Laboratories

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.

Owner Phenotyping Groups: 
eMERGE Northwestern Group
View Phenotyping Groups: 
eMERGE Mayo Group
eMERGE Phenotype WG
Final

Statins and MACE

Phenotype Description:  Patients on statins for primary prevention who develop an AMI or 1st AMI. 

Below are algorithms used to identify AMI and 1st AMI cohort 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

Joshua Denny at josh.denny@vanderbilt.edu

 

             

Owner Phenotyping Groups: 
eMERGE Vanderbilt Group
PGPop WG
View Phenotyping Groups: 
eMERGE Phenotype WG
PGPop WG
Final

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.

Final

Type 1 and type 2 Diabetes Mellitus

This document describes the Stanford University algorithm to extract individuals with diabetes and the type of diabetes from electronic health records (EHRs). There are two main tasks of this phenotype development: 1) to extract patients with diabetes (gestational diabetes is excluded), and 2) to discriminate between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Instead of identifying all diabetes cases, we aim to reduce the number of false positives in our diabetes cohort.

Final

Type1 or Type 2 Diabetes Mellitus

Phenotyping algorithm for the identification of patients with type 1 or type 2 diabetes mellitus (DM) preoperatively using routinely available clinical data from electronic
health records.

View Phenotyping Groups: 
American-Swiss Allergy Phenotyping Group
BioVU Phenotyping
Clostridium Difficile Colitis Phenotype Group
Denny’s Group at Vandy
eMERGE CCHMC Group
eMERGE CCHMC/BCH Group
eMERGE CHOP Group
eMERGE Columbia Group
eMERGE Geisinger Group
eMERGE GHC/University of Washington Group
eMERGE Harvard Group
eMERGE Marshfield Group
eMERGE Mayo Group
eMERGE Meharry Group
eMERGE Mount Sinai Group
eMERGE Northwestern Group
eMERGE Phenotype WG
eMERGE Vanderbilt Group
Farr Institute Kidney Working Group
GPC
NIH Collaboratory Group
PCORI CDRN/PPRN
PCORI MidSouth CDRN
PCORI MidSouth CDRN/Duke Group
PCORI MidSouth CDRN/Greenway Group
PCORI MidSouth CDRN/HSSC Group
PCORI MidSouth CDRN/Meharry Group
PCORI MidSouth CDRN/UNC Group
PCORI MidSouth CDRN/VUMC Group
PGPop WG
PGx model group
PGx Workgroup
PheMA Test Group
Precision Medicine and Health Disparities Collaborative
Resistant hypertension WG
Vanderbilt - SD/RD Group
VESPA - Vanderbilt Electronic Systems for Pharmacogenomic Assessment
Final

Warfarin dose/response

This algorithm identifies patients who have a stable within-range INR (assuming a target INR of 2-3) over at least a three week period and correlates with their warfarin weekly dose.  It is used to identify pharmacogenetics behind warfarin stable dose.

Owner Phenotyping Groups: 
VESPA - Vanderbilt Electronic Systems for Pharmacogenomic Assessment
View Phenotyping Groups: 
VESPA - Vanderbilt Electronic Systems for Pharmacogenomic Assessment
Final

White Blood Cell Indices

Genetic variation that predicts white blood count (WBC) and it differential, a marker of the health of the immune system.

WBC is unique among the identified inflammatory predictors of chronic disease in that it has been routinely measured in healthy patients in an unbiased way for the duration of the electronic medical record data. 

Owner Phenotyping Groups: 
eMERGE GHC/University of Washington Group
View Phenotyping Groups: 
eMERGE Phenotype WG
Final

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