Informed consent, therapeutic misconception, and clinical trials for Alzheimer's disease

The ethical inadequacy of uninformed surrogate consent: advancing respect for persons in clinical research

In clinical research, decision-making capacity is often equated with unspecified conceptions of autonomy, and autonomy is often equated with personhood. On this view, the loss of decision-making capacity is seen as a loss of autonomy, and the loss of autonomy subsumes a loss of personhood. An ethical concern arises at the intersection of those philosophical considerations with the legal considerations in informed consent. Because persons with inadequate decision-making capacity cannot provide legally effective consent, enrollment in research can occur only if a surrogate gives permission on the person's behalf. Federal regulations and resulting institutional policies allow permission from surrogates empowered under state law to consent to medical treatment procedures, typically in a hierarchy of legislatively prioritized relationships lacking regard for what the surrogate actually knows about the current research-related values and preferences of the potential subject. As a result, the research enterprise often countenances reliance on surrogates who have no relational or informational basis for an enrollment decision that aligns with the values and preferences of the subject. Arguing from the perspective that losing decision-making capacity does not alter the moral status of persons, and that respect for persons rather than respect for autonomy is the central ethical obligation, I assess the ethical implications of allowing persons with no knowledge of the values and preferences of the potential subject to make enrollment decisions, concluding that reliance on uninformed surrogates is not an ethically defensible approach to enrolling subjects in clinical research.

Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study

BACKGROUND

Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnout, yet research on the introduction of digital technologies in this field remains limited. The combination of continuous and objective wearable sensor data acquired from patients with deep learning techniques holds the potential to overcome the limitations of traditional psychiatric assessments and support clinical decision-making.

OBJECTIVE

This study aimed to develop and validate wearable-based deep learning models to comprehensively predict patient symptoms across various acute psychiatric wards in South Korea.

METHODS

Participants diagnosed with schizophrenia and mood disorders were recruited from 4 wards across 3 hospitals and prospectively observed using wrist-worn wearable devices during their admission period. Trained raters conducted periodic clinical assessments using the Brief Psychiatric Rating Scale, Hamilton Anxiety Rating Scale, Montgomery-Asberg Depression Rating Scale, and Young Mania Rating Scale. Wearable devices collected patients' heart rate, accelerometer, and location data. Deep learning models were developed to predict psychiatric symptoms using 2 distinct approaches: single symptoms individually (Single) and multiple symptoms simultaneously via multitask learning (Multi). These models further addressed 2 problems: within-subject relative changes (Deterioration) and between-subject absolute severity (Score). Four configurations were consequently developed for each scale: Single-Deterioration, Single-Score, Multi-Deterioration, and Multi-Score. Data of participants recruited before May 1, 2024, underwent cross-validation, and the resulting fine-tuned models were then externally validated using data from the remaining participants.

RESULTS

Of the 244 enrolled participants, 191 (78.3%; 3954 person-days) were included in the final analysis after applying the exclusion criteria. The demographic and clinical characteristics of participants, as well as the distribution of sensor data, showed considerable variations across wards and hospitals. Data of 139 participants were used for cross-validation, while data of 52 participants were used for external validation. The Single-Deterioration and Multi-Deterioration models achieved similar overall accuracy values of 0.75 in cross-validation and 0.73 in external validation. The Single-Score and Multi-Score models attained overall R² values of 0.78 and 0.83 in cross-validation and 0.66 and 0.74 in external validation, respectively, with the Multi-Score model demonstrating superior performance.

CONCLUSIONS

Deep learning models based on wearable sensor data effectively classified symptom deterioration and predicted symptom severity in participants in acute psychiatric wards. Despite lower computational costs, Multi models demonstrated equivalent or superior performance than Single models, suggesting that multitask learning is a promising approach for comprehensive symptom prediction. However, significant variations were observed across wards, which presents a key challenge for developing clinical decision support systems in acute psychiatric wards. Future studies may benefit from recurring local validation or federated learning to address generalizability issues.

Voluntariness of consent in paediatric HIV clinical trials: a mixed-methods, cross-sectional study of participants in the CHAPAS-4 and ODYSSEY trials in Uganda

OBJECTIVES

To examine the voluntariness of consent in paediatric HIV clinical trials and the associated factors.

DESIGN

Mixed-methods, cross-sectional study combining a quantitative survey conducted concurrently with indepth interviews.

SETTING AND PARTICIPANTS

From January 2021 to April 2021, we interviewed parents of children on first-line or second-line Anti-retroviral therapy (ART) in two ongoing paediatric HIV clinical trials [CHAPAS-4 (ISRCTN22964075) and ODYSSEY (ISRCTN91737921)] at the Joint Clinical Research Centre Mbarara, Uganda.

OUTCOME MEASURES

The outcome measures were the proportion of parents with voluntary consent, factors affecting voluntariness and the sources of external influence. Parents rated the voluntariness of their consent on a voluntariness ladder. Indepth interviews described participants' lived experiences and were aimed at adding context.

RESULTS

All 151 parents randomly sampled for the survey participated (84% female, median age 40 years). Most (67%) gave a fully voluntary decision, with a score of 10 on the voluntariness ladder, whereas 8% scored 9, 9% scored 8, 6% scored 7, 8% scored 6 and 2.7% scored 4. Trust in medical researchers (adjusted OR 9.90, 95% CI 1.01 to 97.20, p=0.049) and male sex of the parent (adjusted OR 3.66, 95% CI 1.00 to 13.38, p=0.05) were positively associated with voluntariness of consent. Prior research experience (adjusted OR 0.31, 95% CI 0.12 to 0.78, p=0.014) and consulting (adjusted OR 0.25. 95% CI 0.10 to 0.60, p=0.002) were negatively associated with voluntariness. Consultation and advice came from referring health workers (36%), spouses (29%), other family members (27%), friends (15%) and researchers (7%). The indepth interviews (n=14) identified the health condition of the child, advice from referring health workers and the opportunity to access better care as factors affecting the voluntariness of consent.

CONCLUSIONS

This study demonstrated a high voluntariness of consent, which was enhanced among male parents and by parents' trust in medical researchers. Prior research experience of the child and advice from health workers and spouses were negatively associated with the voluntariness of parents' consent. Female parents and parents of children with prior research experience may benefit from additional interventions to support voluntary participation.

A qualitative study of factors influencing ePHR adoption by caregivers and care providers of Alzheimer's patients: An extension of the unified theory of acceptance and use of technology model

Background and Aims

As the nowadays provision of many healthcare services relies on technology, a better understanding of the factors contributing to the acceptance and use of technology in health care is essential. For Alzheimer's patients, an electronic personal health record (ePHR) is one such technology. Stakeholders should understand the factors affecting the adoption of this technology for its smooth implementation, adoption, and sustainable use. So far, these factors have not fully been understood for Alzheimer's disease (AD)-specific ePHR. Therefore, the present study aimed to understand these factors in ePHR adoption based on the perceptions and views of care providers and caregivers involved in AD care.

Methods

This qualitative study was conducted from February 2020 to August 2021 in Kerman, Iran. Seven neurologists and 13 caregivers involved in AD care were interviewed using semi-structured and in-depth interviews. All interviews were conducted through phone contacts amid Covid-19 imposed restrictions, recorded, and transcribed verbatim. The transcripts were coded using thematic analysis based on the unified theory of acceptance and use of technology (UTAUT) model. ATLAS.ti8 was used for data analysis.

Results

The factors affecting ePHR adoption in our study comprised subthemes under the five main themes of performance expectancy, effort expectancy, social influence, facilitating conditions of the UTAUT model, and the participants' sociodemographic factors. From the 37 facilitating factors and 13 barriers identified for ePHR adoption, in general, the participants had positive attitudes toward the ease of use of this system. The stated obstacles were dependent on the participants' sociodemographic factors (such as age and level of education) and social influence (including concern about confidentiality and privacy). In general, the participants considered ePHRs efficient and useful in increasing neurologists' information about their patients and managing their symptoms in order to provide better and timely treatment.

Conclusion

The present study gives a comprehensive insight into the acceptance of ePHR for AD in a developing setting. The results of this study can be utilized for similar healthcare settings with regard to technical, legal, or cultural characteristics. To develop a useful and user-friendly system, ePHR developers should involve users in the design process to take into account the functions and features that match their skills, requirements, and preferences.

Stakeholders' Perspectives regarding Participation in Neuromodulation-Based Dementia Intervention Research

This study evaluated stakeholders' perspectives regarding participation in two hypothetical neuromodulation trials focused on individuals with Alzheimer's disease and related disorders (ADRDs). Stakeholders (i.e., individuals at risk for ADRDs [n = 56], individuals with experience as a caregiver for someone with a cognitive disorder [n = 60], and comparison respondents [n = 124]) were recruited via MTurk. Primary outcomes were willingness to enroll (or enroll one's loved one), feeling lucky to have the opportunity to enroll, and feeling obligated to enroll in two protocols (transcranial magnetic stimulation, TMS; deep brain stimulation, DBS). Relative to the Comparison group, the At Risk group endorsed higher levels of "feeling lucky" regarding both research protocols, and higher willingness to participate in the TMS protocol. These findings provide tentative reassurance regarding the nature of decision making regarding neurotechnology-based research on ADRDs. Further work is needed to evaluate the full range of potential influences on research participation.

Reconsidering Gold Standards for Surrogate Decision Making for People with Dementia

As dementia progresses and cognitive function declines, surrogate decision making becomes increasingly prevalent. By convention, there is a hierarchical approach to proxy decision making beginning with known wishes, followed by a substituted judgment standard, and then a best-interests standard. For people with dementia, discrepancy in proxy assessments is common and associated with negative behavioral outcomes. Therefore, optimal approaches to proxy decision making for people with dementia should instead prioritize and implement options that encourage direct participation of persons with dementia and standards that explicitly rely on consideration of longitudinal changes in values and preferences for persons with dementia.

Longitudinal differences in everyday preferences: Comparisons between people with cognitive impairment and their care partners

OBJECTIVES

Persons with progressive cognitive impairment (CI) increasingly rely on surrogate decision-makers for everyday activities. Yet, little is known about changes in everyday preferences over time or about concordance between persons with CI and their care partners regarding longitudinal changes.

METHODS

The sample included 48 dyads of persons with CI (Clinical Dementia Rating Scale score ≥0.5) and their care partners. The Preferences for Everyday Living Inventory was used to assess importance of preferences among persons with CI at baseline and follow-up (mean 486 days). Care partners separately completed concurrent proxy assessments. Mixed random and fixed effects longitudinal models were used to evaluate changes in ratings and concordance levels between persons with CI and care partners.

RESULTS

There were significant gender differences regarding importance ratings of "autonomous choice" and "social engagement" preferences over time: women with CI rated these preferences as more important across time as a whole. Higher levels of neuropsychiatric symptoms were associated with less importance of "social engagement" preferences across time as a whole for persons with CI and a more negative discrepancy between persons with CI and care partner proxy assessments as time went on.

CONCLUSION

This study yields new insights into predictors of longitudinal change in everyday preferences among persons with CI and their care partners. Although preferences were largely stable over time, there is increasing support for the relationship between differences in "social engagement" preferences and neuropsychiatric symptoms, which may have implications for monitoring and/or treatment in the context of cognitive impairment.

Data-driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records

Introduction

We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes.

Methods

A retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi‐specialty urban academic medical center in New York was conducted. Subphenotypes were derived using hierarchical clustering from 792 probable AD patients (cases) who had received at least one diagnosis of AD using their clinical data. The other 6795 patients, labeled as controls, were matched on age and gender with the cases and randomly selected in the ratio of 9:1. Prediction models with multiple ML algorithms were trained on this cohort using 5‐fold cross‐validation. XGBoost was used to rank the variable importance.

Results

Four subphenotypes were computationally derived. Subphenotype A (n = 273; 28.2%) had more patients with cardiovascular diseases; subphenotype B (n = 221; 27.9%) had more patients with mental health illnesses, such as depression and anxiety; patients in subphenotype C (n = 183; 23.1%) were overall older (mean (SD) age, 79.5 (5.4) years) and had the most comorbidities including diabetes, cardiovascular diseases, and mental health disorders; and subphenotype D (n = 115; 14.5%) included patients who took anti‐dementia drugs and had sensory problems, such as deafness and hearing impairment.

The 0‐year prediction model for AD risk achieved an area under the receiver operating curve (AUC) of 0.764 (SD: 0.02); the 6‐month model, 0.751 (SD: 0.02); the 1‐year model, 0.752 (SD: 0.02); the 2‐year model, 0.749 (SD: 0.03); and the 3‐year model, 0.735 (SD: 0.03), respectively. Based on variable importance, the top‐ranked comorbidities included depression, stroke/transient ischemic attack, hypertension, anxiety, mobility impairments, and atrial fibrillation. The top‐ranked medications included anti‐dementia drugs, antipsychotics, antiepileptics, and antidepressants.

Conclusions

Four subphenotypes were computationally derived that correlated with cardiovascular diseases and mental health illnesses. ML algorithms based on patient demographics, diagnosis, and treatment demonstrated promising results in predicting the risk of developing AD at different time points across an individual's lifespan.