[K23] Assessing Risk for Poor Outcomes in Antiphospholipid Syndrome
Ente: National Institute of Arthritis and Musculoskeletal and Skin Diseases
Scadenza: 2030-06-30
Importo max: 175.373 EUR
Paese: US
Descrizione
PROJECT SUMMARY/ABSTRACT
Background: This proposal is designed for Medha Barbhaiya, MD, MPH to grow towards becoming an
independent investigator focused on developing and validating a novel method to identify antiphospholipid
syndrome (APS) in large cohorts, evaluating its utility in identifying poor APS outcomes in multi-center EHR
cohorts, and identifying discrete APS subphenotypes based on longitudinal autoantibody and biomarker data.
Currently, there is no accurate way to identify APS patients in large cohorts, limiting understanding of the role
of modifiable risk factors for outcomes across sociodemographic groups. Additionally, while certain aPL
profiles may confer increased thrombotic risks, the extent to which novel biomarkers predict clinical outcomes
is unknown. Preliminary data: For Aim 1, we have begun assessing the feasibility of developing the first
algorithms for APS identification using structured EHR data. In the Hospital for Special Surgery (HSS)
electronic health record (EHR), we have applied a broad screening filter to identify all potential APS patients
(n=1,318 potential adult APS cases with ≥1 APS ICD-10-CM [D68.61] code since January 1, 2016). Under the
guidance of her mentors, Dr. Barbhaiya will randomly select 200 of these subjects as a ‘training set’ for chart
review to identify their true case status. For Aim 2, we have recently descriptively evaluated the APS ACTION
registry, the largest and longest prospective registry of patients with antiphospholipid antibodies (aPL) to study
associations between aPL profile and clinical events. We will now apply bioinformatics approaches to evaluate
the association of novel APS biomarkers with aPL profile and clinical outcomes in this ongoing prospective
registry. Methods: As part of this K23 award, we will develop algorithms using structured and unstructured
data using natural language processing and machine learning approaches. We separately plan to
subphenotype antiphospholipid antibody (aPL) patients in the ongoing prospective APS ACTION registry using
novel biomarkers and aPL profile. We will use classical clustering methods as well as unsupervised machine
learning to cross-sectionally and longitudinally evaluate for an association with APS clinical outcomes after
adjusting for demographic and other clinical factors. Career Development: This proposal employs novel
methods to address gaps in knowledge related to APS epidemiology. Dr. Barbhaiya is an Assistant Professor
in Medicine and Population Health Sciences at Weill Cornell Medicine and an Assistant Attending at Hospital
for Special Surgery with access to outstanding services and environment at these institutions. She has
assembled a strong multi-disciplinary mentoring team and will be able to complete formal training in
bioinformatics, biomarker assay interpretation, immunology, leadership and mentoring skills. This project has
the potential to lead to future grants and studies, and will position Dr. Barbhaiya to achieve
Istituzione: HOSPITAL FOR SPECIAL SURGERY
PI: Medha Barbhaiya
Progetto: 5K23AR085799-02
Settori: National Institute of Arthritis and Musculoskeletal and Skin Diseases
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