PURPOSE: Up to half of the children undergoing epilepsy surgery will continue to have seizures (szs) despite a cortical resection or ablation. Functional connectivity has shown promise in better identifying the epileptogenic zone. We hypothesized that cortical areas showing high information outflow during interictal epileptiform discharges are part of the epileptogenic zone. METHODS: We identified 22 children with focal epilepsy who had undergone stereo electroencephalography, surgical resection or ablation, and had ≥1 year of postsurgical follow-up. The mean phase slope index, a directed measure of functional connectivity, was calculated for each electrode contact during interictal epileptiform discharges. The positive predictive value and negative predictive value for a sz-free outcome were calculated based on whether high information outflow brain regions were resected. RESULTS: Resection of high outflow (z-score ≥ 1) and very high outflow (z-score ≥ 2) electrode contacts was associated with higher sz freedom (high outflow: χ 2 statistic = 59.1; P < 0.001; very high outflow: χ 2 statistic = 31.3; P < 0.001). The positive predictive value and negative predictive value for sz freedom based on resection at the electrode level increased at higher z-score thresholds with a peak positive predictive value of 0.86 and a peak negative predictive value of 0.9. CONCLUSIONS: Better identification of the epileptogenic zone has the potential to improve epilepsy surgery outcomes. If the surgical plan can be modified to include these very high outflow areas, more children might achieve sz freedom. Conversely, if deficits from resecting these areas are unacceptable, ineffective surgeries could be avoided and alternative therapies offered.

The present paper reports on a study of the HVAC energy usage for an EREV (extended range electric vehicle) implementation of a localized cooling/heating system. Components in the localized system use thermoelectric (TE) devices to target the occupant's chest, face, lap and foot areas. A novel contact TE seat was integrated into the system. Human subject comfort rides and a thermal manikin in the tunnel were used to establish equivalent comfort for the baseline and localized system. The tunnel test results indicate that, with the localized system, HVAC energy savings of 37% are achieved for cooling conditions (ambient conditions greater than 10 °C) and 38% for heating conditions (ambient conditions less than 10 °C), respectively based on an annualized ambient and vehicle occupancy weighted method. The driving range extension for an electric vehicle was also estimated based on the HVAC energy saving.

Objective

We sought to examine demographic, socioeconomic, and biological predictors of all-cause, cardiovascular, and noncardiovascular mortality in patients with diabetes.

Research design and methods

Survey, medical record, and administrative data were obtained from 8,733 participants in the Translating Research Into Action for Diabetes Study, a multicenter, prospective, observational study of diabetes care in managed care. Data on deaths (n = 791) and cause of death were obtained from the National Death Index after 4 years. Predictors examined included age, sex, race, education, income, duration, and treatment of diabetes, BMI, smoking, microvascular and macrovascular complications, and comorbidities.

Results

Predictors of adjusted all-cause mortality included older age (hazard ratio [HR] 1.04 [95% CI 1.03-1.05]), male sex (1.57 [1.35-1.83]), lower income (< $15,000 vs. > $75,000, HR 1.82 [1.30-2.54]; $15,000-$40,000 vs. > $75,000, HR 1.58 [1.15-2.17]), longer duration of diabetes (> or = 9 years vs. < 9 years, HR 1.20 [1.02-1.41]), lower BMI (< 26 vs. 26-30 kg/m2, HR 1.43 [1.13-1.69]), smoking (1.44 [1.20-1.74]), nephropathy (1.46 [1.23-2.73]), macrovascular disease (1.46 [1.23-1.74]), and greater Charlson index (> or = 2-3 vs. < 1, HR 2.01 [1.04-3.90]; > or = 3 vs. < 1, HR 4.38 [2.26-8.47]). The predictors of cardiovascular and noncardiovascular mortality were different. Macrovascular disease predicted cardiovascular but not noncardiovascular mortality.

Conclusions

Among people with diabetes and access to medical care, older age, male sex, smoking, and renal disease are important predictors of mortality. Even within an insured population, socioeconomic circumstance is an important independent predictor of health.

Objective

To determine the frequency of reporting of diabetes on death certificates of decedents with known diabetes, define factors associated with reporting of diabetes, and describe trends in reporting over time.

Research design and methods

Data were obtained from 11,927 participants with diabetes who were enrolled in the Translating Research Into Action for Diabetes study, a multicenter prospective observational study of diabetes care in managed care. Data on decedents (n = 540) were obtained from the National Death Index. The primary dependent variable was the presence of ICD-10 codes for diabetes on the death certificate. Covariates included age at death, sex, race/ethnicity, education, income, duration of diabetes, type of diabetes, diabetes treatment, smoking status, and number of comorbidities.

Results

Diabetes was recorded on 39% of death certificates and as the underlying cause of death for 10% of decedents with diabetes. Diabetes was significantly less likely to be reported on the death certificates of decedents with diabetes dying of cancer. Predictors of recording diabetes anywhere on the death certificate included longer duration of diabetes and insulin treatment. Longer duration of diabetes, insulin treatment, and fewer comorbidities were associated with recording of diabetes as the underlying cause of death.

Conclusions

Diabetes is much more likely to be reported on the death certificates of diabetic individuals who die of cardiovascular causes. Reporting of diabetes on death certificates has been stable over time. Death certificates underestimate the prevalence of diabetes among decedents and present a biased picture of the causes of death among people with diabetes.

Autism spectrum disorder is a complex trait with a high degree of heritability as well as documented susceptibility from environmental factors. In this study the contributions of copy number variation, exposure to air pollutants, and the interaction between the two on autism risk, were evaluated in the population-based case-control Childhood Autism Risks from Genetics and Environment (CHARGE) Study. For the current investigation, we included only those CHARGE children (a) who met criteria for autism or typical development and (b) for whom our team had conducted both genetic evaluation of copy number burden and determination of environmental air pollution exposures based on mapping addresses from the pregnancy and early childhood. This sample consisted of 158 cases of children with autism and 147 controls with typical development. Multiple logistic regression models were fit with and without environmental variable-copy number burden interactions. We found no correlation between average air pollution exposure from conception to age 2 years and the child's CNV burden. We found a significant interaction in which a 1SD increase in duplication burden combined with a 1SD increase in ozone exposure was associated with an elevated autism risk (OR 3.4, P < 0.005) much greater than the increased risks associated with either genomic duplication (OR 1.85, 95% CI 1.25-2.73) or ozone (OR 1.20, 95% CI 0.93-1.54) alone. Similar results were obtained when CNV and ozone were dichotomized to compare those in the top quartile relative to those having a smaller CNV burden and lower exposure to ozone, and when exposures were assessed separately for pregnancy, the first year of life, and the second year of life. No interactions were observed for other air pollutants, even those that demonstrated main effects; ozone tends to be negatively correlated with the other pollutants examined. While earlier work has demonstrated interactions between the presence of a pathogenic CNV and an environmental exposure [Webb et al., 2016], these findings appear to be the first indication that global copy number variation may increase susceptibility to certain environmental factors, and underscore the need to consider both genomics and environmental exposures as well as the mechanisms by which each may amplify the risks for autism associated with the other. Autism Res 2017, 10: 1470-1480. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

Background

Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR¹. Host epigenome manipulation post coronavirus infection^2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation.

Methods

We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls.

Results

Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively.

Conclusions

In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.