Outcomes of COVID-19 and Vaccination in Patients With Moderate to Severe Atopic Dermatitis Treated With Tralokinumab.

This case series describes the outcomes of COVID-19 and SARS-CoV-2 vaccination in patients with atopic dermatitis who have been treated with tralokinumab.

Long-term 2-year safety and efficacy of tralokinumab in adults with moderate-to-severe atopic dermatitis: Interim analysis of the ECZTEND open-label extension trial.

BACKGROUND: Additional long-term treatments are needed for moderate-to-severe atopic dermatitis (AD). An ongoing, open-label, 5-year extension trial, ECZTEND (NCT03587805), assesses tralokinumab plus optional topical corticosteroids in participants from previous tralokinumab parent trials (PTs) with moderate-to-severe AD. OBJECTIVE: To evaluate the safety and efficacy of up to 2 years tralokinumab treatment in a post hoc interim analysis. METHODS: Safety analyses included adults from completed PTs enrolled in ECZTEND, regardless of tralokinumab exposure duration. Efficacy analyses included adult participants treated with tralokinumab in ECZTEND for ≥1 year and subgroup analyses of those on tralokinumab for 2 years (1 year from PT, 1 year in ECZTEND). Primary end point was the number of adverse events with additional efficacy end points. RESULTS: Participants on tralokinumab had an exposure-adjusted rate of 237.8 adverse events/100 patient-years' exposure (N = 1174) in the safety analysis set. Exposure-adjusted incidence rates of common adverse events were comparable to PTs, although at lower rates. With 2 years of tralokinumab, improvements in extent and severity of AD were sustained, with Eczema Area and Severity Index (EASI-75) in 82.5% of participants (N = 345). LIMITATIONS: Possible selection bias; no placebo arm; some participants experienced treatment gaps between PTs and ECZTEND. CONCLUSION: Over 2 years, tralokinumab was well tolerated and maintained long-term control of AD signs and symptoms.

Comparing a Single Clinician Versus a Multidisciplinary Consensus Conference Approach for Dementia Diagnostics.

BACKGROUND: Evidence-based recommendations on the optimal evaluation approach for dementia diagnostics are limited. This impedes a harmonized workup across clinics and nations. OBJECTIVE: To evaluate the diagnostic performance of a multidisciplinary consensus conference compared to a single clinician approach. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Comparison of the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers in patients suspected of Alzheimer's disease.

BACKGROUND: The two biomarkers 2-[18F]FDG-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. OBJECTIVE: The aim was to compare the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers on diagnosis, prognosis, and patient management in patients suspected of Alzheimer's disease. METHODS: 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-[18F]FDG-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-[18F]FDG-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. RESULTS: 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-[18F]FDG-PET. CONCLUSION: 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-[18F]FDG-PET.

Prognostic value of complementary biomarkers of neurodegeneration in a mixed memory clinic cohort.

BACKGROUND: Biomarkers of neurodegeneration, e.g. MRI brain atrophy and [18F]FDG-PET hypometabolism, are often evaluated in patients suspected of neurodegenerative disease. OBJECTIVE: Our primary objective was to investigate prognostic properties of atrophy and hypometabolism. METHODS: From March 2015-June 2016, 149 patients referred to a university hospital memory clinic were included. The primary outcome was progression/stable disease course as assessed by a clinician at 12 months follow-up. Intracohort defined z-scores of baseline MRI automatic quantified volume and [18F]FDG-PET standardized uptake value ratios were calculated for all unilaterally defined brain lobes and dichotomized as pronounced atrophy (+A)/ pronounced hypometabolism (+H) at z-score <0. A logistic regression model with progression status as the outcome was carried out with number of lobes with the patterns +A/-H, -A/+H, +A/+H respectively as predictors. The model was mutually adjusted along with adjustment for age and sex. A sensitivity analysis with a z-score dichotomization at -0.1 and -0.5 and dichotomization regarding number of lobes affected at one and three lobes was done. RESULTS: Median follow-up time was 420 days [IQR: 387-461 days] and 50 patients progressed. Patients with two or more lobes affected by the pattern +A/+H compared to patients with 0-1 lobes affected had a statistically significant increased risk of progression (odds ratio, 95 % confidence interval: 4.33, 1.90-9.86) in a multivariable model. The model was partially robust to the applied sensitivity analysis. CONCLUSION: Combined atrophy and hypometabolism as assessed by MRI and [18F]FDG-PET in patients under suspicion of neurodegenerative disease predicts progression over 1 year.

Evaluating 2-[18F]FDG-PET in differential diagnosis of dementia using a data-driven decision model.

2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography (2-[18F]FDG-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-[18F]FDG-PET data. The objective of the study was to evaluate 2-[18F]FDG-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-[18F]FDG-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-[18F]FDG-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-[18F]FDG-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-[18F]FDG-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.

A visual rating scale for cingulate island sign on 18F-FDG-PET to differentiate dementia with Lewy bodies and Alzheimer's disease.

Valid diagnosis of dementia with Lewy bodies (DLB) is essential to establish appropriate treatment and care. However, the diagnostic accuracy is complicated by clinical and pathological overlap with Alzheimer's disease (AD). Cingulate island sign (CIS), defined as sparing of posterior cingulate cortex (PCC) relative to precuneus and cuneus on 18F-fluoro-deoxy-glucose positron emission tomography (18F-FDG-PET), is included in the revised diagnostic DLB criteria. There are no guidelines for the visual grading of CIS, although visual rating is a fast-applicable method in a clinical setting. The objective was to develop a robust visual CIS scale and evaluate the performance in differentiating DLB with and without amyloid beta pathology (Aß+/-), and AD. 18F-FDG-PET scans from 35 DLB patients, 36 AD patients, and 23 healthy controls were rated according to a visual CIS scale based on specific reading criteria. The visual CIS scale was validated against a quantitative CIS ratio derived from a region of interest analysis of PCC, precuneus, and cuneus. DLB patients had a significantly higher visual CIS score compared to AD patients, and controls. A cut-off visual CIS score of 4 significantly differentiated DLB Aß- patients from DLB Aß+ patients. In conclusion, the visual CIS scale is clinically useful to differentiate DLB from AD. The degree of CIS may be related to Aß pathology in DLB patients.

Physical Activity as a Moderator of Alzheimer Pathology: A Systematic Review of Observational Studies.

INTRODUCTION: Observational studies have found that physical activity is associated with a reduced risk of cognitive decline and dementia. Whether physical activity may also reduce the level of AD pathology, remains undetermined. OBJECTIVE: To examine the relationship between physical activity and AD biomarkers (beta-amyloid1- 42, total tau and phosphorylated tau in CSF, amyloid PET, hippocampal atrophy on MRI and parietotemporal hypometabolism on brain 18F-FDG-PET). METHODS: We carried out a systematic review of the observational studies of physical activity and AD biomarkers in healthy subjects, subjective cognitive complaints, mild cognitive impairment (MCI) and AD dementia. RESULTS: We identified a total of 40 papers, which were eligible for inclusion. Thirty-four studies were conducted on healthy subjects, 3 on MCI and healthy subjects, 1 on MCI, and 2 on AD and healthy controls. Six studies reported on CSF biomarkers, 9 on amyloid PET, 29 on MRI and 4 on brain 18FFDG- PET. The majority of studies did not find a significant association between physical activity and AD biomarkers. CONCLUSION: The quality of included studies with only a few longitudinal studies, limits the conclusions which may be drawn from the present findings especially regarding the biomarkers other than hippocampal volume. However, the majority of the identified studies did not find a significant association.

Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study.

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Detecting frontotemporal dementia syndromes using MRI biomarkers.

BACKGROUND: 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. METHODS: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ±â€¯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). RESULTS: 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. CONCLUSION: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.

Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study.

BACKGROUND: 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. OBJECTIVES: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Evaluating combinations of diagnostic tests to discriminate different dementia types.

INTRODUCTION: We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. METHODS: 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. RESULTS: 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%. DISCUSSION: 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.

Effects of Physical Exercise on Alzheimer's Disease Biomarkers: A Systematic Review of Intervention Studies.

Physical exercise may be an important adjunct to pharmacological treatment of Alzheimer's disease (AD). Animal studies indicate that exercise may be disease modifying through several mechanisms including reduction of AD pathology. We carried out a systematic review of intervention studies of physical exercise with hippocampal volume (on MRI), amyloid-ß, total tau, phosphorylated tau in cerebrospinal fluid (CSF), 18F-FDG-PET or amyloid PET as outcome measures in healthy subjects, patients with subjective memory complaints, mild cognitive impairment, or AD. We identified a total of 8 studies of which 6 investigated the effects of exercise on hippocampal volume in healthy subjects and 1 on CSF biomarkers and 1 on hippocampal volume in AD, and none investigating the remaining outcome measures or patient groups. Methodological quality of identified studies was generally low. One study found a detrimental effect on hippocampal volume and one found a positive effect, whereas the remaining studies did not find an effect of exercise on outcome measures. The present systematic study identified a relatively small number of studies, which did not support an effect of exercise on hippocampal volume. Methodological issues such small to moderate sample sizes and inadequate ramdomization procedures further limits conclusions. Our findings highlight the difficulties in conducting high quality studies of exercise and further studies are needed before definite conclusions may be reached.