Paediatric heart failure – understanding the pathophysiology and the current role of cardiac biomarkers in clinical practice
Bibliographic record
Abstract
INTRODUCTION: Paediatric heart failure is a common clinical syndrome that may be experienced by children with congenital heart disease (CHD) and/or cardiomyopathy. It is characterised by clinical signs/symptoms which reflect the underlying pathophysiology based on one of three main clinical states: Pulmonary over-circulation, pressure overload, and ventricular failure. Current diagnosis relies on clinical assessment and echocardiogram imaging as cardiac biomarkers has been predominantly scientific to date. This review provides a comprehensive overview of paediatric heart failure pathophysiology and considers the available evidence for cardiac biomarkers in this setting. METHODS: A literature review was completed using MEDLINE ALL, EMBASE, and PubMed on 10th November, 2022. Search terms included biomarkers, heart failure, heart defects, congenital heart disease, fontan circulation, single ventricle circulation, cardiomyopathy, and child. This allowed the identification of individual cardiac biomarkers which are the focus of this review. These included NT-proBNP, MR-proANP, MR-proADM, troponin, sST2, galectin 3, and growth differentiation factor-15. RESULTS: Paediatric studies have established reference ranges for NT-proBNP and troponin for children with structurally normal hearts. Of all the biomarkers reviewed, NT-proBNP appears to correlate most closely with symptoms of heart failure and ventricular dysfunction on echocardiogram. However, there remains limited longitudinal data for NT-proBNP, and no validated reference ranges for patients with CHD and/or cardiomyopathy. None of the other biomarkers reviewed were consistently superior to NT-proBNP. CONCLUSION: Further large paediatric studies of patients with heart failure are needed to validate NT-proBNP in CHD and to evaluate the role of novel biomarkers in specific types of CHD, e.g. single ventricle physiology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".