Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review.

Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardiovascular events. Objectives: The objectives of this study were to conduct a systematic review of machine learning (ML) algorithms for the identification of LVH and compare them with respect to the classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria for LVH. Methods: A search string was constructed with the operators "left ventricular hypertrophy, electrocardiogram" AND machine learning; then, Medline and PubMed were systematically searched. Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized at least one ML approach. ML approaches encompassed support vector machines, logistic regression, Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural networks, convolutional neural networks, deep neural networks and a back-propagation neural network. Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison with the classical ECG criteria for LVH was performed in nine studies. ML algorithms were universally more sensitive than the Cornell voltage, Cornell product, Sokolow-Lyons or Romhilt-Estes criteria. However, none of the ML algorithms had meaningfully better specificity, and four were worse. Many of the ML algorithms included a large number of clinical (age, sex, height, weight), laboratory and detailed ECG waveform data (P, QRS and T wave), making them difficult to utilize in a clinical screening situation. Conclusions: There are over a dozen different ML algorithms for the detection of LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinical and laboratory variables. Most improved in terms of sensitivity, but most also failed to outperform specificity compared to the classic ECG criteria. ML algorithms should be compared or tested on the same (standard) database.

Relationship between Alzheimer dementia and QT interval: A meta-analysis.

While the link between aging and mortality from dementia is widely appreciated, the mechanism is not clear. The objective of this study was to determine whether there is a direct relationship between Alzheimer dementia (AD) and the QT interval, because the latter has been related to cardiac mortality. A systematic review and meta-analysis were conducted after a Medline and EMBASE search using terms "Alzheimer disease or Dementia AND QT interval, QT dispersion or cardiac repolarization." Four studies with control groups were identified. There were significant differences in QT interval between individuals with AD vs individuals without dementia (controls) (odds ratio (OR)1.665 [random effects model] and 1.879 [fixed effect model]) (p < 0.001). There were significant differences in QT interval between individuals with AD vs individuals with mild cognitive impairment (MCI) (OR 1.760 [random effects] and 1.810 [fixed effect]) (p < 0.001). A significant (p <0.001) correlation exists between the QTc and the Mini-Mental State Exam (MMSE), a test of cognitive function. Two studies examined QT variability (the difference between the longest and shortest QT interval on a 12 lead ECG); the OR for QT variability AD vs MCI was 3.858 [random effects model] and 3.712 [fixed effects model] (p < 0.001). When compared to the control group, the OR for QT dispersion in AD was 6.358 [random effects model] or 5.143 ( P< 0.001) [fixed effects model]. A qualitative analysis of the data raised questions about paucity of data defining the nature of the control groups, the pathophysiologic mechanism, and the uniform use of a poor QT heart rate correction factor. The longer QT in AD, greater QT variability in AD, and the direct relationship between QT interval and AD severity supports a brain-heart connection in AD that might be fundamental to aging-induced AD and mortality. Issues with defining the control group, limited number of studies, conflicting data in population studies, and the lack of a strong electrophysiological basis underscore the need for additional research in this field.

Estimating Left Ventricular Mass from the Electrocardiogram across the Spectrum of LV Mass from Normal to Increased LV Mass in an Older Age Group.

Objectives: To examine the relationship of QRS voltages and left ventricular (LV) mass across the spectrum of individuals with different LV mass. Methods: Twenty QRS voltage measurements or combinations were determined in a consecutive series of 159 adults with an ECG and echocardiogram without previous myocardial infarction, left or right bundle branch block, pre-excitation, or electronic pacemaker. Results: The four strongest and significant correlations between QRS and LV mass were S in V4, deepest S wave in any precordial lead plus S in V4, S in V3, and S in V3 plus R in AVL times QRS duration. For men, the strength of the relationships were S in V3 (F = 33.8), deepest S wave in any precordial lead plus S V4 (F = 33.7), S in V3 plus R aVL (F = 29.9), S in V4 (F = 29.79), and deepest S in precordial leads (F = 17.9). The R wave in AVL alone did not correlate with LV mass. Criteria using the R wave in lateral precordial leads did not correlate as strongly with LV mass. For women, only S in V4 significantly correlated with LV mass. Overall, the R wave voltage in limb leads (AVL I or II) did not correlate with precordial S wave amplitudes. Univariate and multivariate analysis showed that some but not all QRS voltages correlated with each other. In multivariate analysis, using only single variables and not combination of QRS variables, the only significant relationship between QRS voltage and left ventricular mass was for men the S in V3 (p = 0.04) and for women S in V4 (p = 0.016) and R in V6 (p = 0.04). Conclusion: The S wave in V3 and V4 correlate most strongly with LV mass while the R wave in limb leads, including AVL, do not correlate.

Myeloperoxidase, carnitine, and derivatives of reactive oxidative metabolites in heart failure with preserved versus reduced ejection fraction: A meta-analysis.

BACKGROUND: Understanding the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF) continues to be challenging. Several inflammatory and metabolic biomarkers have recently been suggested to be involved in HFpEF. OBJECTIVES: The purpose of this review was to synthesize the evidence on non-traditional biomarkers from metabolomic studies that may distinguish HFpEF from heart failure with reduced ejection fraction (HFrEF) and controls without HF. METHODS: A systematic search was conducted using Medline and PubMed with search terms such as "HFpEF" and "metabolomics", and a meta-analysis was conducted. RESULTS: Myeloperoxidase (MPO) levels were significantly (p < 0.001) higher in HFpEF than controls without HF, but comparable (p = 0.838) between HFpEF and HFrEF. Carnitine levels were significantly (p < 0.0001) higher in HFrEF than HFpEF, but comparable (p = 0.443) between HFpEF and controls without HF. Derivatives of reactive oxidative metabolites (DROMs) were not significantly (p = 0.575) higher in HFpEF than controls without HF. CONCLUSION: These data suggest that MPO is operative in HFpEF and HFrEF and may be a biomarker for HF. Furthermore, circulating carnitine levels may distinguish HFrEF from HFpEF.

Collagen type IV as the link between arterial stiffness and dementia.

Arterial stiffness has been linked to impaired cognitive function and dementia but the reason for the association is uncertain. This review proposes that collagen type IV is a critical factor linking arterial stiffness and dementia. Several genome wide association studies have related arterial stiffness to Collagen type IVα. Proteomic studies of arteries, demonstrated higher levels of collagen IVα1 in persons with high arterial stiffness. Collagen type IV defects are associated genetic causes of dementia as well as dementia of a variety of other causes. There are plausible causal roles for collagen type IV in dementia. Disorders of Collagen type IV can produce (I) fibro-hyalinosis and elastosis of small arterioles leading to cerebral ischemia and infarction; (II) dysfunction of the blood brain barrier leading to cerebral hemorrhage; (III) carotid artery stiffness with increase pulse pressure induces cerebral blood vessel damage leading to cerebral atrophy. The mechanisms by which Collagen type IV can lead to vascular stiffness include its degradation by matrix metalloprotease type 2 that (a) stimulates vascular smooth muscle cells to produce more extracellular matrix or (b) liberates peptides that damage the subendothelial space. Factors, such as TGF-ß1, and LDL cholesterol especially oxidized LDL can increase collagen type IV and produce vascular stiffness and dementia. Fibroblast growth factor 23, and abnormal NO signaling have been linked to collagen type IV or increased vascular stiffness and an increased risk of dementia. Recognition of the central role of collagen type IV in arterial stiffness and dementia will inspire new research focused on determining whether its modification can benefit arterial and brain health.

Epigenetics in Heart Failure: Role of DNA Methylation in Potential Pathways Leading to Heart Failure with Preserved Ejection Fraction.

This review will focus on epigenetic modifications utilizing the DNA methylation mechanism, which is potentially involved in the pathogenesis of heart failure with preserved ejection fraction (HFpEF). The putative pathways of HFpEF will be discussed, specifically myocardial fibrosis, myocardial inflammation, sarcoplasmic reticulum Ca2+-ATPase, oxidative-nitrosative stress, mitochondrial and metabolic defects, as well as obesity. The relationship of HFpEF to aging and atrial fibrillation will be examined from the perspective of DNA methylation.

Identifying Premature Ventricular Complexes from Outflow Tracts Based on PVC Configuration: A Machine Learning Approach.

BACKGROUND: Current inferences about the site of origin (SOO) of premature ventricular complexes (PVC) from the surface ECG have not been subjected to newer data analytic techniques that identify signals that are not recognized by visual inspection. AIMS: The objective of this study was to apply data analytics to PVC characteristics. METHODS: PVCs from 12-lead ECGs of a consecutive series of 338 individuals were examined by unsupervised machine learning cluster analysis, and indexes were compared to a composite criterion for SOO. RESULTS: Data analytics found that V1S plus V2S ≤ 9.25 of the PVC had a LVOT origin (sensitivity 95.4%; specificity 97.5%). V1R + V2R + V3R > 15.0 (a RBBB configuration) likely had a LVOT origin. PVCs with V1S plus V2S > 12.75 (LBBB configuration) likely had a RVOT origin. PVC with V1S plus V2S > 14.25 (LBBB configuration) and all inferior leads positive likely had a RVOT origin. CONCLUSION: Newer data analytic techniques provide a non-invasive approach to identifying PVC SOO, which should be useful for the clinician evaluating a 12-lead ECG.

C-Reactive Protein, Interleukin-6, Trimethylamine-N-Oxide, Syndecan-1, Nitric Oxide, and Tumor Necrosis Factor Receptor-1 in Heart Failure with Preserved Versus Reduced Ejection Fraction: a Meta-Analysis.

PURPOSE OF REVIEW: The purpose of this review was to synthesize the evidence on non-traditional biomarkers from proteomic and metabolomic studies that may distinguish heart failure (HF) with preserved ejection fraction (HFpEF) from heart failure with reduced ejection fraction (HFrEF) and non-HF. RECENT FINDINGS: Understanding the pathophysiology of HFpEF continues to be challenging. A number of inflammatory and metabolic biomarkers that have recently been suggested to be involved include C-reactive protein (CRP), interleukin-6 (IL-6), trimethylamine-N-oxide (TMAO), syndecan-1 (SDC-1), nitric oxide (NO), and tumor necrosis factor receptor-1 (TNFR-1). A systematic search was conducted using Medline, EMBASE, and Web of Science with search terms such as "HFpEF," "metabolomics," and "proteomics," and a meta-analysis was conducted. The results demonstrate significantly higher levels of TMAO, CRP, SDC-1, and IL-6 in HFpEF compared to controls without HF and significantly higher levels of TMAO and CRP in HFrEF compared to controls. The results further suggest that HFpEF might be distinguishable from HFrEF based on higher levels of IL-6 and lower levels of SDC-1 and NO. These data may reflect pathophysiological differences between HFpEF and HFrEF.

Assessment of the QT interval in right bundle branch block.

BACKGROUND: Identifying prolonged QT interval in RBBB has been problematic. METHODS: Four approaches were compared to adjust for the QT prolongation in intermittent RBBB. The implications were assessed in a separate group of 200 persons with established RBBB. RESULTS: In 12 individuals, mean age 74.5 years with intermittent RBBB, the presence of RBBB significantly (p < 0.05) increased the QT interval in each of six different heart rate correction formulae by an amount ranging from 35.4 ms in the Hodges formula to 50.2 ms in the Bazett formula. Four different equations were tested to adjust the QT interval and one approach QTcRBBB = 0.945*QTcRBBB - 26 was the best method to adjust for the increased QT in RBBB as it produced a QT value that was not significantly different from the QT interval in the absence of RBBB in intermittent RBBB.Failure to adjust the QT interval in RBBB produces an overestimate of the QT interval which in some heart rate adjustment formulae was marked. For the Bazett heart rate adjustment approach QTc 450 ms was found in 73.9% of men and QTc over 460 ms was found in 60.6% of women. CONCLUSION: These data suggest the implementation of a new approach to recalculate the QT intervals in RBBB. QTcRBBB = 0.945*QTcRBBB - 26 with an appropriate heart rate adjustment formula (other than the Bazett formula) accurately predicts the QT interval in the absence of RBBB.

Clozapine-induced Myocarditis: Pathophysiologic Mechanisms and Implications for Therapeutic Approaches.

Clozapine, a superior treatment for treatment-resistant schizophrenia can cause potentially life-threatening myocarditis and dilated cardiomyopathy. While the occurrence of this condition is well known, its molecular mechanisms are unclear and may be multifactorial. Putative mechanisms warrant an in-depth review not only from the perspective of toxicity but also for understanding the molecular mechanisms of the adverse cardiac effects of clozapine and the development of novel therapeutic approaches. Clozapine-induced cardiac toxicity encompasses a diverse set of pathways, including (i) immune modulation and proinflammatory processes encompassing an IgEmediated (type I hypersensitivity) response and perhaps a cytokine release syndrome (ii) catecholaminergic activation (iii) induction of free radicals and oxidative stress (iv) activation of cardiomyocyte cell death pathways, including apoptosis, ischemia through impairment in coronary blood flow via changes in endothelial production of NO and vasoconstriction induced by norepinephrine as well as other factors released from cardiac mast cells. (v) In addition, an extensive examination of the effects of clozapine on non-cardiac cellular proteins demonstrates that clozapine can impair enzymes involved in cellular metabolism, such as pyruvate kinase, mitochondrial malate dehydrogenase, and other proteins, including α-enolase, triosephosphate isomerase and cofilin, which might explain clozapine-induced reductions in myocardial energy generation for cell viability as well as contractile function. Pharmacologic antagonism of these cellular protein effects may lead to the development of strategies to antagonize the cardiac damage induced by clozapine.

Differences in circadian variation in QT interval of the ECG in women compared to men.

BACKGROUND: Measurement of the QT interval in the ECG (QT interval) is important in evaluating risk for cardiac death and for assessing the impact of drugs on the heart. The objective of this study is to determine whether the time of day affects the QT interval, QT interval variability and whether these relationships are influenced by an individual's sex. METHODS: Twenty-four hour ECGs were analyzed in detail on 50 individuals, 49 years of age, without evidence of coronary artery disease, structural heart disease, or significant arrhythmias. Four different QT-heart rate adjustment formulae were calculated and compared. RESULTS: There were significant (P=0.0014) differences between the QT-heart rate relationship during three different time-periods (night 00:00 to 08:00 h, day 08:00 to 14:00 h and evening 14:00 to 24:00 h). Women, compared to men, had a steeper relation of QT to RR interval indicating that when heart rate slows at night, the QT interval is more prolonged which is consistent with a greater susceptibility to fatal arrhythmias. The variability of the QT interval (the SD) was significantly (P<0.01) greater in men than women at night and in the evening but not during the day. There were differences in the ability of different QT heart rate adjustment formulae to blunt the effect of heart rate changes on the QT interval during the day. CONCLUSION: The time of the day that the QT interval is assessed should be considered. The QT heart rate relationship is different in women than in men especially at night. QT interval variability is greater at night especially in men. There are differences in the ability of QT heart rate adjustment formulae to blunt the effect of heart rate on the QT interval. Differences in the QTc at night might be the basis for the higher prevalence of sudden death in women at night.

Comparison of Unsupervised Machine Learning Approaches for Cluster Analysis to Define Subgroups of Heart Failure with Preserved Ejection Fraction with Different Outcomes.

Heart failure with preserved ejection (HFpEF) is a heterogenous condition affecting nearly half of all patients with heart failure (HF). Artificial intelligence methodologies can be useful to identify patient subclassifications with important clinical implications. We sought a comparison of different machine learning (ML) techniques and clustering capabilities in defining meaningful subsets of patients with HFpEF. Three unsupervised clustering strategies, hierarchical clustering, K-prototype, and partitioning around medoids (PAM), were used to identify distinct clusters in patients with HFpEF, based on a wide range of demographic, laboratory, and clinical parameters. The study population had a median age of 77 years, with a female majority, and moderate diastolic dysfunction. Hierarchical clustering produced six groups but two were too small (two and seven cases) to be clinically meaningful. The K-prototype methods produced clusters in which several clinical and biochemical features did not show statistically significant differences and there was significant overlap between the clusters. The PAM methodology provided the best group separations and identified six mutually exclusive groups (HFpEF1-6) with statistically significant differences in patient characteristics and outcomes. Comparison of three different unsupervised ML clustering strategies, hierarchical clustering, K-prototype, and partitioning around medoids (PAM), was performed on a mixed dataset of patients with HFpEF containing clinical and numerical data. The PAM method identified six distinct subsets of patients with HFpEF with different long-term outcomes or mortality. By comparison, the two other clustering algorithms, the hierarchical clustering and K-prototype, were less optimal.

Evaluating the adverse outcome of subtypes of heart failure with preserved ejection fraction defined by machine learning: A systematic review focused on defining high risk phenogroups.

The ability to distinguish clinically meaningful subtypes of heart failure with preserved ejection fraction (HFpEF) has recently been examined by machine learning techniques but studies appear to have produced discordant results. The objective of this study is to synthesize the types of HFpEF by examining their features and relating them to phenotypes with adverse prognosis. A systematic search was conducted using the search terms "Diastolic Heart Failure" OR "heart failure with preserved ejection fraction" OR "heart failure with normal ejection fraction" OR "HFpEF" AND "machine learning" OR "artificial intelligence" OR 'computational biology'. Ten studies were identified and they varied in their prevalence of ten clinical variables: age, sex, body mass index (BMI) or obesity, hypertension, diabetes mellitus, coronary artery disease, atrial fibrillation, chronic kidney disease, chronic obstructive pulmonary disease or symptom severity (NYHA class or BNP). The clinical findings associated with the different phenotypes in > 85 % of studies were age, hypertension, atrial fibrillation, chronic kidney disease and worse symptoms severity; an adverse outcome was in 65 % to 85 % of studies identified diabetes mellitus and female sex and in less than 65 % of studies was body mass index or obesity, and coronary artery disease. COPD was a relevant factor in only 33 % of studies. Adverse clinical outcome - death or admission to hospital (for heart failure) defined phenogroups with the worst outcome. Combining the 4 studies that calculated the MAGGIC score showed a significant (p<0.05) linear relationship between MAGGIC score and outcome, using the one-year event rate. A new score based on strength of the evidence of the HFpEF studies analyzed here, using 9 variables (eliminating COPD), showed a significant (p<0.009) linear relationship with one-year event rate. Three studies examined biomarkers in detail and the ones most prominently related to outcome or consistently found in the studies were GDF15, FABP4, FGF23, sST2, renin and TNF. The dominant factors that identified phenotypes of HFpEF with adverse outcome were hypertension, atrial fibrillation, chronic kidney disease and worse symptoms severity. A new simplified score, based on clinical factors, was proposed to assess prognosis in HFpEF. Several biomarkers were consistently elevated in phenogroups with adverse outcomes and may indicate the underlying mechanism or pathophysiology specific for phenotypes with an adverse prognosis.

Hypocalcemia-Induced QT Interval Prolongation.

An 87-year-old man with a history of transcatheter aortic valve replacement, pulmonary hypertension, diastolic dysfunction with preserved systolic function, and myelofibrosis had a 12-lead ECG showed a prolonged QT interval of 508 ms with heart-rate correction placing it in the 99th percentile of the population. Reduction in the dose of furosemide and calcium supplementation increased serum calcium and shortened the QT interval. This case provides an opportunity to examine newer concepts for the understanding of the mechanisms by which hypocalcemia might induce QT prolongation. Hypocalcemia likely produces corrected QT interval prolongation primarily through a calcium-dependent inactivation (CDI) mechanism on the L-type calcium channel (LTCC). Lower extracellular calcium leads to a decreased ICaL, subsequently causing intracellular calcium to take longer to reach the critical threshold to induce CDI of the LTCC. The resulting prolonged repolarization of the ventricular myocyte can lead to early after-depolarizations and ensuing life-threatening ventricular arrhythmias. Genetic polymorphisms in Ca2+-binding protein calmodulin which can prolong QT, underscore the role for disturbances of intracellular myocardial calcium handling in arrhythmogenesis. Hypocalcemia is an under-recognized cause of QT prolongation and should be taken into careful consideration in patients presenting with incidental findings of a prolonged QT interval.

The Fundamental Basis of Palpitations: A Neurocardiology Approach.

BACKGROUND AND OBJECTIVE: Palpitations are a common symptom that may indicate cardiac arrhythmias, be a somatic complaint in anxiety disorders, and can be present in patients without either condition. The objective of this review was to explore the pathways and fundamental mechanisms through which individuals appreciate palpitations. OBSERVATIONS: Cardiac afferents provide beat-to-beat sensory information on the heart to the spinal cord, brain stem, and higher brain centers. Cardioception, a subset of interoception ('the physiological sense of the condition of the body'), refers to sensing of the heartbeat. High cardioception is present in persons with lower body mass index, lower percentages of body fat, and anxiety disorders. Low cardioception (lower interoceptive awareness) is associated with psychiatric disorders, such as depression, personality disorders, and schizophrenia. CNS sites associated with heartbeat detection have been identified by functional magnetic resonance imaging studies and heartbeat-evoked electroencephalogram potentials. The right insula, cingulate gyrus, somatomotor and somatosensory cortices nucleus accumbens, left subthalamic nucleus, and left ventral capsule/striatum are implicated in both palpitations and heartbeat detection. Involvement of the brain as a primary modulator of palpitations rests on the data that various areas of the brain are activated in association with cardioception, the ability of focal brain stimulation to induce palpitations, the ability of central alpha receptor agonists and antagonists to modulate palpitations, and suppression of palpitations by transcranial repetitive magnetic stimulation (rTMS). CONCLUSIONS: Palpitations should be viewed as a pathway extending from the heart to the brain. Palpitations are, in part, a reflection of an individual's cardioception awareness, which is modulated by body size, percentage of body fat, and psychological or psychiatric conditions. Palpitations can originate in the brain and involve central neurotransmitters. Treatment of palpitations unrelated to cardiac arrhythmias or anxiety disorders should consider the use of central alpha-2 agonists and possibly rTMS.

Overcoming Obstacles to Develop High-Performance Teams Involving Physician in Health Care Organizations.

Many health care organizations struggle and often do not succeed to be high-performance organizations that are not only efficient and effective but also enjoyable places to work. This review focuses on the physician and organizational roles in limiting achievement of a high-performance team in health care organizations. Ten dimensions were constructed and a number of competencies and metrics were highlighted to overcome the failures to: (i) Ensure that the goals, purpose, mission and vision are clearly defined; (ii) establish a supportive organizational structure that encourages high performance of teams; (iii) ensure outstanding physician leadership, performance, goal attainment; and (iv) recognize that medical team leaders are vulnerable to the abuses of personal power or may create a culture of intimidation/fear and a toxic work culture; (v) select a good team and team members-team members who like to work in teams or are willing and able to learn how to work in a team and ensure a well-balanced team composition; (vi) establish optimal team composition, individual roles and dynamics, and clear roles for members of the team; (vii) establish psychological safe environment for team members; (viii) address and resolve interpersonal conflicts in teams; (xi) ensure good health and well-being of the medical staff; (x) ensure physician engagement with the organization. Addressing each of these dimensions with the specific solutions outlined should overcome the constraints to achieving high-performance teams for physicians in health care organizations.

The association of polymorphism in PHACTR1 rs9349379 and rs12526453 with coronary artery atherosclerosis or coronary artery calcification. A systematic review.

OBJECTIVE: There is a need to identify genetic factors that may produce coronary artery atherosclerotic disease (CAD) that are not involved in the usual risk factors leading to CAD. Previous studies have often equated coronary artery calcification (CAC) with CAD with coronary stenosis or its sequelae. The objective of this study was to examine the relationship between phosphatase and actin regulator 1 (PHACTR1) single nucleotide polymorphisms (SNPs) and the type of coronary artery disease CAD versus CAC. METHOD: A systematic review of the literature was conducted to answer the question of whether PHACTR1 gene polymorphisms are associated with coronary artery disease expressed as coronary artery atherosclerosis or CAC. RESULTS: Eighteen studies spanning seven PHACTR1 SNPs were identified and evaluated for the relationship between PHACTR1 and coronary artery disease. There were significant relationships between rs9349379, rs12526453, and CAD with odds ratios (ORs) (confidence interval) of, respectively, 1.15 (1.13-1.17), 1.13 (1.09-1.17) but not for rs2026458, 1.03 (0.88-1.19). The OR for CAC was 1.22 (1.18-1.26) for rs9349379 and 1.28 (1.21-1.38) for rs12526453. CONCLUSIONS: Several PHACTR1 specifically rs9349379 and rs12526453 polymorphisms but not rs2026458, are associated with CAD. There are differences in the association of PHACTR1 SNPs with CAC. PHACTR1 warrants more attention and study for the prevention and treatment of CAD.

A new approach to the clinical subclassification of heart failure with preserved ejection fraction.

OBJECTIVE: Heart failure with preserved ejection (HFpEF) represents nearly half of all patients with heart failure (HF). The objective of this study was to determine whether patient characteristics identify discrete kinds of HFpEF. METHODS: Data were collected on 196 patients with HFpEF in a non-hospitalized setting. Clinical and laboratory variables were collected, and 47 candidate variables were examined by the unsupervised clustering strategy partitioning around medoids. The Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk score was calculated. Follow-up data on all-cause mortality, cardiovascular mortality, and HF exacerbation, were collected and were not part of the data used to identify subgroups. RESULTS: Six significantly different groups or clusters were found. There were three groups of women (i) individuals with a low proportion of vascular risk factors (HFpEF1) (ii) individuals with a high proportion of hypertension and diabetes, but lower proportion of kidney disease and diastolic dysfunction (HFpEF3) (iii) older individuals with high rates of atrial fibrillation (AF), chronic kidney disease. They had the worst long-term outcomes (HFpEF4). There were three groups of men (i) individuals with a high proportion of coronary artery disease (CAD), dyslipidemia, higher serum creatinine, and diastolic dysfunction (HFpEF2)(ii) individuals with highest BMI, and high proportion of CAD, obstructive sleep apnea, and poorly controlled diabetes (HFpEF5) (iii) individuals with high rates of AF, elevated BNP, biventricular remodeling (HFpEF6). They had a high cardiovascular mortality. CONCLUSIONS: HFpEF consists of a heterogenous group of individuals with six distinct clinical subsets that have different long-term outcomes.

Circadian variation of the QT interval and heart rate variability and their interrelationship.

BACKGROUND: Whether the QT interval displays circadian rhythm after heart rate correction is unresolved and the relationship of QT interval to heart rate variability (HRV) is uncertain. OBJECTIVES: To test the hypothesis that there is a circadian rhythm to QT interval and HRV and determine the relationship between QTc and HRV. METHODS: The hourly average ECG data from 24-h ECGs were examined in individuals (50 without medications and 9 on beta blockers only) with no evidence of coronary artery disease or structural heart disease. The QT duration of normal QRS complexes from a series of 30-s windows was measured. The presence of circadian rhythm was tested by the data analytic approach of goodness of fit to a cosine function. RESULTS: QT interval with and without heart rate correction showed a circadian rhythm for five heart rate adjustment formulae except for the Bazett formula. HRV also showed circadian rhythm but with different acrophages and nadirs depending on the HRV component. There were significant (p < 0.05) positive correlations of QTc with pNN50 rms-SD and SDNN and significant (p < 0.05) negative correlations with SDANN and Tri. The beta blocker group did not generally show circadian rhythm for QT interval or HRV. CONCLUSION: QT, after heart rate adjustment, and HRV have circadian rhythmicity. There are significant correlations between QT interval and HRV indices. Circadian rhythm was blunted with beta blockers. The data are consistent with the concept of a predominance of parasympathetic activity to increase QTc and sympathetic activity to shorten QTc, even after 'correction' of the QT interval for heart rate.