ICD 10 Codes

Digital Rectal Exam

Described in this document are the Stanford University algorithms for extracting both cases and controls of digital rectal examination (DRE) from electronic health records (EHR) of prostate cancer patients. DRE is a clinical procedure, part of a set of quality metrics used to determine quality care for these patients. In this regard, DRE is defined as quality care when it is performed within a time period of up to six months before first treatment for prostate cancer. For the purposes of this algorithm a case is defined as DRE documented, whereas a control is DRE not documented.

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Diverticular Disease Severity, Left Colonic

This algorithm builds off prior phenotyping work from Pacheco & Thompson available in the PheKB phenotype "Diverticulosis and Diverticulitis" as well as the manuscripts from Joo et al (2023)(1) and De Roo et al (2023) (2) . The objective is to approximate diverticular disease severity from the electronic medical record into groups of Diverticulosis, Mild Diverticulitis, and Operative or Recurrent Inpatient Diverticulitis.

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Hearing Loss

Phenotype Description:  individuals with sensorineural hearing loss (SNHL)
Below are algorithms used to identify individuals with SNHL 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 or Joshua Denny at josh.denny@vanderbilt.edu

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Migraine

Migraine is the most common recurrent headache syndrome in children in which 4-10% of school age children may be affected (1). It is characterized by episodes of headache pain that may be accompanied by nausea, vomiting, and light and sound sensitivity. Migraine occurs at all ages and may even begin in infancy as represented by intermittent colic (1). The genes for familial hemiplegic migraine have been identified.

Owner Phenotyping Groups: 
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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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