Cookies?
Library Header Image
LSE Research Online LSE Library Services

Predicting dementia diagnosis from cognitive footprints in electronic health records: a case-control study protocol

Luo, Hao, Lau, Kui Kai, Wong, Gloria H.Y., Chan, Wai Chi, Mak, Henry K.F., Zhang, Qingpeng, Knapp, Martin ORCID: 0000-0003-1427-0215 and Wong, Ian C.K. (2020) Predicting dementia diagnosis from cognitive footprints in electronic health records: a case-control study protocol. BMJ Open, 10 (11). ISSN 2044-6055

[img] Text (Predicting dementia diagnosis from cognitive footprints in electronic health records) - Published Version
Available under License Creative Commons Attribution Non-commercial.

Download (472kB)

Identification Number: 10.1136/bmjopen-2020-043487

Abstract

INTRODUCTION: Dementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case-control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history. METHODS AND ANALYSIS: We will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared. ETHICS AND DISSEMINATION: This study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients' records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities' Action in Response to Dementia project (https://www.tip-card.hku.hk/).

Item Type: Article
Official URL: https://bmjopen.bmj.com/
Additional Information: © 2020 The Authors
Divisions: Personal Social Services Research Unit
Subjects: R Medicine > RC Internal medicine
R Medicine > RA Public aspects of medicine
Date Deposited: 10 Dec 2020 00:32
Last Modified: 20 Aug 2021 00:06
URI: http://eprints.lse.ac.uk/id/eprint/107822

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics