Computable phENotype To Identify Pulmonary Embolism in chilDrEn (CENTIPEDE)

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dc.contributorChildren's Foundation
dc.contributor.authorRajpukar, Madhvi
dc.contributor.authorBailey, Charles
dc.contributor.otherCentral Michigan University
dc.contributor.otherChildren's Hospital of Philadelphia
dc.date.accessioned2024-09-17T18:29:02Z
dc.descriptionThe rate of pediatric pulmonary embolism (PPE), the most serious and potentially deadly form of venous thromboembolism, has increased by 200% over the last decade. But it remains rare at individual centers and consequently is challenging to study. Currently, the management of PPE is empiric and not derived from scientific evidence. Electronic health records (EHR) are a rich source of clinical information that can be used to study rare diseases provided a disease cohort is accurately identified. Previous studies in adults have revealed that using appropriate combinations of diagnostic and treatment codes leads to accurate identification of a PE cohort from EHR. The purpose of this study is to identify the best combination of diagnostic and treatment codes using EHR data that can accurately diagnose PPE at a single center. #### Study Aims 1. Test the classification accuracy of an algorithm (ASPECT) that has been previously validated in adult PE patients to identify pediatric PE cohort from EHR. 2. Evaluate the accuracy of other combinations of diagnostic and treatment codes using EHR data to diagnose pediatric PE at a single center (CHOP). #### Study Design A retrospective cohort study using structured data and chart reviews to develop a computable phenotype. #### Cohort Description Patients who meet the inclusion and exclusion criteria of the ASPECT computational phenotype from January 2012 to December 2022. ASPECT criteria include an anticoagulant prescription associated with an inpatient or emergency visit at age 21 or younger; a pulmonary embolism diagnosis code associated with an inpatient or emergency visit at age 21 or younger; a diagnostic imaging study performed during or two fewer days before the visit; an anticoagulant dispensed or prescribed during the visit; and excludes patients with an anticoagulant dispensed or prescribed between 7 and 365 days before the visit.
dc.description.abstractStudy to test the classification accuracy of an algorithm that has been previously validated in adult pulmonary embolism (PE) patients (“ASPECT”). The algorithm was used to identify a pediatric PE cohort from EHR and evaluate the accuracy of diagnostic and treatment code combinations using EHR data to diagnose pediatric PE at a single center, the Children’s Hospital of Philadelphia.
dc.identifier.urihttps://pedsnet.org/metadata/handle/20.500.14642/791
dc.publisherPEDSnet
dc.relationPediatric Pulmonary Embolism
dc.relationMRI Scan, Chest Region
dc.relationComputed Tomography (CT Scan)
dc.relationAnti-coagulants
dc.rightsa CC-BY 4.0 Attribution license.
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/
dc.subjectStudy::Funded Study::PEDSnet Study
dc.subjectStudy::Clinical Study::Observational Study
dc.subjectStudy::Cohort Study::Retrospective Study
dc.subject.meshPulmonary Embolism
dc.subject.meshLung Diseases
dc.titleComputable phENotype To Identify Pulmonary Embolism in chilDrEn (CENTIPEDE)
dspace.entity.typeStudy
local.subject.flatPEDSnet Data Source
local.subject.flatRetrospective Study
local.subject.flatObservational Study
local.subject.flatCohort Study
project.startDate2024-06
relation.isConceptSetOfStudy3f6753bf-0bfe-465c-a270-e04d4b49b39d
relation.isConceptSetOfStudye16d5ecd-955d-4c09-a134-31a1adc95868
relation.isConceptSetOfStudy2a77461f-47d2-4094-8a11-7f72a036d25c
relation.isConceptSetOfStudyd2d0e52c-dd2e-4f96-a1a6-d204dea95e3c
relation.isConceptSetOfStudy.latestForDiscovery3f6753bf-0bfe-465c-a270-e04d4b49b39d
relation.isOrgUnitOfStudyda841c56-bb55-405c-8b50-8f221ef62890
relation.isOrgUnitOfStudy.latestForDiscoveryda841c56-bb55-405c-8b50-8f221ef62890
relation.isPhenotypeOfStudy57e754ca-ceed-4512-86ec-3baa6e8d3aa5
relation.isPhenotypeOfStudy.latestForDiscovery9148a8ad-764c-4875-b7b6-261e79096859

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