Global Group In Chronic Heart Failure Research Articles

Heart failure risk scores in advanced heart failure patients: insights from the LEVO-D registry.

The prevalence of advanced heart failure (HF) is increasing due to the growing number of patients with HF and their better treatment and survival. There is a scarcity of data on the accuracy of HF web-based risk scores in this selected population. This study aimed to assess mortality prediction performance of the Meta-Analysis Global Group in Chronic HF (MAGGIC-HF) risk score and the model of the Barcelona Bio-HF Risk Calculator (BCN-Bio-HF) containing N terminal pro brain natriuretic peptide in HF patients receiving intermittent inotropic support with levosimendan as destination therapy. Four hundred and three advanced HF patients from 23 tertiary hospitals in Spain receiving intermittent inotropic support with levosimendan as destination therapy were included. Discrimination for all-cause mortality was compared by area under the curve (AUC) and Harrell's C-statistic at 1year. Calibration was assessed by calibration plots comparing observed versus expected events based on estimated risk by each calculator. The included patients were predominantly men, aged 71.5 [interquartile range 64-78] years, with reduced left ventricular ejection fraction (27.5±9.4%); ischaemic heart disease was the most prevalent aetiology (52.5%). Death rate at 1year was 26.8%, while the predicted 1-year mortality by BCN-Bio-HF and MAGGIC-HF was 17.0% and 22.1%, respectively. BCN-Bio-HF AUC was 0.66 (Harrell's C-statistic 0.64), and MAGGIC-HF AUC was 0.62 (Harrell's C-statistic 0.61). The two evaluated risk scores showed suboptimal discrimination and calibration with an underestimation of risk in advanced HF patients receiving levosimendan as destination therapy. There is a need for specific scores for advanced HF.

Abstract P177: Circulating Ketone Bodies and Mortality in Heart Failure: A Community Cohort Study

Background: Heart failure (HF) is associated with metabolic alterations, including ketogenesis. However, determinants of ketogenesis and risk of mortality in HF is not defined. Total ketone bodies (KB) include β-hydroxybutyrate, acetoacetate, and acetone and can be measured in plasma by nuclear magnetic resonance (NMR). The aim of this study is to determine the relationship between KB and clinical characteristics in a community HF cohort and to assess the association between KB and all-cause mortality. Methods: A population-based cohort of 1,389 HF patients was prospectively enrolled between 2003 and 2012. Plasma KB was measured by LP4 NMR LipoProfile ® assay/test on the Vantera® NMR analyzer platform. A conditional inference tree method (ctree R Package) was used to determine optimal KB group cut points. Associations between clinical characteristics and KB were measured with Wilcoxon rank sum test and Pearson’s Chi-squared test. Kaplan-Meier method estimated survival. Cox regression analyses were used to estimate associations between KB concentrations and mortality. Results: Among the 1,382 HF patients with KB measurements, the median age was 78 years (IQR 68-84) and 52% were men. Median KB was 180 μM (IQR 134-308). Patients were divided into two groups with lower KB (≤471.5 μM) and higher KB (>471.5 μM). Patients with higher KB (N=210) had lower BMI, higher BNP, and were more likely to be in the New York Heart Association class III-IV; however, these patients were less likely to have hyperlipidemia, coronary disease, or diabetes mellitus (P < 0.05). Age, sex, creatinine, ejection fraction, or Meta-Analysis Global Group in Chronic HF (MAGGIC) score did not differ by KB group. Higher KB was associated with worse survival (figure). After adjustment for the MAGGIC score, higher KB was associated with increased risk of mortality (HR 1.3; 95% CI, 1.08-1.48). Conclusions: In this community HF cohort, higher KB was associated with increased mortality, independent of the MAGGIC score.

Abstract 10494: Proteomic Assessment of Novel Kidney Function Biomarkers in Heart Failure: A Community Study

Introduction: Heart failure (HF) is a complex syndrome with high mortality often associated with impaired kidney function. Estimated glomerular filtration rate (eGFR) is central to kidney function assessment in HF. However, novel proteins related to kidney function (lipocalin 2 [LCN2], macrophage inhibitory cytokine 1 [MIC1], T cell immunoglobulin and mucin domain 1 [TIM1]) may improve risk stratification of HF patients, beyond eGFR. Their prognostic contribution remain to be fully explored. Hypothesis: Proteomic assessment of kidney function biomarkers improves risk stratification in HF. Methods: Clinical data and biospecimens were collected from a HF community cohort in Southeastern Minnesota from 2003-2012. Serum creatinine was measured in 1,388 patients and eGFR was calculated with the 2021 Chronic Kidney Disease (CKD) Epidemiology Collaboration equation. CKD was defined as an eGFR <60 mL/min/1.73m 2 . Plasma proteins were measured using the SomaLogic aptamer-based technology SomaScan®. In HF patients with and without CKD, Cox proportional hazard models were used to estimate the association between kidney function biomarkers and mortality, after adjustment for Meta-Analysis Global Group in Chronic HF (MAGGIC) risk score. Results: At HF index, patients were on average 75±13 years of age and 48% female. Patients had a median eGFR of 57 mL/min/1.73m 2 and 64% had CKD. Adjusted geometric mean levels of LCN2, MIC1, and TIM1 levels were 20-30% higher in patients with CKD. Patients with CKD were at higher risk of dying (HR 1.6, 95% CI 1.4-1.8). Patients with above median levels of LCN2, TIM1, MIC1 were at higher risk of dying, irrespective of CKD (See Figure). Conclusions: In a HF community cohort, several novel kidney function biomarkers provided further risk stratification in patients with and without CKD.

1092-P: Lipoprotein Insulin Resistance Index and Mortality in Heart Failure: A Population Study

Heart failure (HF) is associated with metabolic alterations, including insulin resistance (IR) . Perturbations in lipid metabolism and insulin signaling may impact outcomes, but the relationship between IR and mortality is not well characterized. The Lipoprotein Insulin Resistance Index (LPIR) is a score (0-100; higher values signify greater IR) calculated from 6 lipoprotein subclass/size parameters measured by nuclear magnetic resonance (NMR) . We examined the association between LPIR and death in a HF community cohort. LPIR was measured by NMR LipoProfile®. Meta-Analysis Global Group in Chronic HF (MAGGIC) scores were calculated from clinical data. Kaplan-Meier curves were used to evaluate survival; Cox regression was used to estimate risk of death by LPIR quartile. Among 1,381 community-dwelling persons with HF, the median LPIR score was 38 (IQR 21-56) . Higher LPIR was associated with younger age, diabetes, smoking, obesity, and lower MAGGIC scores, but not ejection fraction. Survival increased by LPIR quartile (p<0.001) and at 5 years was 36% [95% CI 31-42], 42% [95% CI 38-48], 51% [95% CI 46-56], and 65% [95% CI 60-70] for quartile 1-4, respectively. The association between LPIR and death was independent of MAGGIC score (Figure) . Higher LPIR was associated with better survival in HF, independently of a validated clinical risk score. More research is needed to examine this paradoxically protective association. Disclosure S. Turecamo: None. A. Wolska: None. J.D. Otvos: Employee; LabCorp. K. Conners: None. S.J. Bielinski: Consultant; Novartis Pharmaceuticals Corporation. J. St Sauver: Research Support; Exact Sciences. V.L. Roger: None. Funding Research was supported by the Intramural Research Program of the National Heart, Lung, and Blood Institute (NHLBI) at National Institutes of Health and by the NIH Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, the American Association for Dental Research, and the Colgate-Palmolive Company.

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.

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.