Collaboration Phenotypes

Contact authors listed below are open to engaging others in the development of their phenotypes. Unless the status of the phenotype is marked as final, these phenotypes cannot be viewed in-depth until the author has shared access with you and you have logged into PheKB. Click on an author's name to send an email to him or her expressing your interest in collaborating.

Title Institution Data Modalities and Methods Used Description Status Contact Author Type of Phenotype
Ovarian/Uterine Cancer (OvUtCa) KPWA/UW CPT Codes, ICD 10 Codes, ICD 9 Codes, Laboratories, Medications, Vital Signs 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 Validated David Carrell Disease or Syndrome
Peanut Allergy Vanderbilt University, Vanderbilt University Medical Center CPT Codes, Laboratories Food allergy is defined as an immune response that occurs reproducibly to a given food, typically an immunoglobulin E (IgE)-mediated clinical reaction to specific protein epitopes.  Over the last 20-30 years, food allergy has grown into a major public health problem. Final Jonathan Hemler Disease or Syndrome
Pediatric Lipid Distribution for Development of Pediatric Familial Hypercholesterolemia Geisinger ICD 10 Codes, ICD 9 Codes, Laboratories, Medications Primary aims of the peds FH phenotyping are: In development Ken Borthwick Other Trait
Post-operative Complications from Major General Surgeries Algorithm Vanderbilt CPT Codes, ICD 9 Codes Post-operative Complications as defined by the American College of Surgeon's National Surgical Quality Improvement Program (NSQIP) are a set of 21 clinical conditions that occur 30 days from the date of operation. Testing Tom Mou Disease or Syndrome
Rheumatology Auto-Immune characteristics Harvard Medical School Laboratories, Medications Dear, To identify cases with auto-immune rheumatologic phenotye (for  NT198) we request information about auto-antibody (whether it was tested and what the restults were) and drug information (whether it was prescribed) for each patients that is enrolled in eMERGE. We are requesting every mention of any of the expanded generic drugs. Final Rachel Knevel Other Trait
Sickle Cell Disease Medical College of Wisconsin ICD 9 Codes Final Daniel Michalik Disease or Syndrome
Statins and MACE Vanderbilt University CPT Codes, ICD 9 Codes, Laboratories, Natural Language Processing 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 Final Wei-Qi Wei Drug Response - adverse effect or efficacy
STOP CRC Cohort Group Health Cooperative CPT Codes, ICD 9 Codes This is a cohort identification phenotype for the STOP CRC trial, which is testing a culturally tailored, health care system–based program to improve CRC screening rates in OCHIN, a community-based collaborative network of more than 200 Federally Qualified Healthcare Centers. Validated Michelle Smerek Disease or Syndrome
Systemic lupus erythematosus (SLE) Vanderbilt University Medical Center ICD 9 Codes, Laboratories, Medications, Natural Language Processing 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 April Barnado Disease or Syndrome
Type1 or Type 2 Diabetes Mellitus Mayo Clinic ICD 9 Codes, Laboratories, Medications, Natural Language Processing 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. Final Sudhi Upadhyaya