Using vascular biomarkers to assess heart failure event risk in hospitalized patients with and without AKI
Notice bibliographique
Résumé
BACKGROUND: Patients with AKI experience higher rates of heart failure (HF). This study seeks to identify criteria to assess the risk of heart failure post-hospitalization, with a special focus on AKI patients. We hypothesized that the combined use of 9 vascular biomarkers would predict future heart failure events after AKI. Using a study of 1497 hospitalized patients with and without AKI, we found that these 9 vascular biomarkers successfully stratified patients into different risk groups for HF, and were able to improve prediction of HF when added to routine clinical variables. METHODS: Using the ASSESS-AKI cohort, we performed an unsupervised spectral cluster analysis with 9 plasma biomarkers measured at 3 months post-hospitalization [Angiopoietin (angpt)-1, angpt-2, vascular endothelial growth factor (VEGF)-A, VEGF-C, VEGF-d, VEGF receptor 1 (R1), solubleTie-2 (sTie-2), placental growth factor (PlGF), and basic fibroblast growth factor (bFGF)] in 1,497 patients, half of whom had AKI. We used a Cox regression analysis to evaluate the associations between the clusters and HF. Models were adjusted for demographics, cardiovascular disease risk factors, medications, ICU status, lung disease, sepsis, clinical center, and 3-month post-discharge serum creatinine and proteinuria. We calculated change in the area under the curve (AUC) for the prediction of HF or death at 3 years by adding the biomarkers to a clinical model selected by a penalized regression with LASSO. We also calculated a net reclassification index for the addition of the biomarkers to the clinical model. RESULTS: Three biomarker-derived clusters were identified: Cluster 1 [n = 302, Vascular Injury (Injury) Phenotype] had higher levels of injury markers, whereas Cluster 2 [n = 728, Vascular Repair (Repair) Phenotype] had higher levels of repair markers. Cluster 3 (n = 467) had lower levels of all markers (Dormant Phenotype). Across the entire cohort, those with the Injury Phenotype had twofold higher risk of a HF event compared to the Repair Phenotype [aHR 2.24 (95% CI: 1.57-3.19)] and noted in both participants with AKI [aHR 2.12 (95% CI: 1.35-3.34)] and without AKI [aHR 2.94 (95%CI: 1.57-5.50)]. The Dormant Phenotype was associated with higher risk of HF events only in participants without AKI. The AUC for the prediction of HF event or death at 3 years by the biomarkers was 0.76 (95% CI: 0.73-0.80), 0.77 (95% CI: 0.73-0.80) for the clinical model, and 0.80 (95% CI: 0.77-0.83) for the combined model. The addition of the biomarkers significantly improved reclassification of HF event or death. CONCLUSIONS: Vascular biomarkers can be used to derive phenotypes capable of stratifying future risk of HF events in recently hospitalized patients with or without AKI.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».