Abdominal Aortic Aneurysm ( AAA )

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).

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Body Mass Index (BMI)

These are the PRIMED Harmonization instructions for body mass index (BMI). 

Overall procedure
1. Extract trait measurements using measurement codes and calculations
2. Convert measurement units to expected units, if necessary
3. Apply measurement exclusion criteria, other than statistical outliers
4. Remove statistical outliers
5. Compute a single value per individual for inclusion in analysis
6. Calculate counts of flagged individuals

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Breast Cancer

These are the PRIMED Harmonization instructions for breast cancer (BC). We are using the breast cancer definition used by the eMERGE program, with some slight modifications. Follow this document for implementation.
Modifications:
We will only include cis females in the analyses
The standard definition requires controls be age 18+. If the age distribution of cases within a study is much older (e.g. 30+), then studies may filter controls based on age >= the minimum age of identified cases

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Lipids (HDL-C, LDL-C, TG)

This document describes the PRIMED Harmonization algorithms for HDL-C, LDL-C, and TG.
• High density lipoprotein (HDL-C)
• Triglycerides (TG):
o Non-fasting
• Low-density lipoprotein (LDL-C):
o Unadjusted non-fasting
o Adjusted non-fasting
Overall procedure
1. Extract trait measurements using measurement codes and calculations
2. Convert measurement units to expected units, if necessary
3. Apply measurement exclusion criteria
4. Prepare measurements for analysis

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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 

Final

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.

Final

Prostate Cancer

We are using the prostate cancer definition used by the eMERGE program, with a slight modification to the control definition (prostate_cancer_status_emerge_mod_1). You can review the eMERGE documentation for reference, but please follow this document for implementation.
Modifications:
Combine “Control A” and “Control B” groups from the provided schematic figure into a single control group for analysis
Controls should be filtered based on age >= the minimum age of identified cases to the age distributions of cases and controls are comparable within each study

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