Predicting heart failure outcome from cardiac and comorbid conditions: The 3C-HF score

Senni, Michele; Parrella, Piervirgilio; Maria, Renata De; Cottini, C.; Böhm, Michael; Ponikowski, Piotr; Filippatos, Gerasimos; Tribouilloy, Christophe; Lenarda, Andrea Di; Oliva, Fabrizio; Pulignano, Giovanni; Cicoira, Mariantonietta; Nodari, Savina; Porcu, Maurizio; Cioffi, Gianni; Gabrielli, Domenico; Parodi, Oberdan; Ferrazzi, Paolo; Gavazzi, Antonello

doi:10.1016/j.ijcard.2011.10.071

Cited by 104 publications

(76 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…26 Nonetheless, recent mortality risk scores in HFPEF have attempted to quantify the risk associated with non-cardiac factors in HFPEF. 32,50 While more research is needed to define this risk fully, it is clear that attention must be paid to non-cardiac co-morbidities in the optimal management of HFPEF.…”

Section: Non-cardiac Co-morbiditiesmentioning

confidence: 99%

How do patients with heart failure with preserved ejection fraction die?

Chan

Lam

2013

European J of Heart Fail

181

148

View full text Add to dashboard Cite

Understanding how patients with heart failure with preserved ejection fraction (HFPEF) die provides insight into the natural history and pathophysiology of this complex syndrome, thereby allowing better prediction of response to therapy in designing clinical trials. This review summarizes the current state of knowledge surrounding mortality rates, modes of death, and prognostic factors in HFPEF. Despite the lack of uniform reporting, the following conclusions may be drawn from previous studies. The mortality burden of HFPEF is substantial, ranging from 10% to 30% annually, and higher in epidemiological studies than in clinical trials. Mortality rates compared with heart failure with reduced ejection fraction (HFREF) appear to be strongly influenced by the type of study, but are clearly elevated compared with age-and co-morbidity-matched controls without heart failure. The majority of deaths in HFPEF are cardiovascular deaths, comprising 51-60% of deaths in epidemiological studies and 70% in clinical trials. Among cardiovascular deaths, sudden death and heart failure death are the leading cardiac modes of death in HFPEF clinical trials. Compared with HFREF, the proportions of cardiovascular deaths, sudden death, and heart failure deaths are lower in HFPEF. Conversely, non-cardiovascular deaths constitute a higher proportion of deaths in HFPEF than in HFREF, particularly in epidemiological studies, where this difference may be related to fewer coronary heart deaths in HFPEF. Key mortality risk factors, including age, gender, body mass index, burden of co-morbidities, and coronary artery disease, offer some explanation for the differences in mortality rates observed across studies.--

show abstract

Section: Non-cardiac Co-morbiditiesmentioning

confidence: 99%

How do patients with heart failure with preserved ejection fraction die?

Chan

Lam

2013

European J of Heart Fail

181

148

View full text Add to dashboard Cite

show abstract

“…The 3C-HF score that combines age, cardiac (NYHA class, left ventricular ejection fraction (LVEF), presence of atrial fibrillation, presence of severe valve heart disease secondary to HF severity, no renin-angiotensin system (RAS) and/or β-blocker treatment) and co-morbid (chronic kidney dysfunction, diabetes with target organ damage, anemia and hypertension) conditions commonly present in HF patients, was calculated for each patient, summing the different contributions from each risk factor as described in the study by Senni et al 3 …”

Section: C-hf Scorementioning

confidence: 99%

“…The 3C-HF score has been developed in a large, multicentre HF population enrolled from different clinical settings, 3 and has been validated in a "real world" population not subject to trial selection bias. Discrimination capacity for 1-year all-cause mortality (14.3% and 0.9%, P=0.0002 and 21.9% and 8.4%, P=0.015, respectively) were identified.…”

Section: Maggic Sorementioning

confidence: 99%

“…While only clinical variables and peak maximal oxygen uptake (peak V O2) were used to calculate the Heart Failure Survival Score (HFSS), 1 the Seattle Heart Failure Model (SHFM) 2 also incorporated the impact of therapy on outcome. More recently, the role of co-morbid conditions has been included in the Cardiac and Comorbid Conditions HF (3C-HF) score, 3 showing significant utility in predicting patients' outcome. Biomarkers have also been shown to improve diagnostic accuracy.…”

mentioning

confidence: 99%

“…On this basis, the aim of our study was to test whether the 6MWT -a simple and easy-to-obtain measure of functional capacity -can significantly add to 2 updated and validated clinical scores such as the 3C-HF and the MAGGIC score, 3,6 in the prediction of all-cause mortality.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Pre-Discharge Evaluation in Heart Failure　– Additive Predictive Value of the 6-Minute Walking Test to Clinical Scores –

Rovere

Maestri

Caporotondi

et al. 2015

Circ J

View full text Add to dashboard Cite

Background:We aimed to investigate whether the assessment of functional capacity by the 6-minute walking test (6MWT) might improve the predictive ability of 2 validated clinical scores for risk stratification in heart failure (HF). Methods and Results:The Cardiac and Comorbid Conditions HF (3C-HF) and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) scores were evaluated in 466 consecutive HF patients who completed a pre-discharge 6MWT. The 12-month event rate was 7.7%. Both the 2 scores and the 6MWT predicted all-cause mortality (all P<0.0001), with a hazard ratio of 2. 6MWTThe test was performed at pre-discharge according to a standardized procedure. 10 Briefly, subjects were asked to walk at their own maximal pace along a 35-m long, flat and straight hospital corridor. The test was symptom limited and supervised by a physical therapist. The distance covered during the test was recorded in meters. Statistical AnalysisComparisons between groups for continuous data were performed by using a t-test. Comparisons for categorical variables were carried out by using the Chi-squared test or Fisher's exact test when appropriate. The association between pairs of variables was assessed by using the Pearson's correlation coefficient.One-year survival curves were estimated by using the Kaplan-Meier method and compared by using the log-rank test after classifying the patients into 3 groups (high, medium and low risk) according to tertiles of the 3C-HF score.

show abstract

Prognostic Models Derived in PARADIGM-HF and Validated in ATMOSPHERE and the Swedish Heart Failure Registry to Predict Mortality and Morbidity in Chronic Heart Failure

et al. 2020

View full text Add to dashboard Cite

IMPORTANCEAccurate prediction of risk of death or hospitalizations in patients with heart failure (HF) may allow physicians to explore how more accurate decisions regarding appropriateness and timing of disease-modifying treatments, advanced therapies, or the need for end-of-life care can be made. OBJECTIVE To develop and validate a prognostic model for patients with HF. DESIGN, SETTING, AND PARTICIPANTS Multivariable analyses were performed in a stepwise fashion. Harrell C statistic was used to assess the discriminative ability. The derivation cohort was Prospective Comparison of ARNI With ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure trial (PARADIGM-HF) participants. The models were validated using the Aliskiren Trial to Minimize Outcomes in Patients with Heart Failure Trial (ATMOSPHERE) study and in the Swedish Heart Failure Registry (SwedeHF). A total of 8399 participants enrolled in PARADIGM-HF. Data were analyzed between June 2016 and June 2018.MAIN OUTCOMES AND MEASURES Cardiovascular death, all-cause mortality, and the composite of cardiovascular death or HF hospitalization at both 1 and 2 years. RESULTSComplete baseline clinical data were available for 8011 patients in PARADIGM-HF. The mean (SD) age of participants was 64 (11.4) years, 78.2% were men (n = 6567 of 8399), and 70.6% were New York Heart Association class II (n = 5919 of 8399). During a mean follow-up of 27 months, 1546 patients died, and 2031 had a cardiovascular death or HF hospitalization. The common variables were: male sex, race/ethnicity (black or Asian), region (Central Europe or Latin America), HF duration of more than 5 years, New York Heart Association class III/ IV, left ventricular ejection fraction, diabetes mellitus, β-blocker use at baseline, and allocation to sacubitril/valsartan. Ranked by χ 2 , N-terminal pro brain natriuretic peptide was the single most powerful independent predictor of each outcome. The C statistic at 1 and 2 years was 0.74 (95% CI, 0.71-0.76) and 0.71 (95% CI, 0.70-0.73) for the primary composite end point, 0.73 (95% CI, 0.71-0.75) and 0.71 (95% CI, 0.69-0.73) for cardiovascular death, and 0.71 (95% CI, 0.69-0.74) and 0.70 (95% CI, 0.67-0.74) for all-cause death, respectively. When validated in ATMOSPHERE, the C statistic at 1 and 2 years was 0.71 (95% CI, 0.69-0.72) and 0.70 (95% CI, 0.68-0.71) for the primary composite end point, 0.71 (95% CI, 0.69-0.74) and 0.70 (95% CI, 0.69-0.72) for cardiovascular death, and 0.71 (95% CI, 0.69-0.74) and 0.70 (95% CI, 0.68-0.72) for all-cause death, respectively. An online calculator was created to allow calculation of an individual's risk (http://www.predict-hf.com). CONCLUSIONS AND RELEVANCEPredictive models performed well and were developed and externally validated in large cohorts of geographically representative patients, comprehensively characterized with clinical and laboratory data including natriuretic peptides, who were receiving contemporary evidence-based treatment.

show abstract

Predicting heart failure outcome from cardiac and comorbid conditions: The 3C-HF score

Cited by 104 publications

References 33 publications

How do patients with heart failure with preserved ejection fraction die?

How do patients with heart failure with preserved ejection fraction die?

Pre-Discharge Evaluation in Heart Failure　– Additive Predictive Value of the 6-Minute Walking Test to Clinical Scores –

Prognostic Models Derived in PARADIGM-HF and Validated in ATMOSPHERE and the Swedish Heart Failure Registry to Predict Mortality and Morbidity in Chronic Heart Failure

Contact Info

Product

Resources

About

Predicting heart failure outcome from cardiac and comorbid conditions: The 3C-HF score

Cited by 104 publications

References 33 publications

How do patients with heart failure with preserved ejection fraction die?

How do patients with heart failure with preserved ejection fraction die?

Pre-Discharge Evaluation in Heart Failure – Additive Predictive Value of the 6-Minute Walking Test to Clinical Scores –

Prognostic Models Derived in PARADIGM-HF and Validated in ATMOSPHERE and the Swedish Heart Failure Registry to Predict Mortality and Morbidity in Chronic Heart Failure

Contact Info

Product

Resources

About

Pre-Discharge Evaluation in Heart Failure　– Additive Predictive Value of the 6-Minute Walking Test to Clinical Scores –