The Coronary Artery Risk Detection in Appalachian Communities (CARDIAC) Project: An 18 Year Review

Elliott, Eloise; Lilly, Christa; Murphy, Emily; Pyles, Lee A.; Cottrell, Lesley; Neal, William A.

doi:10.2174/1573400514666180117093652

Cited by 10 publications

(11 citation statements)

References 25 publications

(23 reference statements)

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“…Although these individuals were invited to have their blood relatives screened, only 4% of families were screened. The West Virginia University Coronary Artery Risk Detection In Appalachian Communities (CARDIAC) Project screened lipids in 5th-grade students throughout rural Appalachian West Virginia ( n = 637) [16] . These children were then seen in pediatric lipid clinics and relatives recruited for screening, but only 5% of families were screened.…”

Section: Discussionmentioning

confidence: 99%

A proof-of-concept study of cascade screening for Familial Hypercholesterolemia in the US, adapted from the Dutch model

McGowan¹,

Cuchel²,

Ahmed³

et al. 2021

American Journal of Preventive Cardiology

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Background The Dutch cascade screening model for FH was the most successful of such programs in the world. It remains unclear whether aspects of the Dutch model (i.e. direct engagement with FH probands and relatives outside usual healthcare settings) are feasible in the US. This is especially important since prior attempts at cascade screening in the US have had very low screening rates (<10% of families screened). Methods We conducted a multi-site single-arm proof-of-concept study in which the US-based FH Foundation (a 501c3 research and advocacy organization) directly engaged with FH probands and relatives similar to the approach taken by the Dutch “Foundation for Tracing FH.” Results Eleven unrelated probands with genetically confirmed FH were enrolled. Mean age was 43 years; 82% were women, and 82% were of European ancestry. Prior to enrolling into the study, only 2 families (18% screening rate) were screened for FH with both lipid measurements and genetic testing. Two probands declined cascade screening due to fear over genetic discrimination. Nine total relatives engaged with the FH Foundation. Mean age was 43 years and 44% were women. Seven of those relatives (from 6 families; 55% screening rate) consented to be screened for FH with lipid measurement and genetic testing. The two additional relatives – men ages 39 and 49 – agreed to lipid measurements but not genetic testing, each noting he would like to think more about genetic testing. Conclusions Our proof-of-concept study demonstrates the feasibility of the FH Foundation engaging FH probands and their relatives outside the usual healthcare settings for cascade screening, similar to the Dutch model. We found only 18% of families had already been screened, and after engaging with the FH Foundation, 55% of families were willing to participate in cascade screening. These findings suggest the methods described here may improve cascade screening rates in the US.

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Section: Discussionmentioning

confidence: 99%

A proof-of-concept study of cascade screening for Familial Hypercholesterolemia in the US, adapted from the Dutch model

McGowan¹,

Cuchel²,

Ahmed³

et al. 2021

American Journal of Preventive Cardiology

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“…The prevalence of adolescent obesity is 20.6% in the United States 15 and approximately 47% of children and adolescents in rural West Virginia are either overweight or obese. 16 In our sample ( n = 648), almost half of the children and adolescents with migraine were overweight or obese and, of these, 26% were obese ( Table 2 ).…”

Section: Discussionmentioning

confidence: 79%

Obesity, Migraine, and Overlapping Comorbidities in a Rural Pediatric Population

Walter

Dai

Wang

2021

JNRP

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Objective This study aims to report the prevalence of obesity and overlapping comorbidities in a rural population of children and adolescents with migraine. Design and Methods A cross-sectional, descriptive, secondary data analysis using a comprehensive patient database from the West Virginia University (WVU) Medicine Epic Clarity system will be reported. A review of electronic medical records of 990 children and adolescents, ages 7 to 17 years, evaluated for headache at a rural clinic from December 1, 2009 to December 31, 2017 was completed. The Chi-square test was used to identify any differences in demographic characteristics (age, gender, and race) and distribution of comorbidities (obstructive sleep apnea syndrome [OSAS], depression, and anxiety) among obese versus nonobese adolescents with migraine. Student’s t-test was used to identify any differences in the number of comorbidities between the two groups. Results A total of 648 children and adolescents with a diagnosis of migraine were identified. Approximately 26.4% of the children and adolescents diagnosed with migraine (n = 648) met the criteria for being obese with a mean body mass index (BMI) of 30.6 kg/m2 (standard deviation [SD] = 6.5), ranging from 20.0 to 58.5 kg/m2. There were no significant differences between migraineurs who were categorized as obese versus nonobese in terms of gender (p = 0.8587), age (p = 0.1703), race (p = 0.7655), anxiety (p = 0.1841), or depression (p = 0.2793). Obese individuals have more comorbidities than nonobese individuals (p = 0.015). Additionally, the prevalence of OSAS was significantly higher among obese versus nonobese migraineurs (20 vs. 9.9%, p = 0.0007). Conclusion Given the prevalence of obesity in rural pediatric populations and the reported neurobiological links between migraine and obesity, BMI needs to be monitored and weight management interventions included in plans of care for rural children and adolescents with migraine.

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“…To detect FH, West Virginia pioneered the Coronary Artery Risk Detection in Appalachian Communities (CARDIAC) project in 1998, a multidimensional screening and research project that obtains fasting lipid profiles from over 60,000 fifth graders around the state. [20][21][22] The earlier a patient with FH can be detected and their lipid levels controlled, the greater the risk reduction for future coronary events. The recent Framingham report highlights the importance of optimum management of LDL in the 20-to 40-year-old age group to prevent CAD in the fifth or sixth decade.…”

Section: Cad Phenotype As a Building Block To Study Familial Hyperchomentioning

confidence: 99%

Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting

et al. 2021

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Background The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH. Objective We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository. Methods We developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification. Results CAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1 F1 score was 92.4%. Specific ICD-10 and CPT codes such as “coronary angiography through a vein graft” were more useful than generic terms. Conclusion We have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH.

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The Coronary Artery Risk Detection in Appalachian Communities (CARDIAC) Project: An 18 Year Review

Cited by 10 publications

References 25 publications

A proof-of-concept study of cascade screening for Familial Hypercholesterolemia in the US, adapted from the Dutch model

A proof-of-concept study of cascade screening for Familial Hypercholesterolemia in the US, adapted from the Dutch model

Obesity, Migraine, and Overlapping Comorbidities in a Rural Pediatric Population

Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting

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