Improving CSF Biomarkers' Performance for Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease by Considering Different Confounding Factors: A Meta-Analysis.
Bottom Line:
By considering several confounding factors we aimed to identify in which situations these CSF biomarkers can be useful.The p-tau had high capacity to identify MCI cases converting to AD in ≤24 months.Explaining how different confounding factors influence CSF biomarkers' predictive performance is mandatory to elaborate a definitive map of situations, where these CSF biomarkers are useful both in clinics and research.
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Affiliation: Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet , Stockholm , Sweden.
ABSTRACT
Background: Cerebrospinal fluid (CSF) biomarkers' performance for predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is still suboptimal. Objective: By considering several confounding factors we aimed to identify in which situations these CSF biomarkers can be useful. Data sources: A systematic review was conducted on MEDLINE, PreMedline, EMBASE, PsycInfo, CINAHL, Cochrane, and CRD (1990-2013). Eligibility criteria: (1) Prospective studies of CSF biomarkers' performance for predicting conversion from MCI to AD/dementia; (2) inclusion of Aβ42 and T-tau and/or p-tau. Several meta-analyses were performed. Results: Aβ42/p-tau ratio had high capacity to predict conversion to AD in MCI patients younger than 70 years. The p-tau had high capacity to identify MCI cases converting to AD in ≤24 months. Conclusions: Explaining how different confounding factors influence CSF biomarkers' predictive performance is mandatory to elaborate a definitive map of situations, where these CSF biomarkers are useful both in clinics and research. No MeSH data available. Related in: MedlinePlus |
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Mentions: For each article, true and false positives/negatives values were calculated from sensitivity, specificity, positive predicted value, negative predicted value, and/or the rate of converters and non-converters. A global meta-analysis was performed for each single CSF biomarker (i.e., Aβ42, T-tau, and p-tau) and two relevant ratios (i.e., Aβ42/T-tau and Aβ42/p-tau). Analyses were performed with the MetaDisc 1.1.1 software (Zamora et al., 2006). Sensitivity and specificity pooled estimates were calculated with random-effects models (DerSimonian and Laird, 1986), which yield more conservative estimates. For a qualitative interpretation of sensitivity and specificity results, values above 80% were considered indicative of satisfactory predictive performance according to international recommendations (The Ronald and Nancy Reagan Research Institute of the Alzheimer’s Association and the national Institute on Aging working Group, 1998). Positive and negative likelihood ratios were calculated from resulting sensitivity and specificity values and interpreted following established guidelines (see these guidelines in Figure 2 footnotes) (Qizilbash, 2002). Likelihood ratios indicate how the pretest probability of disease is increased or decreased by the outcome of a diagnostic test. A positive likelihood ratio [LR + = sensitivity/(1 – specificity)] greater than one increases the probability that the disease is present (in this context progression to AD) and helps to rule-in MCI-C cases. A negative likelihood ratio (LR– = (1 – sensitivity)/specificity) of less than one diminishes the probability that disease is present and helps to rule-out MCI-C cases. Statistical heterogeneity was explored with the Cochran Q-test. As this statistic has low power when few studies are available, a recommended p-value of 0⋅10 was established as statistical significance threshold to detect heterogeneity (Hardy and Thompson, 1998). Differences in sensitivity and specificity values for pairs of subgroup meta-analyses (e.g., MCI cases younger than 70 years vs. older than 70 years) were tested with the formula: QBET = QTOT – (Q1 + Q2). Where QTOT represents the overall inter-study variability, and Q1 and Q2 represents inter-study variability for each subgroup in the comparison (Deeks et al., 2001). The QBET statistic was then compared to a χ2 distribution with J − 1 degrees of freedom using a significance level of 0⋅05, where J is the number of subgroups. |
View Article: PubMed Central - PubMed
Affiliation: Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet , Stockholm , Sweden.
Background: Cerebrospinal fluid (CSF) biomarkers' performance for predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is still suboptimal.
Objective: By considering several confounding factors we aimed to identify in which situations these CSF biomarkers can be useful.
Data sources: A systematic review was conducted on MEDLINE, PreMedline, EMBASE, PsycInfo, CINAHL, Cochrane, and CRD (1990-2013).
Eligibility criteria: (1) Prospective studies of CSF biomarkers' performance for predicting conversion from MCI to AD/dementia; (2) inclusion of Aβ42 and T-tau and/or p-tau. Several meta-analyses were performed.
Results: Aβ42/p-tau ratio had high capacity to predict conversion to AD in MCI patients younger than 70 years. The p-tau had high capacity to identify MCI cases converting to AD in ≤24 months.
Conclusions: Explaining how different confounding factors influence CSF biomarkers' predictive performance is mandatory to elaborate a definitive map of situations, where these CSF biomarkers are useful both in clinics and research.
No MeSH data available.