Patients with heart failure and sleep apnea were assigned to adaptive servo-ventilation or control. The rate of death, lifesaving cardiovascular intervention, or hospitalization for heart failure did ...not differ significantly between groups, but mortality was higher with adaptive servo-ventilation.
Sleep-disordered breathing is common in patients who have heart failure with reduced ejection fraction, with reported prevalence rates of 50 to 75%.
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Obstructive sleep apnea occurs more often in patients with heart failure than in the general population. Central sleep apnea, which may manifest as Cheyne–Stokes respiration, is found in 25 to 40% of patients who have heart failure with reduced ejection fraction.
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The prevalence of central sleep apnea increases in parallel with increasing severity of heart failure
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and worsening cardiac dysfunction.
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There are a number of mechanisms by which central sleep apnea may be detrimental to cardiac function, including . . .
Mineralocorticoid receptor antagonists (MRAs) and sodium glucose co-transporter 2 inhibitors favorably influence the clinical course of patients with heart failure and reduced ejection fraction.
This ...study sought to study the mutual influence of empagliflozin and MRAs in EMPEROR-Reduced (Empagliflozin Outcome Trial in Patients With Chronic Heart Failure With Reduced Ejection Fraction).
Secondary analysis that compared the effects of empagliflozin versus placebo in 3,730 patients with heart failure and a reduced ejection fraction, of whom 71% used MRAs at randomization.
The effects of empagliflozin on the primary endpoint, on most efficacy endpoints, and on safety were similar in patients receiving or not receiving an MRA (interaction p > 0.20). For cardiovascular death, the hazard ratios for the effect of empagliflozin versus placebo were 0.82 (95% confidence interval CI: 0.65 to 1.05) in MRA users and 1.19 (95% CI: 0.82 to 1.71) in MRA nonusers (interaction p = 0.10); a similar pattern was seen for all-cause mortality (interaction p = 0.098). Among MRA nonusers at baseline, patients in the empagliflozin group were 35% less likely than those in the placebo group to initiate treatment with an MRA following randomization (hazard ratio: 0.65; 95% CI: 0.49 to 0.85). Among MRA users at baseline, patients in the empagliflozin group were 22% less likely than those in the placebo group to discontinue treatment with an MRA following randomization (hazard ratio: 0.78; 95% CI: 0.64 to 0.96). Severe hyperkalemia was less common in the empagliflozin group.
In EMPEROR-Reduced, the use of MRAs did not influence the effect of empagliflozin to reduce adverse heart failure and renal outcomes. Treatment with empagliflozin was associated with less discontinuation of MRAs. (Empagliflozin Outcome Trial in Patients With Chronic Heart Failure With Reduced Ejection Fraction EMPEROR-Reduced; NCT03057977)
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The Spin-D (Safety and Cardiovascular Efficacy of Spironolactone in Dialysis-Dependent End-Stage Renal Disease) and MiREnDa (Mineralocorticoid Receptor Antagonists in End-Stage Renal Disease) trials ...taken together provide the reassuring demonstration that up to 25 mg/d spironolactone is reasonably safe, provided maintenance hemodialysis patients are properly monitored and investigators use a per-protocol therapeutic algorithm to manage hyperkalemia. These results should encourage and reassure the investigators of the 2 currently ongoing, large, international, major-outcome clinical trials, both of which are using spironolactone up to 25 mg/d: ACHIEVE (Aldosterone bloCkade for Health Improvement EValuation in End-stage Renal Disease trial; NCT03020303) and ALCHEMIST (ALdosterone antagonist Chronic HEModialysis Interventional Survival Trial; NCT01848639).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In a planned pooled analysis of two trials that evaluated empagliflozin in patients with heart failure, major adverse renal outcomes occurred in 2.8% of the patients who received empagliflozin and in ...3.5% of those who received placebo. Results differed between the two trials.
Aims
Residual pulmonary congestion at discharge is associated with poor prognosis in heart failure (HF), but its quantification through physical examination is challenging. Ultrasound imaging of lung ...comets (B‐lines) could improve congestion evaluation. The aim of this study was to assess the short‐term prognostic value of B‐lines after discharge from HF hospitalisation compared with other indices of haemodynamic congestion (BNP, E/e', and inferior vena cava diameter) or clinical status (NYHA class).
Methods and results
Sixty consecutive HF inpatients underwent clinical examination, echocardiography, and lung ultrasound at discharge, independently of, and in addition to routine management by the attending physicians. The median B‐line count was 8.5 (5–34). Three‐month event‐free survival for the primary endpoint (all‐cause death or HF hospitalisation) was 27 ± 10% in patients with ≥30 B‐lines and 88 ± 5% in those with <30 B‐lines (P < 0.0001). In a multivariable model, ≥30 B‐lines significantly predicted the combined endpoint (hazard ratio 5.66, 95% confidence interval 1.74–18.39, P = 0.04), along with NYHA ≥III and inferior vena cava diameter, while other indirect measures of congestion (BNP and E/e' ≥15) were not retained in the model; furthermore ≥30 B‐lines independently also predicted the secondary outcomes (HF hospitalisation and death). Importantly, B‐line addition to NYHA class and BNP was associated with improved risk classification (integrated discrimination improvement 15%, P = 0.02; continuous net reclassification improvement 65%, P = 0.03).
Conclusion
Residual pulmonary congestion at discharge, as assessed by a B‐line count ≥30, is a strong predictor of outcome. Lung ultrasonography may represent a useful tool to identify and monitor congestion and optimize therapy during and/or after hospitalisation for HF, which should be further validated in multicentre studies.
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BFBNIB, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Patients with heart failure, coronary artery disease, and no atrial fibrillation were randomly assigned to receive 2.5 mg of rivaroxaban twice daily or placebo. Rivaroxaban did not have a significant ...effect on the composite outcome of death, myocardial infarction, or stroke.
The choice of the most appropriate unsupervised machine-learning method for "heterogeneous" or "mixed" data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to ...examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of "ready-to-use" tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model LCM and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Although it is a rare disease, the number of available therapeutic options for treating pulmonary arterial hypertension has increased since the late 1990s, with multiple drugs developed that are ...shown to be effective in phase 3 randomised controlled trials. Despite considerable advancements in pulmonary arterial hypertension treatment, prognosis remains poor. Existing therapies target pulmonary endothelial dysfunction with vasodilation and anti-proliferative effects. Novel therapies that target proliferative vascular remodelling and affect important outcomes are urgently needed. There is need for additional innovations in clinical trial design so that all emerging candidate therapies can be rigorously studied. Pulmonary arterial hypertension trial design has shifted from short-term submaximal exercise capacity as a primary endpoint, to larger clinical event-driven trial outcomes. Event-driven pulmonary arterial hypertension trials could face feasibility and efficiency issues in the future because increasing sample sizes and longer follow-up durations are needed, which would be problematic in such a rare disease. Enrichment strategies, innovative and alternative trial designs, and novel trial endpoints are potential solutions that could improve the efficiency of future pulmonary arterial hypertension trials while maintaining robustness and clinically meaningful evidence.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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