Table 2

Table of study characteristics and findings—cost-effectiveness analysis studies

Author, year of publicationType of studyAimOutcome measureStatistical analysisSummary of findings
Handels et al,42 2017 The NetherlandsCost-effectiveness analysisTo estimate the potential ICER of adding CSF biomarker testing to the standard diagnostic work-up to determine the prognosis for patients with MCIAccuracy of prognosis QALYSimulated data model using a merged datasetImproved the accuracy of prognosis by 11%
Additional cost per patient ICERAverage QALY gain of 0.046 €432 additional costs per patient ICER of €9416 per QALY gained
Lee et al,43 2017 CanadaCost-effectiveness analysisTo estimate the lifetime costs and QALYs of CSF biomarker analysis in a cohort of patients referred to a neurologist or memory clinic with suspected AD who remained without a definitive diagnosis of AD or another condition after neuroimagingAdditional cost per patient


QALY


ICER		Markov modelAD pre-test probability of 12.7%: average QALY gain of 0.015 ICER of $C11 032 per QALY gained $C165 additional costs per patient
Valcárcel-Nazco et al,44 2014 SpainCost-effectiveness analysis—economic evaluationTo determine the cost-effectiveness of the CSF biomarkers to diagnose AD in patients with MCI and those with dementiaCost and effectivenessProbabilistic sensitivity analysis using 2nd-order Monte Carlo simulations. Acceptability curves were calculated and ANCOVA models applied to simulation resultsPatients with MCI: lower average cost per patient of €1832.65


Patients with AD: higher average cost per patient of €1133.82


Dominant ICER for patients with MCI

  • AD, Alzheimer's disease; ANCOVA, analysis of covariance; CSF, cerebrospinal fluid; ICER, incremental cost-effectiveness ratio; MCI, mild cognitive impairment; QALY, quality-adjusted life year.