Group In Chronic Heart Failure Research Articles

Risk Prediction in Transition: MAGGIC Score Performance at Discharge and Incremental Utility of Natriuretic Peptides

Risk stratification for hospitalized patients with heart failure (HF) remains a critical need. The Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score is a robust model derived from patients with ambulatory HF. Its validity at the time of discharge and the incremental value of natriuretic peptides (NPs) in this setting is unclear. This was a single-center study examining a total of 4138 patients with HF from 2 groups; hospital discharge patients from administrative data (n = 2503, 60.5%) and a prospective registry of patients with ambulatory HF (n = 1635, 39.5%). The ambulatory registry patients underwent N-terminal pro-B-type NP (BNP) measurement at enrollment, and in the hospitalize discharge cohort clinical BNP levels were abstracted. The primary endpoint was all-cause mortality within 1 year. MAGGIC score performance was compared between cohorts utilizing Cox regression and calibration plots. The incremental value of NPs was assessed using calculated area under the curve and net reclassification improvement (NRI). The hospitalized and ambulatory cohorts differed with respect to primary outcome (777 and 100 deaths, respectively), sex (52.1% vs 41.7% female) and race (35% vs 49.5% African American). The MAGGIC score showed poor discrimination of mortality risk in the hospital discharge (C statistic: 0.668, hazard ratio [HR]: 1.1 per point, 95% confidence interval [CI]: 0.652, 0.684) but fair discrimination in the ambulatory cohorts (C statistic: 0.784, HR: 1.16 per point, 95% CI: 0.74, 0.83), respectively, a difference that was statistically significant (P = .001 for C statistic, 0.002 for HR). Calibration assessment indicated that the slope and intercept (of MAGGIC-predicted to observed mortality) did not statistically differ from ideal in either cohort and did not differ between the cohorts (all P > .1). NP levels did not significantly improve prediction in the hospitalized cohort (P = .127) but did in the ambulatory cohort (C statistic: 0.784 [95% CI: 0.74, 0.83] vs 0.82 [95% CI: 0.78, 0.85]; P = .018) with a favorable NRI of 0.354 (95% CI: 0.202-0.469; P = .002). The MAGGIC score showed poor discrimination when used in patients with HF at hospital discharge, which was inferior to its performance in patients with ambulatory HF. Discrimination within the hospital discharge group was not improved by including hospital NP levels.

Comparación de mortalidad pronosticada y mortalidad observada en pacientes con insuficiencia cardiaca tratados en una unidad clínica especializada

Introduction and objectivesTo analyze survival in heart failure (HF) patients treated at a specialized unit. MethodsProspective cohort-based study of HF patients treated at a specialized unit from 2011 to 2017. Observed 1- and 3-year mortality rates were compared with those predicted by the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score. ResultsWe studied 1280 patients, whose median MAGGIC risk score was 19 [interquartile range, 13-24]. Prescription rates of beta-blockers, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, mineralocorticoid receptor antagonists, and sacubitril-valsartan were 93%, 67%, 22%, 73%, and 16%, respectively. The MAGGIC risk score showed good discrimination for mortality at 1 year (c-statistic=0.71) and 3 years (c-statistic=0.76). Observed mortality was significantly lower than predicted mortality, both at 1 year (6.2% vs 10.9%; observed/predicted ratio=0.57; P<.001) and at 3 years (16.7% vs 27.7%; observed/predicted ratio=0.60; P<.001). This discrepancy was found in several subgroups, except in patients aged> 70 years (29.9% vs 34.7%; observed/predicted ratio=0.86; P=.126) and in patients with ejection fraction> 40% (19.6% vs 20.7%; observed/predicted ratio=0.95; P=.640). ConclusionsMortality in HF patients treated at a specialized clinic was significantly lower than that predicted by the MAGGIC risk score.

P4516Incremental prognostic values of cognitive impairment diagnosed by mini-mental state examination and mini-cog in older hospitalized patients with heart failure

Abstract Introduction Cognitive impairment (CI) is associated with worse prognosis in patients with heart failure, especially in the elderly; however, its incremental prognostic ability in pre-existing prognostic models has not been well elucidated. Moreover, although some tools have been proposed for evaluating cognitive function, their difference in prognostic prediction has not been explicitly compared. Methods A total of 352 heart failure patients aged ≥75 years admitted to three hospitals were evaluated for their cognitive function using the Mini-Mental State Examination (MMSE) and Mini-cog during index hospitalization. We diagnosed CI if MMSE and Mini-cog were ≤23 and ≤2, respectively. The primary endpoint was all-cause death. Results The median age of the entire cohort was 85 (IQR: 80–88) years, and 47.7% of the subjects were male. Based on the MMSE and Mini-cog, the CI was diagnosed in 167 (47.4%) and 159 (45.2%) patients, respectively. The two diagnostic tools showed poor to moderate agreement (Cohen's kappa coefficient: 0.37, 95% CI: 0.27–0.47). During the follow-up period of median 346 (IQR: 195–489) days, 53 patients (15.1%) died. Although the Kaplan-Meier analysis showed that CI diagnosed using Mini-cog (CI-MC) was associated with significantly higher mortality (P=0.001), this association was not significant for CI diagnosed using MMSE (CI-MMSE) (P=0.059). On multivariate Cox regression analysis, CI-MMSE and CI-MC were individually associated with worse prognosis in older heart failure patients even after adjustment for Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk model and log B-type natriuretic peptide levels (CI-MMSE, HR: 2.05 [95% CI: 1.16–3.61]; and CI-MC, HR: 2.57 [95% CI: 1.46–4.53]). The receiver operating characteristic curve analysis for Mini-cog showed significantly higher area under the curve (AUC) than that for MMSE (0.61 vs. 0.52, p=0.045). To test the incremental prognostic capability, models were constructed by individually adding each score to the MAGGIC risk model, and the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were evaluated. CI-MMSE did not show incremental prognostic predictability (NRI: 0.28, p=0.069; IDI: 0.01, p=0.090), whereas CI-MC (NRI: 0.45, p=0.001; IDI: 0.03, p=0.001) did. Adding CI-MC instead of CI-MMSE to the MAGGIC risk model showed significant reclassification improvement (NRI: 0.45, p=0.002, IDI: 0.02, p=0.041). Conclusion In older patients with heart failure, CI defined by Mini-Cog is superior in providing additive prognostic value than that defined by CI based on MMSE. Acknowledgement/Funding This study is partially funded by Japan Heart Foundation Research Grant and Novartis Research Grants.

P5423Exercise oscillatory ventilation improves the performance of prognosis scores currently used for heart failure

Abstract Background and aim Several prognostic risk scores are available for heart failure with reduced ejection fraction (HFrEF), and are used, together with other criteria, to help decide the ideal timing for listing candidates for a heart transplant. The detection of an oscillatory ventilatory pattern (OVP) during cardiopulmonary exercise testing (CPET) has been associated with more advanced HF and a worse prognosis, but was not considered in the development of all the current risk scores. We evaluated whether OVP adds significant prognostic information to four contemporary HF scores. Methods Single-centre retrospective cohort study of consecutive HFrEF patients undergoing CPET for functional and prognostic assessment from October 1996 till May 2018. The Heart Failure Survival Score (HFSS), Seattle Heart Failure Model (SHFM), Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) and Metabolic Exercise Cardiac Kidney Index (MECKI) were obtained in each patient. Cox model was fit with time to death or urgent transplant (whichever came first within 2 years) as the dependent variable and OVP and respective HF score as the independent variables. We further assessed the added discriminative power by performing ROC curve comparisons. Results We studied 387 patients, median age 58 (IQR 49; 65) years, and 77% were male. The most common HFrEF aetiology was ischemic heart disease (54%). Median peak oxygen consumption was 15,7 mL/kg/min (IQR 12,8; 20,0). OVP was present in 150 (39%) patients. Over the 2-year follow-up period, 48 patients died, and 52 underwent heart transplantation (of which 25 were urgent). HFSS showed the weakest (c-statistic 0,625; 95% [CI] 0,54–0,71) and MECKI score the strongest (c-statistic 0,819; 95% [CI] 0,76–0,88) discriminatory ability. Contrasting with NTproBNP value, the presence of OVP predicted the study endpoint independently of the HF prognosis score used (see table). Adding the occurrence of OVP to the HFSS and the MAGGIC scores significantly improved their prognostic performance (see Table). Conclusion An OVP is a common finding in HFrEF patients undergoing CPET, and adds prognostic information to contemporary HF prognosis scores. Systematic evaluation of this easily available criterion may assist the decision on the appropriate timing for heart transplantation listing.

P1639Prognostic utility of H2FPEF score in heart failure with preserved ejection fraction

Abstract H2FPEF (obesity, atrial fibrillation, age &gt;60 yrs, ≥2 antihypertensives, E/e' &gt;9, and pulmonary artery systolic pressure by echo &gt;35 mmHg) is a newly-developed score used for establishing the likelihood of heart failure with preserved ejection fraction (HFpEF). Given the clinical significance of its components, it is tempting to speculate that this algorithm might be useful for cardiovascular (CV) risk prediction. Aim To investigate the prognostic value of H2FPEF score in a well-characterized HFpEF population. Methods and results A group of 205 patients (64±8yrs) with symptomatic HFpEF, underwent clinical and echocardiographic evaluation. At a mean follow-up of 26.2 months, 64 patients (31%) experienced the composite of CV hospitalization or death, and 51 (25%) HF hospitalization. Cox regression analysis revealed that H2FPEF was significantly associated with both study endpoints (HR: 1.30; 95% CI: 1.10 to 1.54; p=0.002 for CV hospitalization or death, and 1.45; 95% CI: 1.21 to 1.75; p&lt;0.001 for HF hospitalization). The prognostic value of H2FPEF was non-inferior to a traditional prognosticator in HF - MAGGIC (Meta-analysis Global Group in Chronic Heart Failure) risk score (area under ROC curve 0.62 for H2FPEF and 0.65 for MAGGIC, p=0.58, for the composite end-point, and 0.66 for both predictors, p=0.96, for HF hospitalization). Using an externally-derived cutpoint for H2FPEF of 5 (considered as the upper limit of the range corresponding to an intermediate probability of HFpEF), we demonstrated that the subset with the score equal to or above this threshold was characterized by a higher risk of both study end-points (Figure). Figure 1. Kaplan-Meier estimates of survival free of the study outcomes according to H2FPEF score. Conclusions H2FPEF score, originally dedicated to discrimination of HFpEF, is a potent prognosticator in this condition, with the ability to identify increased clinical risk comparable to MAGGIC score.

Impacto de la FRAgilidad y otros síndromes Geriátricos en el manejo clínico y pronóstico del paciente anciano ambulatorio con Insuficiencia Cardiaca (FRAGIC). Estudio prospectivo y multicéntrico

Background and objectivesHeart failure (HF) is a chronic disease that is often associated with ageing. There are predictive models based on variables that associate it with a poor prognosis, although those do not include common conditions in the elderly, such as frailty or comorbidity. The aim of this study is to determine the clinical and epidemiological characteristics of a cohort of elderly outpatients with HF followed-up by cardiologists. This will include a study of the prevalence of frailty and other geriatric syndromes, as well as their impact on the prognosis, and to evaluate whether these may improve predictive ability of such predictive models. Material and methodsObservational, prospective, and multicentre study that will include 400 patients ≥75years old with chronic HF followed-up in Spanish tertiary hospitals by cardiology specialists in HF. Patients will undergo a comprehensive geriatric assessment, and prediction of events will be performed based on MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) and Barcelona-Bio HF calculator scores. The primary endpoint is cardiovascular and overall mortality at 1 and 3years follow-up. ResultsThis study will assess both the characteristics and prognosis of elderly patients with HF followed-up by cardiologists in Spain and the applicability in the elderly population of scores used in the general population with chronic HF. ConclusionThis is the first prospective study that will systematically assess frailty and other geriatric syndromes in the elderly outpatient with HF in Spain and followed-up by cardiologists, thus contributing to improve knowledge about both its prevalence and impact on our patients.

Performance of Prognostic Risk Scores in Heart Failure Patients: Do Sex Differences Exist?

BackgroundSex differences in the performance of prognostic risk scores in heart failure (HF) patients have not previously been investigated. We examined the performance of 2 commonly used scores in predicting mortality and a composite end point consisting of ventricular assist device, heart transplantation, or mortality in women vs men with HF. MethodsThis was a retrospective study of 1,136 (25% women) consecutive ambulatory adult HF patients with reduced left ventricular ejection fraction (≤ 40%) followed at a single institution from 2000 to 2012. Discrimination, calibration, and absolute risk reclassification of the Seattle Heart Failure Model (SHFM) and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score to predict 1- and 3-year outcomes were compared between women and men. ResultsAt 1- and 3-year follow-ups, 116 (22% women) and 231 (21% women) patients died, respectively. Survival was equal between sexes (P = 0.41). The SHFM and the MAGGIC score showed similar discriminatory capacity in women (c-statistics 0.84, 95% CI 0.77-0.92, and 0.74, 95% CI 0.64-0.83) and men (c-statistics 0.74, 95% CI 0.69-0.79, and 0.70, 95% CI 0.64-0.75). There was no difference in the predicted and observed 1-year mortality by the scores in both sexes. Compared with the SHFM, the MAGGIC score better reclassified 10% (95% CI 7%-14%) of women and 18% (95% CI 15%-20%) of men. At 3-year follow-up, similar results were seen for discrimination, whereas both scores overestimated mortality with more marked overestimation in women. The results were reproducible for the composite end point, with improved calibration at 3-year follow-up in both scores. ConclusionsOur findings support the use of the MAGGIC score in both women and men owing to better risk classification.

Physician Judgement vs Model-Predicted Prognosis in Patients With Heart Failure

BackgroundPrevious evidence suggests that cardiologists and family doctors have limited accuracy in predicting patient prognosis. Predictive models with satisfactory accuracy for estimating mortality in patients with heart failure (HF) exist; physicians, however, seldom use these models. We evaluated the relative accuracy of physician vs model prediction to estimate 1-year survival in ambulatory patients with HF. MethodsWe conducted a single-centre cross-sectional study involving 150 consecutive ambulatory patients with HF >18 years of age with a left ventricular ejection fraction ≤40%. Each patient’s cardiologist and family doctor provided their predicted 1-year survival, and predicted survival scores were calculated using 3 models: HF Meta-Score, Seattle Heart Failure Model (SHFM), and Meta-Analysis Global Group in Chronic HF (MAGGIC) score. We compared accuracy between physician and model predictions using intraclass correlation (ICC). ResultsMedian predicted survival by HF cardiologists was lower (median 80%, interquartile range [IQR]: 61%-90%) than that predicted by family physicians (median 90%, IQR 70%-99%, P = 0.08). One-year median survival calculated by the HF Meta-Score (94.6%), SHFM (95.4%), and MAGGIC (88.9%,) proved as high or higher than physician estimates. Agreement among HF cardiologists (ICC 0.28-0.41) and family physicians (ICC 0.43-0.47) when compared with 1-year model-predicted survival scores proved limited, whereas the 3 models agreed well (ICC > 0.65). ConclusionsHF cardiologists underestimated survival in comparison with family physicians, whereas both physician estimates were lower than calculated model estimates. Our results provide additional evidence of potential inaccuracy of physician survival predictions in ambulatory patients with HF. These results should be validated in longitudinal studies collecting actual survival.

Incremental Value of Implantable Cardiac Device Diagnostic Variables Over Clinical Parameters to Predict Mortality in Patients With Mild to Moderate Heart Failure.

BackgroundHeart failure remains a leading cause of morbidity and mortality. Clinical prediction models provide suboptimal estimates of mortality in this population. We sought to determine the incremental value of implantable device diagnostics over clinical prediction models for mortality.Methods and Results RAFT (Resynchronization/Defibrillation for Ambulatory Heart Failure Trial) patients with implanted devices capable of device diagnostic monitoring were included, and demographic and clinical parameters were used to compute Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) heart failure risk scores. Patients were classified according to MAGGIC score into low (0–16), intermediate (17–24), or high (>24) risk groups. Mortality was evaluated from 6 months postimplant in accordance with the RAFT protocol. In a subset of 1036 patients, multivariable analysis revealed that intermediate and high MAGGIC scores, fluid index, atrial fibrillation, and low activity flags were independent predictors of mortality. A device‐integrated diagnostic parameter that included a fluid index flag and either a positive atrial fibrillation flag or a positive activity flag was able to significantly differentiate higher from lower risk for mortality in the intermediate MAGGIC cohort. The effect was more pronounced in the high‐risk MAGGIC cohort, in which device‐integrated diagnostic–positive patients had a shorter time to death than those who were device‐integrated diagnostic negative.ConclusionsDevice diagnostics using a combination of fluid index trends, atrial fibrillation burden, and patient activity provide significant incremental prognostic value over clinical heart failure prediction scores in higher‐risk patients. This suggests that combining clinical and device diagnostic parameters may lead to models with better predictive power. Whether this risk is modifiable with early medical intervention would warrant further studies.Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT00251251.

Artificial intelligence algorithm for predicting mortality of patients with acute heart failure.

AimsThis study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF).Methods and results12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolled to the Korean AHF registry were used as performance test data. The endpoints were in-hospital, 12-month, and 36-month mortality. We compared the DAHF performance with the Get with the Guidelines–Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and other machine-learning models by using the test data. Area under the receiver operating characteristic curve of the DAHF were 0.880 (95% confidence interval, 0.876–0.884) for predicting in-hospital mortality; these results significantly outperformed those of the GWTG-HF (0.728 [0.720–0.737]) and other machine-learning models. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantly outperformed MAGGIC score (0.718 and 0.729). During the 36-month follow-up, the high-risk group, defined by the DAHF, had a significantly higher mortality rate than the low-risk group(p<0.001).ConclusionDAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models.

Performance of the Meta-Analysis Global Group in Chronic Heart Failure Score in Black Patients Compared With Whites

Risk stratification is critical in heart failure (HF) and the Meta-Analysis Global Group in Chronic HF (MAGGIC) score is a validated tool derived from ~40,000 patients. However, few of these patients self-identified as black, raising uncertainty regarding performance in blacks with HF. This study analyzed a racially diverse group of 4046 patients (1646 black and 2400 white) from a single center from 2007 to 2015. Baseline characteristics were collected to tabulate MAGGIC score and test its discrimination and calibration within race groups. The primary end point was all-cause mortality. Death was detected using system records and the social security death master file. Discrimination was tested using Cox models of MAGGIC score stratified by race, and combined analysis including MAGGIC, race, and MAGGIC×race. Calibration was assessed using linear regression models and plots of observed versus predicted data. Overall, 901 (21%) patients died during 1-year follow-up. MAGGIC score discrimination was similar in both race groups in terms of C statistic (0.707±0.027 versus 0.725±0.014, for black versus white; P=0.556) and the hazard ratio (HR) per MAGGIC point was 1.12 in black patients (95% CI, 1.10-1.14) and 1.13 in white patients (95% CI, 1.12-1.14). Race was a significant correlate of survival, with better survival in black patients compared with white (HR, 0.66; 95% CI, 0.56-0.78), but the interaction of MAGGIC×race was not significant (β=-0.013; P=0.16), and adding race to the model did not improve discrimination (C statistic for MAGGIC versus MAGGIC+race, 0.721 versus 0.722; P=0.79). In calibration testing, the slope was not significantly different from 1 in either group, but the groups differed from each other, and it was closer to unity among black patients (0.94 versus 1.4; P=0.004). These data support the use of the MAGGIC score to risk stratify black patients with HF.

Physician Prediction versus Model Predicted Prognosis in Ambulatory Patients with Heart Failure

Purpose Previous evidence suggests that both cardiologists and family doctors have limited accuracy in predicting patient prognosis. Predictive models with satisfactory accuracy for estimating mortality in heart failure (HF) patients exist; physicians, however, seldom use these models. We evaluated the relative accuracy of physician versus model prediction to estimate 1-year survival in ambulatory HF patients. Methods We conducted a single centre prospective cohort study involving 150 consecutive ambulatory HF patients >18 years of age with a left ventricular ejection fraction ≤40%. Each patient's cardiologist and family doctor provided their predicted 1-year survival. Using clinical and laboratory data at the time of enrolment we calculated predicted survival using three models: the HF Meta-Score, the Seattle Heart Failure Model (SHFM) and the Meta-Analysis Global Group in Chronic HF (MAGGIC) score. We compared accuracy between physician and model predictions using intra-class correlation (ICC). Results Median predicted survival by HF cardiologists was lower (median 80%, IQR 61-90%) than that predicted by family doctors (median 90%, IQR 70-99%, p=0.08). The 1-year median predicted survivals calculated by the HF Meta-Score (94.6%), SHFM (95.4%) and MAGGIC (88.9%,) proved as high or higher than physician estimates. Agreement among both HF cardiologists (ICC 0.28-0.41) and family doctors (ICC 0.43-0.47) when compared to 1-year model-predicted survival scores proved limited, while the 3 models agreed well with one another (ICC >0.65). Conclusion We found that median survival estimates are lower among HF cardiologists in comparison to family doctors, while both physician estimates are lower than calculated model estimates. Considering previous evidence that model's accuracy is acceptable and physicians’ is limited, our results provide additional evidence of the superior accuracy of predictive models 1-year survival in ambulatory HF patients. These results should be validated in longitudinal studies collecting actual survival.

Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically-ill patients and for guiding decision making. The existing models, however, cannot be used during initial treatment or screening. This study aimed to derive and validate an echocardiography-based mortality prediction model for HD using deep learning (DL). In this multicenter retrospective cohort study, the subjects were admitted adult (age≥18years) HD patients who underwent echocardiography. The outcome was in-hospital mortality. We extracted predictor variables from echocardiography reports using text mining. We developed deep learning-based prediction model using derivation data of a hospital A. And we conducted external validation using echocardiography report of hospital B. We conducted subgroup analysis of coronary heart disease (CHD) and heart failure (HF) patients of hospital B and compared DL with the currently used predictive models (eg, Global Registry of Acute Coronary Events (GRACE) score, Thrombolysis in Myocardial Infarction score (TIMI), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and Get With The Guidelines-Heart Failure (GWTG-HF) score). The study subjects comprised 25776 patients with 1026 mortalities. The areas under the receiver operating characteristic curve (AUROC) of the DL model were 0.912, 0.898, 0.958, and 0.913 for internal validation, external validation, CHD, and HF, respectively, and these results significantly outperformed other comparison models. This echocardiography-based deep learning model predicted in-hospital mortality among HD patients more accurately than existing prediction models and other machine learning models.

Validation of the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) heart failure risk score and the effect of adding natriuretic peptide for predicting mortality after discharge in hospitalized patients with heart failure.

BackgroundIn clinical practice, a risk prediction model is an effective solitary program to predict prognosis in particular patient groups. B-type natriuretic peptide (BNP)and N-terminal pro-b-type natriuretic peptide (NT-proBNP) are widely recognized outcome-predicting factors for patients with heart failure (HF).This study derived external validation of a risk score to predict 1-year mortality after discharge in hospitalized patients with HF using the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC)program data. We also assessed the effect of adding BNP or NT-proBNP to this risk score model in a Korean HF registry population.Method and resultsWe included 5625 patients from the Korean acute heart failure registry (KorAHF) and excluded those who died in hospital. The MAGGIC constructed a risk score to predict mortality in patients with HF by using 13 routinely available patient characteristics (age, gender, diabetes, chronic obstructive pulmonary disorder (COPD), HF diagnosed within the last 18 months, current smoker, NYHA class, use of beta blocker, ACEI or ARB, body mass index, systolic blood pressure, creatinine, and EF). We added BNP or NT-proBNP, which are the most important biomarkers, to the MAGGIC risk scoring system in patients with HF. The outcome measure was 1-year mortality. In multivariable analysis, BNP or NT-proBNP independently predicted death. The risk score was significantly varied between alive and dead groups (30.61 ± 6.32 vs. 24.80 ± 6.81, p < 0.001). After the conjoint use of BNP or NT-proBNP and MAGGIC risk score in patients with HF, a significant difference in risk score was noted (31.23 ± 6.46 vs. 25.25 ± 6.96, p < 0.001).The discrimination abilities of the risk score model with and without biomarker showed minimal improvement (C index of 0.734 for MAGGIC risk score and 0.736 for MAGGIC risk score plus BNP or NT-proBNP, p = 0.0502) and the calibration was found good. However, we achieved a significant improvement in net reclassification and integrated discrimination for mortality (NRI of 33.4%,p < 0.0001 and IDI of 0.002, p < 0.0001).ConclusionIn the KorAHF, the MAGGIC project HF risk score performed well in a large nationwide contemporary external validation cohort. Furthermore, the addition of BNP or NT-proBNPto the MAGGIC risk score was beneficial in predicting more death in hospitalized patients with HF.

Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score: Validation of a Simple Tool for the Prediction of Morbidity and Mortality in Heart Failure With Preserved Ejection Fraction.

BackgroundThe Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) mortality risk score, derived from a large sample of patients with heart failure (HF) across the spectrum of ejection fraction (EF), has not yet been externally validated in a well‐characterized HF with preserved EF cohort with adjudicated morbidity outcomes.Methods and ResultsWe evaluated the MAGGIC risk score (composed of 13 clinical variables) in 407 patients with HF with preserved EF enrolled in a prospective registry and used Cox regression to evaluate its association with morbidity/mortality. We used receiver‐operating characteristic analysis to compare the predictive ability of the MAGGIC risk score with the more complex Seattle Heart Failure Model, and we determined the value of adding B‐type natriuretic peptide to the MAGGIC risk score for risk prediction. During a mean follow‐up time of 3.6±1.8 years, 28% died, 32% were hospitalized for HF, and 55% had a cardiovascular hospitalization and/or death. The MAGGIC score, a mean±SD of 18±7, was significantly associated with mortality (P<0.0001), HF hospitalizations (P<0.0001), and the combined end point of cardiovascular‐related hospitalizations or death (hazard ratio, 1.8 [95% confidence interval, 1.6–2.1], per 1‐SD increase in the MAGGIC score; P<0.0001). Receiver‐operating characteristic analyses showed that MAGGIC and Seattle Heart Failure Model performed similarly in predicting HF with preserved EF outcomes, but the MAGGIC score demonstrated better calibration for hospitalization outcomes. Further analyses showed that B‐type natriuretic peptide was additive to the MAGGIC risk score for predicting outcomes (P<0.01 by likelihood ratio test).ConclusionsThe MAGGIC risk score is a simple, yet powerful method of risk stratification for both morbidity and mortality in HF with preserved EF.Clinical Trial RegistrationURL: http://www.clinicaltrials.gov. Unique identifier: NCT01030991.

Predicting Mortality in Decompensated Heart Failure: A Retrospective Study of 1,023 Veteran Admissions

Introduction Several risk models have been developed to predict mortality in patients with heart failure to help guide therapy and facilitate shared decision making. Implementation of these predictive models has been cautious due to a lack of generalizability and limitations in estimating mortality for individuals. Hypothesis We hypothesized that the online Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) Risk calculator, derived from patient cohorts with preserved and reduced systolic heart failure, would accurately predict 1- and 3-year mortality rates in veterans hospitalized with acute decompensated heart failure. Methods A retrospective analysis was undertaken of 1023 consecutive, unique patients admitted with the primary diagnosis of heart failure over a 10-year period at the William S. Middleton Memorial Veterans Hospital. The MAGGIC risk calculator was used to predict 1 and 3-year mortality rates based on 13 patient variables. These estimates were compared to actual survival. Results The demographics of our study population were as follows: median age 73, male 99%, diabetes 57%, COPD 48%, HF diagnosis Table 1 . To further clarify if the MAGGIC model could accurately predict mortality across various risk groups, patients were stratified into quintiles based on integer score ( Figures 1a, b ). Patients in the highest risk group had precise risk predictions at 1-year (64.49% actual versus 54.26% predicted) and at 3-years (86.92% actual versus 84.89% predicted) Conclusions Our data demonstrates that the MAGGIC risk calculator is accurate in predicting 1 and 3-year mortality rates in hospitalized veterans, independent of systolic performance. Moreover, the risk calculator effectively predicted patient mortality across multiple integer risk scores. This risk model demonstrates promise in identifying hospitalized patients at high risk for mortality and therefore may assist in decision making regarding appropriateness of pursuing advanced heart failure therapy or palliative care.

A Cardiac-Specific Regulatory Genetic Variant for Protein Kinase C α is Significantly Associated with Mortality in Patients with Heart Failure

Introduction Protein kinase C α (PRKCA) is a fundamental regulator of beta-adrenergic signaling and cardiac contractility. Previous studies suggested that increased cardiac expression of PRKCA is associated with adverse cardiac outcomes. Cardiac expression of PRKCA is partly genetically regulated, with the recently identified rs9909004 (T→C) as the most likely regulatory variant in failing human heart tissue, but the clinical impact of rs9909004 genotype remains unknown. Therefore the objectives of this study were to determine whether PRKCA rs9909004 genotype is associated with mortality in patients with heart failure and reduced ejection fraction (HFrEF), and if so, whether the genotype effects are modified by beta-blocker (BB) exposure. Hypothesis The T allele of rs9909004, associated with increased cardiac expression of PRKCA, will be associated with increased risk for all-cause mortality in HFrEF patients, and its effect will be less pronounced in HFrEF patients treated with higher BB exposure. Methods Insured HFrEF patients (n = 1,122) were enrolled from a single health care system into a prospective registry beginning in 2007, and they were genotyped with the Affymetrix Axiom® biobank array. BB exposure was quantified using pharmacy claims. Time-updating Cox models were used to test the independent association of rs9909004 genotype (additive genetic model for T allele) with all-cause mortality with adjustment for the following covariates: Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score; BB exposure; N-terminal pro B-type natriuretic peptide (NT-proBNP) level; and the patients’ self-reported race. The interaction of rs9909004 genotype with BB exposure was tested in similarly adjusted models stratified by BB status (yes/no for any exposure) and then in the total cohort using a multiplicative interaction term. Results Patients were mean age 68 ± 12 years with left ventricular ejection fraction 35% ± 11; 51% were African-Americans, and 35% were women. Follow-up was median 3.0 years with 255 deaths (23%). The T allele frequency was 59% in African-Americans and 53% in whites. The T allele significantly associated with increased risk for all-cause mortality with an adjusted hazard ratio (HR) = 1.34 (95% CI = 1.06-1.71, p = 0.016). Interestingly, the association of this genotype with all-cause mortality was not modified BB exposure. The adjusted HRs were similar regardless of BB status (no BB, HR = 1.46, p = 0.146; yes BB, HR = 1.33, p = 0.041), and the formal test of interaction with BB was also not significant (p = 0.306, β=0.35). Conclusions The T allele of rs9909004 in PRKCA is a significant risk factor for mortality in patients with HFrEF, independent of other risk predictors, and its effect was not modified by BB exposure.

Performance of Prognostic Risk Scores in Chronic Heart Failure Patients Enrolled in the European Society of Cardiology Heart Failure Long-Term Registry

ObjectivesThis study compared the performance of major heart failure (HF) risk models in predicting mortality and examined their utilization using data from a contemporary multinational registry. BackgroundSeveral prognostic risk scores have been developed for ambulatory HF patients, but their precision is still inadequate and their use limited. MethodsThis registry enrolled patients with HF seen in participating European centers between May 2011 and April 2013. The following scores designed to estimate 1- to 2-year all-cause mortality were calculated in each participant: CHARM (Candesartan in Heart Failure-Assessment of Reduction in Mortality), GISSI-HF (Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure), MAGGIC (Meta-analysis Global Group in Chronic Heart Failure), and SHFM (Seattle Heart Failure Model). Patients with hospitalized HF (n = 6,920) and ambulatory HF patients missing any variable needed to estimate each score (n = 3,267) were excluded, leaving a final sample of 6,161 patients. ResultsAt 1-year follow-up, 5,653 of 6,161 patients (91.8%) were alive. The observed-to-predicted survival ratios (CHARM: 1.10, GISSI-HF: 1.08, MAGGIC: 1.03, and SHFM: 0.98) suggested some overestimation of mortality by all scores except the SHFM. Overprediction occurred steadily across levels of risk using both the CHARM and the GISSI-HF, whereas the SHFM underpredicted mortality in all risk groups except the highest. The MAGGIC showed the best overall accuracy (area under the curve [AUC] = 0.743), similar to the GISSI-HF (AUC = 0.739; p = 0.419) but better than the CHARM (AUC = 0.729; p = 0.068) and particularly better than the SHFM (AUC = 0.714; p = 0.018). Less than 1% of patients received a prognostic estimate from their enrolling physician. ConclusionsPerformance of prognostic risk scores is still limited and physicians are reluctant to use them in daily practice. The need for contemporary, more precise prognostic tools should be considered.

Prognostic value of selected risk scales in patients with end-stage heart failure

Due to the increasing number of patients placed on waiting lists for orthotopic heart transplantation (OHT), the selection of patients with the highest risk of death has become paramount. This study aimed to evaluate the predictive value of the Model for End-stage Liver Disease eXcluding INR (MELD-XI) and Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) scales in ambulatory patients awaiting OHT and compare them to the Heart Failure Survival Score (HFSS). The study was a retrospective review of 370 adult ambulatory patients with end-stage heart failure, who were added to the OHT waiting list at our institution between 2012 and 2016. The median age of the patients was 54.0 (46.0-60.0) years, and 324 (87.6%) of them were male. The overall one-year mortality was 27.6%. The areas under the curve (AUCs) for the MAGGIC and HFSS scales were comparable: 0.771 (95% confidence interval [CI] 0.720-0.823); sensitivity 77%, specificity 68% vs. 0.781 (95% CI 0.732-0.829); sensitivity 90%, specificity 58%, respectively. The AUC for the MELD-XI scale was higher than that for the HFSS scale: 0.812 (95% CI 0.769-0.856); sensitivity 91%, specificity 63% vs. 0.781 (95% CI 0.732-0.829) sensitivity 90%, specificity 58%, respectively. Our study demonstrated that elevated MELD-XI and MAGGIC scores and lowered HFSS scores were associated with an increased risk of death during one-year follow-up. The prognostic utility of the MELD-XI scoring system was better than that of the HFSS scale, while the MAGGIC scale was comparable to the HFSS.

Performance of the MAGGIC heart failure risk score and its modification with the addition of discharge natriuretic peptides

AimsPredictive models for heart failure patients are widely used in the clinical practice to stratify patients' mortality and enable clinicians to tailor and intensify their approach. However, such models have not been validated internationally. In addition, biomarkers are now frequently measured to obtain prognostic information, and the implications of this practice are not known. In this study, we aimed to validate the model performance of the Meta‐analysis Global Group in Chronic Heart Failure (MAGGIC) score in a Japanese acute heart failure registry and further explore the incremental prognostic value of discharge B‐type natriuretic peptide (BNP) level.Methods and ResultsIn this study, we evaluated the registered data of 2215 consecutive acute HF patients (with 694 119 person‐years follow‐up) from a prospective multicentre registry (the West Tokyo Heart Failure) conducted in Japan from April 2006 to August 2016. The mean age was 73.0 ± 13.0, and 61.2% were male. The MAGGIC score demonstrated modest discrimination (c‐index = 0.71, 95% confidence interval 0.67–0.74) and good calibration (R 2 value = 0.97); there was constant overestimation for 1 year mortality. However, when the BNP level was added to the original MAGGIC variables, the model demonstrated good discrimination (c‐index = 0.74, 95% confidence interval 0.70–0.78) with adequate calibration (R 2 value = 0.91). The modified MAGGIC BNP score was externally validated in a separate Japanese registry (NaDEF) and demonstrated moderate discrimination (c‐index = 0.69, 95% confidence interval 0.65–0.73) and calibration (R 2 value = 0.85).ConclusionThe original MAGGIC score performed modestly in Japanese patients, but the addition of discharge BNP level enhanced model performance. The addition of objective biomarkers may result in effective modification of preexisting internationally recognized risk models and aid in multinational comparisons of heart failure patients' outcomes.