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ABSTRACT The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
RESUMO A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.
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ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.
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BACKGROUND: Electronic patient-reported outcome (ePRO) systems are increasingly used in clinical trials to provide evidence of efficacy and tolerability of treatment from the patient perspective. The aim of this study is twofold: (1) to describe how we developed an electronic platform for patients to report their symptoms, and (2) to develop and undertake usability testing of an ePRO solution for use in a study of cell therapy seeking to provide early evidence of efficacy and tolerability of treatment and test the feasibility of the system for use in later phase studies. METHODS: An ePRO system was designed to be used in a single arm, multi-centre, phase II basket trial investigating the safety and activity of the use of ORBCEL-C™ in the treatment of patients with inflammatory conditions. ORBCEL-C™ is an enriched Mesenchymal Stromal Cells product isolated from human umbilical cord tissue using CD362+ cell selection. Usability testing sessions were conducted using cognitive interviews and the 'Think Aloud' method with patient advisory group members and Research Nurses to assess the usability of the system. RESULTS: Nine patient partners and seven research nurses took part in one usability testing session. Measures of fatigue and health-related quality of life, the PRO-CTCAE™ and FACT-GP5 global tolerability question were included in the ePRO system. Alert notifications to the clinical team were triggered by PRO-CTCAE™ and FACT-GP5 scores. Patient participants liked the simplicity and responsiveness of the patient-facing app. Two patients were unable to complete the testing session, due to technical issues. Research Nurses suggested minor modifications to improve functionality and the layout of the clinician dashboard and the training materials. CONCLUSION: By testing the effectiveness, efficiency, and satisfaction of our novel ePRO system (PROmicsR), we learnt that most people with an inflammatory condition found it easy to report their symptoms using an app on their own device. Their experiences using the PROmicsR ePRO system within a trial environment will be further explored in our upcoming feasibility testing. Research nurses were also positive and found the clinical dashboard easy-to-use. Using ePROs in early phase trials is important in order to provide evidence of therapeutic responses and tolerability, increase the evidence based, and inform methodology development. TRIAL REGISTRATION: ISRCTN, ISRCTN80103507. Registered 01 April 2022, https://www.isrctn.com/ISRCTN80103507.
More and more patients tell clinicians how they feel by completing questionnaires electronically. Therefore, it is important to assess how easy it is for patients to do this. In this study, we describe how we developed an electronic platform for patients to report their symptoms and how we tested the usability of this platform with patient partners and research nurses. Once the electronic platform was developed, quality of life and symptoms questionnaires were programmed onto it. Alerts were sent to the clinical team if specific scores were obtained on the symptoms questionnaires. Although two patient partners were not able to finish the testing session because of technical issues, the ones who completed the session liked its simplicity and responsiveness. The research nurses also liked the system and only suggested minor modifications. Following this testing, we refined the electronic platform to test it further in a larger study which investigates the safety and use of a drug. We hope that thanks to this electronic platform, we will obtain useful information on the safety and efficacy of treatment.
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Calidad de Vida , Diseño Centrado en el Usuario , Humanos , Interfaz Usuario-Computador , Electrónica , Medición de Resultados Informados por el PacienteRESUMEN
Patient and public involvement and engagement (PPIE) can provide valuable insights into the experiences of those living with and affected by a disease or health condition. Inclusive collaboration between patients, the public and researchers can lead to productive relationships, ensuring that health research addresses patient needs. Guidelines are available to support effective PPIE; however, evaluation of the impact of PPIE strategies in health research is limited. In this Review, we evaluate the impact of PPIE in the 'Therapies for Long COVID in non-hospitalised individuals' (TLC) Study, using a combination of group discussions and interviews with patient partners and researchers. We identify areas of good practice and reflect on areas for improvement. Using these insights and the results of a survey, we synthesize two checklists of considerations for PPIE, and we propose that research teams use these checklists to optimize the impact of PPIE for both patients and researchers in future studies.
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COVID-19 , Síndrome Post Agudo de COVID-19 , Humanos , COVID-19/epidemiología , Lista de Verificación , Participación del Paciente , PacientesRESUMEN
Androgen receptor splice variants (AR-SVs) contribute to the aggressive growth of castration-resistant prostate cancer (CRPC). AR-SVs, including AR-V7, are expressed in ~30% of CRPC, but minimally in treatment-naïve primary prostate cancer (PCa). Compared to Caucasian American (CA) men, African American (AA) men are more likely to be diagnosed with aggressive/potentially lethal PCa and have shorter disease-free survival. Expression of a truncated AR in an aggressively growing patient-derived xenograft developed with a primary PCa specimen from an AA patient led us to hypothesize that the expression of AR-SVs could be an indicator of aggressive growth both in PCa progression and at the CRPC stage in AA men. Tissue microarrays (TMAs) were created from formalin-fixed paraffin-embedded (FFPE) prostatectomy tumor blocks from 118 AA and 115 CA treatment-naïve PCa patients. TMAs were stained with AR-V7-speicifc antibody and with antibodies binding to the N-terminus domain (NTD) and ligand-binding domain (LBD) of the AR. Since over 20 AR-SVs have been identified, and most AR-SVs do not as yet have a specific antibody, we considered a 2.0-fold or greater difference in the NTD vs. LBD staining as indication of potential AR-SV expression. Two AA, but no CA, patient tumors stained positively for AR-V7. AR staining with NTD and LBD antibodies was robust in most patients, with 21% of patients staining at least 2-fold more for NTD than LBD, indicating that AR-SVs other than AR-V7 are expressed in primary treatment-naïve PCa. About 24% of the patients were AR-negative, and race differences in AR expression were not statistically significant. These results indicate that AR-SVs are not restricted to CRPC, but also are expressed in primary PCa at higher rate than previously reported. Future investigation of the relative expression of NTD vs. LBD AR-SVs could guide the use of newly developed treatments targeting the NTD earlier in the treatment paradigm.
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OBJECTIVES: To estimate the cost and cost-effectiveness of Bright Bodies, a high-intensity, family-based intervention that has been demonstrated to improve body mass index (BMI) among children with obesity in a randomized controlled trial. METHODS: We developed a microsimulation model to project 10-year BMI trajectories of 8 to 16-year-old children with obesity, using data from the National Longitudinal Surveys and Centers for Disease Control and Prevention growth charts, and we validated the model using data from the Bright Bodies trial and a follow-up study. We used the trial data to estimate the average reduction in BMI per person-year over 10 years and the incremental costs of Bright Bodies, compared with the traditional clinical weight management (control), from a health system's perspective in 2020 US dollars. Using results from studies of Medical Expenditure Panel Survey data, we projected the long-term obesity-related medical expenditure. RESULTS: In the primary analysis, assuming depreciating effects postintervention, Bright Bodies is expected to reduce a participant's BMI by 1.67 kg/m2 (95% uncertainty interval 1.43-1.94) per year over 10 years as compared with control. The incremental intervention cost of Bright Bodies was $360 ($292-$421) per person compared with the clinical control. Nevertheless, savings in obesity-related healthcare expenditure offset these costs and the expected cost-savings of Bright Bodies is $1126 ($689-$1693) per person over 10-years. The projected time to achieve cost-savings compared with clinical control was 3.58 (2.63-5.17) years. CONCLUSIONS: Although resource-intensive, our findings suggest that Bright Bodies is cost-saving compared to the clinical control by averting future obesity-related healthcare costs among children with obesity.
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Obesidad Infantil , Humanos , Niño , Adolescente , Obesidad Infantil/prevención & control , Análisis Costo-Beneficio , Estudios de Seguimiento , Índice de Masa CorporalRESUMEN
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ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.
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OBJECTIVE: To describe the development and validation of a novel patient reported outcome measure for symptom burden from long covid, the symptom burden questionnaire for long covid (SBQ-LC). DESIGN: Multiphase, prospective mixed methods study. SETTING: Remote data collection and social media channels in the United Kingdom, 14 April to 1 August 2021. PARTICIPANTS: 13 adults (aged ≥18 years) with self-reported long covid and 10 clinicians evaluated content validity. 274 adults with long covid field tested the draft questionnaire. MAIN OUTCOME MEASURES: Published systematic reviews informed development of SBQ-LC's conceptual framework and initial item pool. Thematic analysis of transcripts from cognitive debriefing interviews and online clinician surveys established content validity. Consensus discussions with the patient and public involvement group of the Therapies for Long COVID in non-hospitalised individuals: From symptoms, patient reported outcomes and immunology to targeted therapies (TLC Study) confirmed face validity. Rasch analysis of field test data guided item and scale refinement and provided initial evidence of the SBQ-LC's measurement properties. RESULTS: SBQ-LC (version 1.0) is a modular instrument measuring patient reported outcomes and is composed of 17 independent scales with promising psychometric properties. Respondents rate their symptom burden during the past seven days using a dichotomous response or 4 point rating scale. Each scale provides coverage of a different symptom domain and returns a summed raw score that can be transformed to a linear (0-100) score. Higher scores represent higher symptom burden. After rating scale refinement and item reduction, all scales satisfied the Rasch model requirements for unidimensionality (principal component analysis of residuals: first residual contrast values <2.00 eigenvalue units) and item fit (outfit mean square values within 0.5 -1.5 logits). Rating scale categories were ordered with acceptable category fit statistics (outfit mean square values <2.0 logits). 14 item pairs had evidence of local dependency (residual correlation values >0.4). Across the 17 scales, person reliability ranged from 0.34 to 0.87, person separation ranged from 0.71 to 2.56, item separation ranged from 1.34 to 13.86, and internal consistency reliability (Cronbach's alpha) ranged from 0.56 to 0.91. CONCLUSIONS: SBQ-LC (version 1.0) is a comprehensive patient reported outcome instrument developed using modern psychometric methods. It measures symptoms of long covid important to people with lived experience of the condition and may be used to evaluate the impact of interventions and inform best practice in clinical management.
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COVID-19 , Adolescente , Adulto , COVID-19/complicaciones , Humanos , Estudios Prospectivos , Reproducibilidad de los Resultados , Encuestas y Cuestionarios , Síndrome Post Agudo de COVID-19RESUMEN
INTRODUCTION: Individuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4-12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies. METHODS AND ANALYSIS: A cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink, and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability and patient-reported outcome measures. Data will be collected monthly for 1 year.Statistical clustering methods will be used to identify distinct Long COVID-19 symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear substudy which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy.We will review existing evidence on interventions for postviral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulative evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation.Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers. TRIAL REGISTRATION NUMBER: 1567490.
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COVID-19 , COVID-19/complicaciones , COVID-19/terapia , Prueba de COVID-19 , Humanos , Medición de Resultados Informados por el Paciente , Calidad de Vida , Síndrome , Síndrome Post Agudo de COVID-19RESUMEN
Functional cognitive disorder is common but underlying mechanisms remain poorly understood. Metacognition, an individual's ability to reflect on and monitor cognitive processes, is likely to be relevant. Local metacognition refers to an ability to estimate confidence in cognitive performance on a moment-to-moment basis, whereas global metacognition refers to long-run self-evaluations of overall performance. Using a novel protocol comprising task-based measures and hierarchical Bayesian modelling, we compared local and global metacognitive performance in individuals with functional cognitive disorder. Eighteen participants with functional cognitive disorder (mean age = 49.2 years, 10 males) were recruited to this cross-sectional study. Participants completed computerized tasks that enabled local metacognitive efficiency for perception and memory to be measured using the hierarchical meta-d' model within a signal detection theory framework. Participants also completed the Multifactorial Memory Questionnaire measuring global metacognition, and questionnaires measuring anxiety and depression. Estimates of local metacognitive efficiency were compared with those estimated from two control groups who had undergone comparable metacognitive tasks. Global metacognition scores were compared with the existing normative data. A hierarchical regression model was used to evaluate associations between global metacognition, depression and anxiety and local metacognitive efficiency, whilst simple linear regressions were used to evaluate whether affective symptomatology and local metacognitive confidence were associated with global metacognition. Participants with functional cognitive disorder had intact local metacognition for perception and memory when compared with controls, with the 95% highest density intervals for metacognitive efficiency overlapping with the two control groups in both cognitive domains. Functional cognitive disorder participants had significantly lower global metacognition scores compared with normative data; Multifactorial Memory Questionnaire-Ability subscale (t = 6.54, P < 0.0001) and Multifactorial Memory Questionnaire-Satisfaction subscale (t = 5.04, P < 0.0001). Mood scores, global metacognitive measures and metacognitive bias were not significantly associated with local metacognitive efficiency. Local metacognitive bias [ß = -0.20 (SE = 0.09), q = 0.01] and higher depression scores as measured by the Patient Health Questionnaire-9 [ß = -1.40 (SE = 2.56), q = 0.01] were associated with the lower global metacognition scores. We show that local metacognition is intact, whilst global metacognition is impaired, in functional cognitive disorder, suggesting a decoupling between the two metacognitive processes. In a Bayesian model, an aberrant prior (impaired global metacognition), may override bottom-up sensory input (intact local metacognition), giving rise to the subjective experience of abnormal cognitive processing. Future work should further investigate the interplay between local and global metacognition in functional cognitive disorder.
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OBJECTIVES: The use of routine remote follow-up of patients with chronic kidney disease (CKD) is increasing exponentially. It has been suggested that online electronic patient-reported outcome measures (ePROMs) could be used in parallel, to facilitate real-time symptom monitoring aimed at improving outcomes. We tested the feasibility of this approach in a pilot trial of ePROM symptom monitoring versus usual care in patients with advanced CKD not on dialysis. DESIGN: A 12-month, parallel, pilot randomised controlled trial (RCT) and qualitative substudy. SETTING AND PARTICIPANTS: Queen Elizabeth Hospital Birmingham, UK. Adult patients with advanced CKD (estimated glomerular filtration rate ≥6 and ≤15 mL/min/1.73 m2, or a projected risk of progression to kidney failure within 2 years ≥20%). INTERVENTION: Monthly online ePROM symptom reporting, including automated feedback of tailored self-management advice and triggered clinical notifications in the advent of severe symptoms. Real-time ePROM data were made available to the clinical team via the electronic medical record. OUTCOMES: Feasibility (recruitment and retention rates, and acceptability/adherence to the ePROM intervention). Health-related quality of life, clinical data (eg, measures of kidney function, kidney failure, hospitalisation, death) and healthcare utilisation. RESULTS: 52 patients were randomised (31% of approached). Case report form returns were high (99.5%), as was retention (96%). Overall, 73% of expected ePROM questionnaires were received. Intervention adherence was high beyond 90 days (74%) and 180 days (65%); but dropped beyond 270 days (46%). Qualitative interviews supported proof of concept and intervention acceptability, but highlighted necessary changes aimed at enhancing overall functionality/scalability of the ePROM system. LIMITATIONS: Small sample size. CONCLUSIONS: This pilot trial demonstrates that patients are willing to be randomised to a trial assessing ePROM symptom monitoring. The intervention was considered acceptable; though measures to improve longer-term engagement are needed. A full-scale RCT is considered feasible. TRIAL REGISTRATION NUMBER: ISRCTN12669006 and the UK NIHR Portfolio (CPMS ID: 36497).
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Diálisis Renal , Insuficiencia Renal Crónica , Adulto , Electrónica , Estudios de Factibilidad , Humanos , Medición de Resultados Informados por el Paciente , Insuficiencia Renal Crónica/terapia , Reino UnidoRESUMEN
BACKGROUND/AIMS: Functional visual symptoms are relatively common symptoms seen by ophthalmologists. However, there are no consensus guidelines on ophthalmological management of this condition, and there is a paucity of knowledge about the collective challenges experienced in treating patients with functional visual symptoms. In order to establish an ophthalmological perspective on this condition, we undertook the first national survey of experience, knowledge and management of functional visual symptoms amongst ophthalmologists. METHODS: An online survey was disseminated to ophthalmologists in the UK via all Royal College of Ophthalmology college tutors. RESULTS: One hundred nineteen ophthalmologists completed the survey. Functional visual symptoms accounted for 3% of all new referrals. Forty per cent of respondents felt they had a good understanding of functional visual symptoms. Two-thirds reported a need for further training in this area. Respondents estimated two-thirds of patients' symptoms improved, but a third experienced severe or extreme disability. Following diagnosis, a minority of patients were referred to mental health or neurology services. The majority of respondents described difficulty discussing psychological factors, with a lack of time or space in a clinic preventing a holistic approach. Free text comments highlighted a lack of access to dedicated psychological support for patients. CONCLUSION: Functional visual symptoms are disabling and are seen relatively frequently by ophthalmologists. This preliminary survey suggests that care pathways for patients with functional visual symptoms could be optimised. Fostering links between ophthalmology and existing services with expertise in functional disorders could improve patient care and clinician education and ultimately encourage research in this area.
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Oftalmólogos , Oftalmología , Humanos , Oftalmología/educación , Encuestas y Cuestionarios , Reino Unido/epidemiologíaRESUMEN
INTRODUCTION: The use of electronic patient-reported outcome (ePRO) systems to capture PRO data in clinical trials is increasing; however, their feasibility, acceptability and utility in clinical trials of advanced therapy medicinal products (ATMPs) are not yet well understood. This protocol describes a qualitative study that aims to evaluate the feasibility and acceptability of ePRO data capture using a trial-specific ePRO system (the PROmics system) within an advanced therapy trial involving patients with immune-mediated inflammatory disease (rheumatoid arthritis, lupus, primary sclerosing cholangitis (PSC) and Crohn's disease). METHODS AND ANALYSIS: This protocol for a remote, qualitative, interview-based feasibility study is embedded within the POLARISE trial, a single-arm, phase II, multisite ATMP basket trial in the UK. 10-15 patients enrolled in the POLARISE trial and 10-15 research team members at the trial sites will be recruited. Participants will take part in semistructured interviews which will be transcribed verbatim and analysed thematically according to the framework method. Data collection and analysis will occur concurrently and iteratively. Researcher triangulation will be used to achieve a consensus-based analysis, enhancing rigour and trustworthiness. ETHICS AND DISSEMINATION: This study was approved by the London-West London and GTAC Research Ethics Committee (Ref: 21/LO/0475). Informed consent will be obtained from all participants prior to data collection. The study findings will be published in peer-review journals and disseminated via conference presentations and other media. Our patient and public involvement and engagement group and ATMP stakeholder networks will be consulted to maximise dissemination and impact. TRIAL REGISTRATION NUMBER: ISRCTN80103507.
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Electrónica , Medición de Resultados Informados por el Paciente , Humanos , Estudios de Factibilidad , Investigación Cualitativa , Recolección de DatosRESUMEN
Globally, there are now over 160 million confirmed cases of COVID-19 and more than 3 million deaths. While the majority of infected individuals recover, a significant proportion continue to experience symptoms and complications after their acute illness. Patients with 'long COVID' experience a wide range of physical and mental/psychological symptoms. Pooled prevalence data showed the 10 most prevalent reported symptoms were fatigue, shortness of breath, muscle pain, joint pain, headache, cough, chest pain, altered smell, altered taste and diarrhoea. Other common symptoms were cognitive impairment, memory loss, anxiety and sleep disorders. Beyond symptoms and complications, people with long COVID often reported impaired quality of life, mental health and employment issues. These individuals may require multidisciplinary care involving the long-term monitoring of symptoms, to identify potential complications, physical rehabilitation, mental health and social services support. Resilient healthcare systems are needed to ensure efficient and effective responses to future health challenges.
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COVID-19/complicaciones , Calidad de Vida , COVID-19/terapia , Atención a la Salud , Diarrea/etiología , Empleo , Fatiga/etiología , Cefalea/etiología , Humanos , Trastornos Mentales/etiología , Salud Mental , Dolor/etiología , Enfermedades Respiratorias/etiología , SARS-CoV-2 , Trastornos de la Sensación/etiología , Síndrome Post Agudo de COVID-19RESUMEN
Patient-reported outcomes (PROs) are used in clinical trials to provide valuable evidence on the impact of disease and treatment on patients' symptoms, function and quality of life. High-quality PRO data from trials can inform shared decision-making, regulatory and economic analyses and health policy. Recent evidence suggests the PRO content of past trial protocols was often incomplete or unclear, leading to research waste. To address this issue, international, consensus-based, PRO-specific guidelines were developed: the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-PRO Extension. The SPIRIT-PRO Extension is a 16-item checklist which aims to improve the content and quality of aspects of clinical trial protocols relating to PRO data collection to minimise research waste, and ultimately better inform patient-centred care. This SPIRIT-PRO explanation and elaboration (E&E) paper provides information to promote understanding and facilitate uptake of the recommended checklist items, including a comprehensive protocol template. For each SPIRIT-PRO item, we provide a detailed description, one or more examples from existing trial protocols and supporting empirical evidence of the item's importance. We recommend this paper and protocol template be used alongside the SPIRIT 2013 and SPIRIT-PRO Extension paper to optimise the transparent development and review of trial protocols with PROs.
Asunto(s)
Calidad de Vida , Proyectos de Investigación , Lista de Verificación , Humanos , Medición de Resultados Informados por el Paciente , Informe de InvestigaciónRESUMEN
OBJECTIVES: (a) To adapt the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-patient-reported outcome (PRO) Extension guidance to a user-friendly format for patient partners and (b) to codesign a web-based tool to support the dissemination and uptake of the SPIRIT-PRO Extension by patient partners. DESIGN: A 1-day patient and public involvement session. PARTICIPANTS: Seven patient partners. METHODS: A patient partner produced an initial lay summary of the SPIRIT-PRO guideline and a glossary. We held a 1-day PPI session in November 2019 at the University of Birmingham. Five patient partners discussed the draft lay summary, agreed on the final wording, codesigned and agreed the final content for both tools. Two additional patient partners were involved in writing the manuscript. The study compiled with INVOLVE guidelines and was reported according to the Guidance for Reporting Involvement of Patients and the Public 2 checklist. RESULTS: Two user-friendly tools were developed to help patients and members of the public be involved in the codesign of clinical trials collecting PROs. The first tool presents a lay version of the SPIRIT-PRO Extension guidance. The second depicts the most relevant points, identified by the patient partners, of the guidance through an interactive flow diagram. CONCLUSIONS: These tools have the potential to support the involvement of patient partners in making informed contributions to the development of PRO aspects of clinical trial protocols, in accordance with the SPIRIT-PRO Extension guidelines. The involvement of patient partners ensured the tools focused on issues most relevant to them.
Asunto(s)
Lista de Verificación , Medición de Resultados Informados por el Paciente , Humanos , Informe de InvestigaciónRESUMEN
INTRODUCTION: Health Data Research UK designated seven UK-based Hubs to facilitate health data use for research. PIONEER is the Hub in Acute Care. PIONEER delivered workshops where patients/public citizens agreed key principles to guide access to unconsented, anonymised, routinely collected health data. These were used to inform the protocol. METHODS: This paper describes the PIONEER infrastructure and data access processes. PIONEER is a research database and analytical environment that links routinely collected health data across community, ambulance and hospital healthcare providers. PIONEER aims ultimately to improve patient health and care, by making health data discoverable and accessible for research by National Health Service, academic and commercial organisations. The PIONEER protocol incorporates principles identified in the public/patient workshops. This includes all data access requests being reviewed by the Data Trust Committee, a group of public citizens who advise on whether requests should be supported prior to licensed access. ETHICS AND DISSEMINATION: East Midlands-Derby REC (20/EM/0158): Confidentiality Advisory Group (20/CAG/0084). www.PIONEERdatahub.co.uk.
