The algorithm uses Structured Query Language to identify AAA cases, controls, and excludes from the Electronic Medical Record. AAA cases were defined as meeting at least one of three criteria: had a AAA repair procedure (Case Type 1), had at least one vascular clinic encounter with a diagnosis of ruptured AAA (Case Type 2), or had at least two vascular clinic encounters with a diagnosis of unruptured AAA (Case Type 3).
ACE-I induced cough is a common side effect of use of ACE inhibitors, one of the most common class of antihypertensives. The frequency of ACEI-induced cough varies based on ancestry. A GWAS of ACEI cough using this algorithm in the eMERGE Network identified KCNIP4 as associated with this phenotype, which was validated in two replication cohorts.
Cases are those with ACEI cough. Controls are those exposed to ACEI without adverse events noted and not switched to angiotensin receptor blockers (ARBs).
Algorithm validated - December 12, 2012.
Appendicitis is one of the most common acquired surgical conditions of childhood. Diagnosis of appendicitis remains difficult. Much work has been done on validation of clinical scores to reduce the number of unnecessary surgeries and radiographic tests while maintaining a high sensitivity for disease. However, no score performs well enough in practice to reduce these risks (Kulik et al., 2013). It is also known that appendicitis has a familial predominance, but little is known about the genetic factors that may increase a certain child's risk for the condition (Oldmeadow et al., 2009).
Carotid artert atherosclerosis disease (CAAD) is measured in cases and controls by both structured data, including ICD diagnosis codes, and quantitative measurements of carotid stenosis based on doppler and other imaging technologies.
The phenotype algorithm includes typical eMERGE pseudo code for implementing the structured data components of the algorithm, as well as a portable natural language processing (NLP) system used to extract percent stenosis measurements from imaging reports.
Algorithm to select subjects with "normal" electrocardiograms. Subjects do not have heart disease, interfering medications, or abnormal electrolytes at the time of the normal ECG. Individuals may, however, develop abnormalities later in life.
Hypothetical timeline for a single patient:
Please refer to attached documents.
We used a multi-modal strategy consisting of structured database querying, natural language processing on free-text documents, and optical character recognition on scanned clinical images to identify cataract subjects and related cataract attributes.