, emergency department; IVC, inferior vena cava; MR-proANP, midregional pro-atrial natriuretic peptide; NT-proBNP, N-terminal pro b-type natriuretic peptide; NPV, negative predictive value.

Data Sharing Statement

The data files that provide insight into the AI-supported screening process and the choices made by the human reviewer are available from https://doi.org/10.5281/zenodo.10517334.

Acknowledgments

We would like to thank all authors of included studies who offered additional study information or data.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

The research has been conducted as part of the RE-SAMPLE project, which has been supported by the European Union’s Horizon 2020 research and innovation program (grant number 965315).