Life-long tailoring of management for patients with hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease, characterised by complex pathophysiology and extensive genetic and clinical heterogeneity. In most patients, HCM is caused by mutations in cardiac sarcomere protein genes and inherited as an autosomal dominant trait. The clinical phenotype ranges from severe presentations at a young age to lack of left ventricular hypertrophy in genotype-positive individuals. No preventative treatment is available as the sequence and causality of the pathomechanisms that initiate and exacerbate HCM are unknown. Sudden cardiac death and end-stage heart failure are devastating expressions of this disease. Contemporary management including surgical myectomy and implantable cardiac defibrillators has shown significant impact on long-term prognosis. However, timely recognition of specific scenarios – including transition to the end-stage phase – may be challenging due to limited awareness of the progression patterns of HCM. This in turn may lead to missed therapeutic opportunities. To illustrate these difficulties, we describe two HCM patients who progressed from the typical hyperdynamic stage of asymmetric septal thickening to end-stage heart failure with severely reduced ejection fraction. We highlight the different stages of this complex inherited cardiomyopathy based on the clinical staging pro-posed by Olivotto and colleagues. In this way, we aim to provide a practical guide for clinicians and hope to increase awareness for this common form of cardiac disease

Effect of statins on atrial fibrillation: collaborative meta-analysis of published and unpublished evidence from randomised controlled trials

Objective To examine whether statins can reduce the risk of atrial fibrillation. Design Meta-analysis of published and unpublished results from larger scale statin trials, with comparison of the findings against the published results from smaller scale or shorter duration studies. Data sources Medline, Embase, and Cochrane's CENTRAL up to October 2010. Unpublished data from longer term trials were obtained through contact with investigators. Study selection Randomised controlled trials comparing statin with no statin or comparing high dose versus standard dose statin; all longer term trials had at least 100 participants and at least six months' follow-up. Results In published data from 13 short term trials (4414 randomised patients, 659 events), statin treatment seemed to reduce the odds of an episode of atrial fibrillation by 39% (odds ratio 0.61, 95% confidence interval 0.51 to 0.74; P<0.001), but there was significant heterogeneity (P<0.001) between the trials. In contrast, among 22 longer term and mostly larger trials of statin versus control (105 791 randomised patients, 2535 events), statin treatment was not associated with a significant reduction in atrial fibrillation (0.95, 0.88 to 1.03; P=0.24) (P<0.001 for test of difference between the two sets of trials). Seven longer term trials of more intensive versus standard statin regimens (28 964 randomised patients and 1419 events) also showed no evidence of a reduction in the risk of atrial fibrillation (1.00, 0.90 to 1.12; P=0.99). Conclusions The suggested beneficial effect of statins on atrial fibrillation from published shorter term studies is not supported by a comprehensive review of published and unpublished evidence from larger scale trials

Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review

OBJECTIVES: Missing data is a common problem during the development, evaluation, and implementation of prediction models. Although machine learning (ML) methods are often said to be capable of circumventing missing data, it is unclear how these methods are used in medical research. We aim to find out if and how well prediction model studies using machine learning report on their handling of missing data. STUDY DESIGN AND SETTING: We systematically searched the literature on published papers between 2018 and 2019 about primary studies developing and/or validating clinical prediction models using any supervised ML methodology across medical fields. From the retrieved studies information about the amount and nature (e.g. missing completely at random, potential reasons for missingness) of missing data and the way they were handled were extracted. RESULTS: We identified 152 machine learning-based clinical prediction model studies. A substantial amount of these 152 papers did not report anything on missing data (n = 56/152). A majority (n = 96/152) reported details on the handling of missing data (e.g., methods used), though many of these (n = 46/96) did not report the amount of the missingness in the data. In these 96 papers the authors only sometimes reported possible reasons for missingness (n = 7/96) and information about missing data mechanisms (n = 8/96). The most common approach for handling missing data was deletion (n = 65/96), mostly via complete-case analysis (CCA) (n = 43/96). Very few studies used multiple imputation (n = 8/96) or built-in mechanisms such as surrogate splits (n = 7/96) that directly address missing data during the development, validation, or implementation of the prediction model. CONCLUSION: Though missing values are highly common in any type of medical research and certainly in the research based on routine healthcare data, a majority of the prediction model studies using machine learning does not report sufficient information on the presence and handling of missing data. Strategies in which patient data are simply omitted are unfortunately the most often used methods, even though it is generally advised against and well known that it likely causes bias and loss of analytical power in prediction model development and in the predictive accuracy estimates. Prediction model researchers should be much more aware of alternative methodologies to address missing data

The association of the Mediterranean diet with heart failure risk in a Dutch population

Background and aims: It is still unclear whether a healthy diet can prevent heart failure (HF). Therefore, this study aimed to investigate the association between adherence to a Mediterranean-style diet, reflected by modified Mediterranean Diet Scores (mMDS), and the incidence of HF in men and women. / Methods and results: This observational study comprised 9316 men and 27,645 women from the EPIC-NL cohort free from cardiovascular disease at baseline. Dietary intakes were assessed using a validated food frequency questionnaire. mMDS was calculated using a 9-point scale based on consumption of vegetables, legumes, fruit, nuts, seeds, grains, fish, fat ratio, dairy, meat and alcohol. HF events were ascertained by linkage to nation-wide registries. Multivariable Hazard Ratios (HR) and 95% confidence intervals (CI) were estimated by Cox proportional hazards regression models. Over a median follow-up of 15 years (IQR 14–16), 633 HF events occurred: 144 in men (1.5%) and 489 in women (1.8%). The median mMDS was 4 (IQR 3–5). There was significant effect modification by sex (P-value for interaction <0.001), therefore results are stratified for men and women. For men, a higher mMDS associated with lower HF risk (HR: 0.88; 95% CI: 0.79, 0.98 per point increase in mMDS; HR upper category: 0.53; 95% CI: 0.33, 0.86), whereas no association was found in women (HR: 0.98; 95% CI: 0.93, 1.04 per point increase; HR upper category: 1.07; 95% CI: 0.83, 1.36). / Conclusion: Adherence to a Mediterranean-style diet may reduce HF risk, particularly in men. The underlying reasons for the differences in findings between men and women need further study

Harnessing publicly available genetic data to prioritize lipid modifying therapeutic targets for prevention of coronary heart disease based on dysglycemic risk

Therapeutic interventions that lower LDL-cholesterol effectively reduce the risk of coronary artery disease (CAD). However, statins, the most widely prescribed LDL-cholesterol lowering drugs, increase diabetes risk. We used genome-wide association study (GWAS) data in the public domain to investigate the relationship of LDL-C and diabetes and identify loci encoding potential drug targets for LDL-cholesterol modification without causing dysglycemia. We obtained summary-level GWAS data for LDL-C from GLGC, glycemic traits from MAGIC, diabetes from DIAGRAM and CAD from CARDIoGRAMplusC4D consortia. Mendelian randomization analyses identified a one standard deviation (SD) increase in LDL-C caused an increased risk of CAD (odds ratio [OR] 1.63 (95 % confidence interval [CI] 1.55, 1.71), which was not influenced by removing SNPs associated with diabetes. LDL-C/CAD-associated SNPs showed consistent effect directions (binomial P = 6.85 × 10−5). Conversely, a 1-SD increase in LDL-C was causally protective of diabetes (OR 0.86; 95 % CI 0.81, 0.91), however LDL-cholesterol/diabetes-associated SNPs did not show consistent effect directions (binomial P = 0.15). HMGCR, our positive control, associated with LDL-C, CAD and a glycemic composite (derived from GWAS meta-analysis of four glycemic traits and diabetes). In contrast, PCSK9, APOB, LPA, CETP, PLG, NPC1L1 and ALDH2 were identified as “druggable” loci that alter LDL-C and risk of CAD without displaying associations with dysglycemia. In conclusion, LDL-C increases the risk of CAD and the relationship is independent of any association of LDL-C with diabetes. Loci that encode targets of emerging LDL-C lowering drugs do not associate with dysglycemia, and this provides provisional evidence that new LDL-C lowering drugs (such as PCSK9 inhibitors) may not influence risk of diabetes

Does Heterogeneity Exist in Treatment Associations With Renin–Angiotensin–System Inhibitors or Beta-blockers According to Phenotype Clusters in Heart Failure with Preserved Ejection Fraction?

BACKGROUND: We explored the association between use of renin–angiotensin system inhibitors and beta-blockers, with mortality/morbidity in 5 previously identified clusters of patients with heart failure with preserved ejection fraction (HFpEF). METHODS AND RESULTS: We analyzed 20,980 patients with HFpEF from the Swedish HF registry, phenotyped into young–low comorbidity burden (12%), atrial fibrillation–hypertensive (32%), older–atrial fibrillation (24%), obese–diabetic (15%), and a cardiorenal cluster (17%). In Cox proportional hazard models with inverse probability weighting, there was no heterogeneity in the association between renin–angiotensin system inhibitor use and cluster membership for any of the outcomes: cardiovascular (CV) mortality, all-cause mortality, HF hospitalisation, CV hospitalisation, or non-CV hospitalisation. In contrast, we found a statistical interaction between beta-blocker use and cluster membership for all-cause mortality (P = .03) and non-CV hospitalisation (P = .001). In the young–low comorbidity burden and atrial fibrillation–hypertensive cluster, beta-blocker use was associated with statistically significant lower all-cause mortality and non-CV hospitalisation and in the obese–diabetic cluster beta-blocker use was only associated with a statistically significant lower non-CV hospitalisation. The interaction between beta-blocker use and cluster membership for all-cause mortality could potentially be driven by patients with improved EF. However, patient numbers were diminished when excluding those with improved EF and the direction of the associations remained similar. CONCLUSIONS: In patients with HFpEF, the association with all-cause mortality and non-CV hospitalisation was heterogeneous across clusters for beta-blockers. It remains to be elucidated how heterogeneity in HFpEF could influence personalized medicine and future clinical trial design

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

Review Purpose: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. Findings: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. Summary: The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. Graphical Abstract: HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease

Chirp management in silicon-graphene electro absorption modulators

We study the frequency chirp properties of graphene-on-silicon electro-absorption modulators (EAMs). By experimentally measuring the chirp of a 100 \ub5m long single layer graphene EAM, we show that the optoelectronic properties of graphene induce a large positive linear chirp on the optical signal generated by the modulator, giving rise to a maximum shift of the instantaneous frequency up to 1.8 GHz. We exploit this peculiar feature for chromatic-dispersion compensation in fiber optic transmission thanks to the pulse temporal lensing effect. In particular, we show dispersion compensation in a 10Gb/s transmission experiment on standard single mode fiber with temporal focusing distance (0-dB optical-signal-to-noise ratio penalty) of 60 km, and also demonstrate 100 km transmission with a bit error rate largely lower than the conventional Reed-Solomon forward error correction threshold of 10 123

Rs964184 (APOA5-A4-C3-A1) Is Related to Elevated Plasma Triglyceride Levels, but Not to an Increased Risk for Vascular Events in Patients with Clinically Manifest Vascular Disease

Background: Single nucleotide polymorphisms in the APOA5-A4-C3-A1 gene complex are associated with elevated plasma triglycerides and elevated vascular risk in healthy populations. In patients with clinically manifest vascular disease, hypertriglyceridemia and metabolic syndrome are frequently present, but the contribution of these single nucleotide polymorphisms to plasma triglycerides, effect modification by obesity and risk of recurrent vascular events is unknown in these patients. Methods: Prospective cohort study of 5547 patients with vascular disease. Rs964184 (APOA5-A4-C3-A1 gene complex) was genotyped, and we evaluated the relation with plasma lipid levels, presence of metabolic syndrome and the risk for new vascular events. Results: The minor allele of rs964184 was strongly associated with log plasma triglycerides (β 0.12; 95%CI 0.10-0.15, p = 1.1*10−19), and was also associated with 0.03 mmol/L lower high-density lipoprotein-cholesterol (95%CI 0.01–0.04), and 0.14 mmol/L higher non-high-density lipoprotein-cholesterol (95%CI 0.09–0.20). The minor allele frequency increased from 10.9% in patients with plasma triglycerides 27 kg/m2, p for interaction = 0.02). The prevalence of the metabolic syndrome increased from 52% for patients with two copies of the major allele to 62% for patients with two copies of the minor allele (p = 0.01). Rs964184 was not related with recurrent vascular events (HR 0.99; 95%CI 0.86–1.13). Conclusion: The single nucleotide polymorphism rs964184 (APOA5-A4-C3-A1) is associated with elevated plasma triglycerides concentrations in patients with clinically manifest vascular disease. In carriers of one minor allele, the effect on plasma triglycerides was modified by body mass index. There is no relation between rs964184 and recurrent vascular events in these patients

Relation of Iron Status to Prognosis After Acute Coronary Syndrome

Iron deficiency has been extensively researched and is associated with adverse outcomes in heart failure. However, to our knowledge, the temporal evolution of iron status has not been previously investigated in patients with acute coronary syndrome (ACS). Therefore, we aimed to explore the temporal pattern of repeatedly measured iron, ferritin, transferrin, and transferrin saturation (TSAT) in relation to prognosis post-ACS. BIOMArCS (BIOMarker study to identify the Acute risk of a Coronary Syndrome) is a prospective, multicenter, observational cohort study conducted in The Netherlands between 2008 and 2015. A total of 844 patients with post-ACS were enrolled and underwent high-frequency (median 17) blood sampling during 1 year follow-up. Biomarkers of iron status were measured batchwise in a central laboratory. We analyzed 3 patient subsets, including the case-cohort (n = 187). The primary endpoint (PE) was a composite of cardiovascular mortality and repeat nonfatal ACS, including unstable angina pectoris requiring revascularization. The association between iron status and the PE was analyzed using multivariable joint models. Mean age was 63 years; 78% were men, and >50% had iron deficiency at first sample in the case-cohort. After adjustment for a broad range of clinical variables, 1 SD decrease in log-iron was associated with a 2.2-fold greater risk of the PE (hazard ratio 2.19, 95% confidence interval 1.34 to 3.54, p = 0.002). Similarly, 1 SD decrease in log-TSAT was associated with a 78% increased risk of the PE (hazard ratio 1.78, 95% confidence interval 1.17 to 2.65, p = 0.006). Ferritin and transferrin were not associated with the PE. Repeated measurements of iron and TSAT predict risk of adverse outcomes in patients with post-ACS during 1 year follow-up. Trial Registration: The Netherlands Trial Register. Unique identifiers: NTR1698 and NTR1106. Registered at https://www.trialregister.nl/trial/1614 and https://www.trialregister.nl/trial/1073