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DECODE
A computerized decision support system for the timely identification of dementia Dr David Llewellyn
University of Exeter Medical School
“Britain unprepared for 'tsunami' of dementia patients”
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Cohort expectation of life at birth according to historic and projected mortality rates, for persons born from 1850 to 2050, England & Wales
Source: Office for National Statistics
Incidence rates of dementia in the United Kingdom compared with meta-analysis results in Europe and worldwide Nature Reviews Neuroscience 2007; 8: 233–9
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A sophisticated treatise on competing risk? Number of people with dementia in the UK, by level of severity and age group
Source: Dementia UK: Update
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What is the probability that a randomly selected elderly patient will have dementia?
What is the probability that a randomly selected elderly person has dementia? 40% 8.3% 6.5% 65%
What is the probability that a randomly selected elderly patient will have dementia?
What is the probability that a randomly selected elderly patient has dementia? 40% 8.3% 6.5% 65% Torbay memory clinic, 2015 England, 1991 England, 2011 English care home, 2011
It depends on context...
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Lancet 2013; 382: 1405–12 Substantial variation by age and time... ~670,000 people with dementia in 2011 (rather than the estimated 884,000) Lancet 2013; 382: 1405–12 Substantial variation by sex and location... (Estimated from Cambridgeshire, Nottingham and Newcastle)
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A common conventional (idealised) approach to case finding This is known to be inaccurate (unsystematic, subjective, complicated!) Limited accuracy (ignores most available information) Which cases are suitable for evaluation in primary care?
SLIDE 7 09/03/2016 7 Dementia case finding – a worked example
~6% of elderly people in England currently have dementia
Population screening would identify most (not all) true cases
MMSE: 88.3% sensitivity
Lin, et al. (2013) Ann Int Med, 159, 9, 601-12.
True positives (blue): 5 False negatives (red): 1
SLIDE 8 09/03/2016 8 Population screening would identify most (not all) true cases
MMSE: 88.3% sensitivity
Lin, et al. (2013) Ann Int Med, 159, 9, 601-12.
True positives (blue): 5 False negatives (red): 1 If this triggered a 100% accurate diagnostic assessment(!) then undiagnosed dementia would drop to 12%
A huge number of ‘false positives’ without dementia though
MMSE: 86.2% specificity
Lin, et al. (2013) Ann Int Med, 159, 9, 601-12.
True negatives (black): 81 False negatives (red): 1 True positives (blue): 5 False positives (green): 13
SLIDE 9 09/03/2016 9 A huge number of ‘false positives’ without dementia though
MMSE: 86.2% specificity
Lin, et al. (2013) Ann Int Med, 159, 9, 601-12.
True negatives (black): 81 False negatives (red): 1 True positives (blue): 5 False positives (green): 13 Proportion of true positives = 29% (Worse than currently seen in memory clinics)
DECODE: Computerized decision support system for timely dementia identification
Features:
- Interactive
- Intuitive
- Evidence-based diagnostic
‘signatures’
web versions
- Incorporate guidelines and
patient information
shared decision making
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Preliminary model development
685 older adults from the population-based Aging, Demographics and Memory Study (ADAMS) A wide range of primary care relevant potential predictors of dementia status evaluated (sociodemographics, medical history, medications, ADL/IADLs, cognition, self-report, informant report) Weighted stepwise multivariable logistic regression used to predict consensus DSM diagnosis of dementia status (yes/no) This is a standard (frequentist) model that ignores prior knowledge about the predictors Our full model will adopt a more complicated (Bayesian) approach Substantial improvement in dementia identification when weighted patient characteristics are used Misclassification rates are reduced from 22% to 6%
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DECODE will not replace clinical judgement!
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Thank you, my funders and colleagues
Sir Halley Stewart Trust
david.llewellyn@exeter.ac.uk
