Risk stratification and subclinical phenotyping of dilated and/or arrhythmogenic cardiomyopathy mutation-positive relatives: CVON eDETECT consortium

In relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy, early detection of disease onset is essential to prevent sudden cardiac death and facilitate early treatment of heart failure. However, the optimal screening interval and combination of diagnostic techniques are unknown. The clinical course of disease in index patients and their relatives is variable due to incomplete and age-dependent penetrance. Several biomarkers, electrocardiographic and imaging (echocardiographic deformation imaging and cardiac magnetic resonance imaging) techniques are promising non-invasive methods for detection of subclinical cardiomyopathy. However, these techniques need optimisation and integration into clinical practice. Furthermore, determining the optimal interval and intensity of cascade screening may require a personalised approach. To address this, the CVON-eDETECT (early detection of disease in cardiomyopathy mutation carriers) consortium aims to integrate electronic health record data from long-term follow-up, diagnostic data sets, tissue and plasma samples in a multidisciplinary biobank environment to provide personalised risk stratification for heart failure and sudden cardiac death. Adequate risk stratification may lead to personalised screening, treatment and optimal timing of implantable cardioverter defibrillator implantation. In this article, we describe non-invasive diagnostic techniques used for detection of subclinical disease in relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy.     

Partial summary measures of the predictiveness curve

In the evaluation of a biomarker for risk prediction, one can assess the performance of the biomarker in the population of interest by displaying the predictiveness curve. In conjunction with an assessment of the classification accuracy of a biomarker, the predictiveness curve is an important tool for assessing the usefulness of a risk prediction model. Inference for a single biomarker or for multiple biomarkers can be performed using summary measures of the predictiveness curve. We propose two partial summary measures, the partial total gain and the partial proportion of explained variation, that summarize the predictiveness curve over a restricted range of risk. The methods we describe can be used to compare two biomarkers when there are existing thresholds for risk stratification. We describe inferential tools for one and two samples that are shown to have adequate power in a simulation study. The methods are illustrated by assessing the accuracy of a risk score for predicting the onset of Alzheimer's disease.     

Clinical aspects of acute coronary syndromes

There are two types of acute coronary syndromes : those with or without ST-segment elevation. The former require urgent therapeutic measures to reopen the culprit artery (intravenous thrombolysis or primary percutaneous coronary intervention). For the latter, risk stratification is essential and is based upon clinical and biochemical markers. Among them, recent and repeated anginal attacks, ST-segment modifications on admission electrocardiogram, and increased markers of myonecrosis (particularly increased troponin levels) are strong predictors of untoward outcome. According to the risk profile, the initial management is based upon an invasive strategy with powerful antithrombotic medications and urgent angiography, or upon a non-invasive strategy using stress testing, preferably coupled with myocardial imaging techniques. In all instances, secondary prevention measures are determinant to try and stop the progression of the atherosclerotic disease.     

Patient Characteristics Associated with Measurement of Routine Diabetes Care: An Observational Study

Background

Non-modifiable patient characteristics, including age, gender, ethnicity as well as the occurrence of multi-morbidities, are associated with processes and outcomes of diabetes care. Information on these factors can be used in case mix adjustment of performance measures. However, the practical relevance of such adjustment is not clear. The aim of this study was to assess the strength of associations between patient factors and diabetes care processes and outcomes.

Methods

We performed an observational study based on routinely collected data of 12,498 diabetes patients in 59 Dutch primary care practices. Data were collected on patient age, gender, whether the patient lived in a deprived area, body mass index and the co-occurrence of cardiovascular disease, chronic obstructive pulmonary disease, depression or anxiety. Outcomes included 6 dichotomous measures (3 process and 3 outcome related) regarding glycosylated hemoglobin, systolic blood pressure and low density lipoprotein-cholesterol. We performed separate hierarchical logistic mixed model regression models for each of the outcome measures.

Results

Each of the process measure models showed moderate effect sizes, with pooled areas under the curve that varied between 0.66 and 0.76. The frequency of diabetes related consultations as a measure of patient compliance to treatment showed the strongest association with all process measures (odds ratios between 5.6 and 14.5). The effect sizes of the outcome measure models were considerably smaller than the process measure models, with pooled areas under the curve varying from 0.57 to 0.61.

Conclusions

Several non-modifiable patient factors could be associated with processes and outcomes of diabetes care. However, associations were small. These results suggest that case-mix correction or stratification in assessing diabetes care has limited practical relevance.     

Sex differences in ischemic heart disease and heart failure biomarkers

Since 1984, each year, more women than men die of ischemic heart disease (IHD) and heart failure (HF), yet more men are diagnosed. Because biomarker assessment is often the first diagnostic employed in such patients, understanding biomarker differences in men vs. women may improve female morbidity and mortality rates.Some key examples of cardiac biomarker utility based on sex include contemporary use of “unisex” troponin reference intervals under-diagnosing myocardial necrosis in women; greater use of hsCRP in the setting of acute coronary syndrome (ACS) could lead to better stratification in women; and greater use of BNP with sex-specific thresholds in ACS could also lead to more timely risk stratification in women.Accurate diagnosis, appropriate risk management, and monitoring are key in the prevention and treatment of cardiovascular diseases; however, the assessment tools used must also be useful or at least assessed for utility in both sexes. In other words, going forward, we need to evaluate sex-specific reference intervals or cutoffs for laboratory tests used to assess cardiovascular disease to help close the diagnostic gap between men and women.     

Echocardiographic Measures of Cardiac Structure and Function Are Associated with Risk of Atrial Fibrillation in Blacks: The Atherosclerosis Risk in Communities (ARIC) Study

Background

Several studies have examined the link between atrial fibrillation (AF) and various echocardiographic measures of cardiac structure and function in whites and other racial groups but not in blacks. Exploring AF risk factors in blacks is important given that the lower incidence of AF in this racial group despite higher risk factors, is not completely explained.

Methods

We examined the association of echocardiographic measures with AF incidence in 2283 blacks (64.5% women, mean age 58.8 years) free of diagnosed AF and enrolled in the Jackson cohort of Atherosclerosis Risk in Communities (ARIC) study, a prospective study of cardiovascular disease. Echocardiography was performed in 1993–1995, and incident AF was determined by electrocardiograms at a follow-up study exam, hospitalization discharge codes and death certificates through the end of 2009. Cox proportional hazards regression was used to estimate hazard ratios and 95% confidence intervals for AF associated with the echocardiographic measures, adjusting for age, sex, and known AF risk factors.

Results

During an average follow-up of 13.5 years, 191 (8.4%) individuals developed AF. Left ventricular (LV) internal diameter 2-D (diastole) and percent fractional shortening of LV diameter displayed a U-shaped relationship with risk of AF, while left atrial diameter displayed a J-shaped nonlinear association. LV mass index was associated positively with AF. E/A ratio <0.7 or >1.5 and ejection fraction (EF <50%) were also associated with higher AF risk. These measures improved risk stratification for AF in addition to traditional risk factors, although not significantly {C-statistic of 0.767 (0.714–0.819) vs. 0.744 (0.691–0.797)}.

Conclusions

In a community-based population of blacks, echocardiographic measures of cardiac structure and function are significantly associated with an increased risk of AF.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

Bias correction with a single null marker for population stratification in candidate gene association studies

Population stratification is a form of confounding by ethnicity that may cause bias to effect estimates and inflate test statistics in genetic association studies. Unlinked genetic markers have been used to adjust for test statistics, but their use in correcting biased effect estimates has not been addressed. We evaluated the potential of bias correction that could be achieved by a single null marker (M) in studies involving one candidate gene (G). When the distribution of M varied greatly across ethnicities, controlling for M in a logistic regression model substantially reduced biases on odds ratio estimates. When M had same distributions as G across ethnicities, biases were further reduced or eliminated by subtracting the regression coefficient of M from the coefficient of G in the model, which was fitted either with or without a multiplicative interaction term between M and G. Correction of bias due to population stratification depended specifically on the distributions of G and M, the difference between baseline disease risks across ethnicities, and whether G had an effect on disease risk or not. Our results suggested that marker choice and the specific treatment of that marker in analysis greatly influenced bias correction.     

Variance calculations and confidence intervals for estimates of the attributable risk based on logistic models

The attributable risk (AR), defined as AR = [Pr(disease) - Pr(disease/no exposure)]/Pr(disease), measures the proportion of disease risk that is attributable to an exposure. Recently Bruzzi et al. (1985, American Journal of Epidemiology 122, 904-914) presented point estimates of AR based on logistic models for case-control data to allow for confounding factors and secondary exposures. To produce confidence intervals, we derived variance estimates for AR under the logistic model and for various designs for sampling controls. Calculations for discrete exposure and confounding factors require covariances between estimates of the risk parameters of the logistic model and the proportions of cases with given levels of exposure and confounding factors. These covariances are estimated from Taylor series expansions applied to implicit functions. Similar calculations for continuous exposures are derived using influence functions. Simulations indicate that those asymptotic procedures yield reliable variance estimates and confidence intervals with near nominal coverage. An example illustrates the usefulness of variance calculations in selecting a logistic model that is neither so simplified as to exhibit systematic lack of fit nor so complicated as to inflate the variance of the estimate of AR.     

Standard errors for attributable risk for simple and complex sample designs

Adjusted attributable risk (AR) is the proportion of diseased individuals in a population that is due to an exposure. We consider estimates of adjusted AR based on odds ratios from logistic regression to adjust for confounding. Influence function methods used in survey sampling are applied to obtain simple and easily programmable expressions for estimating the variance of AR. These variance estimators can be applied to data from case-control, cross-sectional, and cohort studies with or without frequency or individual matching and for sample designs with subject samples that range from simple random samples to (sample) weighted multistage stratified cluster samples like those used in national household surveys. The variance estimation of AR is illustrated with: (i) a weighted stratified multistage clustered cross-sectional study of childhood asthma from the Third National Health and Examination Survey (NHANES III), and (ii) a frequency-matched case-control study of melanoma skin cancer.     

Gender difference in apolipoprotein E-associated risk for familial Alzheimer disease: a possible clue to the higher incidence of Alzheimer disease in women.

Late-onset Alzheimer disease (AD) is associated with the apolipoprotein E (APOE)-epsilon4 allele. In late-onset familial AD, women have a significantly higher risk of developing the disease than do men. The aim of this study was to determine whether the gender difference in familial AD is a function of APOE genotype. We studied 58 late-onset familial AD kindreds. Kaplan-Meier survival analysis was used to assess genotype-specific distributions of age at onset. Odds ratios were estimated by logistic regression with adjustment for age and by conditional logistic regression with stratification on families. All methods detected a significant gender difference for the epsilon4 heterozygous genotype. In women, epsilon4 heterozygotes had higher risk than those without epsilon4; there was no significant difference between epsilon4 heterozygotes and epsilon4 homozygotes. In men, epsilon4 heterozygotes had lower risk than epsilon4 homozygotes; there was not significant difference between epsilon4 heterozygotes and those without epsilon4. A direct comparison of epsilon4 heterozygous men and women revealed a significant twofold increased risk in women. We confirmed these results in 15 autopsy-confirmed AD kindreds from the National Cell Repository at Indiana University Alzheimer Disease Center. These observations are consistent with the increased incidence of familial AD in women and may be a critical clue to the role of gender in the pathogenesis of AD.     

Inter-Model Comparison of the Landscape Determinants of Vector-Borne Disease: Implications for Epidemiological and Entomological Risk Modeling

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.     

Nouveautés en cardiologie nucléaire : une gamma caméra à semi-conducteurs dédiée à l’imagerie cardiaque

During the last few years, cardiac imaging made important breakthroughs thanks to the development of various techniques allowing the risk stratification of patients with coronary artery disease. The well-established single photon emission computed tomography (SPECT) as a myocardial imaging technique made an important progress with the recent improvement of high-speed volumic acquisition, using dedicated semi-conductor gamma camera. These cameras bring significant improvement to the image quality and the image acquisition time, which is now seven times lower. New type of artefacts is expected because of the geometry of detection, thus studies are still needed to assess the exact performance of this revolutionary technology.     

Risk stratification for sudden cardiac death

Recent advances in pharmacological and device-based therapies have provided a range of management options for patients at risk of sudden cardiac death (SCD). Since all such interventions come with their attendant risks, however, stratification procedures aimed at identifying those who stand to benefit overall have gained a new degree of importance. This review assesses the value of risk stratification measures currently available in clinical practice, as well as of others that may soon enter the market. Parameters that may be obtained only by performing invasive cardiac catheterisation procedures are considered separately from those that may be derived using more readily available non-invasive techniques. It is concluded that effective stratification is likely to require the use of composite parameters and that invasive procedures might only be justified in specific sub-groups of patients.     

Hypertension in perspective

Decisions about the management of hypertensive patients should not be based on the level of blood pressure alone, but also on the presence of other risk factors, target organ damage and cardiovascular and renal disease. The results of echocardiography and carotid ultrasonography aids in the stratification of absolute cardiovascular risk as recently advocated by the guidelines of the European Society of Hypertension 2003. Therefore, the detection of target organ damage by ultrasound techniques allows an accurate identification of high-risk patients. Cardiovascular risk stratification only based on a simple routine work-up can often underestimate overall risk, thus leading to a potentially inadequate therapeutic management especially of low-medium risk patients.     

Analytical surveillance of emerging drugs of abuse and drug formulations

Uncontrolled recreational drugs are proliferating in number and variety. Effects of long-term use are unknown, and regulation is problematic, as efforts to control one chemical often lead to several other structural analogs. Advanced analytical instrumentation and methods are continuing to be developed to identify drugs, chemical constituents of products, and drug substances and metabolites in biological fluids. Several mass spectrometry based approaches appear promising, particularly those that involve high resolution chromatographic and mass spectrometric methods that allow unbiased data acquisition and sophisticated data interrogation. Several of these techniques are shown to facilitate both targeted and broad spectrum analyses, the latter of which are often of particular benefit when dealing with misleadingly labeled products or assessing a biological matrix for illicit drugs and metabolites. The development and application of novel analytical approaches such as these will help to assess the nature and degree of exposure and risk and, where necessary, inform forensics and facilitate implementation of specific regulation and control measures.     

Genetic association mapping based on discordant sib pairs: the discordant-alleles test.

Family-based tests of association provide the opportunity to test for an association between a disease and a genetic marker. Such tests avoid false-positive results produced by population stratification, so that evidence for association may be interpreted as evidence for linkage or causation. Several methods that use family-based controls have been proposed, including the haplotype relative risk, the transmission-disequilibrium test, and affected family-based controls. However, because these methods require genotypes on affected individuals and their parents, they are not ideally suited to the study of late-onset diseases. In this paper, we develop several family-based tests of association that use discordant sib pairs (DSPs) in which one sib is affected with a disease and the other sib is not. These tests are based on statistics that compare counts of alleles or genotypes or that test for symmetry in tables of alleles or genotypes. We describe the use of a permutation framework to assess the significance of these statistics. These DSP-based tests provide the same general advantages as parent-offspring trio-based tests, while being applicable to essentially any disease; they may also be tailored to particular hypotheses regarding the genetic model. We compare the statistical properties of our DSP-based tests by computer simulation and illustrate their use with an application to Alzheimer disease and the apolipoprotein E polymorphism. Our results suggest that the discordant-alleles test, which compares the numbers of nonmatching alleles in DSPs, is the most powerful of the tests we considered, for a wide class of disease models and marker types. Finally, we discuss advantages and disadvantages of the DSP design for genetic association mapping.     

Non-linear heart rate variability and risk stratification in cardiovascular disease

Traditional time and frequency domain heart rate variability (HRV) have cardiac patients at risk of mortality post-myocardial infarction. More recently, non linear HRV has been applied to risk stratification of cardiac patients. In this review we describe studies of non linear HRV and outcome in cardiac patients. We have included studies that used the three most common non-linear indices: power law slope, the short term fractal scaling exponent and measures based on Poincare plots. We suggest that a combination of traditional and non-linear HRV may be optimal for risk stratification. Considerations in using non linear HRV in a clinical setting are described.     

Nonparametric variable importance assessment using machine learning techniques

In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression techniques are often suboptimal for predicting the response. Additionally, because the variable importance measures native to different regression techniques generally have a different interpretation, comparisons across techniques can be difficult. In this work, we study a variable importance measure that can be used with any regression technique, and whose interpretation is agnostic to the technique used. This measure is a property of the true data‐generating mechanism. Specifically, we discuss a generalization of the analysis of variance variable importance measure and discuss how it facilitates the use of machine learning techniques to flexibly estimate the variable importance of a single feature or group of features. The importance of each feature or group of features in the data can then be described individually, using this measure. We describe how to construct an efficient estimator of this measure as well as a valid confidence interval. Through simulations, we show that our proposal has good practical operating characteristics, and we illustrate its use with data from a study of risk factors for cardiovascular disease in South Africa.