Objective
Observational studies examining outcomes among opioid-exposed infants are limited by phenotype algorithms that may under identify opioid-exposed infants without neonatal opioid withdrawal syndrome (NOWS). We developed and validated the performance of different phenotype algorithms to identify opioid-exposed infants using electronic health record (EHR) data.
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.
Identify bacterial pneumonia, similar to that reported with genetic association risk in CD143 and TLR4 A229G in literature.
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.
Rheumatoid Arthritis phenotype algorithm for the DNA Databank demonstration project.
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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.
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