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Anthropometric-based classification schemes for excess adiposity do not include direct assessment of obesity-related comorbidity and functional status and thus have limited clinical utility. We examined the ability of the Edmonton obesity staging system, a 5-point ordinal classification system that considers comorbidity and functional status, in predicting mortality in a nationally representative US sample. We analyzed data from the National Health and Human Nutrition Examination Surveys (NHANES) III (1988–1994) and the NHANES 1999–2004, with mortality follow-up through to the end of 2006. Adults (age ≥ 20 yr) with overweight or obesity who had been randomized to the morning session at the mobile examination centre were scored according to the Edmonton obesity staging system. We examined the relationship between staging system scores and mortality, and Cox proportional hazards models were adjusted for the presence of the metabolic syndrome or hypertriglyceridemic waist. Over 75% of the cohort with overweight or obesity were given scores of 1 or 2. Scores of 4 could not be reliably assigned because specific data elements were lacking. Survival curves clearly diverged when stratified by scores of 0–3, but not when stratified by obesity class alone. Within the data from the NHANES 1988–1994, scores of 2 (hazard ratio [HR] 1.57; 95% confidence interval [CI] 1.16 to 2.13) and 3 (HR 2.69; 95% CI 1.98 to 3.67) were associated with increased mortality compared with scores of 0 or 1, even after adjustment for body mass index and the metabolic syndrome. We found similar results after adjusting for hypertriglyceridemic waist (i.e., waist circumference ≥ 90 cm and a triglyceride level ≥ 2 mmol/L for men; the corresponding values for women were ≥? 85 cm and ≥? 1.5 mmol/L), as well as in a cohort eligible for bariatric surgery. The Edmonton obesity staging system independently predicted increased mortality even after adjustment for contemporary methods of classifying adiposity. The Edmonton obesity staging system may offer improved clinical utility in assessing obesity-related risk and prioritizing treatment.

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the \"missing heritability\" for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (∼ 0.80), substantial room for improvement remains.

Background New or worsening cognitive impairment or dementia is common in older adults following an episode of critical illness, and screening post-discharge is recommended for those at increased risk. There is a need for prediction models of post-ICU cognitive impairment to guide delivery of screening and support resources to those in greatest need. We sought to develop and internally validate a machine learning model for new cognitive impairment or dementia in older adults after critical illness using electronic health record (EHR) data. Methods Our cohort included patients > 60 years of age admitted to a large academic health system ICU in North Carolina between 2015 and 2021. Patients were included in the cohort if they were admitted to the ICU for ≥  48 h with ≥  2 ambulatory visits prior to hospitalization and at least one visit in the post-discharge year. We used a machine learning model, oblique random survival forests (ORSF), to examine the multivariable association of 54 structured data elements available by 3 months after discharge with incident diagnoses of cognitive impairment or dementia over 1-year. Results In this cohort of 8,299 adults, 22% died and 4.9% were diagnosed with dementia or cognitive impairment within one year. The ORSF model showed reasonable discrimination (c-statistic = 0.83) and stability with little difference in the model’s c-statistic across time. Conclusion Machine learning using readily available EHR data can predict new cognitive impairment or dementia at 1-year post-ICU discharge in older adults with acceptable accuracy. Further studies are needed to understand how this tool may impact screening for cognitive impairment in the post-discharge period.

Missing data in electronic health records are highly prevalent and result in analytical concerns such as heterogeneous sources of bias and loss of statistical power. One simple analytic method for addressing missing or unknown covariate values is to treat missingness for a particular variable as a category onto itself, which we refer to as the missing indicator method. For cross-sectional analyses, recent work suggested that there was minimal benefit to the missing indicator method; however, it is unclear how this approach performs in the setting of longitudinal data, in which correlation among clustered repeated measures may be leveraged for potentially improved model performance. This study aims to conduct a simulation study to evaluate whether the missing indicator method improved model performance and imputation accuracy for longitudinal data mimicking an application of developing a clinical prediction model for falls in older adults based on electronic health record data. We simulated a longitudinal binary outcome using mixed effects logistic regression that emulated a falls assessment at annual follow-up visits. Using multivariate imputation by chained equations, we simulated time-invariant predictors such as sex and medical history, as well as dynamic predictors such as physical function, BMI, and medication use. We induced missing data in predictors under scenarios that had both random (missing at random) and dependent missingness (missing not at random). We evaluated aggregate performance using the area under the receiver operating characteristic curve (AUROC) for models with and with no missing indicators as predictors, as well as complete case analysis, across simulation replicates. We evaluated imputation quality using normalized root-mean-square error for continuous variables and percent falsely classified for categorical variables. Independent of the mechanism used to simulate missing data (missing at random or missing not at random), overall model performance via AUROC was similar regardless of whether missing indicators were included in the model. The root-mean-square error and percent falsely classified measures were similar for models including missing indicators versus those with no missing indicators. Model performance and imputation quality were similar regardless of whether the outcome was related to missingness. Imputation with or with no missing indicators had similar mean values of AUROC compared with complete case analysis, although complete case analysis had the largest range of values. The results of this study suggest that the inclusion of missing indicators in longitudinal data modeling neither improves nor worsens overall performance or imputation accuracy. Future research is needed to address whether the inclusion of missing indicators is useful in prediction modeling with longitudinal data in different settings, such as high dimensional data analysis.

Background Caring for a child with a chronic condition, such as sickle cell disease, can have a significant impact on parents and families. In order to provide comprehensive care and support to these families, psychometrically sound instruments are needed as an initial step in measuring the impact of chronic diseases on parents and families. We sought to evaluate the psychometric properties of the PedsQL™ Family Impact Module in populations of children with and without sickle cell disease. In addition, we sought to determine the correlation between parent's well being and their proxy report of their child's health-related quality of life (HRQL). Methods We conducted a cross-sectional study of parents of children with and without sickle cell disease who presented to an urban hospital-based sickle cell disease clinic and an urban primary care clinic. We assessed the HRQL and family functioning of both groups of parents utilizing the PedsQL™ Family Impact Module. The reliability, validity and factor structure of the instrument were determined and scores from the instrument were correlated with scores from parent-proxy report of their child's HRQL using the PedsQL™ 4.0 Generic Core Scales. Results Parents of 170 children completed the module (97 parents of children with sickle cell disease and 73 parents of children without sickle cell disease). The Family Impact Module had high ceiling effects but was reliable (Cronbach's alpha > 0.80 in all scales). The empirical factor structure was generally consistent with the theoretical factor structure and supported construct validity. The Family Impact Module discriminated between parents of children with severe sickle cell disease from parents of children with mild disease or no disease in the areas of communication and worry. There were no significant differences across any of the subscales between parents of children with mild sickle cell disease and those with no disease. Parents with higher scores, representing better HRQL and family functioning, generally reported higher HRQL scores for their children. Conclusion The PedsQL™ Family Impact module was reliable, however it displayed large ceiling effects and did not discriminate well between parents of children with and without sickle cell disease. Future research to evaluate the psychometric properties of the Family Impact Module for parents of healthy children may be helpful.

African Americans have been understudied in genome wide association studies of diabetes and related traits. In the current study, we examined the joint association of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) with fasting insulin and an index of insulin resistance (HOMA-IR) in the HyperGEN study, a family based study with proband ascertainment for hypertension. This analysis is restricted to 1,040 African Americans without diabetes. We generated allele specific CNV genotypes at 872,243 autosomal loci using Birdsuite, a freely available multi-stage program. Joint tests of association for SNPs and CNVs were performed using linear mixed models adjusting for covariates and familial relationships. Our results highlight SNPs associated with fasting insulin and HOMA-IR (rs6576507 and rs8026527, 3.7*10(-7)≤P≤1.1*10(-5)) near ATPase, class V, type 10A (ATP10A), and the L Type voltage dependent calcium channel (CACNA1D, rs1401492, P≤5.2*10(-6)). ATP10A belongs to a family of aminophospholipid-transporting ATPases and has been associated with type 2 diabetes in mice. CACNA1D has been linked to pancreatic beta cell generation in mice. The two most significant copy variable markers (rs10277702 and rs361367; P<2.0*10(-4)) were in the beta variable region of the T-cell receptor gene (TCRVB). Human and mouse TCR has been shown to mimic insulin and its receptor and could contribute to insulin resistance. Our findings differ from genome wide association studies of fasting insulin and other diabetes related traits in European populations, highlighting the continued need to investigate unique genetic influences for understudied populations such as African Americans.

A secondary analysis of SPRINT, a trial involving patients with hypertension and high cardiovascular risk in which intensive therapy resulted in lower rates of cardiovascular events than standard therapy, showed that patient-reported outcomes were similar in the two groups.

Background After the passage of the 21st Century Cures Act in the U.S., the Inclusion Across the Lifespan policy eliminates upper-age limits for research participation unless risk justified. Broader inclusion will necessitate the use of reliable instruments in research that characterize the health status and function of older adults with multiple chronic conditions. As there is a plethora of such instruments, the Geriatrics Research Instrument Library (GRIL) was developed as freely available online resource of data collection instruments commonly used in gerontological research. GRIL has been revised and updated by the Advancing Geriatrics Infrastructure and Network Growth (AGING) Initiative, a joint endeavor of the Health Care Systems Research Network (HCSRN) and the Older Americans Independence Centers (OAICs). Methods Extensive PubMed literature searches and domain expert feedback were utilized to inventory and update GRIL through the addition of instruments and compiling of instrument metadata. GRIL is hosted on the National Institute on Aging OAIC Coordinating Center website with a platform utilizing Microsoft Structured Query Language (SQL) and an Adobe ColdFusion application server. Tracking statistics are collected using Google Analytics. Results Presently, GRIL includes 175 instruments across 18 domains, including instrument metadata such as instrument description, copyright information, completion time estimates, keywords, available translations, and a link and reference to the original manuscript describing the instrument. The GRIL website includes user-friendly features such as mobile platforming and resource links. Conclusions GRIL provides a user-friendly public resource that facilitates clinical researchers in efficiently selecting appropriate instruments to measure clinical outcomes relevant to older adults across a full range of domains.

Risk stratification and population management strategies are critical for providing effective and equitable care for the growing population of older adults in the USA. Both frailty and neighborhood disadvantage are constructs that independently identify populations with higher healthcare utilization and risk of adverse outcomes. To examine the joint association of these factors on acute healthcare utilization using two pragmatic measures based on structured data available in the electronic health record (EHR). In this retrospective observational study, we used EHR data to identify patients aged ≥ 65 years at Atrium Health Wake Forest Baptist on January 1, 2019, who were attributed to affiliated Accountable Care Organizations. Frailty was categorized through an EHR-derived electronic Frailty Index (eFI), while neighborhood disadvantage was quantified through linkage to the area deprivation index (ADI). We used a recurrent time-to-event model within a Cox proportional hazards framework to examine the joint association of eFI and ADI categories with healthcare utilization comprising emergency visits, observation stays, and inpatient hospitalizations over one year of follow-up. We identified a cohort of 47,566 older adults (median age = 73, 60% female, 12% Black). There was an interaction between frailty and area disadvantage (P = 0.023). Each factor was associated with utilization across categories of the other. The magnitude of frailty's association was larger than living in a disadvantaged area. The highest-risk group comprised frail adults living in areas of high disadvantage (HR 3.23, 95% CI 2.99-3.49; P < 0.001). We observed additive effects between frailty and living in areas of mid- (RERI 0.29; 95% CI 0.13-0.45; P < 0.001) and high (RERI 0.62, 95% CI 0.41-0.83; P < 0.001) neighborhood disadvantage. Considering both frailty and neighborhood disadvantage may assist healthcare organizations in effectively risk-stratifying vulnerable older adults and informing population management strategies. These constructs can be readily assessed at-scale using routinely collected structured EHR data.

INTRODUCTION Vascular risk factors contribute to cognitive decline suggesting that maintaining cerebrovascular health could reduce dementia risk. The objective of this study is to evaluate the association of cerebrovascular reactivity (CVR), a measure of brain blood vessel elasticity, with mild cognitive impairment (MCI) and dementia. METHODS Participants were enrolled in the Systolic Blood Pressure Intervention Trial Memory and Cognition in Decreased Hypertension (SPRINT‐MIND) magnetic resonance imaging substudy. Baseline CVR in Alzheimer's disease (AD) signature regions were primary variables of interest. The occipital pole and postcentral gyrus were included as control regions. RESULTS Higher AD composite CVR was associated with lower MCI risk. No significant associations between inferior temporal gyrus, occipital pole, or postcentral gyrus CVR and MCI risk, or any regional CVR–combined risk associations were observed. DISCUSSION CVR in AD signature regions is negatively associated with occurrence of MCI, implicating CVR in AD signature regions as a potential mechanism leading to cognitive impairment.