Head-to-head comparison of contemporary heart failure risk scores.
AIMS
Several heart failure (HF) web-based risk scores are currently used in clinical practice. Currently, we lack head-to-head comparison of the accuracy of risk scores. This study aimed to assess correlation and mortality prediction performance of Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, which includes clinical variables + medications; Seattle Heart Failure Model (SHFM), which includes clinical variables + treatments + analytes; PARADIGM Risk of Events and Death in the Contemporary Treatment of Heart Failure (PREDICT-HF) and Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, which also include biomarkers, like N-terminal pro B-type natriuretic peptide (NT-proBNP).


METHODS AND RESULTS
A total of 1166 consecutive patients with HF from different aetiologies that had NT-proBNP measurement at first visit were included. Discrimination for all-cause mortality was compared by Harrell's C-statistic from 1 to 5 years, when possible. Calibration was assessed by calibration plots and Hosmer-Lemeshow test and global performance by Nagelkerke's R2 . Correlation between scores was assessed by Spearman rank test. Correlation between the scores was relatively poor (rho value from 0.66 to 0.79). Discrimination analyses showed better results for 1-year mortality than for longer follow-up (SHFM 0.817, MAGGIC-HF 0.801, PREDICT-HF 0.799, BCN-Bio-HF 0.830). MAGGIC-HF showed the best calibration, BCN-Bio-HF overestimated risk while SHFM and PREDICT-HF underestimated it. BCN-Bio-HF provided the best discrimination and overall performance at every time-point.


CONCLUSIONS
None of the contemporary risk scores examined showed a clear superiority over the rest. BCN-Bio-HF calculator provided the best discrimination and overall performance with overestimation of risk. MAGGIC-HF showed the best calibration, and SHFM and PREDICT-HF tended to underestimate risk. Regular updating and recalibration of online web calculators seems necessary to improve their accuracy as HF management evolves at unprecedented pace.
['Codina|Pau|P|', 'Lupón|Josep|J|', 'Borrellas|Andrea|A|', 'Spitaleri|Giosafat|G|', 'Cediel|Germán|G|', 'Domingo|Mar|M|', 'Simpson|Joanne|J|', 'Levy|Wayne C|WC|', 'Santiago-Vacas|Evelyn|E|', 'Zamora|Elisabet|E|', 'Buchaca|David|D|', 'Subirana|Isaac|I|', 'Santesmases|Javier|J|', 'Diez-Quevedo|Crisanto|C|', 'Troya|Maria I|MI|', 'Boldo|Maria|M|', 'Altmir|Salvador|S|', 'Alonso|Nuria|N|', 'González|Beatriz|B|', 'Rivas|Carmen|C|', 'Nuñez|Julio|J|', 'McMurray|John|J|', 'Bayes-Genis|Antoni|A|']
Heart failure Mortality Risk models Risk prediction