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

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/).

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Faculty Opinions recommendation of Pointers to earlier diagnosis of endometriosis: a nested case-control study using primary care electronic health records.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732101192.793570243 ◽

2020 ◽

Author(s):

Khalid S Khan

Keyword(s):

Primary Care ◽

Electronic Health Records ◽

Case Control Study ◽

Case Control ◽

Health Records ◽

Electronic Health ◽

Nested Case Control ◽

Control Study

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Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study

Age and Ageing ◽

10.1093/ageing/afy022 ◽

2018 ◽

Vol 47 (4) ◽

pp. 564-569 ◽

Cited By ~ 16

Author(s):

Daniel Stow ◽

Fiona E Matthews ◽

Stephen Barclay ◽

Steve Iliffe ◽

Andrew Clegg ◽

...

Keyword(s):

Older Adults ◽

Electronic Health Records ◽

Case Control Study ◽

Population Based ◽

Case Control ◽

Health Records ◽

Electronic Health ◽

Using Data ◽

Control Study

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Pointers to earlier diagnosis of endometriosis: a nested case-control study using primary care electronic health records

British Journal of General Practice ◽

10.3399/bjgp17x693497 ◽

2017 ◽

Vol 67 (665) ◽

pp. e816-e823 ◽

Cited By ~ 5

Author(s):

Christopher Burton ◽

Lisa Iversen ◽

Sohinee Bhattacharya ◽

Dolapo Ayansina ◽

Lucky Saraswat ◽

...

Keyword(s):

Primary Care ◽

Electronic Health Records ◽

Case Control Study ◽

Conditional Logistic Regression ◽

Case Control ◽

Health Records ◽

Menstrual Symptoms ◽

Electronic Health ◽

Nested Case Control ◽

Control Study

BackgroundEndometriosis is a condition with relatively non-specific symptoms, and in some cases a long time elapses from first-symptom presentation to diagnosis.AimTo develop and test new composite pointers to a diagnosis of endometriosis in primary care electronic records.Design and settingThis is a nested case-control study of 366 cases using the Practice Team Information database of anonymised primary care electronic health records from Scotland. Data were analysed from 366 cases of endometriosis between 1994 and 2010, and two sets of age and GP practice matched controls: (a) 1453 randomly selected females and (b) 610 females whose records contained codes indicating consultation for gynaecological symptoms.MethodComposite pointers comprised patterns of symptoms, prescribing, or investigations, in combination or over time. Conditional logistic regression was used to examine the presence of both new and established pointers during the 3 years before diagnosis of endometriosis and to identify time of appearance.ResultsA number of composite pointers that were strongly predictive of endometriosis were observed. These included pain and menstrual symptoms occurring within the same year (odds ratio [OR] 6.5, 95% confidence interval [CI] = 3.9 to 10.6), and lower gastrointestinal symptoms occurring within 90 days of gynaecological pain (OR 6.1, 95% CI = 3.6 to 10.6). Although the association of infertility with endometriosis was only detectable in the year before diagnosis, several pain-related features were associated with endometriosis several years earlier.ConclusionUseful composite pointers to a diagnosis of endometriosis in GP records were identified. Some of these were present several years before the diagnosis and may be valuable targets for diagnostic support systems.

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Temporality of clinical factors associated with pancreatic cancer: a case-control study using linked electronic health records

BMC Cancer ◽

10.1186/s12885-021-09014-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Abu Z. M. Dayem Ullah ◽

Konstantinos Stasinos ◽

Claude Chelala ◽

Hemant M. Kocher

Keyword(s):

Pancreatic Cancer ◽

Electronic Health Records ◽

Cancer Diagnosis ◽

Case Control Study ◽

Case Control ◽

Retrospective Case ◽

Health Records ◽

Recent Onset ◽

Electronic Health ◽

Control Study

Abstract Background Pancreatic cancer risk is poorly quantified in relation to the temporal presentation of medical comorbidities and lifestyle. This study aimed to examine this aspect, with possible influence of demographics. Methods We conducted a retrospective case-control study on the ethnically-diverse population of East London, UK, using linked electronic health records. We evaluated the independent and two-way interaction effects of 19 clinico-demographic factors in patients with pancreatic cancer (N = 965), compared with non-malignant pancreatic conditions (N = 3963) or hernia (control; N = 4355), reported between April 1, 2008 and March 6, 2020. Risks were quantified by odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression models. Results We observed increased odds of pancreatic cancer incidence associated with recent-onset diabetes occurring within 6 months to 3 years before cancer diagnosis (OR 1.95, 95% CI 1.25-3.03), long-standing diabetes for over 3 years (OR 1.74, 95% CI 1.32-2.29), recent smoking (OR 1.81, 95% CI 1.36-2.4) and drinking (OR 1.76, 95% CI 1.31-2.35), as compared to controls but not non-malignant pancreatic conditions. Pancreatic cancer odds was highest for chronic pancreatic disease patients (recent-onset: OR 4.76, 95% CI 2.19-10.3, long-standing: OR 5.1, 95% CI 2.18-11.9), amplified by comorbidities or harmful lifestyle. Concomitant diagnosis of diabetes, upper gastrointestinal or chronic pancreatic conditions followed by a pancreatic cancer diagnosis within 6 months were common, particularly in South Asians. Long-standing cardiovascular, respiratory and hepatobiliary conditions were associated with lower odds of pancreatic cancer. Conclusions Several factors are, independently or via effect modifications, associated with higher incidence of pancreatic cancer, but some established risk factors demonstrate similar magnitude of risk measures of developing non-malignant pancreatic conditions. The findings may inform refined risk-stratification strategies and better surveillance for high-risk individuals, and also provide a means for systematic identification of target population for prospective cohort-based early detection research initiatives.

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