Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET.

BACKGROUND: The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. METHODS: We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. RESULTS: The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). CONCLUSION: Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Impact of COVID-19 pandemic on mortality rate in memory clinic patients.

INTRODUCTION: We investigated whether mortality in memory clinic patients changed due to coronavirus disease 2019 (COVID-19) pandemic. METHODS: We included patients from the Amsterdam Dementia Cohort: (1) n = 923 pandemic patients (baseline visit: 2017-2018, follow-up: until 2021), and (2) n = 830 historical control patients (baseline visit: 2015-2016, follow-up: until 2019). Groups were well-balanced. We compared mortality during pandemic with historical control patients using Cox regression. Differences in cause of death between groups were explored using Fisher's exact test. RESULTS: Pandemic patients had a higher risk of mortality than historical control patients (hazard ratio [HR] [95% confidence interval {CI}] = 1.34 [1.05-1.70]). Stratified for syndrome diagnosis, the effect remained significant in dementia patients (HR [95% CI] = 1.35 [1.03-1.78]). Excluding patients who died of COVID-19-infection, the higher mortality risk in pandemic patients attenuated (HR [95% CI] = 1.24 [0.97-1.58]). Only the difference in cause of death between pandemic patients and historical control patients for death to COVID-19-infection (p = 0.001) was observed. CONCLUSION: Memory clinic patients had increased mortality risk during COVID-19 compared to historical control patients, attributable to dementia patients. Highlights: We investigated if mortality rates in memory clinic patients changed due to COVID-19 pandemic.We included patients along the cognitive continuum, including SCD, MCI, and dementia.We used a well-balanced historical control group.Memory clinic patients had higher risk for mortality during COVID-19 lockdown.Our results indicate that excess mortality is mainly caused by death to COVID-19 infection.

Predicting sojourn times across dementia disease stages, institutionalization, and mortality.

INTRODUCTION: Inferring the timeline from mild cognitive impairment (MCI) to severe dementia is pivotal for patients, clinicians, and researchers. Literature is sparse and often contains few patients. We aim to determine the time spent in MCI, mild-, moderate-, severe dementia, and institutionalization until death. METHODS: Multistate modeling with Cox regression was used to obtain the sojourn time. Covariates were age at baseline, sex, amyloid status, and Alzheimer's disease (AD) or other dementia diagnosis. The sample included a register (SveDem) and memory clinics (Amsterdam Dementia Cohort and Memento). RESULTS: Using 80,543 patients, the sojourn time from clinically identified MCI to death across all patient groups ranged from 6.20 (95% confidence interval [CI]: 5.57-6.98) to 10.08 (8.94-12.18) years. DISCUSSION: Generally, sojourn time was inversely associated with older age at baseline, males, and AD diagnosis. The results provide key estimates for researchers and clinicians to estimate prognosis.

Clinical Effect of Early vs Late Amyloid Positron Emission Tomography in Memory Clinic Patients: The AMYPAD-DPMS Randomized Clinical Trial.

Importance: Amyloid positron emission tomography (PET) allows the direct assessment of amyloid deposition, one of the main hallmarks of Alzheimer disease. However, this technique is currently not widely reimbursed because of the lack of appropriately designed studies demonstrating its clinical effect. Objective: To assess the clinical effect of amyloid PET in memory clinic patients. Design, Setting, and Participants: The AMYPAD-DPMS is a prospective randomized clinical trial in 8 European memory clinics. Participants were allocated (using a minimization method) to 3 study groups based on the performance of amyloid PET: arm 1, early in the diagnostic workup (within 1 month); arm 2, late in the diagnostic workup (after a mean [SD] 8 [2] months); or arm 3, if and when the managing physician chose. Participants were patients with subjective cognitive decline plus (SCD+; SCD plus clinical features increasing the likelihood of preclinical Alzheimer disease), mild cognitive impairment (MCI), or dementia; they were assessed at baseline and after 3 months. Recruitment took place between April 16, 2018, and October 30, 2020. Data analysis was performed from July 2022 to January 2023. Intervention: Amyloid PET. Main Outcome and Measure: The main outcome was the difference between arm 1 and arm 2 in the proportion of participants receiving an etiological diagnosis with a very high confidence (ie, ≥90% on a 50%-100% visual numeric scale) after 3 months. Results: A total of 844 participants were screened, and 840 were enrolled (291 in arm 1, 271 in arm 2, 278 in arm 3). Baseline and 3-month visit data were available for 272 participants in arm 1 and 260 in arm 2 (median [IQR] age: 71 [65-77] and 71 [65-77] years; 150/272 male [55%] and 135/260 male [52%]; 122/272 female [45%] and 125/260 female [48%]; median [IQR] education: 12 [10-15] and 13 [10-16] years, respectively). After 3 months, 109 of 272 participants (40%) in arm 1 had a diagnosis with very high confidence vs 30 of 260 (11%) in arm 2 (P < .001). This was consistent across cognitive stages (SCD+: 25/84 [30%] vs 5/78 [6%]; P < .001; MCI: 45/108 [42%] vs 9/102 [9%]; P < .001; dementia: 39/80 [49%] vs 16/80 [20%]; P < .001). Conclusion and Relevance: In this study, early amyloid PET allowed memory clinic patients to receive an etiological diagnosis with very high confidence after only 3 months compared with patients who had not undergone amyloid PET. These findings support the implementation of amyloid PET early in the diagnostic workup of memory clinic patients. Trial Registration: EudraCT Number: 2017-002527-21.

Steeper memory decline after COVID-19 lockdown measures.

BACKGROUND: During COVID-19 lockdown measures, memory clinic patients reported worries for faster cognitive decline, due to loss of structure and feelings of loneliness and depression. We aimed to investigate the impact of the COVID-19 lockdown on rate of cognitive decline in a mixed memory clinic population, compared to matched historical controls. METHODS: We included patients who visited Alzheimer Center Amsterdam 6 months to 1 week before the first Dutch COVID-19 lockdown, and had a second visit 1 year later, after this lockdown period (n = 113; 66 ± 7 years old; 30% female; n = 55 dementia, n = 31 mild cognitive impairment (MCI), n = 18 subjective cognitive decline (SCD), n = 9 postponed diagnosis). Historical controls (visit in 2016/2017 and second visit 1 year later (n = 640)) were matched 1:1 to lockdown patients by optimal Mahalanobis distance matching (both groups n = 113). Groups were well matched. Differences between lockdown patients and historical controls over time in Mini-Mental State Examination, Trail Making Test part A and B, Rey-Auditory Verbal Learning Test (RAVLT) immediate and delayed recall, and category fluency scores were analyzed using linear mixed effect models with random intercepts. We examined differences in rate of cognitive decline between whole groups, and after stratification in SCD, MCI, and dementia separately. RESULTS: Lockdown patients had a faster rate of memory decline compared to controls on both RAVLT immediate [B(SE) = - 2.62 (1.07), p = 0.015] and delayed recall [B(SE) = - 1.07 (0.34), p = 0.002]. Stratification by syndrome diagnosis showed that this effect was largely attributable to non-demented participants, as we observed faster memory decline during lockdown in SCD and MCI (RAVLT immediate [SCD: B(SE) = - 6.85 (2.97), p = 0.027; MCI: B(SE) = - 6.14 (1.78), p = 0.001] and delayed recall [SCD: B(SE) = - 2.45 (1.11), p = 0.035; MCI: B(SE) = - 1.50 (0.51), p = 0.005]), but not in dementia. CONCLUSION: Memory clinic patients, specifically in pre-dementia stages, showed faster memory decline during COVID-19 lockdown, providing evidence that lockdown regulations had a deleterious effect on brain health. In individuals that may have been able to deal with accumulating, subclinical neuropathology under normal and structured circumstances, the additional stress of lockdown regulations may have acted as a "second hit," resulting in less beneficial disease trajectory.

Determinants of informal care time, distress, depression, and quality of life in care partners along the trajectory of Alzheimer's disease.

Introduction: We evaluated determinants associated with care partner outcomes along the Alzheimer's disease (AD) stages. Methods: We included n = 270 care partners of amyloid-positive patients in the pre-dementia and dementia stages of AD. Using linear regression analysis, we examined determinants of four care partner outcomes: informal care time, caregiver distress, depression, and quality of life (QoL). Results: More behavioral symptoms and functional impairment in patients were associated with more informal care time and depressive symptoms in care partners. More behavioral symptoms were related with more caregiver distress. Spouse care partners spent more time on informal care and QoL was lower in female care partners. Behavioral problems and subtle functional impairment of the patient predisposed for worse care partner outcomes already in the pre-dementia stages. Discussion: Both patient and care partner determinants contribute to the care partner outcomes, already in early disease stages. This study provides red flags for high care partner burden.

A more precise diagnosis by means of amyloid PET contributes to delayed institutionalization, lower mortality, and reduced care costs in a tertiary memory clinic setting.

INTRODUCTION: We aim to study the effect of a more precise diagnosis, by means of amyloid positron emission tomography (PET), on institutionalization, mortality, and health-care costs. METHODS: Between October 27, 2014 and December 31, 2016, we offered amyloid PET to all patients as part of their diagnostic work-up. Patients who accepted to undergo amyloid PET (n = 449) were propensity score matched with patients without amyloid PET (n = 571, i.e., no PET). Matched groups (both n = 444) were compared on rate of institutionalization, mortality, and health-care costs in the years after diagnosis. RESULTS: Amyloid PET patients had a lower risk of institutionalization (10% [n = 45] vs. 21% [n = 92]; hazard ratio [HR] = 0.48 [0.33-0.70]) and mortality rate (11% [n = 49] vs. 18% [n = 81]; HR = 0.51 [0.36-0.73]) and lower health-care costs in the years after diagnosis compared to matched no-PET patients (ß = -4573.49 [-6524.76 to -2523.74], P-value < 0.001). DISCUSSION: A more precise diagnosis in tertiary memory clinic patients positively influenced the endpoints of institutionalization, death, and health-care costs.

Identifying best practices for disclosure of amyloid imaging results: A randomized controlled trial.

INTRODUCTION: Empirical studies on effective communication for amyloid disclosure in mild cognitive impairment (MCI) are lacking. We aimed to study the impact of six communication strategies. METHOD: We performed a randomized controlled trial with seven randomly assigned, video-vignette conditions: six emphasizing a communication strategy and one basic condition. All showed a scripted consultation of a neurologist disclosing positive amyloid positron emission tomography (PET) scan results to an MCI patient. Healthy individuals (N = 1017; mean age ± SD 64 ± 8, 808 (79%) female) were instructed to imagine themselves in the video, answered questionnaires assessing information recall, emotional state, and behavioral intentions, and evaluate the physician/information. RESULTS: "Risk best practice" resulted in highest free recall compared to other strategies (P < .05), except "emotional support". Recall in "emotional support" was better compared to "basic-' and elaborate information"(P < .05). "Risk best practice" resulted in the highest uncertainty (P < .001). "Teach-back" and "emotional support" contributed to the highest evaluations (P -values < .01). CONCLUSION: Risk communication best practices, attending to emotions, and teach-back techniques enhance information recall of amyloid-PET results, and could contribute to positive care evaluations.

Life satisfaction across the entire trajectory of Alzheimer's disease: A mediation analysis.

Introduction: We studied life satisfaction across Alzheimer's disease (AD) stages and studied mobility and meaningful activities as mediators of the associations between these AD stages and life satisfaction. Methods: In this cross-sectional study, we included n = 269 amyloid-positive patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD dementia from the Amsterdam Dementia Cohort. Life satisfaction was measured with the satisfaction with life scale. The mediating role of transportation, work, sports, and hobbies on life satisfaction was examined in single and multiple mediator models. Results: Patients with dementia are less satisfied with life compared to SCD and MCI. These differences in life satisfaction are explained by reduced participation in meaningful activities, which in turn, was largely attributable to decreased transportation use. Discussion: Our findings suggest that improving access to transportation, therewith allowing participation in meaningful activities help to maintain life satisfaction and may be an important target for intervention.

A longitudinal study on quality of life along the spectrum of Alzheimer's disease.

BACKGROUND: Quality of life (QoL) is an important outcome from the perspective of patients and their caregivers, in both dementia and pre-dementia stages. Yet, little is known about the long-term changes in QoL over time. We aimed to compare the trajectories of QoL between amyloid-positive and amyloid-negative SCD or MCI patients and to evaluate QoL trajectories along the Alzheimer's disease (AD) continuum of cognitively normal to dementia. METHODS: We included longitudinal data of 447 subjective cognitive decline (SCD), 276 mild cognitive impairment (MCI), and 417 AD dementia patients from the Amsterdam Dementia Cohort. We compared QoL trajectories (EQ-5D and visual analog scale (VAS)) between (1) amyloid-positive and amyloid-negative SCD or MCI patients and (2) amyloid-positive SCD, MCI, and dementia patients with linear mixed-effect models. The models were adjusted for age, sex, Charlson Comorbidity Index (CCI), education, and EQ-5D scale (3 or 5 level). RESULTS: In SCD, amyloid-positive participants had a higher VAS at baseline but showed a steeper decline over time in EQ-5D and VAS than amyloid-negative participants. Also, in MCI, amyloid-positive patients had higher QoL at baseline but subsequently showed a steeper decline in QoL over time compared to amyloid-negative patients. When we compared amyloid-positive patients along the Alzheimer continuum, we found no difference between SCD, MCI, or dementia in baseline QoL, but QoL decreased at a faster rate in the dementia stage compared with the of SCD and MCI stages. CONCLUSIONS: QoL decreased at a faster rate over time in amyloid-positive SCD or MCI patients than amyloid-negative patients. QoL decreases over time along the entire AD continuum of SCD, MCI and dementia, with the strongest decrease in dementia patients. Knowledge of QoL trajectories is essential for the future evaluation of treatments in AD.

Development of multivariable prediction models for institutionalization and mortality in the full spectrum of Alzheimer's disease.

BACKGROUND: Patients and caregivers express a desire for accurate prognostic information about time to institutionalization and mortality. Previous studies predicting institutionalization and mortality focused on the dementia stage. However, Alzheimer's disease (AD) is characterized by a long pre-dementia stage. Therefore, we developed prediction models to predict institutionalization and mortality along the AD continuum of cognitively normal to dementia. METHODS: This study included SCD/MCI patients (subjective cognitive decline (SCD) or mild cognitive impairment (MCI)) and patients with AD dementia from the Amsterdam Dementia Cohort. We developed internally and externally validated prediction models with biomarkers and without biomarkers, stratified by dementia status. Determinants were selected using backward selection (p<0.10). All models included age and sex. Discriminative performance of the models was assessed with Harrell's C statistics. RESULTS: We included n=1418 SCD/MCI patients (n=123 died, n=74 were institutionalized) and n=1179 patients with AD dementia (n=413 died, n=453 were institutionalized). For both SCD/MCI and dementia stages, the models for institutionalization and mortality included after backward selection clinical characteristics, imaging, and cerebrospinal fluid (CSF) biomarkers. In SCD/MCI, the Harrell's C-statistics of the models were 0.81 (model without biomarkers: 0.76) for institutionalization and 0.79 (model without biomarker: 0.76) for mortality. In AD-dementia, the Harrell's C-statistics of the models were 0.68 (model without biomarkers: 0.67) for institutionalization and 0.65 (model without biomarker: 0.65) for mortality. Models based on data from amyloid-positive patients only had similar discrimination. CONCLUSIONS: We constructed prediction models to predict institutionalization and mortality with good accuracy for SCD/MCI patients and moderate accuracy for patients with AD dementia. The developed prediction models can be used to provide patients and their caregivers with prognostic information on time to institutionalization and mortality along the cognitive continuum of AD.

Clinical applicability of quantitative atrophy measures on MRI in patients suspected of Alzheimer's disease.

OBJECTIVES: Neurodegeneration in suspected Alzheimer's disease can be determined using visual rating or quantitative volumetric assessments. We examined the feasibility of volumetric measurements of gray matter (GMV) and hippocampal volume (HCV) and compared their diagnostic performance with visual rating scales in academic and non-academic memory clinics. MATERIALS AND METHODS: We included 231 patients attending local memory clinics (LMC) in the Netherlands and 501 of the academic Amsterdam Dementia Cohort (ADC). MRI scans were acquired using local protocols, including a T1-weighted sequence. Quantification of GMV and HCV was performed using FSL and FreeSurfer. Medial temporal atrophy and global atrophy were assessed with visual rating scales. ROC curves were derived to determine which measure discriminated best between cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's dementia (AD). RESULTS: Patients attending LMC (age 70.9 ± 8.9 years; 47% females; 19% CN; 34% MCI; 47% AD) were older, had more cerebrovascular pathology, and had lower GMV and HCV compared to those of the ADC (age 64.9 ± 8.2 years; 42% females; 35% CN, 43% MCI, 22% AD). While visual ratings were feasible in > 95% of scans in both cohorts, quantification was achieved in 94-98% of ADC, but only 68-85% of LMC scans, depending on the software. Visual ratings and volumetric outcomes performed similarly in discriminating CN vs AD in both cohorts. CONCLUSION: In clinical settings, quantification of GM and hippocampal atrophy currently fails in up to one-third of scans, probably due to lack of standardized acquisition protocols. Diagnostic accuracy is similar for volumetric measures and visual rating scales, making the latter suited for clinical practice. In a real-life clinical setting, volumetric assessment of MRI scans in dementia patients may require acquisition protocol optimization and does not outperform visual rating scales. KEY POINTS: • In a real-life clinical setting, the diagnostic performance of visual rating scales is similar to that of automatic volumetric quantification and may be sufficient to distinguish Alzheimer's disease groups. • Volumetric assessment of gray matter and hippocampal volumes from MRI scans of patients attending non-academic memory clinics fails in up to 32% of cases. • Clinical MR acquisition protocols should be optimized to improve the output of quantitative software for segmentation of Alzheimer's disease-specific outcomes.

Psychosocial Effects of COVID-19 Measures on (Pre-)Dementia Patients During Second Lockdown.

BACKGROUND: The COVID-19 pandemic poses enormous social challenges, especially during lockdown. People with cognitive decline and their caregivers are particularly at risk of lockdown consequences. OBJECTIVE: To investigate psychosocial effects in (pre-)dementia patients and caregivers during second lockdown and compare effects between first and second lockdown. METHODS: We included n = 511 (pre-)dementia patients and n = 826 caregivers from the Amsterdam Dementia Cohort and via Alzheimer Nederland. All respondents completed a self-designed survey on psychosocial effects of COVID-19. We examined relations between experienced support and psychosocial and behavioral symptoms using logistic regression. In a subset of patients and caregivers we compared responses between first and second lockdown using generalized estimating equation (GEE). RESULTS: The majority of patients (≥58%) and caregivers (≥60%) reported that family and friends, hobbies, and music helped them cope. Support from family and friends was strongly related to less negative feelings in patients (loneliness: OR = 0.3[0.1-0.6]) and caregivers (loneliness: OR = 0.2[0.1-0.3]; depression: OR = 0.4[0.2-0.5]; anxiety: OR = 0.4[0.3-0.6]; uncertainty: OR = 0.3[0.2-0.5]; fatigue: OR = 0.3[0.2-0.4]; stress: OR = 0.3[0.2-0.5]). In second lockdown, less psychosocial and behavioral symptoms were reported compared to first lockdown (patients; e.g., anxiety: 22% versus 13%, p = 0.007; apathy: 27% versus 8%, p < 0.001, caregivers; e.g., anxiety: 23% versus 16%, p = 0.033; patient's behavioral problems: 50% versus 35%, p < 0.001). Patients experienced more support (e.g., family and friends: 52% versus 93%, p < 0.001; neighbors: 28% versus 66%, p < 0.001). CONCLUSION: During second lockdown, patients and caregivers adapted to challenges posed by lockdown, as psychosocial and behavioral effects decreased, while patients experienced more social support compared to first lockdown. Support from family and friends is a major protective factor for negative outcomes in patients and caregivers.

Identifying relevant outcomes in the progression of Alzheimer's disease; what do patients and care partners want to know about prognosis?

BACKGROUND: Prognostic studies in the context of Alzheimer's disease (AD) mainly predicted time to dementia. However, it is questionable whether onset of dementia is the most relevant outcome along the AD disease trajectory from the perspective of patients and their care partners. Therefore, we aimed to identify the most relevant outcomes from the viewpoint of patients and care partners. METHODS: We used a two-step, mixed-methods approach. As a first step we conducted four focus groups in the Netherlands to elicit a comprehensive list of outcomes considered important by patients (n = 12) and care partners (n = 14) in the prognosis of AD. The focus groups resulted in a list of 59 items, divided into five categories. Next, in an online European survey, we asked participants (n = 232; 99 patients, 133 care partners) to rate the importance of all 59 items (5-point Likert scale). As participants were likely to rate a large number of outcomes as "important" (4) or "very important" (5), we subsequently asked them to select the three items they considered most important. RESULTS: The top-10 lists of items most frequently mentioned as "most important" by patients and care partners were merged into one core outcome list, comprising 13 items. Both patients and care partners selected outcomes from the category "cognition" most often, followed by items in the categories "functioning and dependency" and "physical health." No items from the category "behavior and neuropsychiatry" and "social environment" ended up in our core list of relevant outcomes. CONCLUSION: We identified a core list of outcomes relevant to patients and care partner, and found that prognostic information related to cognitive decline, dependency, and physical health are considered most relevant by both patients and their care partners.

Dutch Brain Research Registry for study participant recruitment: Design and first results.

INTRODUCTION: The Dutch Brain Research Registry aims to facilitate online recruitment of participants for brain disease studies. METHODS: Registrants were primarily recruited through an online social media campaign. The registration process included a short questionnaire, which was subsequently used in the prescreening process to match participants to studies. RESULTS: In the first 18 months, 17,218 registrants signed up (58±11 years old, 78% female). Out of 34,696 study invitations that were sent, 36% were accepted by registrants, of which 50% to 84% were finally enrolled, resulting in 10,661 participants in 28 studies. Compared to non-participants, study participants were more often older, male, more highly educated, retired or unemployed, non-smoking, healthier, and more often had a family member with dementia. DISCUSSION: The Dutch Brain Research Registry facilitates effective matching of participants to brain disease studies. Participant factors related to study enrollment may reflect facilitators or barriers for participation, which is useful for improving recruitment strategies.

Biomarker testing in MCI patients-deciding who to test.

BACKGROUND: We aimed to derive an algorithm to define the optimal proportion of patients with mild cognitive impairment (MCI) in whom cerebrospinal fluid (CSF) testing is of added prognostic value. METHODS: MCI patients were selected from the Amsterdam Dementia Cohort (n = 402). Three-year progression probabilities to dementia were predicted using previously published models with and without CSF data (amyloid-beta1-42 (Abeta), phosphorylated tau (p-tau)). We incrementally augmented the proportion of patients undergoing CSF, starting with the 10% patients with prognostic probabilities based on clinical data around the median (percentile 45-55), until all patients received CSF. The optimal proportion was defined as the proportion where the stepwise algorithm showed similar prognostic discrimination (Harrell's C) and accuracy (three-year Brier scores) compared to CSF testing of all patients. We used the BioFINDER study (n = 221) for validation. RESULTS: The optimal proportion of MCI patients to receive CSF testing selected by the stepwise approach was 50%. CSF testing in only this proportion improved the performance of the model with clinical data only from Harrell's C = 0.60, Brier = 0.198 (Harrell's C = 0.61, Brier = 0.197 if the information on magnetic resonance imaging was available) to Harrell's C = 0.67 and Brier = 0.190, and performed similarly to a model in which all patients received CSF testing. Applying the stepwise approach in the BioFINDER study would again select half of the MCI patients and yielded robust results with respect to prognostic performance. INTERPRETATION: CSF biomarker testing adds prognostic value in half of the MCI patients. As such, we achieve a CSF saving recommendation while simultaneously retaining optimal prognostic accuracy.

Psychosocial Effects of Corona Measures on Patients With Dementia, Mild Cognitive Impairment and Subjective Cognitive Decline.

Background: The recent COVID-19 pandemic is not only a major healthcare problem in itself, but also poses enormous social challenges. Though nursing homes increasingly receive attention, the majority of people with cognitive decline and dementia live at home. We aimed to explore the psychosocial effects of corona measures in memory clinic (pre-)dementia patients and their caregivers. Methods: Between April 28th and July 13th 2020, n = 389 patients of Alzheimer center Amsterdam [n = 121 symptomatic (age = 69 ± 6, 33%F, MMSE = 23 ± 5), n = 268 cognitively normal (age = 66 ± 8, 40% F, MMSE = 29 ± 1)] completed a survey on psychosocial effects of the corona measures. Questions related to social isolation, worries for faster cognitive decline, behavioral problems and discontinuation of care. In addition, n = 147 caregivers of symptomatic patients completed a similar survey with additional questions on caregiver burden. Results: Social isolation was experienced by n = 42 (35%) symptomatic and n = 67 (25%) cognitively normal patients and two third of patients [n = 129 (66%); n = 58 (75%) symptomatic, n = 71 (61%) cognitively normal] reported that care was discontinued. Worries for faster cognitive decline were existed in symptomatic patients [n = 44 (44%)] and caregivers [n = 73 (53%)], but were also reported by a subgroup of cognitively normal patients [n = 27 (14%)]. Both patients [n = 56 (46%) symptomatic, n = 102 (38%) cognitively normal] and caregivers [n = 72 (48%)] reported an increase in psychological symptoms. More than three quarter of caregivers [n = 111(76%)] reported an increase in patients' behavioral problems. A higher caregiver burden was experienced by n = 69 (56%) of caregivers and n = 43 (29%) of them reported that a need for more support. Discontinuation of care (OR = 3.3 [1.3-7.9]), psychological (OR = 4.0 [1.6-9.9]) and behavioral problems (OR = 3.0 [1.0-9.0]) strongly related to experiencing a higher caregiver burden. Lastly, social isolation (OR = 3.2 [1.2-8.1]) and psychological symptoms (OR = 8.1 [2.8-23.7]) were red flags for worries for faster cognitive decline. Conclusion: Not only symptomatic patients, but also cognitively normal patients express worries for faster cognitive decline and psychological symptoms. Moreover, we identified patients who are at risk of adverse outcomes of the corona measures, i.e., discontinued care, social isolation, psychological and behavioral problems. This underlines the need for health care professionals to provide ways to warrant the continuation of care and support (informal) networks surrounding patients and caregivers to mitigate the higher risk of negative psychosocial effects.

Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy.

INTRODUCTION: An accurate and timely diagnosis for Alzheimer's disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination. METHODS: We included 535 subjects (139 controls, 286 Alzheimer's disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients. RESULTS: The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed). CONCLUSIONS: We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.

Clinicians' communication with patients receiving a MCI diagnosis: The ABIDE project.

BACKGROUND: We aimed to explore clinicians' communication, including the discussion of diagnosis, cause, prognosis and care planning, in routine post-diagnostic testing consultations with patients with Mild Cognitive Impairment (MCI). METHODS: Thematic content analysis was used to analyze audiotaped consultations in which 10 clinicians (eight neurologists and two geriatricians) from 7 memory clinics, disclosed diagnostic information to 13 MCI patients and their care partners. We assessed clinician-patient communication regarding diagnostic label, cause, prognosis and care planning to identify core findings. RESULTS: Core findings were: clinicians 1) differed in how they informed about the MCI label; 2) tentatively addressed cause of symptoms; 3) (implicitly) steered against further biomarker testing; 4) rarely informed about the patient's risk of developing dementia; 5) often informed about the expected course of symptoms emphasizing potential symptom stabilization and/or improvement, and; 6) did not engage in a conversation on long-term (care) planning. DISCUSSION: Clinicians' information provision about the underlying cause, prognosis and implications for long-term (care) planning in MCI could be more specific. Since most patients and care partners have a strong need to understand the patient's symptoms, and for information on the prognosis and implications for the future, clinicians' current approach may not match with those needs.

Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study.

BACKGROUND: Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. METHODS: In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models-a demographics model, a hippocampal volume model, and a CSF biomarkers model-by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework. FINDINGS: We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59-0·65), validated hippocampal volume model (0·67, 0·62-0·72), and updated CSF biomarkers model (0·72, 0·68-0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71-0·76). INTERPRETATION: We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour. FUNDING: ZonMW-Memorabel.