{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T23:45:44Z","timestamp":1788911144847,"version":"build-2803163510"},"reference-count":27,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T00:00:00Z","timestamp":1574985600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100009947","name":"Merck Sharp and Dohme","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009947","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["agsjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J American Geriatrics Society"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVES<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DESIGN<\/jats:title>\n                    <jats:p>A case\u2010control study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>SETTING<\/jats:title>\n                    <jats:p>The Indiana Network for Patient Care (INPC), a regional health information exchange in Indiana.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>PARTICIPANTS<\/jats:title>\n                    <jats:p>Patients identified with ADRD and matched controls.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>MEASUREMENTS<\/jats:title>\n                    <jats:p>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\u2010 to 10\u2010year, 3\u2010 to 10\u2010year, and 5\u2010 to 10\u2010year 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>The AUROC curves for the validation samples for the 1\u2010 to 10\u2010year, 3\u2010 to 10\u2010year, and 5\u2010 to 10\u2010year 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\u2010 to 10\u2010year, 3\u2010 to 10\u2010year, and 5\u2010 to 10\u2010year models had sensitivity that ranged from 51% to 62% and specificity that ranged from 80% to 89%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>\n                      EMR\u2010based data provide a targeted and scalable process for early identification of risk of ADRD as an alternative to traditional population screening.\n                      <jats:bold>J Am Geriatr Soc 68:511\u2013518, 2020<\/jats:bold>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgs.16218","type":"journal-article","created":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T20:53:51Z","timestamp":1575060831000},"page":"511-518","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Passive Digital Signature for Early Identification of Alzheimer's Disease and Related Dementia"],"prefix":"10.1111","volume":"68","author":[{"given":"Malaz","family":"Boustani","sequence":"first","affiliation":[{"name":"Indiana University Center for Health Innovation and Implementation Science Indiana Clinical Translational Science Institute  Indianapolis Indiana"},{"name":"Sandra Eskenazi Center for Brain Care Innovation Eskenazi Health  Indianapolis Indiana"},{"name":"Indiana University Center for Aging Research Regenstrief Institute, Inc  Indianapolis Indiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony J.","family":"Perkins","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Indiana University School of Medicine  Indianapolis Indiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rezaul Karim","family":"Khandker","sequence":"additional","affiliation":[{"name":"Center for Observational and Real\u2010World Evidence Merck &amp; Co., Inc.  Kenilworth Indiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Duong","sequence":"additional","affiliation":[{"name":"The Gerontology Institute Georgia State University  Atlanta Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul R.","family":"Dexter","sequence":"additional","affiliation":[{"name":"Indiana University Center for Aging Research Regenstrief Institute, Inc  Indianapolis Indiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Lipton","sequence":"additional","affiliation":[{"name":"Department of Neurology Albert Einstein College of Medicine  Bronx New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher M.","family":"Black","sequence":"additional","affiliation":[{"name":"Center for Observational and Real\u2010World Evidence Merck &amp; Co., Inc.  Kenilworth Indiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasu","family":"Chandrasekaran","sequence":"additional","affiliation":[{"name":"Center for Observational and Real\u2010World Evidence Merck &amp; Co., Inc.  Boston Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Craig A.","family":"Solid","sequence":"additional","affiliation":[{"name":"Solid Research Group, LLC  Saint Paul Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Monahan","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Indiana University School of Medicine and School of Public Health  Indianapolis Indiana"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,11,29]]},"reference":[{"key":"e_1_2_6_2_1","unstructured":"US Department of Health and Human Services.National plan to address Alzheimer's disease: 2018 update. 2018.https:\/\/aspe.hhs.gov\/system\/files\/pdf\/259581\/NatPlan2018.pdf. Accessed March 22 2019."},{"key":"e_1_2_6_3_1","unstructured":"Medicare Interactive.Annual wellness visit. 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