التفاصيل البيبلوغرافية
| العنوان: |
Passive Digital Signature for Early Identification of Alzheimer's Disease and Related Dementia. |
| المؤلفون: |
Boustani, Malaz, Perkins, Anthony J., Khandker, Rezaul Karim, Duong, Stephen, Dexter, Paul R., Lipton, Richard, Black, Christopher M., Chandrasekaran, Vasu, Solid, Craig A., Monahan, Patrick |
| المصدر: |
Journal of the American Geriatrics Society; Mar2020, Vol. 68 Issue 3, p511-518, 8p, 4 Charts |
| مصطلحات موضوعية: |
DIGITAL signatures, ALZHEIMER'S disease diagnosis, DIAGNOSIS of dementia, EARLY diagnosis, ELECTRONIC health records, ALZHEIMER'S disease risk factors, DEMENTIA risk factors, RISK assessment, HUMAN services programs, CASE-control method, RECEIVER operating characteristic curves |
| مصطلحات جغرافية: |
INDIANA |
| مستخلص: |
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. J Am Geriatr Soc 68:511–518, 2020 [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of the American Geriatrics Society is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
