Aniek M. van Gils

Objective: Person-centred communication in memory clinics is essential, but often not optimal. This study aimed to develop a solution that supports people with cognitive complaints in expressing their needs and preferences during memory clinic consultations. Methods: Following a human-centred design approach, co-researchers (n = 4 people with dementia) identified a problem statement. This problem was confirmed and elaborated upon via a questionnaire (n = 25) and focus group (n = 18) for triangulation purposes, and in co-design sessions with people with cognitive complaints (n = 3), care partners (n = 2), and clinicians (n = 3). These sessions informed prototype development in collaboration with a design agency. Usability and User eXperience (UX) testing were conducted with people with cognitive complaints (n = 30), care partners (n = 4), and clinicians (n = 17) via think-aloud sessions, interviews, questionnaires, and focus groups. Results: Co-researchers emphasized the importance of clinicians gaining a holistic understanding of someone's life and circumstances, which was confirmed in the ‘triangulation’ questionnaire, focus group, and co-design sessions. Co-design resulted in a digital and analogue prototype of ‘Helder in Gesprek’ (‘Clear in Conversation’), a tool to assist people with cognitive complaints in reflecting on what they wish to share with their clinician and facilitate communication during consultations. Usability testing revealed a generally positive attitude toward the prototypes, while also identifying areas for improvement, such as navigation, system feedback, understandability, distinguishable elements, and cognitive overload. Conclusion: Our human-centred design approach informed the design and development of two prototypes of ‘Helder in Gesprek’. Usability and UX testing provide directions for re-design and feasibility testing in a real-world setting.

Background: Assessment of cognitive decline in the earliest stages of Alzheimer disease (AD) is important but challenging. AD is a neurodegenerative disease characterized by gradual cognitive decline. Disease stages range from preclinical AD, in which individuals are cognitively unimpaired, to mild cognitive impairment (MCI) and dementia. Digital technologies promise to enable detection of early, subtle cognitive changes. Although the field of digital cognitive biomarkers is rapidly evolving, a comprehensive overview of the reporting of psychometric properties (ie, validity, reliability, responsiveness, and clinical meaningfulness) is missing. Insight into the extent to which these properties are evaluated is needed to identify the validation steps toward implementation. Objective: This scoping review aimed to identify the reporting on quality characteristics of smartphone- and tablet-based cognitive tools with potential for remote administration in individuals with preclinical AD or MCI. We focused on both psychometric properties and practical tool characteristics. Methods: This scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. In total, 4 databases (PubMed, Embase, Web of Science, and PsycINFO) were systematically searched from January 1, 2008, to January 5, 2023. Studies were included that assessed the psychometric properties of cognitive smartphone- or tablet-based tools with potential for remote administration in individuals with preclinical AD or MCI. In total, 2 reviewers independently screened titles and abstracts in ASReview, a screening tool that combines manual and automatic screening using an active learning algorithm. Thereafter, we manually screened full texts in the web application Rayyan. For each included study, 2 reviewers independently explored the reported information on practical and psychometric properties. For each psychometric property, examples were provided narratively. Results: In total, 11,300 deduplicated studies were identified in the search. After screening, 50 studies describing 37 different digital tools were included in this review. Average administration time was 13.8 (SD 10.1; range 1-32) minutes, but for 38% (14/37) of the tools, this was not described. Most tools (31/37, 84%) were examined in 1 language. The investigated populations were mainly individuals with MCI (34/37, 92%), and fewer tools were examined in individuals with preclinical AD (8/37, 22%). For almost all tools (36/37, 97%), construct validity was assessed through evaluation of clinical or biological associations or relevant group differences. For a small number of tools, information on structural validity (3/37, 8%), test-retest reliability (12/37, 32%), responsiveness (6/37, 16%), or clinical meaningfulness (0%) was reported. Conclusions: Numerous smartphone- and tablet-based tools to assess cognition in early AD are being developed, whereas studies concerning their psychometric properties are limited. Often, initial validation steps have been taken, yet further validation and careful selection of psychometrically valid outcome scores are required to demonstrate clinical usefulness with regard to the context of use, which is essential for implementation.

Background: The increasing prevalence of dementia and the introduction of disease-modifying therapies (DMTs) highlight the need for efficient diagnostic pathways in memory clinics. We present a data-driven approach to efficiently guide stepwise diagnostic testing for three clinical scenarios: 1) syndrome diagnosis, 2) etiological diagnosis, and 3) eligibility for DMT. Methods: We used data from two memory clinic cohorts (ADC, PredictND), including 504 patients with dementia (302 Alzheimer’s disease, 107 frontotemporal dementia, 35 vascular dementia, 60 dementia with Lewy bodies), 191 patients with mild cognitive impairment, and 188 cognitively normal controls (CN). Tests included digital cognitive screening (cCOG), neuropsychological and functional assessment (NP), MRI with automated quantification, and CSF biomarkers. Sequential testing followed a predetermined order, guided by diagnostic certainty. Diagnostic certainty was determined using a clinical decision support system (CDSS) that generates a disease state index (DSI, 0–1), indicating the probability of the syndrome diagnosis or underlying etiology. Diagnosis was confirmed if the DSI exceeded a predefined threshold based on sensitivity/specificity cutoffs relevant to each clinical scenario. Diagnostic accuracy and the need for additional testing were assessed at each step. Results: Using cCOG as a prescreener for 1) syndrome diagnosis has the potential to accurately reduce the need for extensive NP (42%), resulting in syndrome diagnosis in all patients, with a diagnostic accuracy of 0.71, which was comparable to using NP alone. For 2) etiological diagnosis, stepwise testing resulted in an etiological diagnosis in 80% of patients with a diagnostic accuracy of 0.77, with MRI needed in 77%, and CSF in 37%. When 3) determining DMT eligibility, stepwise testing (100% cCOG, 83% NP, 75% MRI) selected 60% of the patients for confirmatory CSF testing and eventually identified 90% of the potentially eligible patients with AD dementia. Conclusions: Different diagnostic pathways are accurate and efficient depending on the setting. As such, a data-driven tool holds promise for assisting clinicians in selecting tests of added value across different clinical contexts. This becomes especially important with DMT availability, where the need for more efficient diagnostic pathways is crucial to maintain the accessibility and affordability of dementia diagnoses.

BACKGROUND Assessment of cognitive decline in the earliest stages of Alzheimer disease (AD) is important but challenging. AD is a neurodegenerative disease characterized by gradual cognitive decline. Disease stages range from preclinical AD, in which individuals are cognitively unimpaired, to mild cognitive impairment (MCI) and dementia. Digital technologies promise to enable detection of early, subtle cognitive changes. Although the field of digital cognitive biomarkers is rapidly evolving, a comprehensive overview of the reporting of psychometric properties (ie, validity, reliability, responsiveness, and clinical meaningfulness) is missing. Insight into the extent to which these properties are evaluated is needed to identify the validation steps toward implementation. OBJECTIVE This scoping review aimed to identify the reporting on quality characteristics of smartphone- and tablet-based cognitive tools with potential for remote administration in individuals with preclinical AD or MCI. We focused on both psychometric properties and practical tool characteristics. METHODS This scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. In total, 4 databases (PubMed, Embase, Web of Science, and PsycINFO) were systematically searched from January 1, 2008, to January 5, 2023. Studies were included that assessed the psychometric properties of cognitive smartphone- or tablet-based tools with potential for remote administration in individuals with preclinical AD or MCI. In total, 2 reviewers independently screened titles and abstracts in ASReview, a screening tool that combines manual and automatic screening using an active learning algorithm. Thereafter, we manually screened full texts in the web application Rayyan. For each included study, 2 reviewers independently explored the reported information on practical and psychometric properties. For each psychometric property, examples were provided narratively. RESULTS In total, 11,300 deduplicated studies were identified in the search. After screening, 50 studies describing 37 different digital tools were included in this review. Average administration time was 13.8 (SD 10.1; range 1-32) minutes, but for 38% (14/37) of the tools, this was not described. Most tools (31/37, 84%) were examined in 1 language. The investigated populations were mainly individuals with MCI (34/37, 92%), and fewer tools were examined in individuals with preclinical AD (8/37, 22%). For almost all tools (36/37, 97%), construct validity was assessed through evaluation of clinical or biological associations or relevant group differences. For a small number of tools, information on structural validity (3/37, 8%), test-retest reliability (12/37, 32%), responsiveness (6/37, 16%), or clinical meaningfulness (0%) was reported. CONCLUSIONS Numerous smartphone- and tablet-based tools to assess cognition in early AD are being developed, whereas studies concerning their psychometric properties are limited. Often, initial validation steps have been taken, yet further validation and careful selection of psychometrically valid outcome scores are required to demonstrate clinical usefulness with regard to the context of use, which is essential for implementation.

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.

Background: Both memory clinic professionals and patients see value in digital tools, yet these hardly find their way to clinical practice. We explored the usability of a digital tool to support the diagnostic work-up in daily memory clinic practice. We evaluated four modules that integrate multi-modal patient data (1.cognitive test; cCOG, and 2. MRI quantification; cMRI) into useful diagnostic information for clinicians (3. cDSI) and understandable and personalized information for patients (4. patient report). Methods: We conducted a mixed-methods study in five Dutch memory clinics. Fourteen clinicians (11 geriatric specialists/residents, two neurologists, one nurse practitioner) were invited to integrate the tool into routine care with 43 new memory clinic patients. We evaluated usability and user experiences through quantitative data from questionnaires (patients, care partners, clinicians), enriched with thematically analyzed qualitative data from interviews (clinicians). Results: We observed wide variation in tool use among clinicians. Our core findings were that clinicians: 1) were mainly positive about the patient report, since it contributes to patient-centered and personalized communication. This was endorsed by patients and care partners, who indicated that the patient report was useful and understandable and helped them to better understand their diagnosis, 2) considered the tool acceptable in addition to their own clinical competence, 3) indicated that the usefulness of the tool depended on the patient population and purpose of the diagnostic process, 4) addressed facilitators (ease of use, practice makes perfect) and barriers (high workload, lack of experience, data unavailability). Conclusion: This multicenter usability study revealed a willingness to adopt a digital tool to support the diagnostic process in memory clinics. Clinicians, patients, and care partners appreciated the personalized diagnostic report. More attention to education and training of clinicians is needed to utilize the full functionality of the tool and foster implementation in actual daily practice. These findings provide an important step towards a lasting adoption of digital tools in memory clinic practice.

Background: To optimally serve increasing numbers of memory clinic patients through a timely and adequate diagnosis, digital health technologies offer a unique potential. Diagnostic decision support systems (DDSS) can aid clinicians in diagnostics and patient communication, but such tools are not widely used in daily practice. We assessed the feasibility of a DDSS prototype consisting of four modules (Figure 1): (1) web‐based cognitive test tool (cCOG); (2) automated MRI‐quantification (cMRI); (3) AI‐based classifier (cDSI); (4) diagnostic report page, providing an easy‐to‐understand overview of all information for clinicians and patients.Method: We conducted the DAILY feasibility study in five Dutch memory clinics, including 11 clinicians (internal medicine/geriatricians(n = 5), neurologists(n = 2), residents(n = 3), nurse practitioner(n = 1), mean age 38±7y, 11±6y experience), and 43 patients(7 subjective complaints, 19 mild cognitive impairment, 14 dementia, 3 postponed diagnosis, mean age 73±11y), and their care partners(n = 28, 65±11y). Clinicians received instructions to integrate the tool into routine care. Feasibility, usability, and satisfaction were assessed using questionnaires (clinicians, patients, care partners) and interviews (clinicians).Result: Clinicians reported using the tool in 34/43(80%) patients. Their overall satisfaction with the tool was acceptable (median 70(IQR 50‐80), scale 0‐100). Out of the four modules, the diagnostic report was most often used (in 70% of patients) and rated as useful by 89% of clinicians, with 70% indicating it contributed to more efficient patient communication. Patients and care partners evaluated the diagnostic report as understandable and helpful. In the interviews, clinicians were predominantly positive regarding the diagnostic report, indicating that it helped create an overview for themselves and their patients. Widely varying opinions indicated that (time‐efficient) use of the complete tool demands more education and practice to obtain the proficiency level needed to adequately integrate the tool into routine practice.Conclusion: The lasting adoption of digital technologies in healthcare is challenging, and efficient and user‐friendly tools are needed. In this study, clinicians, patients, and care partners valued an automatically‐generated, comprehensible report summarizing individual test results. Implementing such a digital report page could contribute to improved patient communication and, ultimately, better care management. Furthermore, our results identified an educational need regarding the use of digital tools.

Background: Separating Alzheimer’s dementia (AD) from dementia with Lewy bodies (DLB) can be challenging, especially in DLB patients with concomitant AD pathology. It is imperative to distinguish the diseases for optimal patient management. To date, no single MRI feature can accurately separate the two diseases. This study examines whether a linear combination of regional atrophy measures can differentiate cognitively normal (CN), AD, and DLB patients.Method: We computed volumes of 70 brain regions using an automatic segmentation tool. For each region, we derived age, sex, and head size‐dependent z‐scores. A linear classifier with all brain regions was used to predict if a patient belonged to CN, AD, or DLB. Diagnoses were based on international consensus criteria and CSF‐biomarkers. The classifier was tested in 327 CN (mean age 67±10y, 48%F), 501 AD (70±9y, 49%F), and 99 DLB (69±7y, 15%F) patients from three cohorts (ADNI, PredictND, ADC). The ADC cohort was assessed separately, as almost all DLB patients were from the ADC. In addition, we assessed classification performance in a subgroup of DLB patients with AD‐like CSF (DLB+, n = 57). Performance was evaluated using 10‐fold cross‐validation.Result: Performance measures for the classifier are summarized in Table 1. The classifier accuracy was assessed in the three‐class classification task and in two‐class classifications where one of the groups was excluded from the analysis. The balanced accuracy in the three‐class classification was 67% for the DLB group and the DLB+ subgroup. For AD vs. DLB, the AUC was 0.77(95% CI:0.70‐0.82), for AD vs. DLB+ AUC was 0.72(0.64‐0.80). The classifier correctly classified CN and AD patients more often than DLB patients (Table 2). Most DLB+ patients were misclassified as AD. The hippocampus was the best single region for distinguishing AD vs. DLB, with an AUC of 0.70(0.64‐0.76).Conclusion: The joint classifier achieved high accuracy in separating AD vs. DLB patients. Although the accuracy was lower, the classifier could distinguish DLB patients with concomitant AD pathology from AD. Next, we will combine different brain regions to discern disease‐specific patterns and add clinical data to the classifier to improve differential diagnostic accuracy for DLB and DLB with concomitant AD pathology.

Background: The use of amyloid‐PET in daily clinical practice is upcoming. Yet, ttranslation of the appropriate use criteria (AUC) to clinical practice is challenging, hampering successful implementation of amyloid‐PET. Tools that guide selecting patients for whom amyloid‐PET has most clinical utility are needed. Therefore, we developed and evaluated a computerized decision support approach to select patients for amyloid‐PET testing.Method: We included 286 subjects (136 controls, 116 Alzheimer’s disease (AD) dementia, 26 frontotemporal lobe dementia (FTD), and 8 vascular dementia (VaD) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [¹⁸F]florbetaben amyloid‐PET. In our computerized decision support approach, we first classified the subjects using only neuropsychology, APOE, and MRI data. Then, for uncertain subjects (probability of correct class (PPC) < 0.75) we tested the classification by adding amyloid positive (AD like) and negative (normal) visual PET read results, and assessed whether either of the values makes the diagnosis reliable (PPC≥0.75). If the threshold of PCC was reached, the actual visual PET read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient confidence (PPC ≥0.75) in the computerized approach with three scenarios: 1) without amyloid‐PET (only demographics, neuropsychology, APOE and MRI), 2) amyloid‐PET according to the AUC and 3) amyloid‐PET for all patients. The study was performed using five‐fold cross‐validation.Result: The computerized approach advised PET in n = 76(27%) patients, leading to a diagnosis with sufficient confidence in n = 190(66%) patients. This approach outperformed the other three scenarios: 1) without amyloid‐PET, diagnosis was obtained in n = 147(51%), 2) applying the AUC resulted in amyloid‐PET in n = 113(40%) and diagnosis in n = 160(56%), and 3) performing amyloid‐PET in all patients resulted in diagnosis in n = 165(58%).Conclusion: Our computerized data‐driven approach restricted the application of amyloid‐PET to 27% in a memory clinic cohort of controls, AD, FTD, and VaD, without compromising diagnostic accuracy. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing during the dementia workup. Thus, innovative use of resources can be stimulated, which is essential when disease‐modifying drugs increase the demand for biomarker testing.

Inleiding De wereldwijde prevalentie van dementie stijgt naar verwachting tot zo’n 75 miljoen patiënten in 2050. Het stellen van een tijdige en nauwkeurige dementiediagnose is belangrijk voor toekomstplanning, juiste behandeling en zorg op maat. Het diagnosticeren van de onderliggende etiologie, zoals de ziekte van Alzheimer (‘Alzheimer’s…