The two biomarkers 2-18FFDG-PET and cerebrospinal fluid biomarkers are both recommended to support the diagnosis of Alzheimer's disease. However, there is a lack of knowledge for the comparison of ...the two biomarkers in a routine clinical setting.
The aim was to compare the clinical impact of 2-18FFDG-PET and cerebrospinal fluid biomarkers on diagnosis, prognosis, and patient management in patients suspected of Alzheimer's disease.
Eighty-one patients clinically suspected of Alzheimer's disease were retrospectively included from the Copenhagen Memory Clinic. As part of the clinical work-up all patients had a standard diagnostic program examination including MRI and ancillary investigations with 2-18FFDG-PET and cerebrospinal fluid biomarkers. An incremental study design was used to evaluate the clinical impact of the biomarkers. First, the diagnostic evaluation was based on the standard diagnostic program, then the diagnostic evaluation was revised after addition of either cerebrospinal fluid biomarkers or 2-18FFDG-PET. At each diagnostic evaluation, two blinded dementia specialists made a consensus decision on diagnosis, prediction of disease course, and change in patient management. Confidence in the decision was measured on a visual analogue scale (0-100). After 6 months, the diagnostic evaluation was performed with addition of the other biomarker. A clinical follow-up after 12 months was used as reference for diagnosis and disease course.
The two biomarkers had a similar clinical value across all diagnosis when added individually to the standard diagnostic program. However, for the correctly diagnosed patient with Alzheimer's disease cerebrospinal fluid biomarkers had a significantly higher impact on diagnostic confidence (mean scores±SD: 88±11 vs. 82±11, p = 0.046) and a significant reduction in the need for ancillary investigations (23 vs. 18 patients, p = 0.049) compared to 2-18FFDG-PET.
The two biomarkers had similar clinical impact on diagnosis, but cerebrospinal fluid biomarkers had a more significant value in corroborating the diagnosis of Alzheimer's disease compared to 2-18FFDG-PET.
Evidence-based recommendations on the optimal evaluation approach for dementia diagnostics are limited. This impedes a harmonized workup across clinics and nations.
To evaluate the diagnostic ...performance of a multidisciplinary consensus conference compared to a single clinician approach.
In this prospective study, we enrolled 457 patients with suspected cognitive decline, from two European memory clinics. A diagnostic evaluation was performed at baseline independently in two ways: 1) by a single clinician and 2) at a multidisciplinary consensus conference. A syndrome diagnosis and an etiological diagnosis was made. The confidence in the diagnosis was recorded using a visual analogue scale. An expert panel re-evaluation diagnosis served as reference for the baseline syndrome diagnosis and a 12-24-month follow-up diagnosis for the etiological diagnosis.
439 patients completed the study. We observed 12.5%discrepancy (k = 0.81) comparing the baseline syndrome diagnoses of the single clinician to the consensus conference, and 22.3%discrepancy (k = 0.68) for the baseline etiological diagnosis. The accuracy of the baseline etiological diagnosis was significantly higher at the consensus conference and was driven mainly by increased accuracy in the MCI group. Confidence in the etiological diagnosis at baseline was significantly higher at the consensus conference (p < 0.005), especially for the frontotemporal dementia diagnosis.
The multidisciplinary consensus conference performed better on diagnostic accuracy of disease etiology and increased clinicians' confidence. This highlights the importance of a multidisciplinary diagnostic evaluation approach for dementia diagnostics, especially when evaluating patients in the MCI stage.
Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. ...Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another.
In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200).
The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index.
This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.
Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist ...clinicians.
To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics.
In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis.
In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011).
Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians' confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.
•Addition of 2-18FFDG-PET to common diagnostic tests improved the accuracy for DLB and FTD.•Two new 2-18FFDG-PET biomarkers demonstrated specific disease patterns for DLB and FTD.•Different ...combinations of diagnostic tests were valuable for each subtype of dementia.
2-18Ffluoro-2-deoxy-d-glucose positron emission tomography (2-18FFDG-PET) has an emerging supportive role in dementia diagnostic as distinctive metabolic patterns are specific for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Previous studies have demonstrated that a data-driven decision model based on the disease state index (DSI) classifier supports clinicians in the differential diagnosis of dementia by using different combinations of diagnostic tests and biomarkers. Until now, this model has not included 2-18FFDG-PET data.
The objective of the study was to evaluate 2-18FFDG-PET biomarkers combined with commonly used diagnostic tests in the differential diagnosis of dementia using the DSI classifier.
We included data from 259 subjects diagnosed with AD, DLB, FTD, vascular dementia (VaD), and subjective cognitive decline from two independent study cohorts. We also evaluated three 2-18FFDG-PET biomarkers (anterior vs. posterior index (API-PET), occipital vs. temporal index, and cingulate island sign) to improve the classification accuracy for both FTD and DLB.
We found that the addition of 2-18FFDG-PET biomarkers to cognitive tests, CSF and MRI biomarkers considerably improved the classification accuracy for all pairwise comparisons of DLB (balanced accuracies: DLB vs. AD from 64% to 77%; DLB vs. FTD from 71% to 92%; and DLB vs. VaD from 71% to 84%). The two 2-18FFDG-PET biomarkers, API-PET and occipital vs. temporal index, improved the accuracy for FTD and DLB, especially as compared to AD. Moreover, different combinations of diagnostic tests were valuable to differentiate specific subtypes of dementia.
In conclusion, this study demonstrated that the addition of 2-18FFDG-PET to commonly used diagnostic tests provided complementary information that may help clinicians in diagnosing patients, particularly for differentiating between patients with FTD, DLB, and AD.
In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision ...support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics.
In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0-100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy.
After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI - 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI - 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (∆VAS = 4%, p < .0001).
Adding the PredictND tool to the clinical evaluation increased clinicians' confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications.
We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia.
In this multicenter study, we included 356 patients with ...Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types.
Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%.
Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further.
•Performance of diagnostic tests in pairwise diagnostics differs by diagnostic groups.•Accuracy seems to increase when combining diagnostic tests.•Diagnostic groups might be better separated by different combinations of biomarkers.
Abstract Introduction/Background To ensure minimal residual disease and to prevent relapses, recently published consensus reports have defined optimal long-term treatment targets for atopic ...dermatitis (AD).1,2 Tralokinumab, a monoclonal antibody specifically neutralizing interleukin-13, is approved for the treatment of moderate-to-severe AD. ECZTEND (NCT03587805) is an ongoing open-label, 5-year extension trial investigating the long-term safety and efficacy of tralokinumab 300 mg every other week (Q2W) plus optional topical corticosteroids (TCS). Objectives To determine the proportion of patients treated for up to 4 years with tralokinumab in AD clinical trials who: 1) exhibit stable improvement, with no or minimal fluctuations, in lesion extent and severity long-term (ie, response in ≥80% of attended visits), and 2) exhibit a stable long-term composite response (ie, up to 4 years of tralokinumab treatment and response in ≥80% of attended trial visits) in signs and symptoms of AD, and quality of life based on recent treat-to-target recommendations (EASI ≤7 and either DLQI ≤5 or Itch NRS ≤4). Methods This post hoc analysis included 347 patients who were continuously treated with tralokinumab for 52 weeks in the identically designed phase 3 monotherapy trials ECZTRA 1&2 and subsequently for up to 152 weeks in ECZTEND as of the April 30, 2022 data cutoff. Stability of long-term response, with no or minimal fluctuations, was defined as meeting the target endpoints at ≥80% of attended visits between Weeks 16-152 in ECZTEND. Endpoints analyzed were EASI ≤7, EASI ≤2, and a composite long-term treatment target: EASI ≤7 and either DLQI ≤5 or worst weekly pruritus NRS ≤4. Results A stable EASI ≤7 response (at ≥80% of attended visits) was observed in 70.2% (233/332) of tralokinumab-treated patients over Weeks 16-152 of ECZTEND. A stable EASI ≤2 response was observed in 34.0% (113/332) of patients, and a long-term optimal composite target, EASI ≤7 and either DLQI ≤5 or Itch NRS ≤4, was observed in 60.5% (201/332) of patients. Conclusions High proportions of clinical trial patients maintained stable responses, with no or minimal fluctuations in efficacy, with continued tralokinumab 300 mg Q2W plus optional TCS for up to 4 years of treatment.
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