Should non-cardiovascular mortality be considered in the SCORE model? : findings from the Prevention of Renal and Vascular End-stage Disease (PREVEND) cohort

Competing non-cardiovascular related deaths were not accounted for in the Systematic COronary Risk Evaluation (SCORE) model. In this study we assessed the impact of non-cardiovascular related deaths on the prognostic performance and yield of the SCORE model. 5,752 participants from the Prevention of Renal and Vascular End stage Disease cohort aged 40 years and older who were free of atherosclerotic cardiovascular disease (CVD) at baseline were included. A cause-specific hazards (CSH) CVD-related mortality prediction model that accounted for non-CVD-related deaths was developed. The prognostic performance of this model was then compared with a refitted SCORE model. During a median follow-up period of 12.5 years, 139 CVD and 495 non-CVD-related deaths were reported. Discriminatory performance was comparable between the models (C-index = 0.64). The models showed good calibration although the CSH model underestimated risk in the highest decile while the refitted SCORE model showed overestimation. The CSH model classified more non-events into the low risk group compared to the refitted SCORE model (n = 51), yet it was accompanied by a misclassification of six events into the low risk group. The refitted SCORE model classified more individuals as high risk. However, the potential overtreatment that may result from utilizing the refitted SCORE model, when compared with the CSH model, still falls within acceptable limits. Our findings do not support the incorporation of non-cardiovascular mortality into the estimation of total cardiovascular risk in the SCORE model. Keywords: Competing risks; Total cardiovascular risk; SCORE; Primary prevention; Risk misclassification; Overtreatmen

Prevalence, predictors and clinical outcome of residual congestion in acute decompensated heart failure

BACKGROUND: Congestion is the main reason for hospital admission for acute decompensated heart failure (ADHF). A better understanding of the clinical course of congestion and factors associated with decongestion are therefore important. We studied the clinical course, predictors and prognostic value of congestion in a cohort of patients admitted for ADHF by including different indirect markers of congestion (residual clinical congestion, brain natriuretic peptides (BNP) trajectories, hemoconcentration or diuretic response). METHODS AND RESULTS: We studied the prognostic value of residual clinical congestion using an established composite congestion score (CCS) in 1572 ADHF patients. At baseline, 1528 (97.2%) patients were significantly congested (CCS ≥ 3), after 7 days of hospitalization or discharge (whichever came first), 451 (28.7%) patients were still significantly congested (CCS ≥ 3), 751 (47.8%) patients were mildly congested (CCS = 1 or 2) and 370 (23.5%) patients had no signs of residual congestion (CCS = 0). The presence of significant residual congestion at day 7 or discharge was independently associated with increased risk of re-admissions for heart failure by day 60 (HR [95%CI] = 1.88 [1.39-2.55]) and all-cause mortality by day 180 (HR [95%CI] = 1.54 [1.16-2.04]). Diuretic response provided added prognostic value on top of residual congestion and baseline predictors for both outcomes, yet gain in prognostic performance was modest. CONCLUSION: Most patients with acute decompensated heart failure still have residual congestion 7 days after hospitalization. This factor was associated with higher rates of re-hospitalization and death. Decongestion surrogates, such as diuretic response, added to residual congestion, are still significant predictors of outcomes, but they do not provide meaningful additive prognostic information

A network analysis to compare biomarker profiles in patients with and without diabetes mellitus in acute heart failure

Aims It is unclear whether distinct pathophysiological processes are present among patients with acute heart failure (AHF), with and without diabetes. Network analysis of biomarkers may identify correlative associations that reflect different pathophysiological pathways. Methods and results We analysed a panel of 48 circulating biomarkers measured within 24 h of admission for AHF in a subset of patients enrolled in the PROTECT trial. In patients with and without diabetes, we performed a network analysis to identify correlations between measured biomarkers. Compared with patients without diabetes (n = 1111), those with diabetes (n = 922) had a higher prevalence of ischaemic heart disease and traditional coronary risk factors. After multivariable adjustment, patients with and without diabetes had significantly different levels of biomarkers across a spectrum of pathophysiological domains, including inflammation (TNFR-1a, periostin), cardiomyocyte stretch (BNP), angiogenesis (VEGFR, angiogenin), and renal function (NGAL, KIM-1) (adjusted P-value <0.05). Among patients with diabetes, network analysis revealed that periostin strongly clustered with C-reactive protein and interleukin-6. Furthermore, renal markers (creatinine and NGAL) closely associated with potassium and glucose. These findings were not seen among patients without diabetes. Conclusion Patients with AHF and diabetes, compared with those without diabetes, have distinct biomarker profiles. Network analysis suggests that cardiac remodelling, inflammation, and fibrosis are closely associated with each other in patients with diabetes. Furthermore, potassium levels may be sensitive to changes in renal function as reflected by the strong renal–potassium–glucose correlation. These findings were not seen among patients without diabetes and may suggest distinct pathophysiological processes among AHF patients with diabetes

Bio-adrenomedullin as a marker of congestion in patients with new-onset and worsening heart failure.

BACKGROUND: Secretion of adrenomedullin (ADM) is stimulated by volume overload to maintain endothelial barrier function, and higher levels of biologically active (bio-) ADM in heart failure (HF) are a counteracting response to vascular leakage and tissue oedema. This study aimed to establish the value of plasma bio-ADM as a marker of congestion in patients with worsening HF. METHODS AND RESULTS: The association of plasma bio-ADM with clinical markers of congestion, as well as its prognostic value was studied in 2179 patients with new-onset or worsening HF enrolled in BIOSTAT-CHF. Data were validated in a separate cohort of 1703 patients. Patients with higher plasma bio-ADM levels were older, had more severe HF and more signs and symptoms of congestion (all P < 0.001). Amongst 20 biomarkers, bio-ADM was the strongest predictor of a clinical congestion score (r2  = 0.198). In multivariable regression analysis, higher bio-ADM was associated with higher body mass index, more oedema, and higher fibroblast growth factor 23. In hierarchical cluster analysis, bio-ADM clustered with oedema, orthopnoea, rales, hepatomegaly and jugular venous pressure. Higher bio-ADM was independently associated with impaired up-titration of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers after 3 months, but not of beta-blockers. Higher bio-ADM levels were independently associated with an increased risk of all-cause mortality and HF hospitalization (hazard ratio 1.16, 95% confidence interval 1.06-1.27, P = 0.002, per log increase). Analyses in the validation cohort yielded comparable findings. CONCLUSIONS: Plasma bio-ADM in patients with new-onset and worsening HF is associated with more severe HF and more oedema, orthopnoea, hepatomegaly and jugular venous pressure. We therefore postulate bio-ADM as a congestion marker, which might become useful to guide decongestive therapy