000170100 001__ 170100
000170100 005__ 20260318155254.0
000170100 0247_ $$2doi$$a10.3390/hearts6040026
000170100 0248_ $$2sideral$$a148610
000170100 037__ $$aART-2025-148610
000170100 041__ $$aeng
000170100 100__ $$aGallego-Cuenca, Alejandro
000170100 245__ $$aComparison of Two Risk Calculators Based on Clinical Variables (MAGGIC and BCN Bio-HF) in Prediction of All-Cause Mortality After Acute Heart Failure Episode
000170100 260__ $$c2025
000170100 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170100 5203_ $$aBackground: Heart failure (HF) is common and deadly, affecting over 60 million people worldwide, and it remains a leading cause of hospitalization and post-discharge death. One-year mortality after an acute decompensated HF (ADHF) admission often approaches 40%. Prognostic models are critical for stratifying mortality risk in heart failure (HF) patients. This study compared the performance of the MAGGIC and BCN Bio-HF models in predicting 1-year and 3-year all-cause mortality (ACM) in patients discharged after acute decompensated HF (ADHF). Methods: A retrospective analysis was conducted on 229 patients hospitalized for ADHF at the Clinical University Hospital of Zaragoza. The required variables were extracted from medical records, and ACM risks were calculated using web-based tools. Calibration, discrimination (AUC), and Kaplan–Meier survival analysis and calibration curves assessed risk stratification and alignment with observed outcomes. Reclassification metrics (Net Reclassification Index [NRI], Integrated Discrimination Improvement [IDI]) were used to compare the models’ predictive performances. Results: Both of the models demonstrated robust discrimination for 1-year ACM (AUC: MAGGIC = 0.738, BCN Bio-HF = 0.769) but showed lower performance for 3-year predictions. Calibration was poor, with both models exhibiting significant risk underestimation at the individual level. MAGGIC achieved higher sensitivity (1-year: 0.911; 3-year: 0.685), favoring high-risk patient identification, whereas BCN Bio-HF offered superior specificity (1-year: 0.679; 3-year: 0.746) and a positive prediction value, reducing false positives. BCN Bio-HF showed a significant 12.7% reclassification improvement for 1-year mortality prediction. Conclusions: BCN Bio-HF did not outperform MAGGIC in our cohort. MAGGIC is preferable for the initial high-risk patient identification, requiring more intense short-term follow-up, while BCN Bio-HF’s higher specificity is best-suited to avoid overtreatment. Altogether, the clinical utility of both models was limited in our cohort by severe miscalibration, which may render adequate risk stratification difficult.
000170100 536__ $$9info:eu-repo/grantAgreement/ES/DGA/B07-23R
000170100 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000170100 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170100 700__ $$aBueno-Juana, Esperanza
000170100 700__ $$aCampos-Sáenz de Santamaría, Amelia$$uUniversidad de Zaragoza
000170100 700__ $$aGarcés-Horna, Vanesa$$uUniversidad de Zaragoza
000170100 700__ $$0(orcid)0000-0002-2338-7637$$aSánchez-Marteles, Marta$$uUniversidad de Zaragoza
000170100 700__ $$aPérez-Calvo, Juan I.
000170100 700__ $$aGiménez-López, Ignacio
000170100 700__ $$0(orcid)0000-0002-4769-7154$$aRubio-Gracia, Jorge$$uUniversidad de Zaragoza
000170100 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000170100 773__ $$g6, 4 (2025), 26 [17 pp.]$$tHearts (Basel)$$x2673-3846
000170100 8564_ $$s2217158$$uhttps://zaguan.unizar.es/record/170100/files/texto_completo.pdf$$yVersión publicada
000170100 8564_ $$s2573812$$uhttps://zaguan.unizar.es/record/170100/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170100 909CO $$ooai:zaguan.unizar.es:170100$$particulos$$pdriver
000170100 951__ $$a2026-03-18-13:52:17
000170100 980__ $$aARTICLE