ICD 10 Codes

Developmental Language Disorder

APT-DLD
Version 1.0, July 2020

Automated Phenotyping Tool for identifying DLD cases in health-systems data (APT-DLD) is an algorithm for classifying/identifying developmental language disorder cases in electronic health records system data. APT-DLD can be used to:
1. Identify pediatric DLD cases from electronic health record systems using ICD9 and ICD10 codes
2. Study epidemiology and population-level charateristics of DLD from EHRs

The How-To guide for using APT-DLD is provided in the files listed below.

Owner Phenotyping Groups:

Vanderbilt - Music Cognition Lab

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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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Epilepsy/Antiepileptic drug response algorithm

Owner Phenotyping Groups:

eMERGE CHOP Group

View Phenotyping Groups:

eMERGE Phenotype WG

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Functional seizures

Algorithm to detect functional seizures, also known as:

Psychogenic nonepileptic seizures

Nonepileptic attack disorder

Pseudoseizures

Hystero-epilepsy

Conversion disorder with seizures

Dissociative Seizures

Owner Phenotyping Groups:

BioVU Phenotyping

PsycheMERGE

Vanderbilt - SD/RD Group

View Phenotyping Groups:

BioVU Phenotyping

PsycheMERGE

Vanderbilt - SD/RD Group

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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

Owner Phenotyping Groups:

Denny’s Group at Vandy

eMERGE Phenotype WG

eMERGE Vanderbilt Group

View Phenotyping Groups:

Denny’s Group at Vandy

eMERGE CCHMC Group

eMERGE CCHMC/BCH Group

eMERGE CHOP Group

eMERGE Columbia Group

eMERGE Geisinger Group

eMERGE GHC/University of Washington Group

eMERGE Harvard Group

eMERGE Marshfield Group

eMERGE Mayo Group

eMERGE Meharry Group

eMERGE Mount Sinai Group

eMERGE Northwestern Group

eMERGE Phenotype WG

eMERGE Vanderbilt Group

Vanderbilt - SD/RD Group

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Intellectual Disability

Owner Phenotyping Groups:

eMERGE CHOP Group

View Phenotyping Groups:

eMERGE CHOP Group

eMERGE Phenotype WG

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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:

eMERGE CCHMC Group

View Phenotyping Groups:

eMERGE Phenotype WG

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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.

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