Multimodal Analgesia
Described in this document are the Stanford University algorithms for extracting both cases and controls of Multimodal analgesia from electronic health records (EHR) for surgical patients.
Described in this document are the Stanford University algorithms for extracting both cases and controls of Multimodal analgesia from electronic health records (EHR) for surgical patients.
Non-alcoholic fatty liver disease (NAFLD)
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.
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.
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. Peanut allergy is a common type of food allergy that accounts for a disproportionate number of fatal and near-fatal anaphylactic events amongst all the common food allergens.
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:
https://phekb.org/phenotype/benign-prostatic-hyperplasia-bph
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.
This algorithm describes the ongoing resistant hypertension algorithm used in eMERGE, which was a network phenotype within the eMERGE-I and eMERGE-II sites.