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Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data

BACKGROUND: Cerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently avai...

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Autores principales: Shaklai, Sigal, Gilad-Bachrach, Ran, Yom-Tov, Elad, Stern, Naftali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196360/
https://www.ncbi.nlm.nih.gov/pubmed/34047699
http://dx.doi.org/10.2196/27084
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author Shaklai, Sigal
Gilad-Bachrach, Ran
Yom-Tov, Elad
Stern, Naftali
author_facet Shaklai, Sigal
Gilad-Bachrach, Ran
Yom-Tov, Elad
Stern, Naftali
author_sort Shaklai, Sigal
collection PubMed
description BACKGROUND: Cerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently available to predict a near/impending stroke, which might alert patients at risk to seek immediate preventive action (eg, anticoagulants for atrial fibrillation, control of hypertension). OBJECTIVE: Here, we propose that an algorithm based on internet search queries can identify people at increased risk for a near stroke event. METHODS: We analyzed queries submitted to the Bing search engine by 285 people who self-identified as having undergone a stroke event and 1195 controls with regard to attributes previously shown to reflect cognitive function. Controls included random people 60 years and above, or those of similar age who queried for one of nine control conditions. RESULTS: The model performed well against all comparator groups with an area under the receiver operating characteristic curve of 0.985 or higher and a true positive rate (at a 1% false-positive rate) above 80% for separating patients from each of the controls. The predictive power rose as the stroke date approached and if data were acquired beginning 120 days prior to the event. Good prediction accuracy was obtained for a prospective cohort of users collected 1 year later. The most predictive attributes of the model were associated with cognitive function, including the use of common queries, repetition of queries, appearance of spelling mistakes, and number of queries per session. CONCLUSIONS: The proposed algorithm offers a screening test for a near stroke event. After clinical validation, this algorithm may enable the administration of rapid preventive intervention. Moreover, it could be applied inexpensively, continuously, and on a large scale with the aim of reducing stroke events.
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spelling pubmed-81963602021-06-28 Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data Shaklai, Sigal Gilad-Bachrach, Ran Yom-Tov, Elad Stern, Naftali J Med Internet Res Original Paper BACKGROUND: Cerebrovascular disease is a leading cause of mortality and disability. Common risk assessment tools for stroke are based on the Framingham equation, which relies on traditional cardiovascular risk factors to predict an acute event in the near decade. However, no tools are currently available to predict a near/impending stroke, which might alert patients at risk to seek immediate preventive action (eg, anticoagulants for atrial fibrillation, control of hypertension). OBJECTIVE: Here, we propose that an algorithm based on internet search queries can identify people at increased risk for a near stroke event. METHODS: We analyzed queries submitted to the Bing search engine by 285 people who self-identified as having undergone a stroke event and 1195 controls with regard to attributes previously shown to reflect cognitive function. Controls included random people 60 years and above, or those of similar age who queried for one of nine control conditions. RESULTS: The model performed well against all comparator groups with an area under the receiver operating characteristic curve of 0.985 or higher and a true positive rate (at a 1% false-positive rate) above 80% for separating patients from each of the controls. The predictive power rose as the stroke date approached and if data were acquired beginning 120 days prior to the event. Good prediction accuracy was obtained for a prospective cohort of users collected 1 year later. The most predictive attributes of the model were associated with cognitive function, including the use of common queries, repetition of queries, appearance of spelling mistakes, and number of queries per session. CONCLUSIONS: The proposed algorithm offers a screening test for a near stroke event. After clinical validation, this algorithm may enable the administration of rapid preventive intervention. Moreover, it could be applied inexpensively, continuously, and on a large scale with the aim of reducing stroke events. JMIR Publications 2021-05-28 /pmc/articles/PMC8196360/ /pubmed/34047699 http://dx.doi.org/10.2196/27084 Text en ©Sigal Shaklai, Ran Gilad-Bachrach, Elad Yom-Tov, Naftali Stern. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.05.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Shaklai, Sigal
Gilad-Bachrach, Ran
Yom-Tov, Elad
Stern, Naftali
Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data
title Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data
title_full Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data
title_fullStr Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data
title_full_unstemmed Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data
title_short Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data
title_sort detecting impending stroke from cognitive traits evident in internet searches: analysis of archival data
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196360/
https://www.ncbi.nlm.nih.gov/pubmed/34047699
http://dx.doi.org/10.2196/27084
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