Non-alcoholic fatty liver disease (NALFD) & Alcoholic Fatty Liver Disease (ALD)
Non-alcoholic fatty liver disease (NAFLD)
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Final
Opioid-exposed infant clinical indicators
Objective
We leveraged existing data from a single electronic health care system in the southeastern United States to demonstrate the feasibility of measuring quality indicators for the hospital-based care of opioid-exposed newborns using existing data infrastructure. Additionally, we identified other key variables related to the care of opioid-exposed maternal-infant dyads.
Patients and Methods
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Opioid-exposed infants
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.
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Final
Ovarian/Uterine Cancer (OvUtCa)
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.
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Final
Pneumonia- VUMC eMERGE v5.1
Identify bacterial pneumonia, similar to that reported with genetic association risk in CD143 and TLR4 A229G in literature.
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Final
Post-event Pain algorithm
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.
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Final
Rheumatoid Arthritis (RA)
This rheumatoid arthritis (RA) algorithm was created using a machine-learning logistic regression model.
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Final
SLE (Systemic Lupus Erythematosus) using SLICC (Systemic Lupus Internation Collaborating Clinics) Criteria
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.
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Final
Type 1 and type 2 Diabetes Mellitus
This document describes the Stanford University algorithm to extract individuals with diabetes and the type of diabetes from electronic health records (EHRs). There are two main tasks of this phenotype development: 1) to extract patients with diabetes (gestational diabetes is excluded), and 2) to discriminate between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Instead of identifying all diabetes cases, we aim to reduce the number of false positives in our diabetes cohort.
Final
Type 1 Diabetes
Phenotype Algorithm for Type 1 Diabetes – eMERGE Phase-IV Program
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Final