Passive Digital Signature for Early Identification of Alzheimer’s Disease and Related Dementia


OBJECTIVES

Developing scalable strategies for the early identification of Alzheimer’s disease and related dementia (ADRD) is important. We aimed to develop a passive digital signature for early identification of ADRD using electronic medical record (EMR) data.

DESIGN

A case‐control study.

SETTING

The Indiana Network for Patient Care (INPC), a regional health information exchange in Indiana.

PARTICIPANTS

Patients identified with ADRD and matched controls.

MEASUREMENTS

We used data from the INPC that includes structured and unstructured (visit notes, progress notes, medication notes) EMR data. Cases and controls were matched on age, race, and sex. The derivation sample consisted of 10 504 cases and 39 510 controls; the validation sample included 4500 cases and 16 952 controls. We constructed models to identify early 1‐ to 10‐year, 3‐ to 10‐year, and 5‐ to 10‐year ADRD signatures. The analyses included 14 diagnostic risk variables and 10 drug classes in addition to new variables produced from unstructured data (eg, disorientation, confusion, wandering, apraxia, etc). The area under the receiver operating characteristics (AUROC) curve was used to determine the best models.

RESULTS

The AUROC curves for the validation samples for the 1‐ to 10‐year, 3‐ to 10‐year, and 5‐ to 10‐year models that used only structured data were .689, .649, and .633, respectively. For the same samples and years, models that used both structured and unstructured data produced AUROC curves of .798, .748, and .704, respectively. Using a cutoff to maximize sensitivity and specificity, the 1‐ to 10‐year, 3‐ to 10‐year, and 5‐ to 10‐year models had sensitivity that ranged from 51% to 62% and specificity that ranged from 80% to 89%.

CONCLUSION

EMR‐based data provide a targeted and scalable process for early identification of risk of ADRD as an alternative to traditional population screening.

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