Skip to content
DraconiticResearch
Search Sources API
222,075 studies31 sourcesUpdated 31 min ago
Peer-reviewedCC BY-NC-ND 4.0NPJ Digital Medicine

Passive early screening for Alzheimer’s disease and related dementias using EHR comorbidity patterns

  • Dmytro Onishchenko1
  • James A. Mastrianni2,3
  • Ishanu Chattopadhyay1
  1. 1Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY USA
  2. 2Department of Neurology, University of Chicago, Chicago, IL USA
  3. 3Director, Memory Center, University of Chicago, Chicago, IL USA
Read full textPDFDOICite

Abstract

Early identification of Alzheimer’s disease and related dementias (ADRD) remains limited by specialized tests and late-stage diagnosis. The Zero-burden Risk Assessment (ZeBRA) is an AI-driven score that predicts incident ADRD up to a decade before diagnosis using only routine electronic health record (EHR) data, without laboratory tests, imaging, or questionnaires. Trained on 487,989 cases and 12,483,718 controls from nationwide U.S. insurance claims and validated on held-out National samples and two independent cohorts, ZeBRA achieved AUC = 0.93 and 0.83 in the 50+ cohort for 1-year and 10-year horizons, respectively, with positive likelihood ratios exceeding 10 in the National 50+ held-out cohort at 95% specificity and stable discrimination over time. Performance was consistent across age, sex, race, and ethnicity subgroups. In a prospective feasibility pilot, higher ZeBRA scores showed concordance with lower Montreal Cognitive Assessment (MoCA) scores, indicating greater cognitive impairment (R = −0.78, 95% CI: −0.94 to −0.37). Compared with prior EHR-based models, ZeBRA provides superior accuracy, cross-site generalizability, and noise-corrected interpretability via our novel Λ-OR attribution metric. Scalability and low burden suggest application in population-level early detection and presymptomatic trial enrichment.

Related

  1. Comorbidity patterns and sex differences in late-onset Alzheimer’s disease using electronic records
    Yukari Katsuhara, Umair Khan, Zachary A. Miller +4
    iSciencePeer-reviewedCC BY 4.0Full textNeuroscienceNeurologyCell PressCrossrefEurope PMC
  2. Potential links between PCOS/PMOS and Alzheimer disease and related dementias
    Journal of the Endocrine SocietyPeer-reviewedCC BYFull textEndocrinology & metabolismEurope PMC
  3. Patient-Reported Shared Decision-Making and Documented Advance Care Planning Among Adults With Alzheimer Disease and Related Dementias
    X. Wang, T. G. R. Macieira, Y.-R. Hong +2
    medRxivPreprintAll rights reservedFull textEpidemiology & public healthmedRxiv
  4. The effects of improved access to diabetes medications on risk of Alzheimer's disease and related dementias
    Douglas Barthold, Julie Zissimopoulos, Anirban Basu +1
    Alzheimer's & dementia : the journal of the Alzheimer's AssociationPeer-reviewedCC BY-NCFull textGeneral medicineEurope PMC
  5. Performance of Claims-based Alzheimer’s Disease and Related Dementias Algorithms in Medicare Advantage and Fee-for-Service Populations
    You Wu, Chan Mi Park, Xiecheng Chen +5
    The journals of gerontology. Series A, Biological sciences and medical sciencesPeer-reviewedLicence not statedFull textPsychiatry & mental healthPMC

Cite

BibTeXRIS
BibTeX
@article{onishchenko2026passive,
  title = {{Passive early screening for Alzheimer’s disease and related dementias using EHR comorbidity patterns}},
  author = {Dmytro Onishchenko and James A. Mastrianni and Ishanu Chattopadhyay},
  journal = {NPJ Digital Medicine},
  year = {2026},
  month = jul,
  doi = {10.1038/s41746-026-02954-2},
  url = {https://doi.org/10.1038/s41746-026-02954-2},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Passive early screening for Alzheimer’s disease and related dementias using EHR comorbidity patterns
AU  - Dmytro Onishchenko
AU  - James A. Mastrianni
AU  - Ishanu Chattopadhyay
PY  - 2026
DA  - 2026/07/11
JO  - NPJ Digital Medicine
DO  - 10.1038/s41746-026-02954-2
UR  - https://doi.org/10.1038/s41746-026-02954-2
AB  - Early identification of Alzheimer’s disease and related dementias (ADRD) remains limited by specialized tests and late-stage diagnosis. The Zero-burden Risk Assessment (ZeBRA) is an AI-driven score that predicts incident ADRD up to a decade before diagnosis using only routine electronic health record (EHR) data, without laboratory tests, imaging, or questionnaires. Trained on 487,989 cases and 12,483,718 controls from nationwide U.S. insurance claims and validated on held-out National samples and two independent cohorts, ZeBRA achieved AUC = 0.93 and 0.83 in the 50+ cohort for 1-year and 10-year horizons, respectively, with positive likelihood ratios exceeding 10 in the National 50+ held-out cohort at 95% specificity and stable discrimination over time. Performance was consistent across age, sex, race, and ethnicity subgroups. In a prospective feasibility pilot, higher ZeBRA scores showed concordance with lower Montreal Cognitive Assessment (MoCA) scores, indicating greater cognitive impairment (R = −0.78, 95% CI: −0.94 to −0.37). Compared with prior EHR-based models, ZeBRA provides superior accuracy, cross-site generalizability, and noise-corrected interpretability via our novel Λ-OR attribution metric. Scalability and low burden suggest application in population-level early detection and presymptomatic trial enrichment.
SN  - 2398-6352
ER  -