Disease or Syndrome

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
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Sleep Apnea Phenotype

  • The computable phenotype for the Sleep Apnea Patient Centered Outcomes Network uses existing and well established ICD codes for different types of sleep apnea including 327.23 (adult and pediatric obstructive sleep apnea), 780.51 (insomnia with sleep apnea), 780.53 (hypersomnia with sleep apnea), and 780.57 (unspecified sleep apnea).
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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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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.

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Type 2 Diabetes - PRS Evaluation

NOTE: 

The following files were updated on 4/9/2021 so that the output of the #feature table in the eMERGE_IV_OMOP_T2DM_PRS_algorithm script matches the data dictionary.

Files:

  • T2DM_DD_Feature_Count_OMOP_2021049.csv
  • eMERGE_IV_OMOP_T2DM_PRS_algorithm_20210409.txt
  • eMERGE_IV_OMOP_T2DM_PRS_algorithm_20210409.zip

 

 

Owner Phenotyping Groups: 
eMERGE Harvard Group
View Phenotyping Groups: 
eMERGE Phenotype WG
Final

Urinary Incontinence

Description of a weakly supervised machine learning approach for extracting treatment-related side effects (Urinary Incontinence) following prostate cancer therapy from multiple types of free-text clinical narratives, including progress notes, discharge summaries, history and physical notes. Prostatectomy surgery and radiation therapy are our treatments of interest for prostate cancer.

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