Glomerular Disease, SNOMED-Only Codes

Created

Last Modified

2021-03-01

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Abstract

This concept set is inteded to identify patients with a diagnosis of glomerular disease.

Funder(s)

Provenance

This concept set was developed for the Membranoproliferative Glomerulonephritis (MPGN) in PEDSnet study.

Description

This concept set contains codes pertaining to glomerular disease, glomerulonephritis, nephritic syndrome, and nephrotic syndrome. It is intended to identify a cohort of patients with glomerular disease when incorporated into the computational phenotype originally developed and validated by Denburg et al 2019 (DOI: 10.1681/ASN.2019040365). Validation indicated that this computational phenotype had excellent classification accuracy across PEDSnet: sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%).

The computational phenotype identifies patients via two pathways: two or more glomerular inclusion diagnoses on different days of service, OR (one or more glomerular inclusion diagnosis AND one or more kidney biopsy which is not post-transplant). Codes flagged with “other_code_req” alone are not sufficient for a patient to be included in the cohort, i.e., these codes must be accompanied by another code without this flag or a kidney biopsy which is not post-transplant. Codes flagged with “neph_req” must be associated with a nephrology specialty visit.

The concept set was developed by mapping a list of ICD-9-CM codes provided by Michelle Denburg (clinician) to SNOMED. It is limited to SNOMED-CT-only diagnosis codes.

Vocabulary

Data Source

Related Phenotype

Related Person

Related Study

Membranoproliferative Glomerulonephritis (MPGN) in PEDSnet
Children's Hospital of Philadelphia
Study to identify and conduct phenotypic characterization of children with C3G and IC-MPGN in PEDSnet using data science and chart review methods. The long-term goal of this work is to perform a large multi-institutional natural history study, compare clinical outcomes in C3G and IC-MPGN, and identify predictors of disease response or progression.

Related Concept Set

Related Publications

Denburg, Michelle R.; Razzaghi, Hanieh; Bailey, L. Charles; Soranno, Danielle E.; Pollack, Ari H.; Dharnidharka, Vikas R.; Mitsnefes, Mark M.; Smoyer, William E.; Somers, Michael J. G.; Zaritsky, Joshua J.; Flynn, Joseph T.; Claes, Donna J.; Dixon, Bradley P.; Benton, Maryjane; Mariani, Laura H.; Forrest, Christopher B.2; Furth, Susan L. Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research. JASN 30(12):p 2427-2435, December 2019. | DOI: 10.1681/ASN.2019040365

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Except where otherwised noted, this item's license is described as a CC-BY Attribution 4.0 License.

Version History

Now showing 1 - 2 of 2
VersionDateSummary
2024-10-07 16:13:28
Updated to be more comprehensive and suitable for reuse.
1*
2024-06-10 15:03:24
Restricted to diagnoses that occurred in the PEDSnet database at the time of creation.
* Selected version