Glomerular Disease, SNOMED-Only Codes

dc.contributor.advisorDenburg, Michelle
dc.contributor.advisorRazzaghi, Hanieh
dc.contributor.advisorBailey, Charles
dc.contributor.authorGoodwin Davies, Amy
dc.contributor.otherChildren's Hospital of Philadelphia
dc.date2021-03-01
dc.date.accessioned2024-06-10T19:03:24Z
dc.date.available2024-06-10T19:03:24Z
dc.date.created2019
dc.descriptionThis 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.
dc.description.abstractThis concept set is inteded to identify patients with a diagnosis of glomerular disease.
dc.identifier.urihttps://pedsnet.org/metadata/handle/20.500.14642/545
dc.language.isoen-US
dc.provenanceThis concept set was developed for the Membranoproliferative Glomerulonephritis (MPGN) in PEDSnet study.
dc.publisherPEDSnet
dc.relation.isreferencedbyDenburg, 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](https://doi.urg/10.1681/ASN.2019040365)
dc.relation.urihttps://atlassian.chop.edu/bitbucket/projects/PRP/repos/denburg_mpgn/browse/descriptives_tbl4/specs/glean_inclusion.csv
dc.rightsa CC-BY Attribution 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectConcept Set Development::Content Data Model::PCORnet Data Model
dc.subjectConcept Set Development::Concept Set Development Strategy::Cohort Characterization::Cohort Definition
dc.subjectConcept Set Development::Concept Set Evaluation::Clinician Reviewed
dc.subject.meshGlomerulonephritis
dc.subject.meshNepritis
dc.subject.meshKidney Diseases
dc.subject.meshUrologic Diseases
dc.subject.otherSNOMED
dc.titleGlomerular Disease, SNOMED-Only Codes
dspace.entity.typeConceptSet
relation.isConceptSetOfConceptSet0f62d22d-ebb0-4656-97ca-5a10ef85fa2d
relation.isConceptSetOfConceptSet.latestForDiscovery0f62d22d-ebb0-4656-97ca-5a10ef85fa2d
relation.isStudyOfConceptSetb9ba1a84-3c64-47c4-a896-53957d51d720

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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.
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