Dataset Information

Development of a dementia prediction model for primary care: The Hisayama Study.

ABSTRACT:

Introduction

We aimed to develop a risk prediction model for incident dementia using predictors that are available in primary-care settings.

Methods

A total of 795 subjects aged 65 years or over were prospectively followed-up from 1988 to 2012. A Cox proportional-hazards regression was used to develop a multivariable prediction model. The developed model was translated into a simplified scoring system based on the beta-coefficient. The discrimination of the model was assessed by Harrell's C statistic, and the calibration was assessed by a calibration plot.

Results

During the follow-up period, 364 subjects developed dementia. In the multivariable model, age, female sex, low education, leanness, hypertension, diabetes, history of stroke, current smoking, and sedentariness were selected as predictors. The developed model and simplified score showed good discrimination and calibration.

Discussion

The developed risk prediction model is feasible and practically useful in primary-care settings to identify individuals at high risk for future dementia.

SUBMITTER: Honda T

PROVIDER: S-EPMC8319663 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Development of a dementia prediction model for primary care: The Hisayama Study.

Honda Takanori T Ohara Tomoyuki T Yoshida Daigo D Shibata Mao M Ishida Yuki Y Furuta Yoshihiko Y Oishi Emi E Hirakawa Yoichiro Y Sakata Satoko S Hata Jun J Nakao Tomohiro T Ninomiya Toshiharu T

Alzheimer's & dementia (Amsterdam, Netherlands) 20210728 1

<h4>Introduction</h4>We aimed to develop a risk prediction model for incident dementia using predictors that are available in primary-care settings.<h4>Methods</h4>A total of 795 subjects aged 65 years or over were prospectively followed-up from 1988 to 2012. A Cox proportional-hazards regression was used to develop a multivariable prediction model. The developed model was translated into a simplified scoring system based on the beta-coefficient. The discrimination of the model was assessed by H ...[more]

PMID: 34337134

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Project description:IntroductionHealth policy in the UK and globally regarding dementia, emphasises prevention and risk reduction. These goals could be facilitated by automated assessment of dementia risk in primary care using routinely collected patient data. However, existing applicable tools are weak at identifying patients at high risk for dementia. We set out to develop improved risk prediction models deployable in primary care.MethodsElectronic health records (EHRs) for patients aged 60-89 from 393 English general practices were extracted from the Clinical Practice Research Datalink (CPRD) GOLD database. 235 and 158 practices respectively were randomly assigned to development and validation cohorts. Separate dementia risk models were developed for patients aged 60-79 (development cohort n = 616,366; validation cohort n = 419,126) and 80-89 (n = 175,131 and n = 118,717). The outcome was incident dementia within 5 years and more than 60 evidence-based risk factors were evaluated. Risk models were developed and validated using multivariable Cox regression.ResultsThe age 60-79 development cohort included 10,841 incident cases of dementia (6.3 per 1,000 person-years) and the age 80-89 development cohort included 15,994 (40.2 per 1,000 person-years). Discrimination and calibration for the resulting age 60-79 model were good (Harrell's C 0.78 (95% CI: 0.78 to 0.79); Royston's D 1.74 (1.70 to 1.78); calibration slope 0.98 (0.96 to 1.01)), with 37% of patients in the top 1% of risk scores receiving a dementia diagnosis within 5 years. Fit statistics were lower for the age 80-89 model but dementia incidence was higher and 79% of those in the top 1% of risk scores subsequently developed dementia.ConclusionOur models can identify individuals at higher risk of dementia using routinely collected information from their primary care record, and outperform an existing EHR-based tool. Discriminative ability was greatest for those aged 60-79, but the model for those aged 80-89 may also be clinical useful.

Project description:BackgroundCoeliac disease (CD) affects approximately 1% of the population, although only a fraction of patients are diagnosed. Our objective was to develop diagnostic prediction models to help decide who should be offered testing for CD in primary care.MethodsLogistic regression models were developed in Clinical Practice Research Datalink (CPRD) GOLD (between Sep 9, 1987 and Apr 4, 2021, n=107,075) and externally validated in CPRD Aurum (between Jan 1, 1995 and Jan 15, 2021, n=227,915), two UK primary care databases, using (and controlling for) 1:4 nested case-control designs. Candidate predictors included symptoms and chronic conditions identified in current guidelines and using a systematic review of the literature. We used elastic-net regression to further refine the models.FindingsThe prediction model included 24, 24, and 21 predictors for children, women, and men, respectively. For children, the strongest predictors were type 1 diabetes, Turner syndrome, IgA deficiency, or first-degree relatives with CD. For women and men, these were anaemia and first-degree relatives. In the development dataset, the models showed good discrimination with a c-statistic of 0·84 (95% CI 0·83-0·84) in children, 0·77 (0·77-0·78) in women, and 0·81 (0·81-0·82) in men. External validation discrimination was lower, potentially because 'first-degree relative' was not recorded in the dataset used for validation. Model calibration was poor, tending to overestimate CD risk in all three groups in both datasets.InterpretationThese prediction models could help identify individuals with an increased risk of CD in relatively low prevalence populations such as primary care. Offering a serological test to these patients could increase case finding for CD. However, this involves offering tests to more people than is currently done. Further work is needed in prospective cohorts to refine and confirm the models and assess clinical and cost effectiveness.FundingNational Institute for Health Research Health Technology Assessment Programme (grant number NIHR129020).

Project description:IntroductionCare is often inadequate and poorly integrated after a dementia diagnosis. Research and policy highlight the unaffordability and unsustainability of specialist-led support, and instead suggest a task-shared model, led by primary care. This study is part of the PriDem primary care led postdiagnostic dementia care research programme and will assess delivery of an evidence-informed, primary care based, person-centred intervention. The intervention involves Clinical Dementia Leads (CDLs) working in primary care to develop effective dementia care systems that build workforce capacity and support teams to deliver tailored support to people living with dementia and their carers.Methods and analysisThis is a 15-month mixed-methods feasibility and implementation study, situated in four National Health Service (NHS) primary care networks in England. The primary outcome is adoption of personalised care planning by participating general practices, assessed through a patient records audit. Feasibility outcomes include recruitment and retention; appropriateness and acceptability of outcome measures; acceptability, feasibility and fidelity of intervention components. People living with dementia (n=80) and carers (n=66) will be recruited through participating general practices and will complete standardised measures of health and well-being. Participant service use data will be extracted from electronic medical records. A process evaluation will explore implementation barriers and facilitators through methods including semistructured interviews with people living with dementia, carers and professionals; observation of CDL engagement with practice staff; and a practice fidelity log. Process evaluation data will be analysed qualitatively using codebook thematic analysis, and quantitatively using descriptive statistics. Economic analysis will determine intervention cost-effectiveness.Ethics and disseminationThe study has received favourable ethical opinion from Wales REC4. NHS Confidentiality Advisory Group support allows researchers preconsent access to patient data. Results will inform intervention adaptations and a future large-scale evaluation. Dissemination through peer-review journals, engagement with policy-makers and conferences will inform recommendations for dementia services commissioning.Trial registration numberISRCTN11677384.

Dataset Information

Development of a dementia prediction model for primary care: The Hisayama Study.

Introduction

Methods

Results

Discussion

Publications

Development of a dementia prediction model for primary care: The Hisayama Study.

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