High-Sensitive Cardiac Troponin for Prediction of Clinical Heart Failure
Notice bibliographique
Résumé
Heart failure (HF) prevalence continues to rise, and projections show that in the next 2 decades, ≈45% more HF cases will occur, with a mortality rate remaining as high as 50% within 5 years of diagnosis and high healthcare costs.Hence, there is an unmet need to apply successful preventive programs and reduce HF incidence.Also, according to current guidelines, an effective HF preventive program requires adequately targeting the preclinical stages of the disease, including risk factors for HF, such as hypertension, diabetes mellitus, renal dysfunction, coronary artery disease, and abnormalities of cardiac structure/function associated with HF, such as left ventricular (LV) hypertrophy and low LV ejection fraction. 1ecently, efforts to better identify subjects at the highest risk were undertaken.Different biomarkers have been studied for this purpose.Although many candidate biomarkers have been described, few have made the difficult translation from initial promise to clinical application. 2mong biomarkers, high-sensitivity cardiac troponin (hs-cTn) can detect small amounts of myocyte injury.Using high-sensitivity assays, detectable levels of cardiac troponin have been demonstrated among apparently healthy individuals in the general population, including stage A HF, as well as in asymptomatic individuals with stable cardiovascular disease, stage B HF, with a prevalence of detectable levels, ranging from 60% to 80% in asymptomatic individuals. 3,4s-cTn elevation may be caused by multiple mechanisms, in addition to myocardial necrosis.These include cardiomyocyte damage from inflammatory cytokines or oxidative stress, apoptosis, increased cell membrane permeability induced by increased stretch or stress with troponin release by injured but still viable cells, fragmentation of altered troponins with release into the circulation of fragments with an affinity for the troponins immunoassays, and production of membranous blebs containing troponins that could release them in the bloodstream. 5Thus, hs-cTn release may not only occur in the setting of myocardial injury related to atherosclerotic coronary heart disease but may be also an expression of other structural phenotypes correlated to HF risk, such as increased LV mass. 3 Among 4221 participants in the Cardiovascular Health Study, those with the highest troponin T (TnT) concentrations had a 5-and 6-fold increase in the incidence of death and HF, respectively, compared with those with undetectable cTnT levels, and serial measurements further improved risk classification. 4Of note, it has been demonstrated that the predictive characteristics of hs-cTn for HF or major adverse cardiovascular events in the community are superior as compared with other biomarkers, such as galectin-3 and high-sensitivity C-reactive protein. 6Conversely, hs-cTn and NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) predictivity seem to be complementary, reflecting different mechanisms of HF, such as myocardial injury as compared with increased wall stress. 7High-Sensitive Cardiac Troponin for Prediction of Clinical Heart Failure Are We Ready for Prime Time?
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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 ».