Markers of Iron Metabolism and Outcomes in Patients with Heart Failure: A Systematic Review
Notice bibliographique
Résumé
Iron deficiency (ID) in conjunction with heart failure (HF) poses a challenge for clinicians and is associated with worse HF outcomes. Treatment of ID with IV iron supplementation for patients with HF has demonstrated benefits in quality of life (QoL) and HF-related hospitalizations. The aim of this systematic review was to summarize the evidence linking iron metabolism biomarkers with outcomes in patients with HF to assist in the optimal use of these biomarkers for patient selection. A systematic review of observational studies in English from 2010 to 2022 was conducted using PubMed, with keywords of "Heart Failure" and respective iron metabolism biomarkers ("Ferritin", "Hepcidin", "TSAT", "Serum Iron", and "Soluble Transferrin Receptor"). Studies pertaining to HF patients, with available quantitative data on serum iron metabolism biomarkers, and report of specific outcomes (mortality, hospitalization rates, functional capacity, QoL, and cardiovascular events) were included, irrespective of left ventricular ejection fraction (LVEF) or other HF characteristics. Clinical trials of iron supplementation and anemia treatment were removed. This systematic review was conducive to formal assessment of risk of bias via Newcastle-Ottawa Scale. Results were synthesized based on their respective adverse outcomes and iron metabolism biomarker(s). Initial and updated searches identified 508 unique titles once duplicates were removed. The final analysis included 26 studies: 58% focused on reduced LVEF; age range was 53-79 years; males composed 41-100% of the reported population. Statistically significant associations of ID were observed with all-cause mortality, HF hospitalization rates, functional capacity, and QoL. Increased risk for cerebrovascular events and acute renal injury have also been reported, but these findings were not consistent. Varying definitions of ID were utilized among the studies; however, most studies employed the current European Society of Cardiology criteria: serum ferritin < 100 ng/mL or the combination of ferritin between 100-299 ng/mL and transferrin saturation (TSAT) < 20%. Despite several iron metabolism biomarkers demonstrating strong association with several outcomes, TSAT better predicted all-cause mortality, as well as long-term risk for HF hospitalizations. Low ferritin was associated with short-term risk for HF hospitalizations, worsening functional capacity, poor QoL, and development of acute renal injury in acute HF. Elevated soluble transferrin receptor (sTfR) levels were associated with worse functional capacity and QoL. Finally, low serum iron was significantly associated with increased risk for cardiovascular events. Considering the lack of consistency among the iron metabolism biomarkers for association with adverse outcomes, it is important to incorporate additional biomarker data, beyond ferritin and TSAT, when assessing for ID in HF patients. These inconsistent associations question how best to define ID to ensure proper treatment. Further research, potentially tailored to specific HF phenotypes, is required to optimize patient selection for iron supplementation therapy and appropriate targets for iron stores replenishment.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,008 |
| Bibliométrie | 0,009 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».