The next generation of evidence-based medicine

Service Lines, Neurology, and Academic Medicine: Departmental Perspectives, Implementation Strategies, and Keys to Success

Service lines are increasingly common for organizing multidisciplinary patient care. Concerns regarding impacts of neuroscience service lines were voiced at several national neurology department chair summits, prompting the American Academy of Neurology to convene a Service Lines Workgroup. Neurology department leaders nationally at institutions that had created or considered a neuroscience service line were interviewed to elicit their experiences and lessons learned. Potential benefits identified stemmed from additional resources that the service line structure yielded (patient navigators, quality improvement staff, technicians) and strengthening of cross-department collaboration. Potential pitfalls included top-down institutional decision-making regarding service line creation, lack of explicit goals, late involvement of neurology, imbalances in neurology representation in leadership, unclear impacts on department finances, and lack of education and research mission integration into service lines. Establishing a satisfactory decision-making structure in a matrixed arrangement and ensuring that funds flow allocations acknowledged neurology's "upstream" contributions were also challenges.

Ongoing prospective studies on reirradiation: A systematic review of a clinical trials database

INTRODUCTION

Reirradiation has gained increasing interest, as advances in systemic therapy increase the survival of patients with cancer, and modern radiation techniques allow more precise treatments. However, high-quality prospective evidence on the safety and efficacy of reirradiation to guide clinical practice remains scarce. This systematic review evaluates ongoing prospective studies on reirradiation to identify research gaps and priorities.

METHODS

A systematic review of ClinicalTrials.gov was conducted on July 11, 2024, using search terms related to reirradiation. Inclusion criteria were prospective studies that were "recruiting," "not yet recruiting," or "active, not recruiting." Studies with published results, retrospective, and in-silico studies were excluded. The review followed PRISMA 2020 guidelines and recommendations for systematic searches of clinical trial registries.

RESULTS

Among 1026 identified studies, 307 were screened, 99 were included. Fourty (40%) focused on central nervous system (CNS), 23 (23%) head and neck, and 17 (17%) on pelvic reirradiation. Most studies (90%) were interventional, with 32 (32%) phase II and 4 (4%) phase III trials. Sixteen trials were randomized (RCTs), including the 4 phase III trials for recurrent glioblastoma, rectal and nasopharyngeal cancer. Ten dose escalation trials focus on recurrent prostate, rectal, and non-small cell lung cancer as well as glioma. Modern high-precision radiotherapy techniques were frequently used, with 21 (21%) studies using stereotactic radiotherapy and 17 (17%) using particle therapy. Combinations with systemic therapies were investigated in 41 (41%) studies.

CONCLUSION

Ongoing studies most frequently focus on CNS, head and neck, and pelvic reirradiation. There remains a critical need for RCTs, in particular for lung, breast, and gynecological cancers. Dose escalation trials, application of precision radiation techniques and combinations with modern systemic therapy may help define the optimal multimodality treatment schedules.

The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems

Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications focused on supply chain operation, patient management, and capacity building, among other use cases, can improve the health system and public health performance. We present the Causal Foundry Artificial Intelligence and Reinforcement Learning platform, which allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices, and to send personalized recommendations based on past data and predictions, can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be decisive, is discussed. This framework is similarly applicable to improving efficiency in health systems where scarcity is not an issue.

Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes

INTRODUCTION

Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects. AI-supported nanomedicine faces challenges like data fusion, ethics, and regulation, requiring better tools and interdisciplinary collaboration. This review highlights the importance of AI regarding patient care and urges scientists, medical professionals, and regulators to adopt AI for better outcomes.

AREAS COVERED

Personalized Nanomedicine, Material Discovery, AI-Driven Therapeutics, Data Integration, Drug Delivery, Patient Centric Care.

EXPERT OPINION

Today, AI can improve personalized health wellness through the discovery of new types of drug nanocarriers, nanomedicine of specific properties to tackle targeted medical needs, and an increment in efficacy along with safety. Nevertheless, problems such as ethical issues, data security, or unbalanced data sets need to be addressed. Potential future developments involve using AI and quantum computing together and exploring telemedicine i.e. the Internet-of-Medical-Things (IoMT) approach can enhance the quality of patient care in a personalized manner by timely decision-making.

Nanotechnological approaches to improve corticosteroids ocular therapy

The administration of corticosteroids is the first-line treatment of the clinical conditions with ocular inflammation. Nonetheless, ocular physiological mechanisms, anatomical barriers and corticosteroid properties prevent it from reaching the target site. Thus, frequent topical administered doses or ocular injections are required, leading to a higher risk of adverse events and poor patient compliance. Designing novel drug delivery systems based on nanotechnological tools is a useful approach to overcome disadvantages associated with the ocular delivery of corticosteroids. Nanoparticle-based drug delivery systems represent an alternative to the current dosage forms for the ocular administration of corticosteroids, since due to their particle size and the properties of their materials, they can increase their solubility, improve ocular permeability, control their release and increase bioavailability after their ocular administration. In this way, lipid and polymer-based nanoparticles have been the main strategies developed, giving rise to novel patent applications to protect these innovative drug delivery systems as a product, its preparation or administration method. Additionally, it should be noted that at least 10 clinical trials are being carried out to evaluate the ocular application of different pharmaceutical formulations based on corticosteroid-loaded nanoparticles. Through a comprehensive and extensive analysis, this review highlights the impact of nanotechnology applications in ocular inflammation therapy with corticosteroids.

Advancing the role of real-world evidence in comparative effectiveness research

Aim: Comparative effectiveness research (CER) is essential for making informed decisions about drug access. It provides insights into the effectiveness and safety of new drugs compared with existing treatments, thereby guiding better healthcare decisions and ensuring that new therapies meet the real-world needs of patients and healthcare systems. Objective: To provide a tool that assists analysts and decision-makers in identifying the most suitable analytical approach for answering a CER question, given specific data availability contexts. Methods: A systematic literature review of the scientific literature was performed and existing regulatory and health technology assessment (HTA) guidance were evaluated to identify and compare recommendations and best practices. Based on this review a methods flowchart that synthesizes current practices and requirements was proposed. Results: The review did not find any papers that clearly identified the most appropriate analytical approach for answering CER questions under various conditions. Therefore, a methods flowchart was designed to inform analyst and decision makers choices starting from a well-defined scientific question. Conclusion: The proposed methods flowchart offers clear guidance on CER methodologies across a range of settings and research needs. It begins with a well-defined research question and considers multiple feasibility aspects related to CER. This tool aims to standardize methods, ensure rigorous and consistent research quality and promote a culture of evidence-based decision-making in healthcare.

Rapid SARS-CoV-2 sensing through oxygen reduction reaction catalysed by Au@Pt/Au core@shell nanoparticles

The development of rapid, accurate, sensitive, and low-cost diagnostic methods for COVID-19 detection in real-time is the unique way to control infection sources and monitor illness progression. In this work, we propose an electrochemical biosensor for the rapid and accuracy diagnosis of COVID-19, through the determination of ORF1ab specific sequence. The biosensor is based on the immobilization of a thiolated sequence partially complementary (domain 1) to ORF1ab on gold screen-printed electrodes and the use of bifunctional Au@Pt/Au core@shell nanoparticles modified with a second thiolated sequence partially complementary to ORF1ab (domain 2) as electrochemical indicator of the hybridization of DNA sequences. The synthesized Au@Pt/Au nanoparticles consist of an Au core, a shell of Pt (Au@Pt NPs), that provides an excellent electrocatalytic activity toward the oxygen reduction reaction (ORR) even after formation of hybrid biomaterials by modification, through the Au protuberances growth on the NPs surface, with an oligonucleotide with recognition ability. The ORR electrochemical activity, enhanced by the label element (Au@Pt/Au NPs), has been employed, for the first time, as indicator of the hybridization event. Based on this strategy, target sequences of the SARS-CoV-2 virus have been detected with a detection limit of 32 pM. The selectivity of the biosensor was confirmed by analysing ORF1ab sequence in the presence of DNA sequences from other viruses. The biosensor has been successfully applied to the direct detection of the virus in non-amplified samples of nasopharyngeal swabs from infected and non-infected patients. Results compare well with those obtained through RT-qPCR but our method is more rapid since does not need any amplification process.

Translating proof-of-concept for platelet slip into improved antithrombotic therapeutic regimens

Platelets are central to thrombosis. Research at the intersection of biological and physical sciences provides proof-of-concept for shear rate-dependent platelet slip at vascular stenosis and near device surfaces. Platelet slip extends the observed biological "slip-bonds" to the boundary of functional gliding without contact. As a result, there is diminished engagement of the coagulation cascade by platelets at these surfaces. Comprehending platelet slip would more precisely direct antithrombotic regimens for different shear environments, including for percutaneous coronary intervention (PCI). In this brief report we promote translation of the proof-of-concept for platelet slip into improved antithrombotic regimens by: (1) reviewing new supporting basic biological science and clinical research for platelet slip; (2) hypothesizing the principal variables that affect platelet slip; (3) applying the consequent construct model in support of-and in some cases to challenge-relevant contemporary guidelines and their foundations (including for urgent, higher-risk PCI); and (4) suggesting future research pathways (both basic and clinical). Should future research demonstrate, explain and control platelet slip, then a paradigm shift for choosing and recommending antithrombotic regimens based on predicted shear rate should follow. Improved clinical outcomes with decreased complications accompanying this paradigm shift for higher-risk PCI would also result in substantive cost savings.

Charting the therapeutic landscape: a comprehensive evidence map on medical cannabis for health outcomes

The therapeutic potential of medical cannabis has garnered significant attention in recent years, prompting an urgent need for a comprehensive understanding of its effectiveness across various health outcomes. This article presents an Evidence Map that systematically summarizes clinical evidence on the use of medical cannabis, including the health conditions it addresses, the interventions employed, and the resulting clinical outcomes. The objective is to map the effectiveness of medical cannabis in relation to a wide range of health outcomes. The systematic review process involved two independent, blinded literature researchers who screened the search output using Rayyan software. For studies deemed relevant, full texts were obtained to clarify inclusion or exclusion criteria, and any disagreements were resolved through group discussion. Out of 1,840 initial references, 279 potential studies were selected and read in full, resulting in the inclusion of 194 studies in this evidence map. The results highlight the use of various cannabis formulations, including those based on isolated cannabidiol (CBD). Seventy-one distinct health outcomes were identified in the systematic reviews, with the most reported outcomes being related to various types of pain and patient safety. Other frequently studied outcomes included appetite regulation, chemotherapy-induced nausea and vomiting, and muscle spasticity. Notably, 278 out of 489 descriptions of treatment effects for these health outcomes reported either "Positive" or "Potentially Positive" effects. When considering only high-quality systematic reviews, as evaluated by the AMSTAR 2 tool, 42 out of 67 descriptions of treatment effects for up to 20 health outcomes were classified as "Positive" or "Potentially Positive." These outcomes included pain, insomnia, seizures, anxiety, muscle spasticity, multiple sclerosis, urinary incontinence, anorexia, and patient safety. This evidence map provides a comprehensive overview of the current clinical evidence on medical cannabis, highlighting its potential therapeutic benefits across a range of health conditions and emphasizing the need for further high-quality research.

Risk factors for surgical site infection after general surgery in HIV-infected patients: a retrospective study

BACKGROUND

As the number of HIV-infected patients increased, the number of patients requiring general surgery has subsequently increased. However, impairment of immune function due to HIV infection increases the risk of postoperative surgical-site infection and significant harm to patient health. This study aimed to examine the risk factors for surgical-site infection after general surgery.

METHODS

The patients' data were from Zunyi fourth hospital medical information system. Machine learning based Boruta algorithm were used for variable screening. Univariable and multivariable logistic regression and restricted cubic spline analysis were performed to examine the relationship between significant variables and surgical-site infection.

RESULTS

A total of 125 general surgery postoperative HIV-infected patients participated in the study. Surgical-site pathogen culture identified Escherichia coli, Klebsiella pneumoniae, and mixed bacteria as the three most common pathogens causing Surgical-site infection. Univariable and multivariable logistic regression analysis to adjust for risk factors identified type III surgical incision (OR = 9.92, 95% CI = 1.28-76.75) and elevated preoperative white blood cell (WBC) count (OR = 1.30, 95% CI = 1.12-1.51) as independent risk factors for postoperative surgical-site infection, whereas CD4 + T lymphocyte count greater than 400 cells/µL was identified as a protective factor (OR = 0.23, 95% CI = 0.09-0.60) while. The restricted cubic spline analysis results directly reflected the dose-response relationship between continuous variables and postoperative surgical-site infection.

CONCLUSIONS

Type III incision and an elevated WBC count pose a higher risk of postoperative surgical-site infection. A CD4 + T lymphocyte counts greater than 400 cells/µL provided a protective effect of lower risk of surgical site infection. Preoperative serum neutrophil percentage, albumin level, red blood cell count, and serum urea level within a specific range were beneficial in reducing the risk of incisional infections. Our research provides a theoretical basis for clinical practice.

Advancing Precision Oncology with Digital and Virtual Twins: A Scoping Review

Digital twins (DTHs) and virtual twins (VTHs) in healthcare represent emerging technologies towards precision medicine, providing opportunities for patient-centric healthcare. Our scoping review aimed to map the current DTH and VTH technologies in oncology, summarize their technical solutions, and assess their credibility. A systematic search was conducted in the main bibliographic databases, identifying 441 records, of which 30 were included. The studies covered a wide range of cancers, including breast, lung, colorectal, and gastrointestinal malignancies, with DTH and VTH applications focusing on diagnosis, therapy, and monitoring. The results revealed heterogeneity in targeted topics, technical approaches, and outcomes. Most twining solutions use synthetic or limited real-world data, raising concerns regarding their reliability. Few studies have integrated real-time data and machine learning for predictive modeling. Technical challenges include data integration, scalability, and ethical considerations, such as data privacy and security. Moreover, the evidence lacks sufficient clinical validation, with only partial credibility in most cases. Our findings underscore the need for multidisciplinary collaboration among end-users and developers to address the technical and ethical challenges of DTH and VTH systems. Although promising for the future of personalized oncology, substantial steps are required to move beyond experimental frameworks and to achieve clinical implementation.

Interoception, Insula, and Autonomic Integration: Relevance to the Expression and Treatment of Psychiatric Symptoms

The mind is embodied, and this is relevant to mental health and psychiatric illness. Interoception is the body-to-brain axis of sensory information flow and its representation at the neural and psychological levels. Interoception is the purported basis for motivation and emotion, and as an inescapable stream of information about the health and functioning of the whole organism, it is proposed to be the foundation to the conscious unitary sense of self. Correspondingly, this central representation of internal state is relevant to understanding the expression of psychological symptoms and behaviours and ultimately psychiatric disorders. Here we review interoception, particularly from a cardiovascular perspective, and how understanding theoretical neural and psychological aspects of interoception relates to perceptions, thoughts, and feelings. We examine how perturbations in interoceptive processing are expressed in mental symptoms and psychiatric disorders and show how this knowledge may yield new treatment targets.

Nutritional guidance needs and influence factors for gastric cancer survivors in primary healthcare setting: a cross-sectional survey

PURPOSE

This study aimed to build a comprehensive understanding of the nutritional guidance needs of gastric cancer survivors in the primary healthcare setting that could be fulfilled by community health service centers as part of their primary healthcare services.

METHODS

Gastric cancer survivors were invited to participate in a questionnaire-based survey by convenience sampling method from the community health center. Relationships between nutritional demand and potential influence factors were examined by a multivariable logistic regression model.

RESULTS

A total of 200 gastric cancer survivors were recruited from the community health service center in Shanghai, China, from whom we obtained 194 valid questionnaires, resulting in a 97.0% response rate. Of these gastric survivor participants, 48 individuals (24.7%) expressed a need for nutritional guidance administered by community health service centers, whereas 146 participants (75.3%) held the perspective that such guidance was unnecessary. Preferences for nutritional guidance included having a dietitian as the provider (68.8%), home-based engagement (72.9%), face-to-face consultation (68.8%), individual counseling (87.5%), and beginning immediately post-discharge (89.6%). A notable reluctance (66.7%) towards financial contribution was also observed. After adjusting for confounders, participants with higher income level (odds ratio (OR) = 4.45, 95% confidence interval (CI) = 1.39-15.50), history of food intake reduction (OR = 14.96, 95% CI = 3.49-82.28), and gastrointestinal symptoms (OR = 4.40, 95% CI = 1.35-14.33) were more likely to seek nutritional guidance.

CONCLUSIONS

Gastric cancer survivors have a certain need for nutritional guidance administered by primary health service centers. Personal guidance by the primary health service center should be provided to gastric cancer survivors to support their dietary and nutritional intake needs.

IMPLICATIONS FOR CANCER SURVIVORS

There is a need to develop and implement nutritional guidance programs in community health service centers as part of their primary healthcare services for gastric cancer survivors.

Ethical challenges for Health Technology Assessment (HTA) in the evolving evidence landscape

Since its inception, Health Technology Assessment (HTA) has typically determined the value of a technology by collecting information derived from randomized clinical trials (RCTs), in line with the principles of evidence-based medicine (EBM). However, data from RCTs did not constitute the sole source of information, as other types of evidence (such as primary qualitative research) have often been utilized. Recent advances in both generating and collecting other types of evidence are broadening the landscape of evidence, adding complexity to the discussion of "robustness of evidence." What are the consequences of these recent developments for the methodology and conduct of HTA, the HTA community, and its ethical commitments? The aim of this article is to explore some ethical challenges that are emerging in the current evolving evidence landscape, particularly changes in evidence generation and collection (e.g., diversification of data sources), and shifting standards of evidence in the field of HTA (e.g., increasing acceptability of evidence that is thought of as lower quality). Our conclusion is that deciding how to best maintain trustworthiness is common to all these issues.

The ESMO Tumour-Agnostic Classifier and Screener (ETAC-S): a tool for assessing tumour-agnostic potential of molecularly guided therapies and for steering drug development

BACKGROUND

Advances in precision oncology led to approval of tumour-agnostic molecularly guided treatment options (MGTOs). The minimum requirements for claiming tumour-agnostic potential remain elusive.

METHODS

The European Society for Medical Oncology (ESMO) Precision Medicine Working Group (PMWG) coordinated a project to optimise tumour-agnostic drug development. International experts examined and summarised the publicly available data used for regulatory assessment of the tumour-agnostic indications approved by the US Food and Drug Administration and/or the European Medicines Agency as of December 2023. Different scenarios of minimum objective response rate (ORR), number of tumour types investigated, and number of evaluable patients per tumour type were assessed for developing a screening tool for tumour-agnostic potential. This tool was tested using the tumour-agnostic indications approved during the first half of 2024. A taxonomy for MGTOs and a framework for tumour-agnostic drug development were conceptualised.

RESULTS

Each tumour-agnostic indication had data establishing objective response in at least one out of five patients (ORR ≥ 20%) in two-thirds (≥4) of the investigated tumour types, with at least five evaluable patients in each tumour type. These minimum requirements were met by tested indications and may serve as a screening tool for tumour-agnostic potential, requiring further validation. We propose a conceptual taxonomy classifying MGTOs based on the therapeutic effect obtained by targeting a driver molecular aberration across tumours and its modulation by tumour-specific biology: tumour-agnostic, tumour-modulated, or tumour-restricted. The presence of biology-informed mechanistic rationale, early regulatory advice, and adequate trial design demonstrating signs of biology-driven tumour-agnostic activity, followed by confirmatory evidence, should be the principles for tumour-agnostic drug development.

CONCLUSION

The ESMO Tumour-Agnostic Classifier (ETAC) focuses on the interplay of targeted driver molecular aberration and tumour-specific biology modulating the therapeutic effect of MGTOs. We propose minimum requirements to screen for tumour-agnostic potential (ETAC-S) as part of tumour-agnostic drug development. Definition of ETAC cut-offs is warranted.

Clinical Evidence Supporting FDA Approval of Gene and RNA Therapies for Rare Inherited Conditions

BACKGROUND

Gene and RNA therapies have potential to transform the treatment of rare inherited diseases, but there are concerns about the evidence supporting their use and high costs.

OBJECTIVE

We analyze the evidence supporting Food and Drug Administration (FDA) approval of gene and RNA therapies for rare inherited diseases and discuss implications for clinical practice and policy.

METHODS

We conducted a qualitative analysis of FDA documents outlining the basis of approval for gene and RNA therapies approved for rare inherited diseases between 2016 and 2023. For each drug, we gathered five characteristics of the evidence supporting FDA approval (no phase 3 trial, nonrandomized, no clinical endpoint, lack of demonstrated benefit, and significant protocol deviation) and four characteristics of the FDA approval process (prior rejection or complete response, negative committee vote, discrepancy between label and trial population, and boxed warning). The main outcome was the number of drugs with each characteristic.

RESULTS

Between 2016 and 2023, 19 gene and RNA therapies received FDA approval to treat rare inherited diseases. The most common limitations in the evidence supporting approval of these drugs were nonrandomized studies (8/19, 42%), no clinical endpoint (7/19, 37%), lack of demonstrated benefit or inconsistent results (4/19, 21%), and no phase 3 trial (4/19, 21%). Half (3/6) of accelerated approvals and 57% (5/9) of drugs with breakthrough designation had nonrandomized trials, and gene therapies with one-time dosing were overrepresented (5/7, 71%) among the drugs with nonrandomized trials. Five of six accelerated approvals (83%) and five of nine pediatric drugs (56%), most of which were indicated for Duchenne muscular dystrophy, had no clinical endpoint. Four of nine (44%) pediatric drugs and four of six (67%) accelerated approvals failed to demonstrate benefit compared with none of the nonpediatric drugs and none of the traditional approvals. Five drugs, which all had different indications and represented a mix of RNA and gene therapies, did not have any of these evidence characteristics. Among drugs that received prior rejections or negative committee opinions, all four had nonrandomized trials and lacked a clinical endpoint, and 75% (3/4) lacked demonstrated benefit. Five of nine (56%) pediatric drugs were indicated for broader age groups according to the drug label compared with the trial populations. Of the three drugs with boxed warnings, two had pediatric indications and nonrandomized studies, and one had no phase 3 trial.

CONCLUSIONS

Issues related to trial design, outcome, and data integrity in the evidence supporting FDA approval of rare inherited disease gene and RNA therapies raise questions about whether this evidence is adequate to inform prescribing decisions. Gene and RNA therapies with accelerated approval and pediatric indications were overrepresented among drugs lacking clinical endpoints or demonstrated benefit and should be the focus of efforts to reduce uncertainty in the evidence.

Effectiveness of Pelvic Floor Muscle and Education-Based Therapies on Bladder, Bowel, Vaginal, Sexual, Psychological Function, Quality of Life, and Pelvic Floor Muscle Function in Females Treated for Gynecological Cancer: A Systematic Review

PURPOSE OF REVIEW

Gynecological malignancies are prevalent in females, and this population is likely to experience symptoms of pelvic floor disorders and sexual dysfunction. Non-surgical, non-pharmaceutical conservative therapies, namely pelvic floor muscle (PFM) therapies and education-based interventions, could be beneficial for this population. The purpose of this systematic review was to examine the evidence regarding their effectiveness on bladder, bowel, vaginal, sexual, psychological function, quality of life, and PFM function in gynecological cancer populations.

RECENT FINDINGS

Six databases were searched to identify studies employing any interventional study design, except case studies, to investigate the effect of PFM therapies, education-based interventions, or combined therapies on any outcome of interest. The search yielded 4467 results, from which 20 studies were included. Of these, 11 (55%) were RCTs, two (10%) were non-RCTs with two groups, and seven (35%) were non-RCTs with a single group. Findings suggest that combined (multimodal) therapies, specifically PFM (active > passive) + education therapies, appear more effective for vaginal, overall pelvic floor, sexual, and PFM function. PFM therapies (active and/or electrostimulation) may improve bladder outcomes. Limited evidence suggests PFM (active) + education therapies may improve bowel function. Conservative therapies may improve psychological function, although available data do not appear to favor a particular therapy. Given the conflicting findings regarding quality of life, no clear conclusions can be made. Interpretation of findings highlighted the importance of intervention dosage, adherence, and supervision for optimal effectiveness. Despite the limitations of the included studies, this review provides new and valuable insights for future research and clinical practice.

Stakeholders' perceptions of personal health data sharing: A scoping review

The rapid advancement of digital health technologies has heightened demand for health data for secondary uses, highlighting the importance of understanding global perspectives on personal information sharing. This article examines stakeholder perceptions and attitudes toward the use of personal health data to improve personalized treatments, interventions, and research. It also identifies barriers and facilitators in health data sharing and pinpoints gaps in current research, aiming to inform ethical practices in healthcare settings that utilize digital technologies. We conducted a scoping review of peer reviewed empirical studies based on data pertaining to perceptions and attitudes towards sharing personal health data. The authors searched three electronic databases-Embase, MEDLINE, and Web of Science-for articles published (2015-2023), using terms relating to health data and perceptions. Thirty-nine articles met the inclusion criteria with sample size ranging from 14 to 29,275. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines for the design and analysis of this study. We synthesized the included articles using narrative analysis. The review captured multiple stakeholder perspectives with an up-to-date range of diverse barriers and facilitators that impact data-sharing behavior. The included studies were primarily cross-sectional and geographically concentrated in high-income settings; often overlooking diverse demographics and broader global health challenges. Most of the included studies were based within North America and Western Europe, with the United States (n = 8) and the United Kingdom (n = 7) representing the most studied countries. Many reviewed studies were published in 2022 (n = 11) and used quantitative methods (n = 23). Twenty-nine studies examined the perspectives of patients and the public while six looked at healthcare professionals, researchers, and experts. Many of the studies we reviewed reported overall positive attitudes about data sharing with variations around sociodemographic factors, motivations for sharing data, type and recipient of data being shared, consent preference, and trust.

Caregiver and Youth Characteristics That Influence Trust in Digital Health Platforms in Pediatric Care: Mixed Methods Study

BACKGROUND

Combining patient-generated health data and digital health platforms may improve patient experience and population health, mitigate rising health care costs, reduce clinician burnout, and enable health equity. However, lack of trust may be a notable barrier to the data-sharing required by such platforms. Understanding sociodemographic, health, and personal characteristics will enable developers and implementers of such technologies to consider these in their technical design requirements.

OBJECTIVE

This study aims to understand relationships between sociodemographic characteristics of caregivers of children or adolescents and trust in and willingness to use digital platforms to store and share personal health information for clinical care and research.

METHODS

This study used a mixed methods approach, including surveys of caregivers of youth aged <18 years living in Canada or the United States and youth aged 16 to 17 years living in Canada, as well as web-based bulletin board discussions to further explore topics of trust in data sharing. Sociodemographic and survey data were tabulated and explored using proportional odds ordinal regression models. Comments from web-based group discussions were analyzed thematically using a coding approach to identify issues important to the participants.

RESULTS

Survey data from 1128 caregivers (female participants: n=549, 48.7%; 36-50 years old: n=660, 58.5%; Canadian: n=603, 53.5%; urban population: n=494, 43.8%) were collected, of which 685 (60.7%) completed all questions. Data from 173 youth (female participants: n=73, 42.2%; urban population: n=94, 54.3%) were collected, of which 129 (74.6%) completed all questions, and data were available for analysis. Furthermore, among 40 participants, 23 (58%) caregivers contributed to the web-based discussion boards. Related to trust, living in a rural area (vs urban; odds ratio [OR] 0.66, 95% CI 0.46-0.95) resulted in lower concern for data privacy and security, while having an undergraduate (OR 1.82, 95% CI 1.30-2.55) or graduate degree (vs secondary or trade school; OR 2.50, 95% CI 1.68-3.73) resulted in higher levels of concern. Living with a chronic disease (OR 1.81, 95% CI 1.35-2.44) increased levels of concern regarding data privacy and security. Interestingly, those with chronic disease were more willing to use digital platforms for clinical care and share personal health information for not-for-profit research. Caregivers were most concerned about data breaches involving data from their children but also highlighted that digital platforms would allow for better coordination of care for their children.

CONCLUSIONS

Our research confirms the willingness of caregivers and youth to use digital platforms for both clinical care delivery and research and suggests that the value of a digital platform may outweigh the risks of its use. Engagement of end users in co-designing such platforms has the potential to enhance digital trust. However, digital trust varies across sociodemographic groups; therefore, diverse end user engagement is necessary when designing digital applications.

The Ubuntu Way: Ensuring Ethical AI Integration in Health Research

The integration of artificial intelligence (AI) in health research has grown rapidly, particularly in African nations, which have also been developing data protection laws and AI strategies. However, the ethical frameworks governing AI use in health research are often based on Western philosophies, focusing on individualism, and may not fully address the unique challenges and cultural contexts of African communities. This paper advocates for the incorporation of African philosophies, specifically Ubuntu, into AI health research ethics frameworks to better align with African values and contexts. This study explores the concept of Ubuntu, a philosophy that emphasises communalism, interconnectedness, and collective well-being, and its application to AI health research ethics. By analysing existing global AI ethics frameworks and contrasting them with the Ubuntu philosophy, a new ethics framework is proposed that integrates these perspectives. The framework is designed to address ethical challenges at individual, community, national, and environmental levels, with a particular focus on the African context. The proposed framework highlights four key principles derived from Ubuntu: communalism and openness, harmony and support, research prioritisation and community empowerment, and community-oriented decision-making. These principles are aligned with global ethical standards such as justice, beneficence, transparency, and accountability but are adapted to reflect the communal and relational values inherent in Ubuntu. The framework aims to ensure that AI-driven health research benefits communities equitably, respects local contexts and promotes long-term sustainability. Integrating Ubuntu into AI health research ethics can address the limitations of current frameworks that emphasise individualism. This approach not only aligns with African values but also offers a model that could be applied more broadly to enhance the ethical governance of AI in health research worldwide. By prioritising communal well-being, inclusivity, and environmental stewardship, the proposed framework has the potential to foster more responsible and contextually relevant AI health research practices in Africa.

Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis

BACKGROUND/OBJECTIVE

Atrial fibrillation (AF) complicates the management of acute ischemic stroke (AIS), necessitating precise predictive models to enhance clinical outcomes. This meta-analysis evaluates the efficacy of machine learning (ML) models in three key areas: stroke prognosis in AF patients, stroke prediction in AF patients, and AF prediction in stroke patients. The study aims to assess the accuracy and variability of ML models in forecasting AIS outcomes and detecting AF in stroke patients, while exploring the clinical benefits and limitations of integrating these models into practice.

METHODS

We conducted a systematic search of PubMed, Embase, and Cochrane databases up to June 2024, selecting studies that evaluated ML accuracy in stroke prognosis and prediction in AF patients and AF prediction in stroke patients. Data extraction and quality assessment were performed independently by two reviewers, with random-effects modeling applied to estimate pooled accuracy metrics.

RESULTS

The meta-analysis included twenty-four studies comprising 7,391,645 patients, categorized into groups for stroke prognosis in AF patients (eight studies), stroke prediction in AF patients (thirteen studies), and AF prediction in stroke patients (three studies). The pooled AUROC was 0.79 for stroke prognosis and 0.68 for stroke prediction in AF, with higher accuracy noted in short-term predictions. The mean AUROC across studies was 0.75, with models such as Extreme Gradient Boosting (XGB) and Random Forest (RF) showing superior performance. For stroke prognosis in AF, the mean AUROC was 0.78, whereas stroke prediction yielded a mean AUROC of 0.73. AF prediction post-stroke had an average AUROC of 0.75. These findings indicate moderate predictive capability of ML models, underscoring the need for further refinement and standardization. The absence of comprehensive sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) metrics limited the ability to conduct full meta-analytic modeling.

CONCLUSIONS

While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility.

Analyzing determinants of premature trial discontinuation in leukemia clinical trials

Clinical trials are crucial for improving patient outcomes. Although a significant number of trials are discontinued prematurely, our understanding of factors influencing early termination is limited. We conducted a comprehensive search of ClinicalTrials.gov to identify leukemia trials from 2000 to 2020, followed by data abstraction performed by two independent reviewers. Among 3522 leukemia clinical trials identified, 28.4% were terminated prematurely. Slow accrual was the leading cause of termination 38.2%. The termination rate increased significantly from 17.0% between 2000 and 2005 to 30.9% between 2010 and 2015 (p < .001). Large trials had a lower termination rate than small trials (p < .001). Academic-sponsored trials had the highest termination rates compared to other sponsors' trials (p < .001). Early-phase trials showed higher termination rates compared to late-phase (p < .001). Other significant factors included a sequential assignment, single-center, and non-randomized trials (p < .001). Much of leukemia trials are terminated prematurely, with slow accrual being the most common reason for early termination.

Enhancing Nursing Practices in Critical Care for Older Adults: A Systematic Review of Age-Friendly Nursing Interventions

OBJECTIVES

To synthesize evidence on the implementation and effectiveness of age-friendly interventions in intensive care units (ICUs) to optimize care for older adults.

DESIGN

Systematic review of studies published up to February 2024, focusing on interventions tailored to meet the needs of older adults in ICU settings.

SETTING AND PARTICIPANTS

Comparative studies conducted in ICUs worldwide, involving older adults receiving critical care, were reviewed.

METHODS

A systematic search of databases including Embase, MEDLINE, and Cochrane was performed. The quality of studies was assessed using the ROSVIS-II tool, and findings were synthesized narratively and thematically.

RESULTS

Out of 1200 articles initially identified, 45 studies met inclusion criteria. Age-friendly interventions (eg, geriatric assessment teams, multimodal care bundles) demonstrated significant benefits, including a 19% reduction in major in-hospital complications and shorter hospital stays by an average of 3 days.

CONCLUSIONS AND IMPLICATIONS

The review supports the effectiveness of age-friendly interventions in improving clinical outcomes for older ICU patients. However, consistent reporting of effect sizes was lacking, and more high-quality comparative effectiveness research is needed. Implications for practice include integrating these interventions into standard ICU protocols to enhance older adult care. Policy implications involve advocating for health care policies that support the dissemination and implementation of effective age-friendly practices. Further research should focus on establishing a robust evidence base to guide implementation and policy decisions.

Challenges and applications in generative AI for clinical tabular data in physiology

Recent advancements in generative approaches in AI have opened up the prospect of synthetic tabular clinical data generation. From filling in missing values in real-world data, these approaches have now advanced to creating complex multi-tables. This review explores the development of techniques capable of synthesizing patient data and modeling multiple tables. We highlight the challenges and opportunities of these methods for analyzing patient data in physiology. Additionally, it discusses the challenges and potential of these approaches in improving clinical research, personalized medicine, and healthcare policy. The integration of these generative models into physiological settings may represent both a theoretical advancement and a practical tool that has the potential to improve mechanistic understanding and patient care. By providing a reliable source of synthetic data, these models can also help mitigate privacy concerns and facilitate large-scale data sharing.

SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials

Background

Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making.

Methods

We collected clinical trial abstracts from key conferences and PubMed (2012-2023). The SEETrials system was developed with three modules: preprocessing, prompt engineering with knowledge ingestion, and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types- multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies.

Results

SEETrials achieved high precision (0.964), recall (sensitivity) (0.988), and F1 score (0.974) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.979-0.992 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88 %, 95 % CI: 84-92 %; CR: 95 %, 95 % CI: 53-66 %), bispecific antibodies (ORR: 64 %, 95 % CI: 55-73 %; CR: 27 %, 95 % CI: 16-37 %), and ADC (ORR: 51 %, 95 % CI: 37-65 %; CR: 26 %, 95 % CI: 1-51 %). Notable study heterogeneity was identified (>75 % I 2 heterogeneity index scores) across several outcome entities analyzed within therapy subgroups.

Conclusion

SEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.

Proof-of-concept study of a small language model chatbot for breast cancer decision support - a transparent, source-controlled, explainable and data-secure approach

PURPOSE

Large language models (LLM) show potential for decision support in breast cancer care. Their use in clinical care is currently prohibited by lack of control over sources used for decision-making, explainability of the decision-making process and health data security issues. Recent development of Small Language Models (SLM) is discussed to address these challenges. This preclinical proof-of-concept study tailors an open-source SLM to the German breast cancer guideline (BC-SLM) to evaluate initial clinical accuracy and technical functionality in a preclinical simulation.

METHODS

A multidisciplinary tumor board (MTB) is used as the gold-standard to assess the initial clinical accuracy in terms of concordance of the BC-SLM with MTB and comparing it to two publicly available LLM, ChatGPT3.5 and 4. The study includes 20 fictional patient profiles and recommendations for 5 treatment modalities, resulting in 100 binary treatment recommendations (recommended or not recommended). Statistical evaluation includes concordance with MTB in % including Cohen's Kappa statistic (κ). Technical functionality is assessed qualitatively in terms of local hosting, adherence to the guideline and information retrieval.

RESULTS

The overall concordance amounts to 86% for BC-SLM (κ = 0.721, p < 0.001), 90% for ChatGPT4 (κ = 0.820, p < 0.001) and 83% for ChatGPT3.5 (κ = 0.661, p < 0.001). Specific concordance for each treatment modality ranges from 65 to 100% for BC-SLM, 85-100% for ChatGPT4, and 55-95% for ChatGPT3.5. The BC-SLM is locally functional, adheres to the standards of the German breast cancer guideline and provides referenced sections for its decision-making.

CONCLUSION

The tailored BC-SLM shows initial clinical accuracy and technical functionality, with concordance to the MTB that is comparable to publicly-available LLMs like ChatGPT4 and 3.5. This serves as a proof-of-concept for adapting a SLM to an oncological disease and its guideline to address prevailing issues with LLM by ensuring decision transparency, explainability, source control, and data security, which represents a necessary step towards clinical validation and safe use of language models in clinical oncology.

Innovation bundles and platforms - a qualitative analysis of health system responses to the COVID-19 pandemic

BACKGROUND

Health systems underwent substantial changes to respond to COVID-19. Learning from the successes and failures of health system COVID-19 responses may help us understand how future health service responses can be designed to be both effective and sustainable. This study aims to identify the role that innovation played in crafting health service responses during the COVID-19 pandemic.

METHODS

Semi-structured interviews were conducted online, exploring 19 health professionals' experiences in responding to COVID-19 in a large State health system in Australia. The data were collected from April to September 2022 and analysed utilising constant comparative analysis. The degree of innovation in health service responses was assessed by comparing them to pre-pandemic services using 5 categories adopted from the IMPISCO (Investigators, Methods, Population, Intervention, Setting, Comparators and Outcomes) framework, which classifies interventional fidelity as: 1/ Identical: No differences are found between health services; 2/ Substitution with alternatives that perform the same function, 3/ In-class replacement with elements that delivers roughly the same functionality, 4/ Augmentation with new functions, 5/ Creation of new elements. Services were decomposed into bundles and fidelity labels were assigned to individual bundle elements.

RESULTS

New services were typically created by reconfiguring existing ones rather than being created de novo. The presence of pre-existing infrastructure (foundational technologies) was seen as critical in mounting fast health service responses. Absence of infrastructure was associated with delays and impaired system responses.

CONCLUSIONS

The need to reconfigure rapidly and use infrastructure to support this suggests we reconceive health services as a platform (a general-purpose service upon which other elements can be added for specific functions), where a common core service (such as a primary care practice) can be extended by adding specialised functions using mediators which facilitate the connection (such as virtual service capabilities). Innovation can be costly and time consuming in crises, and during the COVID-19 pandemic, innovations were typically patched together from pre-existing services. The notion of platforms seems a promising way to prepare the health system for future shocks.

Exploring Deep Eutectic Solvents as Pharmaceutical Excipients: Enhancing the Solubility of Ibuprofen and Mefenamic Acid

Objectives: The study explores the potential of various deep eutectic solvents (DESs) to serve as drug delivery systems and pharmaceutical excipients. The research focuses on two primary objectives: evaluating the ability of the selected DES systems to enhance the solubility of two poorly water-soluble model drugs (IBU and MFA), and evaluating their physicochemical properties, including density, viscosity, flow behavior, surface tension, thermal stability, and water dilution effects, to determine their suitability for pharmaceutical applications. Methods: A range of DES systems containing pharmaceutically acceptable constituents was explored, encompassing organic acid-based, sugar- and sugar alcohol-based, and hydrophobic systems, as well as menthol (MNT)-based DES systems with common pharmaceutical excipients. MNT-based DESs exhibited the most significant solubility enhancements. Results: IBU solubility reached 379.69 mg/g in MNT: PEG 400 (1:1) and 356.3 mg/g in MNT:oleic acid (1:1), while MFA solubility peaked at 17.07 mg/g in MNT:Miglyol 812®N (1:1). In contrast, solubility in hydrophilic DES systems was significantly lower, with choline chloride: glycerol (1:2) and arginine: glycolic acid (1:8) showing the best results. While demonstrating lower solubility compared to the MNT-based systems, sugar-based DESs exhibited increased tunability via water and glycerol addition both in terms of solubility and physicochemical properties, such as viscosity and surface tension. Conclusions: Our study introduces novel DES systems, expanding the repertoire of pharmaceutically acceptable DES formulations and opening new avenues for the rational design of tailored solvent systems to overcome solubility challenges and enhance drug delivery.

Advancing healthcare through data: the BETTER project's vision for distributed analytics

Introduction

Data-driven medicine is essential for enhancing the accessibility and quality of the healthcare system. The availability of data plays a crucial role in achieving this goal.

Methods

We propose implementing a robust data infrastructure of FAIRification and data fusion for clinical, genomic, and imaging data. This will be embedded within the framework of a distributed analytics platform for healthcare data analysis, utilizing the Personal Health Train paradigm.

Results

This infrastructure will ensure the findability, accessibility, interoperability, and reusability of data, metadata, and results among multiple medical centers participating in the BETTER Horizon Europe project. The project focuses on studying rare diseases, such as intellectual disability and inherited retinal dystrophies.

Conclusion

The anticipated impacts will benefit a wide range of healthcare practitioners and potentially influence health policymakers.

How can quantum computing be applied in clinical trial design and optimization?

Clinical trials are necessary for assessing the safety and efficacy of treatments. However, trial timelines are severely delayed with minimal success due to a multitude of factors, including imperfect trial site selection, cohort recruitment challenges, lack of efficacy, absence of reliable biomarkers, etc. Each of these factors possesses a unique computational challenge, such as data management, trial simulations, statistical analyses, and trial optimization. Recent advancements in quantum computing offer a promising opportunity to overcome these hurdles. In this opinion we uniquely explore the application of quantum optimization and quantum machine learning (QML) to the design and execution of clinical trials. We examine the current capabilities and limitations of quantum computing and outline its potential to streamline clinical trials.

Integration of new technologies in the multidisciplinary approach to primary liver tumours: The next-generation tumour board

Primary liver tumours, including benign liver tumours, hepatocellular carcinoma and cholangiocarcinoma, present a multifaceted challenge, necessitating a collaborative approach, as evidenced by the role of the multidisciplinary tumour board (MDTB). The approach to managing primary liver tumours involves specialised teams, including surgeons, radiologists, oncologists, pathologists, hepatologists, and radiation oncologists, coming together to propose individualised treatment plans. The evolving landscape of primary liver cancer treatment introduces complexities, particularly with the expanding array of systemic and locoregional therapies, alongside the potential integration of molecular biology and artificial intelligence (AI) into MDTBs in the future. Precision medicine demands collaboration across disciplines, challenging traditional frameworks. In the next decade, we anticipate the convergence of AI, molecular biology, pathology, and advanced imaging, requiring adaptability in MDTB structure to incorporate these cutting-edge technologies. Navigating this evolution also requires a focus on enhancing basic, translational, and clinical research, as well as boosting clinical trials through an upgraded use of MDTBs as hubs for scientific collaboration and raising literacy about AI and new technologies. In this review, we will delineate the current unmet needs in the clinical management of primary liver cancers, discuss our perspective on the future role of MDTBs in primary liver cancers ("next generation" MDTBs), and unravel the potential power and limitations of novel technologies that may shape the multidisciplinary care landscape for primary liver cancers in the coming decade.

Hospital Factors Influencing the Mobility of Patients for Systemic Therapies in Breast and Bowel Cancer in the Metastatic Setting: A National Population-based Evaluation

AIMS

This national study investigated hospital quality and patient factors associated with treatment location for systemic anticancer treatment (SACT) in patients with metastatic cancers.

MATERIALS AND METHODS

Using linked administrative datasets from the English NHS, we identified all patients diagnosed with metastatic breast and bowel cancer between 1 January 2016 and 31 December 2018, who subsequently received SACT within 4 months from diagnosis. The extent to which patients bypassed their nearest hospital was investigated using a geographic information system (ArcGIS). Conditional logistic regression models were used to estimate the impact of travel time, hospital quality and patient characteristics on where patients underwent SACT.

RESULTS

541 of 2,364 women (22.9%) diagnosed with metastatic breast cancer, and 2,809 of 10,050 (28.0%) patients diagnosed with metastatic bowel cancer bypassed their nearest hospital providing SACT. There was a strong preference for receiving treatment at hospitals near where patients lived (p < 0.001). However, patients who were younger (p = 0.043 for breast cancer; p < 0.001 for bowel cancer) or from rural areas (p = 0.001 for breast cancer; p < 0.001 for bowel cancer) were more likely to travel to more distant hospitals. Patients diagnosed with rectal cancer were more likely to travel further for SACT than patients with colon cancer (p = 0.002). Patients were more likely to travel to comprehensive cancer centres (p = 0.019 for bowel cancer) and designated Experimental Cancer Medicine Centres (ECMCs) although the latter association was not significant. Patients were less likely to receive SACT in hospitals with the highest readmission rates (p = 0.046 for bowel cancer).

CONCLUSION

Patients with metastatic cancer receiving primary SACT are prepared to travel to alternative more distant hospitals for treatment with a preference for larger comprehensive centres providing multimodal care or hospitals which offer early phase cancer clinical trials.

Chinese herbal medicine for the treatment of intestinal cancer: preclinical studies and potential clinical applications

Intestinal cancer (IC) poses a significant global health challenge that drives continuous efforts to explore effective treatment modalities. Conventional treatments for IC are effective, but are associated with several limitations and drawbacks. Chinese herbal medicine (CHM) plays an important role in the overall cancer prevention and therapeutic strategies. Recent years have seen a growing body of research focus on the potential of CHM in IC treatment, showing promising results in managing IC and mitigating the adverse effects of radiotherapy and chemotherapy. This review provides updated information from preclinical research and clinical observation on CHM's role in treatment of IC, offering insights into its comprehensive management and guiding future prevention strategies and clinical practice.

Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGPT in Uveitis

INTRODUCTION

Accurate diagnosis and timely management are vital for favorable uveitis outcomes. Artificial Intelligence (AI) holds promise in medical decision-making, particularly in ophthalmology. Yet, the diagnostic precision and management advice from AI-based uveitis chatbots lack assessment.

METHODS

We appraised diagnostic accuracy and management suggestions of an AI-based chatbot, ChatGPT, versus five uveitis-trained ophthalmologists, using 25 standard cases aligned with new Uveitis Nomenclature guidelines. Participants predicted likely diagnoses, two differentials, and next management steps. Comparative success rates were computed.

RESULTS

Ophthalmologists excelled (60-92%) in likely diagnosis, exceeding AI (60%). Considering fully and partially accurate diagnoses, ophthalmologists achieved 76-100% success; AI attained 72%. Despite an 8% AI improvement, its overall performance lagged. Ophthalmologists and AI agreed on diagnosis in 48% cases, with 91.6% exhibiting concurrence in management plans.

CONCLUSIONS

The study underscores AI chatbots' potential in uveitis diagnosis and management, indicating their value in reducing diagnostic errors. Further research is essential to enhance AI chatbot precision in diagnosis and recommendations.

India's Potential as a Leader in Cancer Care Progress in the Future: A Synthetic Interdisciplinary Perspective

This paper comprehensively analyzes India's potential to become a leader in cancer care in the Global South, particularly in precision population cancer medicine (PPCM). Through an interdisciplinary lens, it examines the current landscape of cancer care in India, highlighting its strengths, weaknesses, opportunities, and threats in this domain. This review explores the concept of knowledge translation and its importance in bridging the gap between knowledge generation and implementation in medical sciences and applies this to the Indian healthcare scenario. The review then delves into India's technological prowess, exemplified by its digital health initiatives such as the CoWIN (winning over COVID-19) app and the Ayushman Bharat Digital Mission, which provide a strong foundation for leveraging advanced technologies in healthcare. The authors discuss India's pharmaceutical industry, often referred to as the "pharmacy of the world," emphasizing its crucial role in global drug manufacturing and distribution. It also examines the country's emerging genomic research landscape, including initiatives such as GenomeIndia and the Indian Cancer Genome Atlas Foundation, which are pivotal for advancing personalized medicine. A significant portion of the review is dedicated to analyzing India's clinical trial ecosystem. It traces the evolution of regulatory frameworks governing clinical research in the country and highlights recent reforms that have made India an increasingly attractive destination for global studies, the potential adoption of innovative trial designs and artificial intelligence (AI)-driven analyses. Crucially, the authors confront the formidable obstacles inherent in India's complex healthcare landscape, illuminating the unique challenges that must be overcome. The review acknowledges India's underrepresentation in global clinical trials despite its large population and significant cancer burden. The issue of financial toxicity in cancer care is discussed, underscoring the need for affordable treatment options. The study also points out the nascent state of India's genomic databases, which account for only a small percentage of global genetic data. Despite these challenges, the authors posit that by effectively leveraging its information technology (IT) infrastructure, robust pharmaceutical sector, and large, diverse population, India has the potential to develop unique, country-specific solutions for cancer care. The study suggests that by fostering genomic research, strategically reforming its clinical trial ecosystem, and harnessing its digital capabilities, India could transform its cancer care landscape and emerge as a model for other developing nations in the Global South. In essence, this paper provides a roadmap for India's journey towards becoming a leader in PPCM, offering valuable insights for policymakers, healthcare professionals, and researchers in the field of oncology and precision medicine. Indeed, by using PPCM as a "pilot project," India can learn to use its new strategies to improve non-cancer care disease prevention, early detection, and improved and more cost-effective management. This approach could revolutionize cancer care in India and serve as a model for other developing nations in the Global South. By leveraging the strategies and technologies developed for PPCM, India could significantly enhance its healthcare system, highlighting the importance and urgency of improving cancer care in the region.

Integrating large language models in care, research, and education in multiple sclerosis management

Use of techniques derived from generative artificial intelligence (AI), specifically large language models (LLMs), offer a transformative potential on the management of multiple sclerosis (MS). Recent LLMs have exhibited remarkable skills in producing and understanding human-like texts. The integration of AI in imaging applications and the deployment of foundation models for the classification and prognosis of disease course, including disability progression and even therapy response, have received considerable attention. However, the use of LLMs within the context of MS remains relatively underexplored. LLMs have the potential to support several activities related to MS management. Clinical decision support systems could help selecting proper disease-modifying therapies; AI-based tools could leverage unstructured real-world data for research or virtual tutors may provide adaptive education materials for neurologists and people with MS in the foreseeable future. In this focused review, we explore practical applications of LLMs across the continuum of MS management as an initial scope for future analyses, reflecting on regulatory hurdles and the indispensable role of human supervision.

The circular paradox of including people with severe brain injuries and reduced decisional capacity in research: A feasibility study exploring randomized research, consent-based recruitment biases, and the resultant health inequities

BACKGROUND

People with severe brain injuries (PSBI) and reduced capacity to consent (CTC) frequently develop muscle contractures. Standard care includes prolonged stretch (PS) but there is limited condition-specific evidence from randomized controlled trials (RCTs).

PURPOSE

Identify factors affecting the inclusion of PSBI and reduced CTC in a PS RCT and methodologies more capable of generating condition-specific outcomes.

METHODS

Mixed-method feasibility studies, including a pilot RCT (PSBI, adults with reduced CTC) comparing PS treatments (serial casting and splinting) and focus groups/interviews with physiotherapists involved in PS treatment. Reflexive thematic analysis developed themes.

RESULTS

Two PSBI were included in the pilot RCT with no significant safety concerns or adverse effects. Twelve physiotherapists participated in two focus groups and two interviews. Four themes were identified: 1) complexity of contracture management; 2) burden of decision making; 3) lack of evidence and uncertainty; and 4) challenges to RCT acceptability and feasibility.

CONCLUSIONS

Reduced CTC contributes to the exclusion of PSBI from experimental research, and a circular paradox where poor research inclusion contributes to generalized healthcare and "evidence-biased medicine." Due to the complexity of their condition, simply including PSBI in randomized research is unlikely to create meaningful health outcomes. Improving their care requires a paradigm shift toward pluralistic methods of knowledge generation.

Challenges and Alternatives to Evaluation Methods and Regulation Approaches for Medical Apps as Mobile Medical Devices: International and Multidisciplinary Focus Group Discussion

BACKGROUND

The rapid proliferation of medical apps has transformed the health care landscape by giving patients and health care providers unprecedented access to personalized health information and services. However, concerns regarding the effectiveness and safety of medical apps have raised questions regarding the efficacy of randomized controlled trials (RCTs) in the evaluation of such apps and as a requirement for their regulation as mobile medical devices.

OBJECTIVE

This study aims to address this issue by investigating alternative methods, apart from RCTs, for evaluating and regulating medical apps.

METHODS

Using a qualitative approach, a focus group study with 46 international and multidisciplinary public health experts was conducted at the 17th World Congress on Public Health in May 2023 in Rome, Italy. The group was split into 3 subgroups to gather in-depth insights into alternative approaches for evaluating and regulating medical apps. We conducted a policy analysis on the current regulation of medical apps as mobile medical devices for the 4 most represented countries in the workshop: Italy, Germany, Canada, and Australia. We developed a logic model that combines the evaluation and regulation domains on the basis of these findings.

RESULTS

The focus group discussions explored the strengths and limitations of the current evaluation and regulation methods and identified potential alternatives that could enhance the quality and safety of medical apps. Although RCTs were only explicitly mentioned in the German regulatory system as one of many options, an analysis of chosen evaluation methods for German apps on prescription pointed toward a "scientific reflex" where RCTs are always the chosen evaluation method. However, this method has substantial limitations when used to evaluate digital interventions such as medical apps. Comparable results were observed during the focus group discussions, where participants expressed similar experiences with their own evaluation approaches. In addition, the participants highlighted numerous alternatives to RCTs. These alternatives can be used at different points during the life cycle of a digital intervention to assess its efficacy and potential harm to users.

CONCLUSIONS

It is crucial to recognize that unlike analog tools, digital interventions constantly evolve, posing challenges to inflexible evaluation methods such as RCTs. Potential risks include high dropout rates, decreased adherence, and nonsignificant results. However, existing regulations do not explicitly advocate for other evaluation methodologies. Our research highlighted the necessity of overcoming the gap between regulatory demands to demonstrate safety and efficacy of medical apps and evolving scientific practices, ensuring that digital health innovation is evaluated and regulated in a way that considers the unique characteristics of mobile medical devices.

DNA Adductomics: A Narrative Review of Its Development, Applications, and Future

DNA adductomics is the global study of all DNA adducts and was first proposed in 2006 by the Matsuda group. Its development has been greatly credited to the advances in mass spectrometric techniques, particularly tandem and multiple-stage mass spectrometry. In fact, liquid chromatography-mass spectrometry (LC-MS)-based methods are virtually the sole technique with practicality for DNA adductomic studies to date. At present, DNA adductomics is primarily used as a tool to search for DNA adducts, known and unknown, providing evidence for exposure to exogenous genotoxins and/or for the molecular mechanisms of their genotoxicity. Some DNA adducts discovered in this way have the potential to predict cancer risks and/or to be associated with adverse health outcomes. DNA adductomics has been successfully used to identify and determine exogenous carcinogens that may contribute to the etiology of certain cancers, including bacterial genotoxins and an N-nitrosamine. Also using the DNA adductomic approach, multiple DNA adducts have been observed to show age dependence and may serve as aging biomarkers. These achievements highlight the capability and power of DNA adductomics in the studies of medicine, biological science, and environmental science. Nonetheless, DNA adductomics is still in its infancy, and great advances are expected in the future.

Pharmacovigilance in the Age of Legalized Cannabis: Using Social Media to Monitor Drug-Drug Interactions Between Immunosuppressants and Cannabis-Derived Products

INTRODUCTION

A clinical trial of Epidiolex®, the only US FDA-approved cannabis-derived consumer product (CDP), discovered an interaction with an immunosuppressant (tacrolimus) that led to drug toxicity, highlighting the unique intersection of prescription and commonly unregulated consumer products.

OBJECTIVE

We aimed to identify if similar drug-drug interactions (DDIs) are occurring among the consumer CDP market, even though they cannot be identified through trials.

METHODS

We searched Reddit for subreddits related to CDPs or health, resulting in 63,561,233 posts. From these, we identified 190 posts discussing both immunosuppressants and CDPs. Two blinded investigators evaluated the following. (1) Was there a concern about a potential DDI between consumer CDPs and immunosuppressants? (2) Was there a unique adverse event attributed to a DDI between consumer CDPs and immunosuppressants?

RESULTS

Of these, 66 posts (35%) expressed concern about a potential DDI, such as "Hey, my partner wants to try my edibles … she's on Prograf [tacrolimus] and wants to talk to a stoner who's had a heart transplant." Four posts (2%) reported a unique DDI, such as "I have clinical results that are semi-anecdotal, showing the coordination to my halting substance use … It's the CBD. Shot my prograf to 30 at like 4 mg." Two of the four reported DDIs are similar to those first reported for Epidiolex. The remaining two reported DDIs include a potential cannabidiol (CBD)/sirolimus or delta-9-tetrahydrocannabinol (THC)/sirolimus interaction and a THC/tacrolimus interaction, both resulting in drug toxicity.

CONCLUSION

This case study is the first to report on DDIs involving consumer CDPs, including both CBD and THC products, as well as a broader class of immunosuppressants. This demonstrates the risks associated with using consumer CDPs alongside prescription medications while highlighting the need for development of increased surveillance to monitor consumer CDPs for drug safety signals, as well as comprehensive regulations that take into account the unique characteristics of the consumer marketplace.

FGFR2-fusions define a clinically actionable molecular subset of pancreatic cancer

Genomic alterations in fibroblast growth factor receptor (FGFR) genes are present in a small number of metastatic pancreatic ductal adenocarcinomas (PDAC) and may represent an emerging subgroup of patients likely to benefit from FGFR targeted therapies. Here we present four FGFR2 fusion-positive metastatic PDAC patients who exhibited durable responses or disease control to FGFR kinase inhibitors. Utilizing our custom FGFR focused cell-free DNA assay, FGFR-Dx, we serially monitored variant allele fractions of FGFR2 fusions during FGFR inhibitor treatment and observed dynamic changes correlating with clinical responses. Genomic analysis of 30,229 comprehensively profiled pancreatic cancers revealed FGFR1-3 fusions in 245 cases, an incidence of 0.81%. FGFR fusions were generally mutually exclusive from other known oncogenes. Our findings provide clinical evidence for identifying and treating FGFR2 fusion-positive PDAC patients with FGFR targeted therapy.

The critical elements of digital health in diabetes and cardiometabolic care

Digital innovations provide novel opportunities to individualize a person's care to best match their lifestyle needs and circumstances and to support them as they live their daily lives with diabetes. These innovations also serve to provide actionable data and insights for the care team giving them a "Webb telescope-like" view into their individual self-management journey, allowing them to see what cannot be seen during infrequent and limited office visits, thereby facilitating collaboration and communication to optimize the care plan on a timely basis. Technology advances are enabling diabetes care to transition from episodic, synchronous, primarily in-person care to include synchronous virtual care options and to continuous, on-demand, data-informed, asynchronous digital care better matching the demands of living with a relentless 24/7 chronic condition. In this paper we will discuss the critical elements and considerations in designing and implementing successful diabetes digital health tools in clinical practice.

Recommendations for clinical trial design in acute kidney injury from the 31st acute disease quality initiative consensus conference. A consensus statement

PURPOSE

Novel interventions for the prevention or treatment of acute kidney injury (AKI) are currently lacking. To facilitate the evaluation and adoption of new treatments, the use of the most appropriate design and endpoints for clinical trials in AKI is critical and yet there is little consensus regarding these issues. We aimed to develop recommendations on endpoints and trial design for studies of AKI prevention and treatment interventions based on existing data and expert consensus.

METHODS

At the 31st Acute Disease Quality Initiative (ADQI) meeting, international experts in critical care, nephrology, involving adults and pediatrics, biostatistics and people with lived experience (PWLE) were assembled. We focused on four main areas: (1) patient enrichment strategies, (2) prevention and attenuation studies, (3) treatment studies, and (4) innovative trial designs of studies other than traditional (parallel arm or cluster) randomized controlled trials. Using a modified Delphi process, recommendations and consensus statements were developed based on existing data, with > 90% agreement among panel members required for final adoption.

RESULTS

The panel developed 12 consensus statements for clinical trial endpoints, application of enrichment strategies where appropriate, and inclusion of PWLE to inform trial designs. Innovative trial designs were also considered.

CONCLUSION

The current lack of specific therapy for prevention or treatment of AKI demands refinement of future clinical trial design. Here we report the consensus findings of the 31st ADQI group meeting which has attempted to address these issues including the use of predictive and prognostic enrichment strategies to enable appropriate patient selection.

Comparing code-free and bespoke deep learning approaches in ophthalmology

AIM

Code-free deep learning (CFDL) allows clinicians without coding expertise to build high-quality artificial intelligence (AI) models without writing code. In this review, we comprehensively review the advantages that CFDL offers over bespoke expert-designed deep learning (DL). As exemplars, we use the following tasks: (1) diabetic retinopathy screening, (2) retinal multi-disease classification, (3) surgical video classification, (4) oculomics and (5) resource management.

METHODS

We performed a search for studies reporting CFDL applications in ophthalmology in MEDLINE (through PubMed) from inception to June 25, 2023, using the keywords 'autoML' AND 'ophthalmology'. After identifying 5 CFDL studies looking at our target tasks, we performed a subsequent search to find corresponding bespoke DL studies focused on the same tasks. Only English-written articles with full text available were included. Reviews, editorials, protocols and case reports or case series were excluded. We identified ten relevant studies for this review.

RESULTS

Overall, studies were optimistic towards CFDL's advantages over bespoke DL in the five ophthalmological tasks. However, much of such discussions were identified to be mono-dimensional and had wide applicability gaps. High-quality assessment of better CFDL applicability over bespoke DL warrants a context-specific, weighted assessment of clinician intent, patient acceptance and cost-effectiveness. We conclude that CFDL and bespoke DL are unique in their own assets and are irreplaceable with each other. Their benefits are differentially valued on a case-to-case basis. Future studies are warranted to perform a multidimensional analysis of both techniques and to improve limitations of suboptimal dataset quality, poor applicability implications and non-regulated study designs.

CONCLUSION

For clinicians without DL expertise and easy access to AI experts, CFDL allows the prototyping of novel clinical AI systems. CFDL models concert with bespoke models, depending on the task at hand. A multidimensional, weighted evaluation of the factors involved in the implementation of those models for a designated task is warranted.

Shifting from Theoretical Best Evidence to Practical Best Evidence: an Approach to Overcome Structural Conservatism of Evidence-Based Medicine and Health Policy

There is disparity in the healthcare sector between the extent of innovation in medical products (e. g., drugs) and healthcare structures. The reason is not a lack of ideas, concepts, or (quasi-) experimental studies on structural innovations. Instead, we argue that the slow implementation of structural innovations has created this disparity partly because evidence-based medicine (EBM) instruments are well suited to evaluate product innovations but less suited to evaluate structural innovations. This article argues that the unintentional interplay between EBM, which has changed significantly over time to become primarily theoretical, on the one hand, and caution and inertia in health policy, on the other, has resulted in structural conservatism. Structural conservatism is present when healthcare structures persistently and essentially resist innovation. We interpret this phenomenon as an unintended consequence of deliberate EBM action. Therefore, we propose a new assessment framework to respond to structural innovations in healthcare, centered on the differentiation between the theoretical best (possible) evidence, the practical best (possible) evidence, and the best available evidence.