Selects patients with presumptive Alzheimer's disease. The algorithm is looking for records with a minimum of 5 visits with the Diagnosis of interest, using ICD 9 codes OR a dementia drug fill.This algorithm was validated against a curated research cohort.
Depression accounts for substantial morbidity and mortality worldwide and risk of experiencing it may have a genetic component. Depressive disorders manifest along a gradient from mild to severe.[1] Electronic health record (EHR) data linked to large, multi-site biobanks[2] facilitate exploration of the genetic component of depression.
Algorithm to identify patients with diabetic retinopathy.
An algorithm for finding patients with diverticulosis, and of those, patients who also have diverticulitis, and to also find control patients. Control patients will have had a colonoscopy but have no evidence of diverticula.
Simple NLP (a portable program is posted here, with instructions, and support is availabe from NU as needed) of colonoscopy reports is the gold standard algorithm, but if the text of colonoscopy reports is not available, an alternate algorithm using CPT & ICD-9 codes can be used, which is also posted.
An algorithm to identify inpatients who have had an acute episode of drug induced liver injury (DILI).
Summary of drug-induced liver injury algorithm
Inclusion criteria
A. Suspect DILI? (NOTE: baseline population is institution specific. See institution implementation details)
1. Liver injury AND Exposure to drug (NOTE: medications are institution specific. See institution implementation details)
2. Temporal relationship of exposure to drug and liver injury diagnosis.
Familial hypercholesterolemia (FH) is a relatively common Mendelian genetic disorder that is associated with elevated plasma low-density lipoprotein cholesterol (LDL-C) levels and dramatically increased lifetime risk for premature atherosclerotic cardiovascular disease (ASCVD). FH can be diagnosed based on clinical presentation and/or genetic testing results, with a positive genetic testing considered to be the “gold standard”.
The purpose of this PheKB page is to store and share code and supporting documents for the clinical data elements for GIRA generation data pull. The data are from eMERGE site electronic medical records for conditions that need additional information than what is included in the participant survey and will be stored in the R4 portal. Please send any questions or concerns to Alanna DiVietro (alanna.divietro@vumc.org).
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