ICD 10 Codes

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.

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

Post-event Pain algorithm

Pain is a personal, multidimensional experience in which genetic biomarkers has a main role in determining pain sensitivity, perception and tolerance. Pain is a major concern for surgical patients and post-operative pain management still present a major challenge both in inpatient or outpatient settings. Apart from genetic factors, there are many other variables that may affect pain perception for example, pretreated patients may require less post-surgical medications, and they may recover more quickly.

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

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

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

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.

Final

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