TY - JOUR
T1 - Passive Digital Signature for Early Identification of Alzheimer's Disease and Related Dementia
AU - Boustani, Malaz
AU - Perkins, Anthony J.
AU - Khandker, Rezaul Karim
AU - Duong, Stephen
AU - Dexter, Paul R.
AU - Lipton, Richard
AU - Black, Christopher M.
AU - Chandrasekaran, Vasu
AU - Solid, Craig A.
AU - Monahan, Patrick
N1 - Funding Information:
This study was supported by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. It was also supported in part by the Einstein Aging Study: NIH/NIA 2PO1 AG 003949.
Funding Information:
Malaz Boustani is the founding director of the Sandra Eskenazi Center for Brain Care Innovation. Richard Lipton is the Edwin S. Lowe Professor of Neurology at the Albert Einstein College of Medicine in New York. He receives research support from the National Institutes of Health (NIH): 2PO1 AG003949 (multiple Principal Investigator), 5U10 NS077308 (Principal Investigator), R21 AG056920 (Investigator), 1RF1 AG057531 (Site PI), RF1 AG054548 (Investigator), 1RO1 AG048642 (Investigator), R56 AG057548 (Investigator), U01062370 (Investigator), RO1 AG060933 (Investigator), K23 NS09610 (Mentor), K23AG049466 (Mentor), K23 NS107643 (Mentor). He also receives support from the Migraine Research Foundation and the National Headache Foundation. He serves on the editorial board of Neurology , is a senior advisor to Headache , and associate editor at Cephalalgia . He has reviewed for the National Institute on Aging and the National Institute for Neurological Disorders, holds stock options in eNeura Therapeutics and Biohaven Holdings; serves as consultant, advisory board member, or has received honoraria from: American Academy of Neurology, Alder, Allergan, American Headache Society, Amgen, Avanir, Biohaven, Biovision, Boston Scientific, Dr. Reddy's, Electrocore, Eli Lilly, eNeura Therapeutics, GlaxoSmithKline, Merck, Pernix, Pfizer, Supernus, Teva, Trigemina, Vector, and Vedanta. He receives royalties from Wolff's Headache , 7th and 8th eds. (Oxford University Press, 2009), Wiley, and Informa. Rezaul Karim Khandker, Christopher Black, and Vasu Chandrasekaran are employees of Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. Anthony Perkins, Stephen Duong, Paul R. Dexter, Craig Solid, and Patrick Monahan have no interests to disclose.
Funding Information:
Malaz Boustani is the founding director of the Sandra Eskenazi Center for Brain Care Innovation. Richard Lipton is the Edwin S. Lowe Professor of Neurology at the Albert Einstein College of Medicine in New York. He receives research support from the National Institutes of Health (NIH): 2PO1 AG003949 (multiple Principal Investigator), 5U10 NS077308 (Principal Investigator), R21 AG056920 (Investigator), 1RF1 AG057531 (Site PI), RF1 AG054548 (Investigator), 1RO1 AG048642 (Investigator), R56 AG057548 (Investigator), U01062370 (Investigator), RO1 AG060933 (Investigator), K23 NS09610 (Mentor), K23AG049466 (Mentor), K23 NS107643 (Mentor). He also receives support from the Migraine Research Foundation and the National Headache Foundation. He serves on the editorial board of Neurology, is a senior advisor to Headache, and associate editor at Cephalalgia. He has reviewed for the National Institute on Aging and the National Institute for Neurological Disorders, holds stock options in eNeura Therapeutics and Biohaven Holdings; serves as consultant, advisory board member, or has received honoraria from: American Academy of Neurology, Alder, Allergan, American Headache Society, Amgen, Avanir, Biohaven, Biovision, Boston Scientific, Dr. Reddy's, Electrocore, Eli Lilly, eNeura Therapeutics, GlaxoSmithKline, Merck, Pernix, Pfizer, Supernus, Teva, Trigemina, Vector, and Vedanta. He receives royalties from Wolff's Headache, 7th and 8th eds. (Oxford University Press, 2009), Wiley, and Informa. Rezaul Karim Khandker, Christopher Black, and Vasu Chandrasekaran are employees of Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. Anthony Perkins, Stephen Duong, Paul R. Dexter, Craig Solid, and Patrick Monahan have no interests to disclose. The authors have declared no conflicts of interest for this article. Study concept and design, acquisition of data/information: Boustani, Perkins, and Monahan. Data analysis and interpretation: Perkins and Monahan. Critical revision of the manuscript for important intellectual content and final approval of manuscript for submission: All authors. This study was supported by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. It was also supported in part by the Einstein Aging Study: NIH/NIA 2PO1 AG 003949.
Publisher Copyright:
© 2019 The American Geriatrics Society
PY - 2020/3/1
Y1 - 2020/3/1
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - dementia
KW - risk factors
UR - http://www.scopus.com/inward/record.url?scp=85075790761&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075790761&partnerID=8YFLogxK
U2 - 10.1111/jgs.16218
DO - 10.1111/jgs.16218
M3 - Article
C2 - 31784987
AN - SCOPUS:85075790761
SN - 0002-8614
VL - 68
SP - 511
EP - 518
JO - Journal of the American Geriatrics Society
JF - Journal of the American Geriatrics Society
IS - 3
ER -