A systematic review of clinical prediction rules for the diagnosis of chronic heart failure. (10th March 2019)

• Record Type: Journal Article
• Authors: Gallagher, Joe; McCormack, Darren; Zhou, Shuaiwei; Ryan, Fiona; Watson, Chris; McDonald, Kenneth; Ledwidge, Mark T.
• Is Part Of: ESC heart failure. Volume 6:Number 3(2019)
• Page Start: 499
• Page End: 508
• Publication Date: 2019-03-10
• Subjects: Heart failure -- Clinical prediction rules -- Diagnosis -- Models; Heart failure -- Periodicals; 616.129005
• DOI: 10.1002/ehf2.12426
• Languages: English
• ISSNs: 2055-5822

Abstract

Abstract: Aims: This study sought to review the literature for clinical prediction models for the diagnosis of patients with chronic heart failure in the community and to validate the models in a novel cohort of patients with a suspected diagnosis of chronic heart failure. Methods and results: MEDLINE and Embase were searched from 1946 to Q4 2017. Studies were eligible if they contained at least one multivariable model for the diagnosis of chronic heart failure applicable to the primary care setting. The CHARMS checklist was used to evaluate models. We also validated models, where possible, in a novel cohort of patients with a suspected diagnosis of heart failure referred to a rapid access diagnostic clinic. In total, 5310 articles were identified with nine articles subsequently meeting the eligibility criteria. Three models had undergone internal validation, and four had undergone external validation. No clinical impact studies have been completed to date. Area under the curve (AUC) varied from 0.74 to 0.93 and from 0.60 to 0.65 in the novel cohort for clinical models alone with AUC up to 0.89 in combination with electrocardiogram and B‐type natriuretic peptide (BNP). The AUC for BNP was 0.86 (95% confidence interval 83.3–88.6%). Conclusions: This review demonstrates that there are a number of clinical prediction rules relevant to the diagnosis of chronic heart failure in the literature. Clinical impact studies are required to compare the use of clinical prediction rules and biomarker strategies in this setting.