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

Abstract

IntroductionDementia 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 analysisWe 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 disseminationThis 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/).

Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

“…It has been reported that more than 10% of community-dwelling subjects older than 70 years suffer from very mild or mild dementia [ 22 ]. Data-informed decision-making strategies to identify individuals at high risk of dementia could be essential to facilitating large-scale prevention and early intervention [ 25 ]. Triage tests such as the MMSE are used in clinical practice to rapidly assess the cognitive condition [ 2 ].…”

Accuracy of Support-Vector Machines for Diagnosis of Alzheimer's Disease, Using Volume of Brain Obtained by Structural MRI at Siriraj Hospital

“…As a consequence, the incidence of AD has been reported to be higher in those individuals with lower education (16). Previous studies showed that digital health data, cognitive performance such as memory, and neuropsychiatric symptoms can help identify those with dementia from normal subjects (17)(18)(19)(20)(21). Some research groups (19) have suggested that a diagnosis of dementia can be made from health recording data.…”

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

“…According to the cognitive footprint theory, age at exposures was classified into three age periods: earlier (21–45 years), mid (45–64), and late-life (≥65) as specified in the protocol published earlier. 17 Only mid and late life were considered in this study since the observation period before the dementia diagnosis was shorter than 20 years. All factors were broken down into mid and late-life factors, based on the exposure period, except education.…”