Evaluating three biomarkers as prognostic factors of in-hospital mortality and severity in heart failure: A prospective cohort
- Pedro A. Segura-Saldaña,
- ,
- Javier E. Alarcón-Santos,
- Cristian Aguilar,
- Mayita L. Alvarez-Vargas,
- Marcos Padilla-Reyes
- Hospital Nacional Edgardo Rebagliati Martins, EsSalud,
- Universidad Peruana Cayetano Heredia,
- Torres de Salud National Research Center,
- ,
- Red Latinoamericana de Cardiología,
- Heart National Institute
Open access
Publication Information
Output type
Original language
EnglishPages from-to (Number of pages)
Pages 31-40 (10 pages)Journal (Volume, Issue Number)
Revista Portuguesa de Cardiologia (Volume 41, Issue 1)Publication milestones
- Published - 01/2022
Publication status
ISSN
0870-2551Publication IDs
- Scopus: 85115802542
Abstract
Objective: To identify the relationship between red blood cell distribution width (RDW, %), interleukin-6 (IL-6) (pg/ml), high sensitivity-c-reactive protein (hs-CRP) (mg/l), in-hospital mortality and disease severity among patients with heart failure (HF). Methods: Prospective cohort. We included adults diagnosed with acute non-ischemic HF in 2015. The dependent variables were in-hospital mortality (yes or no) and disease severity. The latter was assessed with the Get With The Guidelines-HF score. We used hierarchical regression models to describe the pattern of association between biomarkers, mortality, and severity. We used the Youden index to identify the best cut-off for mortality prediction. Results: We included 167 patients; the mean age was 72.61 (SD: 11.06). The majority of patients presented with New York Heart Association classification II (40.12%) or III (43.11%). After adjusting for age and gender, all biomarkers were associated with mortality. After adding comorbidities, only IL-6 was associated. The final model with all clinical variables showed no effect from any biomarker. The best cut-off for RDW, hs-CRP and IL-6 for mortality were 14.8, 68.7 and 52.9, respectively. IL-6 presented the highest sensitivity (100%), specificity (75.35%) and area under the curve (0.91). Conclusions: No biomarker is independent from the most important clinical variables; therefore it should not be used for management modifications.
