Caucasian (North African, Middle East, Other)

Hearing Loss

Phenotype Description:  individuals with sensorineural hearing loss (SNHL)
Below are algorithms used to identify individuals with SNHL at BioVU. If you have questions regarding any of the information presented on this page, you may contact either:
Wei-Qi Wei at wei-qi.wei@vanderbilt.edu or Joshua Denny at josh.denny@vanderbilt.edu

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Herpes Zoster

Herpes zoster, also known as zoster or shingles, is caused by a virus called varicella zoster virus (VZV). Initial infection with the virus causes chickenpox. After chickenpox resolves the virus continues to resides in certain nerve cells. It may remain latent for many years. It may also re-activate, many years later, and cause shingles which is a painful skin rash. How the virus remains latent in the body is not well understood.

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Ovarian/Uterine Cancer (OvUtCa)

The KPWA/UW-led ovarian/uterine cancer phenotype has been validated at Mayo Clinic, the secondary phenotype development site.  Validation results at both the primary and secondary sites were strong and the phenotype is ready for network wide implementation.  The pseudo code document posted 11/30/2017 is correct as is and should be used by network sites for phenotype implementation.  A validated data dictionary of covariates for this phenotype will be added to PheKB by 2/15/2018, but sites are encouraged to begin implementing the phenotype algorithm now.

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Peanut Allergy

Food allergy is defined as an immune response that occurs reproducibly to a given food, typically an immunoglobulin E (IgE)-mediated clinical reaction to specific protein epitopes.  Over the last 20-30 years, food allergy has grown into a major public health problem.  Peanut allergy is a common type of food allergy that accounts for a disproportionate number of fatal and near-fatal anaphylactic events amongst all the common food allergens.

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PGx medication risk prediction model

This algorithm predicts those who are going to be exposed to warfarin, simvastatin, or clopidogrel as three medications that have known pharmacogenomic influences.  This algorithm was used to select individuals for the Vanderbilt PREDICT (Pharmacogenomic Resource for Enhanced Decisions in Care & Treatment) program, which prospectively tests individuals at risk of needing medications whose efficacy is effected by genetic variants.  

 

For more information on PREDICT, see http://mydruggenome.org.

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SLE (Systemic Lupus Erythematosus) using SLICC (Systemic Lupus Internation Collaborating Clinics) Criteria

Systemic Lupus Erythematosus (SLE) is a chronic, systemic autoimmune disease that can affect many parts of the body including skin, lungs, brain, heart, kidneys, joints, and blood vessels. SLE presentation can vary significantly between patients. Because of this, it can be challenging to identify a patient as having SLE. Between 300,000 and 2,000,000 people in the US are estimated to have SLE. Determination of an exact number of people affected is challenging as the disease is difficult to identify given the diverse presentations and the length of time it may take for symptoms to appear.

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Systemic lupus erythematosus (SLE)

We used Vanderbilt’s Synthetic Derivative (SD), a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least one SLE ICD-9 code (710.0) yielding 5959 individuals. To create a training set, 200 were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist.

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