Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging.

PURPOSE: Artificial intelligence (AI) in the form of automated machine learning (AutoML) offers a new potential breakthrough to overcome the barrier of entry for non-technically trained physicians. A Clinical Decision Support System (CDSS) for screening purposes using AutoML could be beneficial to ease the clinical burden in the radiological workflow for paranasal sinus diseases. METHODS: The main target of this work was the usage of automated evaluation of model performance and the feasibility of the Vertex AI image classification model on the Google Cloud AutoML platform to be trained to automatically classify the presence or absence of sinonasal disease. The dataset is a consensus labelled Open Access Series of Imaging Studies (OASIS-3) MRI head dataset by three specialised head and neck consultant radiologists. A total of 1313 unique non-TSE T2w MRI head sessions were used from the OASIS-3 repository. RESULTS: The best-performing image classification model achieved a precision of 0.928. Demonstrating the feasibility and high performance of the Vertex AI image classification model to automatically detect the presence or absence of sinonasal disease on MRI. CONCLUSION: AutoML allows for potential deployment to optimise diagnostic radiology workflows and lay the foundation for further AI research in radiology and otolaryngology. The usage of AutoML could serve as a formal requirement for a feasibility study.

How Telemedicine Can Improve the Quality of Care for Patients with Alzheimer's Disease and Related Dementias? A Narrative Review.

Background and Objectives: Dementia affects more than 55 million patients worldwide, with a significant societal, economic, and psychological impact. However, many patients with Alzheimer's disease (AD) and other related dementias have limited access to effective and individualized treatment. Care provision for dementia is often unequal, fragmented, and inefficient. The COVID-19 pandemic accelerated telemedicine use, which holds promising potential for addressing this important gap. In this narrative review, we aim to analyze and discuss how telemedicine can improve the quality of healthcare for AD and related dementias in a structured manner, based on the seven dimensions of healthcare quality defined by the World Health Organization (WHO), 2018: effectiveness, safety, people-centeredness, timeliness, equitability, integrated care, and efficiency. Materials and Methods: MEDLINE and Scopus databases were searched for peer-reviewed articles investigating the role of telemedicine in the quality of care for patients with dementia. A narrative synthesis was based on the seven WHO dimensions. Results: Most studies indicate that telemedicine is a valuable tool for AD and related dementias: it can improve effectiveness (better access to specialized care, accurate diagnosis, evidence-based treatment, avoidance of preventable hospitalizations), timeliness (reduction of waiting times and unnecessary transportation), patient-centeredness (personalized care for needs and values), safety (appropriate treatment, reduction of infection risk),integrated care (interdisciplinary approach through several dementia-related services), efficiency (mainly cost-effectiveness) and equitability (overcoming geographical barriers, cultural diversities). However, digital illiteracy, legal and organizational issues, as well as limited awareness, are significant potential barriers. Conclusions: Telemedicine may significantly improve all aspects of the quality of care for patients with dementia. However, future longitudinal studies with control groups including participants of a wide educational level spectrum will aid in our deeper understanding of the real impact of telemedicine in quality care for this population.

Prioritizing problems in and solutions to homecare safety of people with dementia: supporting carers, streamlining care.

BACKGROUND: Dementia care is predominantly provided by carers in home settings. We aimed to identify the priorities for homecare safety of people with dementia according to dementia health and social care professionals using a novel priority-setting method. METHODS: The project steering group determined the scope, the context and the criteria for prioritization. We then invited 185 North-West London clinicians via an open-ended questionnaire to identify three main problems and solutions relating to homecare safety of people with dementia. 76 clinicians submitted their suggestions which were thematically synthesized into a composite list of 27 distinct problems and 30 solutions. A group of 49 clinicians arbitrarily selected from the initial cohort ranked the composite list of suggestions using predetermined criteria. RESULTS: Inadequate education of carers of people with dementia (both family and professional) is seen as a key problem that needs addressing in addition to challenges of self-neglect, social isolation, medication nonadherence. Seven out of top 10 problems related to patients and/or carers signalling clearly where help and support are needed. The top ranked solutions focused on involvement and education of family carers, their supervision and continuing support. Several suggestions highlighted a need for improvement of recruitment, oversight and working conditions of professional carers and for different home safety-proofing strategies. CONCLUSIONS: Clinicians identified a range of suggestions for improving homecare safety of people with dementia. Better equipping carers was seen as fundamental for ensuring homecare safety. Many of the identified suggestions are highly challenging and not easily changeable, yet there are also many that are feasible, affordable and could contribute to substantial improvements to dementia homecare safety.

Clinician-identified problems and solutions for delayed diagnosis in primary care: a PRIORITIZE study.

BACKGROUND: Delayed diagnosis in primary care is a common, harmful and costly patient safety incident. Its measurement and monitoring are underdeveloped and underutilised. We created and implemented a novel approach to identify problems leading to and solutions for delayed diagnosis in primary care. METHODS: We developed a novel priority-setting method for patient safety problems and solutions called PRIORITIZE. We invited more than 500 NW London clinicians via an open-ended questionnaire to identify three main problems and solutions relating to delayed diagnosis in primary care. 113 clinicians submitted their suggestions which were thematically grouped and synthesized into a composite list of 33 distinct problems and 27 solutions. A random group of 75 clinicians from the initial cohort scored these and an overall ranking was derived. The agreement between the clinicians' scores was presented using the Average Expert Agreement. RESULTS: The top ranked problems were poor communication between secondary and primary care and the inverse care law, i.e. a mismatch between patients' medical needs and healthcare supply. The highest ranked solutions included: a more rigorous system of communicating abnormal results of investigations to patients, direct hotlines to specialists for GPs to discuss patient problems and better training of primary care clinicians in relevant areas. A priority highlighted throughout the findings is a need to improve communication between clinicians as well as with patients. The highest ranked suggestions had the highest consensus between experts. CONCLUSIONS: The novel method we have developed is highly feasible, informative and scalable, and merits wider exploration with a view of becoming part of a routine pro-active and preventative system for patient safety assessment. Clinicians proposed a range of concrete suggestions with an emphasis on improving communication among clinicians and with patients and better GP training. In their view, delayed diagnosis can be largely prevented with interventions requiring relatively minor investment. Rankings of identified problems and solutions can serve as an aid to policy makers and commissioners of care in prioritization of scarce healthcare resources.

Identification of priorities for improvement of medication safety in primary care: a PRIORITIZE study.

BACKGROUND: Medication error is a frequent, harmful and costly patient safety incident. Research to date has mostly focused on medication errors in hospitals. In this study, we aimed to identify the main causes of, and solutions to, medication error in primary care. METHODS: We used a novel priority-setting method for identifying and ranking patient safety problems and solutions called PRIORITIZE. We invited 500 North West London primary care clinicians to complete an open-ended questionnaire to identify three main problems and solutions relating to medication error in primary care. 113 clinicians submitted responses, which we thematically synthesized into a composite list of 48 distinct problems and 45 solutions. A group of 57 clinicians randomly selected from the initial cohort scored these and an overall ranking was derived. The agreement between the clinicians' scores was presented using the average expert agreement (AEA). The study was conducted between September 2013 and November 2014. RESULTS: The top three problems were incomplete reconciliation of medication during patient 'hand-overs', inadequate patient education about their medication use and poor discharge summaries. The highest ranked solutions included development of a standardized discharge summary template, reduction of unnecessary prescribing, and minimisation of polypharmacy. Overall, better communication between the healthcare provider and patient, quality assurance approaches during medication prescribing and monitoring, and patient education on how to use their medication were considered the top priorities. The highest ranked suggestions received the strongest agreement among the clinicians, i.e. the highest AEA score. CONCLUSIONS: Clinicians identified a range of suggestions for better medication management, quality assurance procedures and patient education. According to clinicians, medication errors can be largely prevented with feasible and affordable interventions. PRIORITIZE is a new, convenient, systematic, and replicable method, and merits further exploration with a view to becoming a part of a routine preventative patient safety monitoring mechanism.

Scavenger Receptor Class B Type I Regulates Plasma Apolipoprotein E Levels and Dietary Lipid Deposition to the Liver.

Scavenger receptor class B type I (SR-BI) is primarily responsible for the selective uptake of cholesteryl esters (CE) of high-density lipoprotein (HDL) by the liver and other tissues. In the present study, we show that SR-BI-deficient (scarb1(-/-)) mice are resistant to diet-induced obesity, hepatic lipid deposition, and glucose intolerance after 24 weeks of being fed a western-type diet. No differences in energy expenditure or mitochondrial function could account for the observed phenotype. Kinetic and gene expression analyses suggested reduced de novo fatty acid synthesis in scarb1(-/-) mice. Furthermore, adenosine monophosphate-activated protein kinase (AMPK)-stimulated hepatic FFA catabolism was reduced in these mice, leaving direct dietary lipid uptake from plasma as the major modulator of hepatic lipid content. Analysis of the apolipoprotein composition of plasma lipoproteins revealed a significant accumulation of apolipoprotein E (ApoE)-containing HDL and TG-rich lipoproteins in scarb1(-/-) mice that correlated with reduced plasma LpL activity. Our data suggest that scarb1(-/-) mice fed a western-type diet for 24 weeks accumulate CE- and ApoE-rich HDL of abnormal density and size. The elevated HDL-ApoE levels inhibit plasma LpL activity, blocking the clearance of triglyceride-rich lipoproteins and preventing the shuttling of dietary lipids to the liver.

Translation, cultural adaptation and pilot testing of a questionnaire measuring the factors affecting the acceptance of telemedicine by Greek cancer patients.

The emergency presented through the COVID-19 pandemic exposed the need to adopt remote, technology-driven solutions and make healthcare services more resilient. To do so, we need technological applications (i.e., telemedicine) that are designed and tailored to the end-users (i.e., chronic patients) needs and the type of healthcare service they get (i.e., cancer care). The requirements above are especially relevant to Greece, being a country with numerous sparsely populated regions (e.g., islands, regions at the borders) and a deteriorating access to healthcare for all citizens. Trying to address such diverse problems and needs, there have been multiple, different telemedicine and telecare projects in Greece in the past years. To support the future design and implementation of such endeavours, in this study we translated a questionnaire measuring the acceptance of telemedicine by patients and adapted it to the Greek context. We continued by running a small-scale pilot with 73 Greek women with breast cancer to assess the adapted instrument for its reliability and construct validity. The created questionnaire had good overall and internal reliability scores for most sub-scales. Factor analysis did not identify the same number of latent dimensions as the original theoretical model. Reverse wording items needing to be recoded were identified, and items that could be omitted in future versions of the questionnaire. Increasing the sample size for the purposes of a longitudinal study, the construct, convergent, and discriminant validity are elements to be further examined in future studies. It is envisaged that the creation of this questionnaire will support the adoption of telemedicine by Greek healthcare services into more routine areas of patient care provision.

A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform.

PURPOSE: This study is to evaluate healthcare needs, preferences, and expectations in supportive cancer care as perceived by cancer survivors, family caregivers, and healthcare professionals. METHODS: Key stakeholders consisted of cancer survivors diagnosed with breast cancer, prostate cancer, or melanoma; adult family caregivers; and healthcare professionals involved in oncology. Recruitment was via several routes, and data were collected via either online surveys or telephone interviews in Greece, Spain, Sweden, and the UK. Framework analysis was applied to the dataset. RESULTS: One hundred and fifty-five stakeholders participated: 70 cancer survivors, 23 family caregivers, and 62 healthcare professionals (13 clinical roles). Cancer survivors and family caregivers' needs included information and support on practical/daily living, as frustration was apparent with the lack of follow-up services. Healthcare professionals agreed on a multidisciplinary health service with a "focus on the patient" and availability closer to home. Most healthcare professionals acknowledged that patient-reported outcomes may provide "better individualised care". Cancer survivors and family caregivers generally felt that the digital platform would be useful for timely personalised support and aided communication. Healthcare professionals were supportive of the "proactive" functionality of the platform and the expected advantages. Anticipated challenges were integration obstacles such as workload/infrastructure and training/support in using the new technology. CONCLUSIONS: Obtaining key stakeholders' insights provided a foundation for action to further co-create the LifeChamps digital platform to meet needs and priorities and deliver enhanced supportive care to "older" cancer survivors. IMPLICATIONS FOR CANCER SURVIVORS: Co-creation provided insight into gaps where digital support may enhance health and well-being.

Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field.

OBJECTIVES: To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions. DATA SOURCES: Peer-reviewed scientific publications and expert opinion. CONCLUSION: The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services. IMPLICATIONS FOR NURSING PRACTICE: As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.

Involving Patients into the LifeChamps Project: Preliminary Findings.

Patient involvement in research has been highlighted as a major requirement for the development of products and services that cover actual patients' needs. However, there has not been an agreement on a commonly used standard for patient involvement in research, at least not in the EU, partially because of lack of common terminology and implementation methodology. Within the standardization activities of "LifeChamps: A Collective Intelligent Platform To Support Cancer Champions", this qualitative study was developed to discover patients' views for their engagement in research. This is an ongoing qualitative study of semi-structured interviews of cancer survivors aged over 65 years of age, exiting the feasibility studies of the LifeChamps project in Stockholm and Thessaloniki. Findings from the thematic analysis of this study are expected to indicate requirements for involvement of patients in research studies as participants.

A Smart Digital Health Platform to Enable Monitoring of Quality of Life and Frailty in Older Patients with Cancer: A Mixed-Methods, Feasibility Study Protocol.

OBJECTIVES: LifeChamps is an EU Horizon 2020 project that aims to create a digital platform to enable monitoring of health-related quality of life and frailty in patients with cancer over the age of 65. Our primary objective is to assess feasibility, usability, acceptability, fidelity, adherence, and safety parameters when implementing LifeChamps in routine cancer care. Secondary objectives involve evaluating preliminary signals of efficacy and cost-effectiveness indicators. DATA SOURCES: This will be a mixed-methods exploratory project, involving four study sites in Greece, Spain, Sweden, and the United Kingdom. The quantitative component of LifeChamps (single-group, pre-post feasibility study) will integrate digital technologies, home-based motion sensors, self-administered questionnaires, and the electronic health record to (1) enable multimodal, real-world data collection, (2) provide patients with a coaching mobile app interface, and (3) equip healthcare professionals with an interactive, patient-monitoring dashboard. The qualitative component will determine end-user usability and acceptability via end-of-study surveys and interviews. CONCLUSION: The first patient was enrolled in the study in January 2023. Recruitment will be ongoing until the project finishes before the end of 2023. IMPLICATIONS FOR NURSING PRACTICE: LifeChamps provides a comprehensive digital health platform to enable continuous monitoring of frailty indicators and health-related quality of life determinants in geriatric cancer care. Real-world data collection will generate "big data" sets to enable development of predictive algorithms to enable patient risk classification, identification of patients in need for a comprehensive geriatric assessment, and subsequently personalized care.

A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture.

Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the "4+1 architectural model".

Application of ensemble clustering and survival tree analysis for identifying prognostic clinicogenomic features in patients with colorectal cancer from the 100,000 Genomes Project.

OBJECTIVE: The objective of this study was to employ ensemble clustering and tree-based risk model approaches to identify interactions between clinicogenomic features for colorectal cancer using the 100,000 Genomes Project. RESULTS: Among the 2211 patients with colorectal cancer (mean age of diagnosis: 67.7; 59.7% male), 16.3%, 36.3%, 39.0% and 8.4% had stage 1, 2, 3 and 4 cancers, respectively. Almost every patient had surgery (99.7%), 47.4% had chemotherapy, 7.6% had radiotherapy and 1.4% had immunotherapy. On average, tumour mutational burden (TMB) was 18 mutations/Mb and 34.4%, 31.3% and 25.7% of patients had structural or copy number mutations in KRAS, BRAF and NRAS, respectively. In the fully adjusted Cox model, patients with advanced cancer [stage 3 hazard ratio (HR) = 3.2; p < 0.001; stage 4 HR = 10.2; p < 0.001] and those who had immunotherapy (HR = 1.8; p < 0.04) or radiotherapy (HR = 1.5; p < 0.02) treatment had a higher risk of dying. The ensemble clustering approach generated four distinct clusters where patients in cluster 2 had the best survival outcomes (1-year: 98.7%; 2-year: 96.7%; 3-year: 93.0%) while patients in cluster 3 (1-year: 87.9; 2-year: 70.0%; 3-year: 53.1%) had the worst outcomes. Kaplan-Meier analysis and log rank test revealed that the clusters were separated into distinct prognostic groups (p < 0.0001). Survival tree or recursive partitioning analyses were performed to further explore risk groups within each cluster. Among patients in cluster 2, for example, interactions between cancer stage, grade, radiotherapy, TMB, BRAF mutation status were identified. Patients with stage 4 cancer and TMB ≥ 1.6 mutations/Mb had 4 times higher risk of dying relative to the baseline hazard in that cluster.

Network Analysis of the Multidimensional Symptom Experience of Oncology.

Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature and structure of interactions for three different dimensions of patients' symptom experience (i.e., occurrence, severity, distress). Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network.

Learning from data to predict future symptoms of oncology patients.

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient's treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions.

Prioritizing medication safety in care of people with cancer: clinicians' views on main problems and solutions.

BACKGROUND: Cancer care is liable to medication errors due to the complex nature of cancer treatment, the common presence of comorbidities and the involvement of a number of clinicians in cancer care. While the frequency of medication errors in cancer care has been reported, little is known about their causal factors and effective prevention strategies. With a unique insight into the main safety issues in cancer treatment, frontline staff can help close this gap. In this study, we aimed to identify medication safety priorities in cancer patient care according to clinicians in North West London using PRIORITIZE, a novel priority-setting approach. METHODS: The project steering group determined the scope, the context and the criteria for prioritization. We then invited North West London cancer care clinicians to identify and prioritize main causes for, and solutions to, medication errors in cancer care. Forty cancer care providers submitted their suggestions which were thematically synthesized into a composite list of 20 distinct problems and 22 solutions. A group of 26 clinicians from the initial cohort ranked the composite list of suggestions using predetermined criteria. RESULTS: The top ranked problems focused on patients' poor understanding of treatments due to language or education difficulties, clinicians' insufficient attention to patients' psychological distress, and inadequate information sharing among health care providers. The top ranked solutions were provision of guidance to patients and their carers on what to do when unwell, pre-chemotherapy work-up for all patients and better staff training. Overall, clinicians considered improved communication between health care providers, quality assurance procedures (during prescription and monitoring stages) and patient education as key strategies for improving cancer medication safety. Prescribing stage was identified as the most vulnerable to medication safety threats. The highest ranked suggestions received the strongest agreement among the clinicians. CONCLUSIONS: Clinician-identified priorities for reducing medication errors in cancer care addressed various aspects of cancer treatment. Our findings open up an opportunity to assess the congruence between health care professional suggestions, currently implemented patient safety policies and evidence base.

Preventing delayed diagnosis of cancer: clinicians' views on main problems and solutions.

BACKGROUND: Delayed diagnosis is a major contributing factor to the UK's lower cancer survival compared to many European countries. In the UK, there is a significant national variation in early cancer diagnosis. Healthcare providers can offer an insight into local priorities for timely cancer diagnosis. In this study, we aimed to identify the main problems and solutions relating to delay cancer diagnosis according to cancer care clinicians. METHODS: We developed and implemented a new priority-setting approach called PRIORITIZE and invited North West London cancer care clinicians to identify and prioritize main causes for and solutions to delayed diagnosis of cancer care. RESULTS: Clinicians identified a number of concrete problems and solutions relating to delayed diagnosis of cancer. Raising public awareness, patient education as well as better access to specialist care and diagnostic testing were seen as the highest priorities. The identified suggestions focused mostly on the delays during referrals from primary to secondary care. CONCLUSIONS: Many identified priorities were feasible, affordable and converged around common themes such as public awareness, care continuity and length of consultation. As a timely, proactive and scalable priority-setting approach, PRIORITZE could be implemented as a routine preventative system for determining patient safety issues by frontline staff.

Deficiency in apolipoprotein A-I ablates the pharmacological effects of metformin on plasma glucose homeostasis and hepatic lipid deposition.

Recently, we showed that deficiency in apolipoprotein A-I (ApoA-I) sensitizes mice to diet-induced obesity, glucose intolerance and NAFLD. Here we investigated the potential involvement of ApoA-I in the pharmacological effects of metformin on glucose intolerance and NAFLD development. Groups of apoa1-deficient (apoa1(-/-)) and C57BL/6 mice fed western-type diet were either treated with a daily dose of 300 mg/kg metformin for 18 weeks or left untreated for the same period. Then, histological and biochemical analyses were performed. Metformin treatment led to a comparable reduction in plasma insulin levels in both C57BL/6 and apoa1(-/-) mice following intraperitoneal glucose tolerance test. However, only metformin-treated C57BL/6 mice maintained sufficient peripheral insulin sensitivity to effectively clear glucose following the challenge, as indicated by a [(3)H]-2-deoxy-D-glucose uptake assay in isolated soleus muscle. Similarly, deficiency in ApoA-I ablated the effect of metformin on hepatic lipid deposition and NAFLD development. Gene expression analysis indicated that the effects of ApoA-I on metformin treatment may be independent of adenosine monophosphate-activated protein kinase (AMPK) activation and de novo lipogenesis. Interestingly, metformin treatment reduced mitochondrial oxidative phosphorylation function only in apoa1(-/-) mice. Our data show that the role of ApoA-I in diabetes extends to the modulation of the pharmacological actions of metformin, a common drug for the treatment of type 2 diabetes.