FRONTOTEMPORAL LOBAR DEGENERATION
DLB INTERNATIONAL WORKSHOP
1(8) F-FDFG uptake by brain tissue as measured by positron emission tomography (PET) is a well-established method for assessment of brain function in people with dementia. Certain findings on brain PET scans can potentially predict the decline of mild cognitive Impairment (MCI) to Alzheimer's disease dementia or other dementias.
To determine the diagnostic accuracy of the 1(8) F-FDG PET index test for detecting people with MCI at baseline who would clinically convert to Alzheimer's disease dementia or other forms of dementia at follow-up.
We searched the Cochrane Register of Diagnostic Test Accuracy Studies, MEDLINE, EMBASE, Science Citation Index, PsycINFO, BIOSIS previews, LILACS, MEDION, (Meta-analyses van Diagnostisch Onderzoek), DARE (Database of Abstracts of Reviews of Effects), HTA (Health Technology Assessment Database), ARIF (Aggressive Research Intelligence Facility) and C-EBLM(International Federation of Clinical Chemistry and Laboratory Medicine Committee for Evidence-based Laboratory Medicine) databases to January 2013. We checked the reference lists of any relevant studies and systematic reviews for additional studies.
We included studies that evaluated the diagnostic accuracy of 1(8) F-FDG PET to determine the conversion from MCI to Alzheimer's disease dementia or to other forms of dementia, i.e. any or all of vascular dementia, dementia with Lewy bodies, and fronto-temporal dementia. These studies necessarily employ delayed verification of conversion to dementia and are sometimes labelled as 'delayed verification cross-sectional studies'.
Data collection and analysis
Two blinded review authors independently extracted data, resolving disagreement by discussion, with the option to involve a third review author as arbiter if necessary. We extracted and summarised graphically the data for two-by-two tables. We conducted exploratory analyses by plotting estimates of sensitivity and specificity from each study on forest plots and in receiver operating characteristic (ROC) space. When studies had mixed thresholds, we derived estimates of sensitivity and likelihood ratios at fixed values (lower quartile, median and upper quartile) of specificity from the hierarchical summary ROC (HSROC) models.
We included 14 studies (421 participants) in the analysis. The sensitivities for conversion from MCI to Alzheimer's disease dementia were between 25% and 100% while the specificities were between 15% and 100%. From the summary ROC curve we fitted we estimated that the sensitivity was 76% (95% confidence interval (CI): 53.8 to 89.7) at the included study median specificity of 82%. This equates to a positive likelihood ratio of 4.03 (95% CI: 2.97 to 5.47), and a negative likelihood ratio of 0.34 (95% CI: 0.15 to 0.75). Three studies recruited participants from the same Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort but only the largest ADNI study (Herholz 2011) is included in the meta-analysis. In order to demonstrate whether the choice of ADNI study or discriminating brain region (Chetelat 2003) or reader assessment (Pardo 2010) make a difference to the pooled estimate, we performed five additional analyses. At the median specificity of 82%, the estimated sensitivity was between 74% and 76%. There was no impact on our findings. In addition to evaluating Alzheimer's disease dementia, five studies evaluated the accuracy of 1(8) F-FDG PET for all types of dementia. The sensitivities were between 46% and 95% while the specificities were between 29% and 100%; however, we did not conduct a meta-analysis because of too few studies, and those studies which we had found recruited small numbers of participants. Our findings are based on studies with poor reporting, and the majority of included studies had an unclear risk of bias, mainly for the reference standard and participant selection domains. According to the assessment of Index test dom
Type of Publication:
Journal article published