Dataset Information

Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study.

ABSTRACT:

Introduction

Preventing dementia, or modifying disease course, requires identification of presymptomatic or minimally symptomatic high-risk individuals.

Methods

We used longitudinal electronic health records from two large academic medical centers and applied a validated natural language processing tool to estimate cognitive symptomatology. We used survival analysis to examine the association of cognitive symptoms with incident dementia diagnosis during up to 8 years of follow-up.

Results

Among 267,855 hospitalized patients with 1,251,858 patient years of follow-up data, 6516 (2.4%) received a new diagnosis of dementia. In competing risk regression, an increasing cognitive symptom score was associated with earlier dementia diagnosis (HR 1.63; 1.54-1.72). Similar results were observed in the second hospital system and in subgroup analysis of younger and older patients.

Discussion

A cognitive symptom measure identified in discharge notes facilitated stratification of risk for dementia up to 8 years before diagnosis.

SUBMITTER: McCoy TH

PROVIDER: S-EPMC7067642 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study.

McCoy Thomas H TH Han Larry L Pellegrini Amelia M AM Tanzi Rudolph E RE Berretta Sabina S Perlis Roy H RH

Alzheimer's & dementia : the journal of the Alzheimer's Association 20200116 3

<h4>Introduction</h4>Preventing dementia, or modifying disease course, requires identification of presymptomatic or minimally symptomatic high-risk individuals.<h4>Methods</h4>We used longitudinal electronic health records from two large academic medical centers and applied a validated natural language processing tool to estimate cognitive symptomatology. We used survival analysis to examine the association of cognitive symptoms with incident dementia diagnosis during up to 8 years of follow-up. ...[more]

PMID: 31859230

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Project description:BackgroundIndividuals from minority ethnic groups in the UK are thought to be at higher risk of developing dementia while facing additional barriers to receiving timely care. However, few studies in the UK have examined if there are ethnic disparities in survival once individuals receive a dementia diagnosis.MethodsWe conducted a retrospective cohort study using electronic health record data of individuals diagnosed with dementia from a large secondary mental healthcare provider in London. Patients from Black African, Black Caribbean, South Asian, White British, and White Irish ethnic backgrounds were followed up for a 10-year period between 01 January 2008 and 31 December 2017. Data were linked to death certificate data from the Office of National Statistics to determine survival from dementia diagnosis. Standardised mortality ratios were calculated to estimate excess deaths in each ethnicity group as compared to the gender- and age-standardised population of England and Wales. We used Cox regression models to compare survival after dementia diagnosis across each ethnicity group.ResultsMortality was elevated at least twofold across all ethnicity groups with dementia compared to the general population in England and Wales. Risk of death was lower in Black Caribbean, Black African, White Irish, and South Asian groups as compared to the White British population, even after adjusting for age, gender, neighbourhood-level deprivation, indicators of mental and physical comorbidities. Risk of death remained lower after additionally accounting for those who emigrated out of the cohort.ConclusionsWhile mortality in dementia is elevated across all ethnic groups as compared to the general population, reasons for longer survival in minority ethnic groups in the UK as compared to the White British group are unclear and merit further exploration. Implications of longer survival, including carer burden and costs, should be considered in policy and planning to ensure adequate support for families and carers of individuals with dementia.

Project description:BackgroundTuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. The St. Michael's Hospital TB database (SMH-TB) was established to address gaps in EHR-derived TB clinical cohorts and provide researchers and clinicians with detailed, granular data related to TB management and treatment.MethodsWe collected and validated multiple layers of EHR data from the TB outpatient clinic at St. Michael's Hospital, Toronto, Ontario, Canada to generate the SMH-TB database. SMH-TB contains structured data directly from the EHR, and variables generated using natural language processing (NLP) by extracting relevant information from free-text within clinic, radiology, and other notes. NLP performance was assessed using recall, precision and F1 score averaged across variable labels. We present characteristics of the cohort population using binomial proportions and 95% confidence intervals (CI), with and without adjusting for NLP misclassification errors.ResultsSMH-TB currently contains retrospective patient data spanning 2011 to 2018, for a total of 3298 patients (N = 3237 with at least 1 associated dictation). Performance of TB diagnosis and medication NLP rulesets surpasses 93% in recall, precision and F1 metrics, indicating good generalizability. We estimated 20% (95% CI: 18.4-21.2%) were diagnosed with active TB and 46% (95% CI: 43.8-47.2%) were diagnosed with latent TB. After adjusting for potential misclassification, the proportion of patients diagnosed with active and latent TB was 18% (95% CI: 16.8-19.7%) and 40% (95% CI: 37.8-41.6%) respectively.ConclusionSMH-TB is a unique database that includes a breadth of structured data derived from structured and unstructured EHR data by using NLP rulesets. The data are available for a variety of research applications, such as clinical epidemiology, quality improvement and mathematical modeling studies.

Dataset Information

Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study.

Introduction

Methods

Results

Discussion

Publications

Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study.

Similar Datasets

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