Rapid and noninvasive estimation of human arsenic exposure based on 4-photo-set of the hand and foot photos through artificial intelligence.

Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmentation and characteristic pitted keratosis on the hands and feet, which often precede the onset of internal cancers. Traditionally, human arsenic exposure is estimated through arsenic levels in biological tissues; however, these methods are invasive and time-consuming. This study aims to develop a noninvasive approach to predict arsenic exposure using artificial intelligence (AI) to analyze photographs of hands and feet. By incorporating well water consumption data and arsenic concentration levels, we developed an AI algorithm trained on 9988 hand and foot photographs from 2497 subjects. This algorithm correlates visual features of palmoplantar hyperkeratosis with arsenic exposure levels. Four pictures per patient, capturing both ventral and dorsal aspects of hands and feet, were analyzed. The AI model utilized existing arsenic exposure data, including arsenic concentration (AC) and cumulative arsenic exposure (CAE), to make binary predictions of high and low arsenic exposure. The AI model achieved an optimal area under the curve (AUC) values of 0.813 for AC and 0.779 for CAE. Recall and precision metrics were 0.729 and 0.705 for CAE, and 0.750 and 0.763 for AC, respectively. While biomarkers have traditionally been used to assess arsenic exposure, efficient noninvasive methods are lacking. To our knowledge, this is the first study to leverage deep learning for noninvasive arsenic exposure assessment. Despite challenges with binary classification due to imbalanced and sparse data, this approach demonstrates the potential for noninvasive estimation of arsenic concentration. Future studies should focus on increasing data volume and categorizing arsenic concentration statistics to enhance model accuracy. This rapid estimation method could significantly contribute to epidemiological studies and aid physicians in diagnosis.

A discrete choice experiment to understand preferences of patients with type 2 diabetes about the attributes of GLP1 receptor agonists in Spain.

OBJECTIVE: To determine the preferences regarding injection, medication frequency and complexity of GLP1 receptor agonists among patients with type 2 diabetes, treatment-naïve for such drugs in Spain. Additionally, patients' willingness to pay according to these attributes was evaluated. METHODS: A discrete-choice experiment survey designed to evaluate patients' preferences over three attributes discriminating by age, sex and patients experience with previous injectable treatment was fulfilled by patients. The resulting model was analyzed using a conditional (fixed-effects) logistic regression. RESULTS: A total of 180 patients (63.35 ± 11.49 years, 63.28% men, 48.41% with previous cardiovascular disease, 54.69% with a time of evolution of diabetes >10 years) recruited from 5 health care centers in Spain completed the survey. Patients viewed positively weekly injections (vs daily injections), but rated negatively a complex preparation of the dose (vs simple preparation). Whereas naïve patients for injectable medications did not consider administration timing of importance, no naïve patients considered it relevant. No relevant differences were observed according to age or gender. Patients were willing to pay 83.25€for a "no preparation required" dose. No naïve and naïve patients were willing to pay 34.61€ and 14.35€; p = 0.000, to change daily injection for a weekly injection. CONCLUSIONS: Patients highly valued the avoidance of injections, with weekly dosing clearly preferred over daily dosing, as well as reducing the treatment complexity. These findings may provide a better understanding of what patients prefer and value in their treatment and provide guidance for clinicians making therapeutic decisions regarding treatments of patients with type 2 diabetes.

Development and validation of a multimodal deep learning framework for vascular cognitive impairment diagnosis.

Cerebrovascular disease (CVD) is the second leading cause of dementia worldwide. The accurate detection of vascular cognitive impairment (VCI) in CVD patients remains an unresolved challenge. We collected the clinical non-imaging data and neuroimaging data from 307 subjects with CVD. Using these data, we developed a multimodal deep learning framework that combined the vision transformer and extreme gradient boosting algorithms. The final hybrid model within the framework included only two neuroimaging features and six clinical features, demonstrating robust performance across both internal and external datasets. Furthermore, the diagnostic performance of our model on a specific dataset was demonstrated to be comparable to that of expert clinicians. Notably, our model can identify the brain regions and clinical features that significantly contribute to the VCI diagnosis, thereby enhancing transparency and interpretability. We developed an accurate and explainable clinical decision support tool to identify the presence of VCI in patients with CVD.

Unleashing the potential: the imperative of political support for health technology assessment in Iran.

Health Technology Assessment (HTA) is essential for evidence-based healthcare decision-making, yet its integration into Iran's healthcare system faces political and logistical challenges. Despite HTA's potential to improve resource allocation, limited awareness, data gaps, and competing priorities hinder its implementation. This commentary emphasizes the need for political support, advocating capacity-building, collaboration, and alignment with long-term health policies. Leveraging international partnerships and monitoring outcomes can enhance HTA's role in improving healthcare in Iran and contributing to global health advancements.

Co-Designing a Conversational Agent With Older Adults With Chronic Obstructive Pulmonary Disease Who Age in Place: Qualitative Study.

BACKGROUND: As a reaction to the global demographic increase in older adults (aged 60+ years), policy makers call for initiatives to enable healthy aging. This includes a focus on person-centered care and access to long-term care for older adults, such as developing different services and digital health technologies. This can enable patients to engage in their health and reduce the burden on the health care systems and health care professionals. The European Union project Smart Inclusive Living Environments (SMILE) focuses on well-being and aging in place using new digital health technologies. The novelty of the SMILE project is the use of a cocreational approach focused on the needs and preferences of older adults with chronic obstructive pulmonary disease (COPD) in technology development, to enhance access, adaptation, and usability and to reduce stigma. OBJECTIVE: The study aimed to describe the perspective, needs, and preferences of older adults living with COPD in the context of the design and development of a conversational agent. METHODS: This study carried out a data-driven thematic analysis of interview data from 11 cocreation workshops with 33 older adults living with COPD. RESULTS: The three particular features that the workshop participants wanted to implement in a new technology were (1) a "my health" function, to use technology to manage and learn more about their condition; (2) a "daily activities" function, including an overview and information about social and physical activities in their local area; and (3) a "sleep" function, to manage circadian rhythm and enhance sleep quality, for example, through online video guides. In total, 2 overarching themes were identified for the 3 functions: measurements, which were actively discussed and received mixed interest among the participants, and health literacy, due to an overall interest in learning more about their condition in relation to everyday life. CONCLUSIONS: The future design of digital health technology must embrace the complexities of the everyday life of an older adult living with COPD and cater to their needs and preferences. Measurements should be optional and personalized, and digital solutions should be used as a supplement to health care professionals, not as substitute.

Developing SysteMatic: Prevention, precision and equity by design for people living with multiple long-term conditions.

Background: The number of individuals living with multiple (≥2) long term conditions (MLTCs) is a growing global challenge. People with MLTCs experience reduced life expectancy, complex healthcare needs, higher healthcare utilisation, increased burden of treatment, poorer quality of life and higher mortality. Evolving technologies including artificial intelligence (AI) could address some of these challenges by enabling more preventive and better integrated care, however, they may also exacerbate inequities. Objective: We aim to deliver an equity focused, action-ready plan for transforming MLTC prevention and care, co-designed by people with lived experience of MLTCs and delivered through an Innovation Hub: SysteMatic. Design: Our Hub is being co-designed by people with lived experience of MLTCs, practitioners, academics and industry partners in Liverpool and Glasgow, UK. This work builds on research into mental-physical health interdependence across the life-course, and on mobilisation of large-scale quantitative data and technology validation in health and care systems serving deprived populations in Glasgow and Liverpool. We work with 3 population segments: 1) Children & Families: facing psychosocial and environmental challenges with lifetime impacts; 2). Working Life: people with poorly integrated mental, physical and social care; and 3) Pre-Frailty: older people with MLTCs. We aim to understand their experiences and in parallel look at routinely collected health data on people with MLTCs to help us identify targets for intervention. We are co-identifying opportunities for systems transformation with our patient partners, healthcare professionals and through discussion with companies and public-sector organisations. We are co-defining 3/5/7-year MLTC innovation/transition targets and sustainable learning approaches. Discussion: SysteMatic will deliver an actionable MLTC Innovation Hub strategic plan, with investment from the UK National Health Service, civic health and care partners, universities, and industry, enabling feedback of well-translated, patient and public prioritised problems into the engineering, physical, health and social sciences to underpin future equitable innovation delivery.

A compound analysis of medical device clinical trials registered in Africa on clinicaltrials.gov.

BACKGROUND: Africa, specifically the Sub-Saharan region, has had numerous medical technology clinical trials to address the various healthcare challenges around infectious diseases, non-communicable diseases, and nutritional disorders it is facing. Medical device clinical trials provide performance data in terms of safety, efficacy, and efficiency, which is a requirement before commercialization. Key players such as academicians, governments, international organizations, and funders collaborate to drive these trials, but their growth in Africa remains slower compared to other parts of the globe. This paper aims to evaluate the number of medical device clinical trials conducted in different African countries that are registered on the clinicaltrials.gov website. METHODS: Data on medical device clinical trials was mined from clinicaltrials.gov website accessed on 22nd September, 2022. The data extracted was analyzed and cleaned in Microsoft Excel and R. Countries were grouped into regions and descriptive statistical analyses for each region were done. Additionally, frequency distributions were also generated and no inferential statistical tests were performed, as the primary focus of this analysis was to describe the distribution of medical conditions across regions. RESULTS: Thirty-one African countries had registered medical device clinical trials on the website with the majority taking place in Egypt and South Africa. Medical device trials for heart related issues took longer to complete compared to other conditions. Malaria, HIV, and male circumcision related device trials were mainly conducted in Eastern and Southern Africa while trials related to dental, fertility, and obesity were concentrated in Northern Africa. Female reproductive health issues were studied equally across all regions. Some African countries did not have any trials registered on clinicaltrials.gov website. CONCLUSION: Findings from this study clearly show the disparity in the number, status, and duration of medical device clinical trials across various African countries.

Access in all areas? A round up of developments in market access and HTA: part 5.

In this latest update, we explore the recent announcement by Canada's Drug Agency (CDA-AMC, formerly CADTH) on their pilot to include the societal perspective in the evaluation of certain new medicines; a recent Office of Health Economics (OHE) report on the evaluation of HTA agency methods over time; and publications examining the impact of Project Orbis on patient access to oncology treatments.

GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed.

Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.

Stool-Based Testing for Post-Polypectomy Colorectal Cancer Surveillance Safely Reduces Colonoscopies: The Molecular Stool Testing for Colorectal Cancer Surveillance Study.

BACKGROUND & AIMS: Colonoscopy-based surveillance to prevent colorectal cancer (CRC) causes substantial burden for patients and health care. Stool tests may help to reduce surveillance colonoscopies by limiting colonoscopies to individuals at increased risk of advanced neoplasia. METHODS: This cross-sectional observational study included individuals aged 50-75 years with surveillance indication. Before bowel preparation, participants collected samples for a multitarget stool DNA test and 2 fecal immunochemical tests (FITs). Test accuracy was calculated for all surveillance indications. For the post-polypectomy indication only, which is the most common and is associated with a relatively low CRC risk, long-term impact of stool-based surveillance was evaluated with the Adenoma and Serrated Pathway to Colorectal Cancer model. Stool-based strategies were simulated to tune each test's positivity threshold to obtain strategies at least as effective as colonoscopy surveillance. RESULTS: There were 3453 individuals with results for all stool tests and colonoscopy; 2226 had previous polypectomy, 1003 had previous CRC, and 224 had a familial risk. Areas under the receiver operating characteristic curve for advanced neoplasia were 0.72 (95% CI, 0.69-0.75) for the multitarget stool DNA test, 0.61 (95% CI, 0.58-0.64) for the FIT OC-SENSOR (Eiken Chemical Co, Tokyo, Japan) and 0.59 (95% CI, 0.56-0.61) for the FIT FOB-Gold (Sentinel, Milan, Italy). Stool-based post-polypectomy surveillance strategies at least as effective as colonoscopy surveillance reduced the number of colonoscopies by 15%-41% and required 5.6-9.5 stool tests over a person's lifetime. Multitarget stool DNA-based surveillance was more costly than colonoscopy surveillance, whereas FIT-based surveillance saved costs. CONCLUSIONS: This study found that stool-based post-polypectomy surveillance strategies can be safe and cost-effective, with potential to reduce the number of colonoscopies by up to 41%. CLINICALTRIALS: gov, Number: NCT02715141.

What methods are currently available for incorporating implementation considerations within the economic evaluation of health technologies? A scoping review.

BACKGROUND: When clinically effective, cost-effective health interventions are not fully implemented in clinical practice, population health suffers. Economic factors are among the most commonly cited reasons for suboptimal implementation. Despite this, implementation and economic evaluation are not routinely performed in conjunction with one another. This review sought to identify and describe what methods are available for researchers to incorporate implementation within economic evaluation, how these methods differ, when they should be used, and where gaps remain. METHODS: We conducted a scoping review using systematic methods. A pearl-growing approach was used to identify studies. References and citations were identified using Web of Science and Scopus. We included for review any study that contained terms relating to economic evaluation and a series of implementation-related terms in the title or abstract. The search was conducted and validated using two independent researchers. RESULTS: Our review identified 42 unique studies that included a methodology for combining implementation and economic evaluation. The methods identified could be categorized into four broad themes: (i) policy cost-effectiveness approach (11 studies), (ii) value of information and value of implementation approach (16 studies), (iii) mixed methods approach (6 studies), and (iv) costing approach (9 studies). We identified a trend over time from methods that adopted the policy cost-effectiveness approach to methods that considered the trade-off between the value of information and value of implementation. More recently, mixed methods approaches to incorporate economic evaluation and implementation have been developed, alongside methods to define, measure and cost individual components of the implementation process for use in economic evaluation. CONCLUSION: Our review identified a range of methods currently available for researchers considering implementation alongside economic evaluation. There is no single method or tool that can incorporate all the relevant issues to fully incorporate implementation within an economic evaluation. Instead, there are a suite of tools available, each of which can be used to answer a specific question relating to implementation. Researchers, reimbursement agencies and national and local decision-makers need to consider how best to utilize these tools to improve implementation.

Impact of Health Informatics Analyst Education on Job Role, Career Transition, and Skill Development: Survey Study.

Background: Professionals with expertise in health informatics play a crucial role in the digital health sector. Despite efforts to train experts in this field, the specific impact of such training, especially for individuals from diverse academic backgrounds, remains undetermined. Objective: This study therefore aims to evaluate the effectiveness of an intensive health informatics training program on graduates with respect to their job roles, transitions, and competencies and to provide insights for curriculum design and future research. Methods: A survey was conducted among 206 students who completed the Advanced Health Informatics Analyst program between 2018 and 2022. The questionnaire comprised four categories: (1) general information about the respondent, (2) changes before and after program completion, (3) the impact of the program on professional practice, and (4) continuing education requirements. Results: The study received 161 (78.2%) responses from the 206 students. Graduates of the program had diverse academic backgrounds and consequently undertook various informatics tasks after their training. Most graduates (117/161, 72.7%) are now involved in tasks such as data preprocessing, visualizing results for better understanding, and report writing for data processing and analysis. Program participation significantly improved job performance (P=.03), especially for those with a master's degree or higher (odds ratio 2.74, 95% CI 1.08-6.95) and those from regions other than Seoul or Gyeonggi-do (odds ratio 10.95, 95% CI 1.08-6.95). A substantial number of respondents indicated that the training had a substantial influence on their career transitions, primarily by providing a better understanding of job roles and generating intrinsic interest in the field. Conclusions: The integrated practical education program was effective in addressing the diverse needs of trainees from various fields, enhancing their capabilities, and preparing them for the evolving industry demands. This study emphasizes the value of providing specialized training in health informatics for graduates regardless of their discipline.

Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study.

BACKGROUND: Depressive disorders have substantial global implications, leading to various social consequences, including decreased occupational productivity and a high disability burden. Early detection and intervention for clinically significant depression have gained attention; however, the existing depression screening tools, such as the Center for Epidemiologic Studies Depression Scale, have limitations in objectivity and accuracy. Therefore, researchers are identifying objective indicators of depression, including image analysis, blood biomarkers, and ecological momentary assessments (EMAs). Among EMAs, user-generated text data, particularly from diary writing, have emerged as a clinically significant and analyzable source for detecting or diagnosing depression, leveraging advancements in large language models such as ChatGPT. OBJECTIVE: We aimed to detect depression based on user-generated diary text through an emotional diary writing app using a large language model (LLM). We aimed to validate the value of the semistructured diary text data as an EMA data source. METHODS: Participants were assessed for depression using the Patient Health Questionnaire and suicide risk was evaluated using the Beck Scale for Suicide Ideation before starting and after completing the 2-week diary writing period. The text data from the daily diaries were also used in the analysis. The performance of leading LLMs, such as ChatGPT with GPT-3.5 and GPT-4, was assessed with and without GPT-3.5 fine-tuning on the training data set. The model performance comparison involved the use of chain-of-thought and zero-shot prompting to analyze the text structure and content. RESULTS: We used 428 diaries from 91 participants; GPT-3.5 fine-tuning demonstrated superior performance in depression detection, achieving an accuracy of 0.902 and a specificity of 0.955. However, the balanced accuracy was the highest (0.844) for GPT-3.5 without fine-tuning and prompt techniques; it displayed a recall of 0.929. CONCLUSIONS: Both GPT-3.5 and GPT-4.0 demonstrated relatively reasonable performance in recognizing the risk of depression based on diaries. Our findings highlight the potential clinical usefulness of user-generated text data for detecting depression. In addition to measurable indicators, such as step count and physical activity, future research should increasingly emphasize qualitative digital expression.

Analytical Methods for Comparing Uncontrolled Trials With External Controls From Real-World Data: A Systematic Literature Review and Comparison With European Regulatory and Health Technology Assessment Practice.

OBJECTIVES: This study aimed to provide an overview of analytical methods in scientific literature for comparing uncontrolled medicine trials with external controls from individual patient data real-world data (IPD-RWD) and to compare these methods with recommendations made in guidelines from European regulatory and health technology assessment (HTA) organizations and with their evaluations described in assessment reports. METHODS: A systematic literature review (until March 1, 2023) in PubMed and Connected Papers was performed to identify analytical methods for comparing uncontrolled trials with external controls from IPD-RWD. These methods were compared descriptively with methods recommended in method guidelines and encountered in assessment reports of the European Medicines Agency (2015-2020) and 4 European HTA organizations (2015-2023). RESULTS: Thirty-four identified scientific articles described analytical methods for comparing uncontrolled trial data with IPD-RWD-based external controls. The various methods covered controlling for confounding and/or dependent censoring, correction for missing data, and analytical comparative modeling methods. Seven guidelines also focused on research design, RWD quality, and transparency aspects, and 4 of those recommended analytical methods for comparisons with IPD-RWD. The methods discussed in regulatory (n = 15) and HTA (n = 35) assessment reports were often based on aggregate data and lacked transparency owing to the few details provided. CONCLUSIONS: Literature and guidelines suggest a methodological approach to comparing uncontrolled trials with external controls from IPD-RWD similar to target trial emulation, using state-of-the-art methods. External controls supporting regulatory and HTA decision making were rarely in line with this approach. Twelve recommendations are proposed to improve the quality and acceptability of these methods.

Real time monitoring of respiratory viral infections in cohort studies using a smartphone app.

Cohort studies investigating respiratory disease pathogenesis aim to pair mechanistic investigations with longitudinal virus detection but are limited by the burden of methods tracking illness over time. In this study, we explored the utility of a purpose-built AERIAL TempTracker smartphone app to assess real-time data collection and adherence monitoring and overall burden to participants, while identifying symptomatic respiratory illnesses in two birth cohort studies. We observed strong adherence with daily app usage over the six-month study period, with positive feedback from participant families. A total of 648 symptomatic respiratory illness events were identified with significant variability between individuals in the frequency, duration, and virus detected. Collectively, our data show that a smartphone app provides a reliable method to capture the longitudinal virus data in cohort studies which facilitates the understanding of early life infections in chronic respiratory disease development.

A roadmap towards implementing health technology assessment in Oman.

PURPOSE: Health technologies are advancing rapidly and becoming more expensive, posing a challenge for financing healthcare systems. Health technology assessment (HTA) improves the efficiency of resource allocation by facilitating evidence-informed decisions on the value of health technologies. Our study aims to create a customized HTA roadmap for Oman based on a gap analysis between the current and future status of HTA implementation. DESIGN/METHODOLOGY/APPROACH: We surveyed participants of an advanced HTA training program to assess the current state of HTA implementation in Oman and explore long-term goals. A list of draft recommendations was developed in areas with room for improvement. The list was then validated for its feasibility in a round table discussion with senior health policy experts to conclude on specific actions for HTA implementation. FINDINGS: Survey results aligned well with expert discussions. The round table discussion concluded with a phasic action plan for HTA implementation. In the short term (1-2 years), efforts will focus on building capacity through training programs. For medium-term actions (3-5 years), plans include expanding the HTA unit and introducing multiple cost-effectiveness thresholds while from 6-10 years, publishing of HTA recommendations, critical appraisal reports, and timelines is recommended. ORIGINALITY/VALUE: Although the HTA system in Oman is still in its early stages, strong initiatives are being taken for its advancement. This structured approach ensures a comprehensive integration of HTA into the healthcare system, enhancing decision-making and promoting a sustainable, evidence-based system addressing the population's needs.

Leveraging Personal Technologies in the Treatment of Schizophrenia Spectrum Disorders: Scoping Review.

BACKGROUND: Digital mental health is a rapidly growing field with an increasing evidence base due to its potential scalability and impacts on access to mental health care. Further, within underfunded service systems, leveraging personal technologies to deliver or support specialized service delivery has garnered attention as a feasible and cost-effective means of improving access. Digital health relevance has also improved as technology ownership in individuals with schizophrenia has improved and is comparable to that of the general population. However, less digital health research has been conducted in groups with schizophrenia spectrum disorders compared to other mental health conditions, and overall feasibility, efficacy, and clinical integration remain largely unknown. OBJECTIVE: This review aims to describe the available literature investigating the use of personal technologies (ie, phone, computer, tablet, and wearables) to deliver or support specialized care for schizophrenia and examine opportunities and barriers to integrating this technology into care. METHODS: Given the size of this review, we used scoping review methods. We searched 3 major databases with search teams related to schizophrenia spectrum disorders, various personal technologies, and intervention outcomes related to recovery. We included studies from the full spectrum of methodologies, from development papers to implementation trials. Methods and reporting follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: This search resulted in 999 studies, which, through review by at least 2 reviewers, included 92 publications. Included studies were published from 2010 to 2023. Most studies examined multitechnology interventions (40/92, 43%) or smartphone apps (25/92, 27%), followed by SMS text messaging (16/92, 17%) and internet-based interventions (11/92, 12%). No studies used wearable technology on its own to deliver an intervention. Regarding the stage of research in the field, the largest number of publications were pilot studies (32/92, 35%), followed by randomized control trials (RCTs; 20/92, 22%), secondary analyses (16/92, 17%), RCT protocols (16/92, 17%), development papers (5/92, 5%), and nonrandomized or quasi-experimental trials (3/92, 3%). Most studies did not report on safety indices (55/92, 60%) or privacy precautions (64/92, 70%). Included studies tend to report consistent positive user feedback regarding the usability, acceptability, and satisfaction with technology; however, engagement metrics are highly variable and report mixed outcomes. Furthermore, efficacy at both the pilot and RCT levels report mixed findings on primary outcomes. CONCLUSIONS: Overall, the findings of this review highlight the discrepancy between the high levels of acceptability and usability of these digital interventions, mixed efficacy results, and difficulties with sustained engagement. The discussion highlights common patterns that may underscore this observation in the field; however, as this was a scoping review, a more in-depth systematic review or meta-analysis may be required to better understand the trends outlined in this review.

A network meta-analysis of KarXT and commonly used pharmacological interventions for schizophrenia.

BACKGROUND: Dopaminergic antipsychotics for schizophrenia have modest effects on symptoms and can cause important side effects. KarXT is an investigational drug for schizophrenia with a novel mechanism targeting muscarinic receptors that may limit these side effects. METHODS: We conducted a systematic review and Bayesian random-effects network meta-analyses of short-term RCTs (3-8 weeks) that enrolled adults with schizophrenia. We compared KarXT to aripiprazole, risperidone, and olanzapine. We sought evidence for symptoms (Positive and Negative Symptoms Scale [PANSS]), weight gain, and all-cause discontinuation. RESULTS: We included 33 trials with 7193 participants. For total, positive, and negative symptoms, KarXT and the three antipsychotics were significantly more efficacious than placebo (mean difference [MD] vs placebo range for total symptoms: -10.67 to -8.05; positive symptoms: -3.46 to -2.53; negative symptoms: -1.99 to -1.44) but not significantly different from each other. KarXT was ranked as least likely to lead to weight gain. This was significant versus risperidone (-2.06 kg; 95 % CrI: -3.28, -0.87) and olanzapine (-2.86 kg; 95 % CrI: -3.97, -1.82). However, KarXT was ranked highest for all-cause discontinuation. This was significant versus risperidone (RR: 0.64; 95 % CrI: 0.46, 0.89) and olanzapine (RR: 0.6; 95 % CrI: 0.44, 0.83). CONCLUSIONS: KarXT and commonly used antipsychotics were more efficacious than placebo at reducing symptoms, but there were no clear differences in short-term efficacy among the active interventions. KarXT was less likely to cause weight gain, an important outcome for those with schizophrenia; short-term data do not permit evaluation of the risk for tardive dyskinesia. Long-term data are needed.

Blood Bonds: Transforming Blood Donation Through Innovation, Inclusion, and Engagement.

The journey of receiving blood as a patient with transfusion-dependent beta thalassemia has profoundly shaped my understanding of the life-saving power of blood donation. This personal experience underscores the critical importance of blood donors, not just for individual recipients but for the broader community, enhancing public health, productivity, and well-being. There are several challenges to securing a blood donor pool in current health care climate. Solutions that focus on the engagement of donors, clinicians, and patients are key to improving the donor pool and utilizing the blood supply in a judicious manner.