Gastroesophageal Reflux Disease (GERD) Phenotype Algorithm
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East Asian (Mongol, Tibetan, Korean, Japanese, Thai, etc.)
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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Intellectual Disability
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Opioid-exposed infants
Objective
Observational studies examining outcomes among opioid-exposed infants are limited by phenotype algorithms that may under identify opioid-exposed infants without neonatal opioid withdrawal syndrome (NOWS). We developed and validated the performance of different phenotype algorithms to identify opioid-exposed infants using electronic health record (EHR) data.
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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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Pneumonia- VUMC eMERGE v5.1
Identify bacterial pneumonia, similar to that reported with genetic association risk in CD143 and TLR4 A229G in literature.
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Rheumatoid Arthritis (RA)
This rheumatoid arthritis (RA) algorithm was created using a machine-learning logistic regression model.
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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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