Effect of Macrotroponin in a Cohort of Community Patients with Elevated Cardiac Troponin.

BACKGROUND: Macrotroponin is an important cause of discrepancy between current high-sensitivity cardiac troponin (hs-cTn) assays, however, its clinical significance is unclear. This study examined the effects of macrotroponin and repeat testing by different hs-cTnI assays in a cohort of community patients with elevated hs-cTnI. METHODS: The first residual serum specimen from each patient in the community admitted to hospital with elevated hs-cTnI (Siemens hs-cTnI Centaur) was retested after immunoglobulin depletion and by 5 other hs-cTn assays. Low recovery of cTnI (<40%) following immunoglobulin depletion was considered as macrotroponin. A retrospective chart review was performed for these participants. Investigator-adjudicated diagnosis served as the reference standard. RESULTS: In our cohort of community patients with elevated troponin (n = 188), participants with macrotroponin (n = 99) often had a multifactorial or indeterminate myocardial injury (56% vs 25%) and were less likely to have acute coronary syndrome (9% vs 28%). On repeat testing of cTn on other platforms, better diagnostic performance (c-statistics) for ischemic and non-ischemic cardiac causes was observed on the Beckman Access hs-cTnI (0.74; 95% confidence interval [CI] 0.67-0.81) or the Abbott hs-cTnI Architect (0.75; CI 0.68-0.82) compared to the Siemens hs-cTnI Vista (0.62; CI 0.54-0.70; P < 0.05). This could be attributed to differences in assay reactivity for macrotroponin. Interestingly, better diagnostic performance was observed in patients without macrotroponin. Although a small number of deaths occurred (n = 16), participants with macrotroponin had better overall survival. CONCLUSIONS: In the low-risk setting, the presence of macrotroponin was clinically associated with multifactorial or indeterminate causes of troponin elevation.

In silico investigation of the mechanisms underlying atrial fibrillation due to impaired Pitx2.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is a major cause of stroke and morbidity. Recent genome-wide association studies have shown that paired-like homeodomain transcription factor 2 (Pitx2) to be strongly associated with AF. However, the mechanisms underlying Pitx2 modulated arrhythmogenesis and variable effectiveness of antiarrhythmic drugs (AADs) in patients in the presence or absence of impaired Pitx2 expression remain unclear. We have developed multi-scale computer models, ranging from a single cell to tissue level, to mimic control and Pitx2-knockout atria by incorporating recent experimental data on Pitx2-induced electrical and structural remodeling in humans, as well as the effects of AADs. The key findings of this study are twofold. We have demonstrated that shortened action potential duration, slow conduction and triggered activity occur due to electrical and structural remodelling under Pitx2 deficiency conditions. Notably, the elevated function of calcium transport ATPase increases sarcoplasmic reticulum Ca2+ concentration, thereby enhancing susceptibility to triggered activity. Furthermore, heterogeneity is further elevated due to Pitx2 deficiency: 1) Electrical heterogeneity between left and right atria increases; and 2) Increased fibrosis and decreased cell-cell coupling due to structural remodelling slow electrical propagation and provide obstacles to attract re-entry, facilitating the initiation of re-entrant circuits. Secondly, our study suggests that flecainide has antiarrhythmic effects on AF due to impaired Pitx2 by preventing spontaneous calcium release and increasing wavelength. Furthermore, our study suggests that Na+ channel effects alone are insufficient to explain the efficacy of flecainide. Our study may provide the mechanisms underlying Pitx2-induced AF and possible explanation behind the AAD effects of flecainide in patients with Pitx2 deficiency.

Routinely reported ejection fraction and mortality in clinical practice: where does the nadir of risk lie?

AIMS: We investigated the relationship between clinically assessed left ventricular ejection fraction (LVEF) and survival in a large, heterogeneous clinical cohort. METHODS AND RESULTS: Physician-reported LVEF on 403 977 echocardiograms from 203 135 patients were linked to all-cause mortality using electronic health records (1998-2018) from US regional healthcare system. Cox proportional hazards regression was used for analyses while adjusting for many patient characteristics including age, sex, and relevant comorbidities. A dataset including 45 531 echocardiograms and 35 976 patients from New Zealand was used to provide independent validation of analyses. During follow-up of the US cohort, 46 258 (23%) patients who had undergone 108 578 (27%) echocardiograms died. Overall, adjusted hazard ratios (HR) for mortality showed a u-shaped relationship for LVEF with a nadir of risk at an LVEF of 60-65%, a HR of 1.71 [95% confidence interval (CI) 1.64-1.77] when ≥70% and a HR of 1.73 (95% CI 1.66-1.80) at LVEF of 35-40%. Similar relationships with a nadir at 60-65% were observed in the validation dataset as well as for each age group and both sexes. The results were similar after further adjustments for conditions associated with an elevated LVEF, including mitral regurgitation, increased wall thickness, and anaemia and when restricted to patients reported to have heart failure at the time of the echocardiogram. CONCLUSION: Deviation of LVEF from 60% to 65% is associated with poorer survival regardless of age, sex, or other relevant comorbidities such as heart failure. These results may herald the recognition of a new phenotype characterized by supra-normal LVEF.

Afterdepolarizations and abnormal calcium handling in atrial myocytes with modulated SERCA uptake: a sensitivity analysis of calcium handling channels.

Delayed afterdepolarizations (DADs) and spontaneous depolarizations (SDs) are typically triggered by spontaneous diastolic Ca2+ release from the sarcoplasmic reticulum (SR) which is caused by an elevated SR Ca2+-ATPase (SERCA) uptake and dysfunctional ryanodine receptors. However, recent studies on the T-box transcription factor gene (TBX5) demonstrated that abnormal depolarizations could occur despite a reduced SERCA uptake. Similar findings have also been reported in experimental or clinical studies of diabetes and heart failure. To investigate the sensitivity of SERCA in the genesis of DADs/SDs as well as its dependence on other Ca2+ handling channels, we performed systematic analyses using the Maleckar et al. model. Results showed that the modulation of SERCA alone cannot trigger abnormal depolarizations, but can instead affect the interdependency of other Ca2+ handling channels in triggering DADs/SDs. Furthermore, we discovered the existence of a threshold value for the intracellular concentration of Ca2+ ([Ca2+]i) for abnormal depolarizations, which is modulated by the maximum SERCA uptake and the concentration of Ca2+ in the uptake and release compartments in the SR ([Ca2+]up and [Ca2+]rel). For the first time, our modelling study reconciles different mechanisms of abnormal depolarizations in the setting of 'lone' AF, reduced TBX5, diabetes and heart failure, and may lead to more targeted treatment for these patients. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Polygenic Risk Scores in Coronary Artery Disease and Atrial Fibrillation.

Coronary artery disease (CAD) and atrial fibrillation (AF) are two highly prevalent cardiovascular disorders that are associated with substantial morbidity and mortality. Conventional clinical risk factors for these disorders may not be identified prior to mid-adult life when pathophysiological processes are already established. A better understanding of the genetic underpinnings of disease should facilitate early detection of individuals at risk and preventative intervention. Single rare variants of large effect size that are causative for CAD, AF, or predisposing factors such as hypertension or hyperlipidaemia, may give rise to familial forms of disease. However, in most individuals, CAD and AF are complex traits in which combinations of genetic and acquired factors play a role. Common genetic variants that affect disease susceptibility have been identified by genome-wide association studies, but the predictive value of any single variant is limited. To address this issue, polygenic risk scores (PRS), comprised of suites of disease-associated common variants have been devised. In CAD and AF, incorporation of PRS into risk stratification algorithms has provided incremental prognostic information to clinical factors alone. The long-term health and economic benefits of PRS-guided clinical management remain to be determined however, and further evidence-based data are required.

A multiplex pharmacogenetics assay using the MinION nanopore sequencing device.

OBJECTIVES: The MinION nanopore sequencing device opens the opportunity to cost-effective and point-of-care DNA sequencing. As a proof of principle, we developed a multiplex assay targeting pharmacogenetic variants related to clopidogrel and warfarin, the two commonly used drugs that show response variability due to genetic polymorphisms. METHODS: Six reference and 78 clinical DNA samples were amplified by PCR to generate 15 amplicons targeting 27 key variants. These products were then barcoded to enable sample multiplexing in one sequencing run. Four variant calling tools (marginCaller, VarScan 2, nanopolish, Clairvoyante) were used to compare genotyping accuracy. RESULTS: In our cohort, 81 out of 84 samples were successfully sequenced and genotyped. Using nanopolish as the variant calling tool achieved accuracy >95% for all except two variants. A known single base deletion (CYP2C9*6) was successfully detected. CONCLUSION: While minor misgenotyping issues exist, this work demonstrates that drug-specific or broad pharmacogenetic screening assays using small PCR amplicons are possible on the MinION sequencing device.

ICMA: an integrated cardiac modeling and analysis platform.

UNLABELLED: ICMA, a software framework to create 3D finite element models of the left ventricle from cardiac ultrasound or magnetic resonance imaging (MRI) data, has been made available as an open-source code. The framework is hardware vendor independent and uses speckle tracking (endocardial border detection) on ultrasound (MRI) imaging data in the form of DICOM. Standard American Heart Association segment-based strain analysis can be performed using a browser-based interface. The speckle tracking, border detection and model fitting methods are implemented in C++ using open-source tools. They are wrapped as web services and orchestrated via a JBOSS-based application server. AVAILABILITY AND IMPLEMENTATION: The source code for ICMA is freely available under MPL 1.1 or GPL 2.0 or LGPL 2.1 license at https://github.com/ABI-Software-Laboratory/ICMA and a standalone virtual machine at http://goo.gl/M4lJKH for download. CONTACT: r.jagir@auckland.ac.nz SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Beatwise ECG Classification for the Detection of Atrial Fibrillation with Deep Learning.

Atrial fibrillation (AF) is the most common, sustained cardiac arrhythmia. Early intervention and treatment could have a much higher chance of reversing AF. An electrocardiogram (ECG) is widely used to check the heart's rhythm and electrical activity in clinics. The current manual processing of ECGs and clinical classification of AF types (paroxysmal, persistent and permanent AF) is ill-founded and does not truly reflect the seriousness of the disease. In this paper, we proposed a new machine learning method for beat-wise classification of ECGs to estimate AF burden, which was defined by the percentage of AF beats found in the total recording time. Both morphological and temporal features for categorizing AF were extracted via two combined classifiers: a 1D U-Net that evaluates fiducial points and segmentation to locate each heartbeat; and the other Recurrent Neural Network (RNN) to enhance the temporal classification of an individual heartbeat. The output of the classifiers had four target classes: Normal Sinus Rhythm (SN), AF, Noises (NO), and Others (OT). The approach was trained and validated on the Icentia11k dataset, with 1001 and 250 patients' ECGs, respectively. The testing accuracy for the four classes was found to be 0.86, 0.81, 0.79, and 0.75, respectively. Our study demonstrated the feasibility and superior performance of combing U-net and RNN to conduct a beat-wise classification of ECGs for AF burden. However, further investigation is warranted to validate this deep learning approach.Clinical relevance- This paper proposes a novel machine learning network for ECG beatwise classification, specifically for aiding AF burden determination.

RNA interference targeting ANGPTL3 for triglyceride and cholesterol lowering: phase 1 basket trial cohorts.

Elevated triglycerides and non-high-density lipoprotein cholesterol (HDL-C) are risk factors for atherosclerotic cardiovascular disease (ASCVD). ARO-ANG3 is an RNA interference therapy that targets angiopoietin-like protein 3 (ANGPTL3), a regulator of lipoprotein metabolism. This first-in-human, phase 1, randomized, placebo-controlled, open-label trial investigated single and repeat ARO-ANG3 doses in four cohorts of fifty-two healthy participants and one cohort of nine participants with hepatic steatosis, part of a basket trial. Safety (primary objective) and pharmacokinetics (in healthy participants) and pharmacodynamics (secondary objectives) of ARO-ANG3 were evaluated. ARO-ANG3 was generally well tolerated, with similar frequencies of treatment-emergent adverse events in active and placebo groups. Systemic absorption of ARO-ANG3 in healthy participants was rapid and sustained, with a mean Tmax of 6.0-10.5 h and clearance from plasma within 24-48 h after dosing with a mean t½ of 3.9-6.6 h. In healthy participants, ARO-ANG3 treatment reduced ANGPTL3 (mean -45% to -78%) 85 days after dose. Reductions in triglyceride (median -34% to -54%) and non-HDL-C (mean -18% to -29%) (exploratory endpoints) concentrations occurred with the three highest doses. These early-phase data support ANGPTL3 as a potential therapeutic target for ASCVD treatment. ClinicalTrials.gov identifier: NCT03747224.

Prognostic modelling of clinical outcomes after first-time acute coronary syndrome in New Zealand.

OBJECTIVE: The Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS) was established to investigate the drivers of secondary events after first-time acute coronary syndrome (ACS), including addressing inequitable outcomes by ethnicity. Herein, the first clinical outcomes and prognostic modelling approach are reported. METHODS: First, in 28 176 New Zealanders with first-time ACS from a national registry, a clinical summary score for predicting 1-year death/cardiovascular readmission was created using Cox regression of 20 clinical variables. This score was then calculated in the 2015 participant MENZACS study to represent clinical risk. In MENZACS, Cox regression was used to assess N-terminal pro-B-type natriuretic peptide (NT-proBNP) as a prognostic marker for death/cardiovascular readmission in four models, adjusting for (1) age and sex; (2) age, sex, ethnicity; (3) clinical summary score; (4) clinical summary score and ethnicity. RESULTS: Of the 2015 MENZACS participants (mean age 61 years, 79% male, 73% European, 14% Maori, 5% Pacific people), 2003 were alive at discharge. Of the 2003, 416 (20.8%) experienced all-cause death/cardiovascular readmission over a median of 3.5 years. In a simple model, age, male sex, Maori ethnicity and NT-proBNP levels were significant predictors of outcome. After adjustment for the clinical summary score, which includes age and sex, NT-proBNP and ethnicity were no longer statistically significant: log2(NT-proBNP) hazard ratio (HR) 1.03, 95% confidence interval (95% CI) 0.98 to 1.08, p=0.305; Maori ethnicity HR 1.26, 95% CI 0.97 to 1.62, p=0.084. CONCLUSIONS: In 2015 patients with first-time ACS, recurrent events were common (20.8%). Increasing NT-proBNP levels and Maori ethnicity were predictors of death/cardiovascular readmission, but not after adjustment for the 20 clinical risk factors represented by the clinical summary score. TRIAL REGISTRATION NUMBER: ACTRN12615000676516.

Metabolomics and a Breath Sensor Identify Acetone as a Biomarker for Heart Failure.

BACKGROUND: Multi-omics delivers more biological insight than targeted investigations. We applied multi-omics to patients with heart failure with reduced ejection fraction (HFrEF). METHODS: 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography mass spectrometry (LC-MS/GC-MS) and solid-phase microextraction (SPME) volatilomics in plasma and urine. HFrEF was defined using left ventricular global longitudinal strain, ejection fraction and NTproBNP. A consumer breath acetone (BrACE) sensor validated results in n = 73. RESULTS: 28 metabolites were identified by GCMS, 35 by LCMS and 4 volatiles by SPME in plasma and urine. Alanine, aspartate and glutamate, citric acid cycle, arginine biosynthesis, glyoxylate and dicarboxylate metabolism were altered in HFrEF. Plasma acetone correlated with NT-proBNP (r = 0.59, 95% CI 0.4 to 0.7), 2-oxovaleric and cis-aconitic acid, involved with ketone metabolism and mitochondrial energetics. BrACE > 1.5 ppm discriminated HF from other cardiac pathology (AUC 0.8, 95% CI 0.61 to 0.92, p < 0.0001). CONCLUSION: Breath acetone discriminated HFrEF from other cardiac pathology using a consumer sensor, but was not cardiac specific.

Trans-fatty acids in New Zealand patients with coronary artery disease.

BACKGROUND: Dietary surveys indicate that New Zealanders have a low intake of trans-fatty acids (TFA) with little derived from industrial sources. This observational cross-sectional study in New Zealand patients with surgical coronary artery disease assesses the plasma levels of TFA and the association of TFA levels with clinical markers of vascular disease. METHODS: 390 patients with severe coronary artery had fasting blood tests taken. Plasma levels of four TFA derived from hydrogenated vegetable oils and ruminant animal products were measured by gas chromatography. Relations between plasma TFA levels and the recent occurrence of myocardial infarction, the presence of polyvascular disease, and serum levels of C-reactive protein (CRP) were assessed. RESULTS: The median TFA level was 0.85% by weight of total fatty acids (IQR 0.59-1.79%), with skewed distribution to the right. For the lowest (<0.74%), middle (0.74-1.07%), and highest (>1.07%) thirds of total TFA, respectively, the proportion of patients with polyvascular disease was 10%, 16%, and 27% (p = 0.0004). Plasma CRP also increased by tertile of TFA (median 2.0, 2.9, 3.2 mg/l, p = 0.007). The association with polyvascular diseases and CRP remained significant after adjustment for risk factors. Significant associations were present between plasma TFA from both ruminant and hydrogenated vegetable oil sources and these markers of cardiovascular risk. CONCLUSIONS: There is an association between relatively low plasma levels of total TFA, mostly derived from ruminant sources, and an increased risk of polyvascular disease and increased CRP in patients with severe coronary artery disease. These high-risk patients may benefit from a targeted approach to minimize all sources of TFA in the diet.

Effect of macrotroponin on the utility of cardiac troponin I as a prognostic biomarker for long term total and cardiovascular disease mortality.

Macrotroponin is a complex formed between endogenous cardiac troponin autoantibodies and circulating cardiac troponin (cTn). It is a recognised cause of discrepancy between current high sensitivity troponin (hs-cTn) assays; and immunoglobulin-bound (macrotroponin) and unbound cTn can coexist in varying proportions in the acute setting. Increasingly it is considered when laboratory cTn results do not match a patient's clinical picture. However, despite the better understanding of macrotroponin as an analytical interference, its clinical significance remains unclear. The aim of this study was to determine the potential impact of macrotroponin on the use of cTn as a long-term prognostic marker. We repeated cTnI testing after polyethylene glycol (PEG) precipitation on consecutive participants (n=159) with a first elevated cTn above 0.2 µg/L during their hospital admission episode. Because this paper is looking at outcomes in years, the initial data were generated at a time when non-hs-cTn assays were in use. We divided the cohort into two groups based on an exploratory PEG recovery cut-off of <34.6% to indicate the presence of possible macrotroponin and compared the overall and cardiovascular related mortality. The median follow-up time for the overall cohort was 8.35 years (8.32-8.40 interquartile range) with no difference between the two groups. The overall median survival was 8.1 years. Our findings indicate a hazard ratio of 0.54 (0.32-0.91 95% CI) for all-cause mortality and 0.48 (0.24-0.95) for cardiovascular mortality in patients with possible macrotroponin compared to those patients with troponin elevation without evidence of macrotroponin, after adjustment for common cardiovascular disease risk factors. Furthermore, an association was observed between PEG% recovery and all-cause mortality (p<0.05). This study showed that patients with macrotroponin have comparatively favourable long-term all-cause and cardiovascular mortality in a cohort of patients with elevated troponin. We illustrate the importance of recognising cTn results as being a summation of heterogeneous components, including those bound to antibodies, and the potential role of macrotroponin to further improve our interpretation and use of cTn as a biomarker.

A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data.

AIM: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). MATERIALS & METHODS: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. RESULTS: Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73-0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67-0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79-0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77-0.78; p < 0.0001. CONCLUSION: ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.

Multiomics, virtual reality and artificial intelligence in heart failure.

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Lay abstract Multiomics is the integration of multiple sources of health information, for example, genomic, metabolite, etc. This delivers more insight than targeted single investigations and provides an ability to perceive subtle individual differences between people. In this study we applied multiomics to patients with heart failure (HF) using DNA sequencing, metabolomics and machine learning applied to ECG echocardiography. We demonstrated significant differences between subsets of patients with HF using these methods. We also showed that machine learning has significant diagnostic potential in identifying HF patients more efficiently than manual or conventional techniques.

Correction to: The Cost Effectiveness of Genetic Testing for CYP2C19 Variants to Guide Thienopyridine Treatment in Patients with Acute Coronary Syndromes.

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Antiplatelet drug nonresponsiveness.

The response to most medication, including antiplatelet drugs, is highly variable between individuals. Observational studies have shown that nonresponders to antiplatelet agents appear to have an increased incidence of vascular events. This review article reviews the background, mechanisms, and evidence in support of the clinical significance of this phenomenon.

The antiplatelet effect of six non-steroidal anti-inflammatory drugs and their pharmacodynamic interaction with aspirin in healthy volunteers.

Patients with cardiovascular disease taking aspirin and some nonsteroidal anti-inflammatory drugs (NSAIDs) appear to have increased vascular events. This study was conducted to compare the ex vivo antiplatelet effects of 6 commonly used NSAIDs and to determine whether these agents antagonize the effect of aspirin. Platelet function was assessed by Platelet Function Analyzer 100 closure time in normal subjects in a randomized, blinded, multiple-crossover study. Platelet function was measured 12 hours after the administration of each NSAID. The NSAID was then given 2 hours before aspirin 300 mg, and platelet function was reassessed 24 hours later. At 12 hours after the administration of naproxen and tiaprofenic acid, closure time was significantly prolonged, whereas the other NSAIDs did not cause significant prolongations. Compared with placebo plus aspirin, closure time was significantly reduced when ibuprofen, indomethacin, naproxen, or tiaprofenic acid was given before aspirin. In conclusion, ibuprofen, indomethacin, naproxen, and tiaprofenic acid all block the antiplatelet effect of aspirin. Sulindac and celecoxib did not demonstrate any significant antiplatelet effect or reduce the antiplatelet of aspirin and, therefore, of the NSAIDs evaluated may be the drugs of choice for patients requiring aspirin and NSAIDs.

Ionic and cellular mechanisms underlying TBX5/PITX2 insufficiency-induced atrial fibrillation: Insights from mathematical models of human atrial cells.

Transcription factors TBX5 and PITX2 involve in the regulation of gene expression of ion channels and are closely associated with atrial fibrillation (AF), the most common cardiac arrhythmia in developed countries. The exact cellular and molecular mechanisms underlying the increased susceptibility to AF in patients with TBX5/PITX2 insufficiency remain unclear. In this study, we have developed and validated a novel human left atrial cellular model (TPA) based on the ten Tusscher-Panfilov ventricular cell model to systematically investigate how electrical remodeling induced by TBX5/PITX2 insufficiency leads to AF. Using our TPA model, we have demonstrated that spontaneous diastolic depolarization observed in atrial myocytes with TBX5-deletion can be explained by altered intracellular calcium handling and suppression of inward-rectifier potassium current (IK1). Additionally, our computer simulation results shed new light on the novel cellular mechanism underlying AF by indicating that the imbalance between suppressed outward current IK1 and increased inward sodium-calcium exchanger current (INCX) resulted from SR calcium leak leads to spontaneous depolarizations. Furthermore, our simulation results suggest that these arrhythmogenic triggers can be potentially suppressed by inhibiting sarcoplasmic reticulum (SR) calcium leak and reversing remodeled IK1. More importantly, this study has clinically significant implications on the drugs used for maintaining SR calcium homeostasis, whereby drugs such as dantrolene may confer significant improvement for the treatment of AF patients with TBX5/PITX2 insufficiency.

A Machine Learning Aided Systematic Review and Meta-Analysis of the Relative Risk of Atrial Fibrillation in Patients With Diabetes Mellitus.

Background: Meta-analysis is a widely used tool in which weighted information from multiple similar studies is aggregated to increase statistical power. However, the exponential growth of publications in key areas of medical science has rendered manual identification of relevant studies increasingly time-consuming. The aim of this work was to develop a machine learning technique capable of robust automatic study selection for meta-analysis. We have validated this approach with an up-to-date meta-analysis to investigate the association between diabetes mellitus (DM) and new-onset atrial fibrillation (AF). Methods: The PubMed online database was searched from 1960 to September 2017 where 4,177 publications that mentioned both DM and AF were identified. Relevant studies were selected as follows. First, publications were clustered based on common text features using an unsupervised K-means algorithm. Clusters that best matched the selected set of potentially relevant studies (a "training" set of 139 articles) were then identified by using maximum entropy classification. The 139 articles selected automatically on this basis were screened manually to identify potentially relevant studies. To determine the validity of the automated process, a parallel set of studies was also assembled by manually screening all initially searched publications. Finally, detailed manual selection was performed on the full texts of the studies in both sets using standard criteria. Quality assessment, meta-regression random-effects models, sensitivity analysis and publication bias assessment were then conducted. Results: Machine learning-assisted screening identified the same 29 studies for meta-analysis as those identified by using manual screening alone. Machine learning enabled more robust and efficient study selection, reducing the number of studies needed for manual screening from 4,177 to 556 articles. A pooled analysis using the most conservative estimates indicated that patients with DM had ~49% greater risk of developing AF compared with individuals without DM. After adjusting for three additional risk factors i.e., hypertension, obesity and heart disease, the relative risk was 23%. Using multivariate adjusted models, the risk for developing AF in patients with DM was similar for all DM subtypes. Women with DM were 24% more likely to develop AF than men with DM. The risk for new-onset AF in patients with DM has also increased over the years. Conclusions: We have developed a novel machine learning method to identify publications suitable for inclusion in meta-analysis.This approach has the capacity to provide for a more efficient and more objective study selection process for future such studies. We have used it to demonstrate that DM is a strong, independent risk factor for AF, particularly for women.