Dementia is one of the most common and serious disorders in later life and the economic and personal cost of caring for people with dementia is immense. There is a need to be able to evaluate ...interventions in dementia using cost-effectiveness analyses, but the generic preference-based measures typically used to measure effectiveness do not work well in dementia. Existing dementia-specific measures can effectively measure health-related quality of life but in their current form cannot be used directly to inform cost-effectiveness analysis using quality-adjusted life-years as the measure of effectiveness.
The aim was to develop two brief health-state classifications, one from DEMQOL and one from DEMQOL-Proxy, to generate health states amenable to valuation. These classification systems consisted of items taken from DEMQOL and DEMQOL-Proxy so they can be derived from any study that has used these instruments.
In the first stage of the study we used a large, clinically representative sample aggregated from two sources: a sample of patients and carers attending a memory service in south London and a sample of patients and carers from other community services in south London. This included 644 people with a diagnosis of mild/moderate dementia and 689 carers of those with mild/moderate dementia. For the valuation study, the general population sample of 600 respondents was drawn to be representative of the UK general population. Households were sampled in urban and rural areas in northern England and balanced to the UK population according to geodemographic profiles. In the patient/carer valuation study we interviewed a sample of 71 people with mild dementia and 71 family carers drawn from a memory service in south London. Finally, the instruments derived were applied to data from the HTA-SADD (Study of Antidepressants for Depression in Dementia) trial.
This was a complex multiphase study with four linked phases: phase 1 - derivation of the health-state classification system; phase 2 - general population valuation survey and modelling to produce values for every health state; phase 3 - patient/carer valuation survey; and phase 4 - application of measures to trial data.
All four phases were successful and this report details this development process leading to the first condition-specific preference-based measures in dementia, an important new development in this field.
The first limitation relates to the lack of an external data set to validate the DEMQOL-U and DEMQOL-Proxy-U classification systems. Throughout the development process we have made decisions about which methodology to use. There are other valid techniques that could be used and it is possible to criticise the choices that we have made. It is also possible that the use of a mild to moderate dementia sample has resulted in classification systems that do not fully reflect the challenges of severe dementia.
The results presented are sufficiently encouraging to recommend that the DEMQOL instruments be used alongside a generic measure such as the European Quality of Life-5 Dimensions (EQ-5D) in future studies of interventions in dementia as there was evidence that they can be more sensitive for patients at the milder end of disease and some limited evidence that the person with dementia measure may be able to reflect deterioration.
The National Institute for Health Research Health Technology Assessment programme.
OBJECTIVETo determine the optimal cut point on the NIH Stroke Scale (NIHSS) for predicting poor 90-day clinical outcome in patients with supratentorial and infratentorial acute ischemic stroke (AIS).
...METHODSData are from participants of the alteplase-dose arm of the randomized controlled trial, Enhanced Control of Hypertension and Thrombolysis Stroke Study (ENCHANTED). Associations between baseline characteristics of clinically defined supratentorial and infratentorial AIS patients and poor functional outcome, defined by scores 3–6 on the modified Rankin Scale, were evaluated in logistic regression models, with area under the curve (AUC) receiver operating characteristics defining the optimal NIHSS predictor cut point.
RESULTSPatients with infratentorial AIS (n = 289) had lower baseline NIHSS scores than those with supratentorial AIS (n = 2,613) (median 7 vs 9; p < 0.001). NIHSS cut points for poor outcome were 10 (AUC 76, sensitivity 65%, specificity 73%) and 6 (AUC 69, sensitivity 72%, specificity 56%) in supratentorial and infratentorial AIS, respectively. There was no significant difference in functional outcome or symptomatic intracranial hemorrhage between AIS types.
CONCLUSIONSIn thrombolysis-eligible AIS patients, the NIHSS may underestimate clinical severity for infratentorial compared to supratentorial lesions for a similar prognosis for recovery. Because thrombolysis treatment has low effect on stroke outcome in patients with infratentorial AIS when baseline NIHSS score is more than 6, additional treatment such as endovascular treatment should be considered to improve stroke outcome.
CLINICALTRIALS.GOV IDENTIFIERNCT01422616.
Utility values to estimate quality-adjusted life years (QALYs) for use in cost-utility analyses are usually elicited from members of the general population. Public attitudes and understanding of ...dementia in particular may mean that values elicited from the general population may differ from patients and carers for dementia health states. This study examines how the population impacts utility values elicited for dementia health states using interviewer-administered time tradeoff valuation of health states defined by the dementia-specific preference-based measures DEMQOL-U (patient-report) and DEMQOL-Proxy-U (carer-report). Eight DEMQOL-U states were valued by 78 members of the UK general population and 71 patients with dementia of mild severity. Eight DEMQOL-Proxy-U states were valued by 77 members of the UK general population and 71 carers of patients with dementia of mild severity. Random-effects generalized least squares regression estimated the impact of population, dementia health state, and respondent sociodemographic characteristics on elicited values, finding that values for dementia health states differed by population and that the difference varied across dementia health states. Patients with dementia and carers of patients with dementia gave systematically lower values than members of the general population that were not due to differences in the sociodemographic characteristics of the populations. Our results suggest that the population used to produce dementia health state values could impact the results of cost-utility analyses and potentially affect resource allocation decisions; yet, currently, only general population values are available for usage.
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