Peripheral Arterial Disease (PAD) is prevalent with approximately 10-12 million adults in US affected. For those with PAD, morbidity and mortality are high and quality of life is markedly impaired. The genetic basis of PAD is poorly understood and is the focus of the Mayo Clinic Electronic Medical Records and Genomics (eMERGE) Network study.
This algorithm predicts those who are going to be exposed to warfarin, simvastatin, or clopidogrel as three medications that have known pharmacogenomic influences. This algorithm was used to select individuals for the Vanderbilt PREDICT (Pharmacogenomic Resource for Enhanced Decisions in Care & Treatment) program, which prospectively tests individuals at risk of needing medications whose efficacy is effected by genetic variants.
For more information on PREDICT, see http://mydruggenome.org.
This is PhEMA (Phenotype Execution Modeling Architecture, projectphema.org)'s implementation of the following BPH (Benign Prostatic Hyperplasia) case algorithm from the following BPH case and control algorithm on PheKB:
Artifacts for this phenotype, inc. an HQMF representation, a KNIME workflow that can run against an i2b2 instance, and a snapshot of the PhAT graphical representation, are posted on GitHub:
Identify bacterial pneumonia, similar to that reported with genetic association risk in CD143 and TLR4 A229G in literature.
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.
Laboratory results for ESR and RBC indices (hemoglobin, MCV, MCH, RDW… etc) should be extracted from the Laboratory databases. For Mayo, from January 1994 till October 2009, ESR and RBC test results were populated for our 3336 participants. All samples were collected on an outpatient basis. Samples collected during an inpatient hospitalization (admit date ≤ collection date ≤ discharge date) should be excluded unless this sample was the only one available for a patient.
This algorithm describes the ongoing resistant hypertension algorithm used in eMERGE, which was a network phenotype within the eMERGE-I and eMERGE-II sites.
This rheumatoid arthritis (RA) algorithm was created using a machine-learning logistic regression model.
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 email@example.com
Joshua Denny at firstname.lastname@example.org