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Abstract 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.

Abstract Background Angelman syndrome (AS) is a rare neurodevelopmental disorder characterized by severe intellectual disability, little to no expressive speech, visual and motor problems, emotional/behavioral challenges, and a tendency towards hyperphagia and weight gain. The characteristics of AS make it difficult to measure these children’s functioning with standard clinical tests. Feasible outcome measures are needed to measure current functioning and change over time, in clinical practice and clinical trials. Aim Our first aim is to assess the feasibility of several functional tests. We target domains of neurocognitive functioning and physical growth using the following measurement methods: eye-tracking, functional Near-Infrared Spectroscopy (fNIRS), indirect calorimetry, bio-impedance analysis (BIA), and BOD POD (air-displacement plethysmography). Our second aim is to explore the results of the above measures, in order to better understand the AS phenotype. Methods The study sample consisted of 28 children with AS aged 2–18 years. We defined an outcome measure as feasible when (1) at least 70% of participants successfully finished the measurement and (2) at least 60% of those participants had acceptable data quality. Adaptations to the test procedure and reasons for early termination were noted. Parents rated acceptability and importance and were invited to make recommendations to increase feasibility. The results of the measures were explored. Results Outcome measures obtained with eye-tracking and BOD POD met the definition of feasibility, while fNIRS, indirect calorimetry, and BIA did not. The most important reasons for early termination of measurements were showing signs of protest, inability to sit still and poor/no calibration (eye-tracking specific). Post-calibration was often applied to obtain valid eye-tracking results. Parents rated the BOD POD als most acceptable and fNIRS as least acceptable for their child. All outcome measures were rated to be important. Exploratory results indicated longer reaction times to high salient visual stimuli (eye-tracking) as well as high body fat percentage (BOD POD). Conclusions Eye-tracking and BOD POD are feasible measurement methods for children with AS. Eye-tracking was successfully used to assess visual orienting functions in the current study and (with some practical adaptations) can potentially be used to assess other outcomes as well. BOD POD was successfully used to examine body composition. Trial registration Registered d.d. 23-04-2020 under number ‘NL8550’ in the Dutch Trial Register: https://onderzoekmetmensen.nl/en/trial/23075

BackgroundThe 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.MethodsWe 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.ResultsThe 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%).ConclusionOur 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: Research clerkships are usually designed as individual learning projects focusing on research skills training, such as research design, data analysis and reporting. When the COVID-19 pandemic triggered an urgent need for digital education, we redesigned a research clerkship with the challenging aim to maintain original quality for more students than usual with limited teaching staff., Approach: We introduced the concept of a research learning community (RLC) with co-teaching and co-learning to a group of 14 students and seven teaching faculty using digital platforms. Small groups of students participated in the RLC, which was supervised weekly by the teachers. Research experts were continuously involved and led workshops., Evaluation: Using a qualitative design, we analysed experiences from the perspectives of students and faculty. We performed an inductive thematic content analysis of three focus group interviews and used 14 student reports for triangulation. The results indicate that apart from developing research skills, students valued peer assistance, attention to uncertainty and learning beyond individual research projects. The teachers/research experts reported that co-teaching and co-learning had contributed to their professional development. In terms of organisation, students and faculty recognised that the RLC model allowed for interdisciplinary learning, facilitated by a digital platform., Implications: To develop students' research skills, embedding a clerkship in an RLC is an attractive alternative to individual research clerkships. The obligatory learning goals are met. Co-learning and co-teaching foster faculty's and students' professional development. When translating to other curricula, we recommend stating common goals in addition to individual objectives., (© 2023 The Authors. The Clinical Teacher published by Association for the Study of Medical Education and John Wiley & Sons Ltd.)

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Neurofilament light (NF-L) is an axonal protein that has shown promise as a traumatic brain injury (TBI) biomarker. Serum NF-L shows a rather slow rise after injury, peaking after 1-2 weeks, although some studies suggest that it may remain elevated for months after TBI. The aim of this study was to examine if plasma NF-L levels several months after the injury correlate with functional outcome in patients who have sustained TBIs of variable initial severity. In this prospective study of 178 patients with TBI and 40 orthopedic injury controls, we measured plasma NF-L levels in blood samples taken at the follow-up appointment on average 9 months after injury. Patients with TBI were divided into two groups (mild [mTBI] vs. moderate-to-severe [mo/sTBI]) according to the severity of injury assessed with the Glasgow Coma Scale upon admission. Recovery and functional outcome were assessed using the Extended Glasgow Outcome Scale (GOSE). Higher levels of NF-L at the follow-up correlated with worse outcome in patients with moderate-to-severe TBI (Spearman's rho = -0.18; p  < 0.001). In addition, in computed tomography-positive mTBI group, the levels of NF-L were significantly lower in patients with GOSE 7-8 (median 18.14; interquartile range [IQR] 9.82, 32.15) when compared with patients with GOSE <7 (median 73.87; IQR 32.17, 110.54; p  = 0.002). In patients with mTBI, late NF-L levels do not seem to provide clinical benefit for late-stage assessment, but in patients with initially mo/sTBI, persistently elevated NF-L levels are associated with worse outcome after TBI and may reflect ongoing brain injury.

Blood biomarkers have been studied to improve the clinical assessment and prognostication of patients with moderate-severe traumatic brain injury (mo/sTBI). To assess their clinical usability, one needs to know of potential factors that might cause outlier values and affect clinical decision making. In a prospective study, we recruited patients with mo/sTBI ( n  = 85) and measured the blood levels of eight protein brain pathophysiology biomarkers, including glial fibrillary acidic protein (GFAP), S100 calcium-binding protein B (S100B), neurofilament light (Nf-L), heart-type fatty acid-binding protein (H-FABP), interleukin-10 (IL-10), total tau (T-tau), amyloid β40 (Aβ40) and amyloid β42 (Aβ42), within 24 h of admission. Similar analyses were conducted for controls ( n  = 40) with an acute orthopedic injury without any head trauma. The patients with TBI were divided into subgroups of normal versus abnormal ( n  = 9/76) head computed tomography (CT) and favorable (Glasgow Outcome Scale Extended [GOSE] 5-8) versus unfavorable (GOSE <5) ( n  = 38/42, 5 missing) outcome. Outliers were sought individually from all subgroups from and the whole TBI patient population. Biomarker levels outside Q1 - 1.5 interquartile range (IQR) or Q3 + 1.5 IQR were considered as outliers. The medical records of each outlier patient were reviewed in a team meeting to determine possible reasons for outlier values. A total of 29 patients (34%) combined from all subgroups and 12 patients (30%) among the controls showed outlier values for one or more of the eight biomarkers. Nine patients with TBI and five control patients had outlier values in more than one biomarker (up to 4). All outlier values were > Q3 + 1.5 IQR. A logical explanation was found for almost all cases, except the amyloid proteins. Explanations for outlier values included extremely severe injury, especially for GFAP and S100B. In the case of H-FABP and IL-10, the explanation was extracranial injuries (thoracic injuries for H-FABP and multi-trauma for IL-10), in some cases these also were associated with abnormally high S100B. Timing of sampling and demographic factors such as age and pre-existing neurological conditions (especially for T-tau), explained some of the abnormally high values especially for Nf-L. Similar explanations also emerged in controls, where the outlier values were caused especially by pre-existing neurological diseases. To utilize blood-based biomarkers in clinical assessment of mo/sTBI, very severe or fatal TBIs, various extracranial injuries, timing of sampling, and demographic factors such as age and pre-existing systemic or neurological conditions must be taken into consideration. Very high levels seem to be often associated with poor prognosis and mortality (GFAP and S100B).

Background and Objective: Fibromyalgia is a chronic disease that causes pain and affects patients' quality of life. Current treatments focus on pharmacological therapies for pain reduction. However, patients' psychological well-being is also affected, with depression and pain catastrophizing being common. This research addresses the clinicians' need to assess the influence of mental health factors on FM severity compared to pain factors., Methods: A co-development study between FM clinicians and data scientists analyzed data from 166 FM-diagnosed patients to assess the influence of mental health factors on FM severity in comparison to pain factors. The study used the Polysymptomatic Distress Scale (PDS) and Fibromyalgia Impact Questionnaire (FIQ) as FM severity indicators and collected 15 variables including regarding demographics, pain intensity perceived, and mental health factors. The team used an author's developed framework to identify the optimal FM severity classifier and explainability by selecting a number of features that lead to obtaining the best classification result. Machine learning classifiers employed in the framework were: decision trees, logistic regression, support vector machines, random forests, AdaBoost, extra trees, and RUSBoost. Explainability analyses were conducted using the following explainable AI techniques: SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Mean Decrease Impurity (MDI)., Results: A balanced random forest with 6 features achieved the best performance with PDS (AUC&#95;ROC, mean = 0.81, std = 0.07). Being FIQ the target variable, due to the imbalance in FM severity levels, a binary and a multiclass classification approaches were considered achieving the optimal performance, respectively, a logistic regression classifier (AUC&#95;ROC, mean = 0.83, std = 0.08) with 6 selected features, and a random forest (AUC&#95;ROC, mean = 0.91, std = 0.04) with 8 selected features. Next, the explainability analysis determined mental health factors were found to be more relevant than pain perceived factors for FM severity., Conclusions: This study's findings, validated by clinicians, are potentially aligned with FM international guidelines that promote non-pharmacological interventions such as promoting mental well-being of FM patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)