{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:39:39Z","timestamp":1777448379236,"version":"3.51.4"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T00:00:00Z","timestamp":1617408000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T00:00:00Z","timestamp":1617408000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We used a nested case\u2013control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2\u00a0years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients\u2009\u2265\u200918\u00a0years, later restricted to age\u2009\u2265\u200940\u00a0years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new and two previous logistic regression models were evaluated using receiver-operating characteristics. Number, characteristics, and CHA<jats:sub>2<\/jats:sub>DS<jats:sub>2<\/jats:sub>-VASc scores of patients identified by the model in the pilot are presented.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>After restricting age to\u2009\u2265\u200940\u00a0years, 31,474 AF cases (mean age, 71.5\u00a0years; female 49%) and 22,078 controls (mean age, 59.5\u00a0years; female 61%) comprised the development cohort. A 10-variable model using age, acute heart disease, albumin, body mass index, chronic obstructive pulmonary disease, gender, heart failure, insurance, kidney disease, and shock yielded the best performance (C-statistic, 0.80 [95% CI 0.79\u20130.80]). The model performed well in the validation cohort (C-statistic, 0.81 [95% CI 0.8\u20130.81]). In the EHR pilot, 7916\/22,272 (35.5%; mean age, 66\u00a0years; female 50%) were identified as higher risk for AF; 5582 (70%) had CHA<jats:sub>2<\/jats:sub>DS<jats:sub>2<\/jats:sub>-VASc score\u2009\u2265\u20092.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-021-01482-1","type":"journal-article","created":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T07:02:52Z","timestamp":1617433372000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED)"],"prefix":"10.1186","volume":"21","author":[{"given":"Randall W.","family":"Grout","sequence":"first","affiliation":[]},{"given":"Siu L.","family":"Hui","sequence":"additional","affiliation":[]},{"given":"Timothy D.","family":"Imler","sequence":"additional","affiliation":[]},{"given":"Sarah","family":"El-Azab","sequence":"additional","affiliation":[]},{"given":"Jarod","family":"Baker","sequence":"additional","affiliation":[]},{"given":"George H.","family":"Sands","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Ateya","sequence":"additional","affiliation":[]},{"given":"Francis","family":"Pike","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,3]]},"reference":[{"issue":"8","key":"1482_CR1","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1161\/01.STR.22.8.983","volume":"22","author":"PA Wolf","year":"1991","unstructured":"Wolf PA, Abbott RD, Kannel WB. 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A waiver of informed consent was granted by the Indiana University Institutional Review Board.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"RWG, SLH, TDI, SE, JB, and FP received institutional funding from McKinsey and Company. RWG has received institutional funding from Digital Health Solutions, LLC. GHS and MA are employees and shareholders of Pfizer Inc.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"112"}}