RESUMO
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.
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.
RESUMO
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.
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.
RESUMO
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.
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.
RESUMO
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.
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.
RESUMO
Importance: Patient-reported outcomes (PROs) can inform health care decisions, regulatory decisions, and health care policy. They also can be used for audit/benchmarking and monitoring symptoms to provide timely care tailored to individual needs. However, several ethical issues have been raised in relation to PRO use. Objective: To develop international, consensus-based, PRO-specific ethical guidelines for clinical research. Evidence Review: The PRO ethics guidelines were developed following the Enhancing the Quality and Transparency of Health Research (EQUATOR) Network's guideline development framework. This included a systematic review of the ethical implications of PROs in clinical research. The databases MEDLINE (Ovid), Embase, AMED, and CINAHL were searched from inception until March 2020. The keywords patient reported outcome* and ethic* were used to search the databases. Two reviewers independently conducted title and abstract screening before full-text screening to determine eligibility. The review was supplemented by the SPIRIT-PRO Extension recommendations for trial protocol. Subsequently, a 2-round international Delphi process (n = 96 participants; May and August 2021) and a consensus meeting (n = 25 international participants; October 2021) were held. Prior to voting, consensus meeting participants were provided with a summary of the Delphi process results and information on whether the items aligned with existing ethical guidance. Findings: Twenty-three items were considered in the first round of the Delphi process: 6 relevant candidate items from the systematic review and 17 additional items drawn from the SPIRIT-PRO Extension. Ninety-six international participants voted on the relevant importance of each item for inclusion in ethical guidelines and 12 additional items were recommended for inclusion in round 2 of the Delphi (35 items in total). Fourteen items were recommended for inclusion at the consensus meeting (n = 25 participants). The final wording of the PRO ethical guidelines was agreed on by consensus meeting participants with input from 6 additional individuals. Included items focused on PRO-specific ethical issues relating to research rationale, objectives, eligibility requirements, PRO concepts and domains, PRO assessment schedules, sample size, PRO data monitoring, barriers to PRO completion, participant acceptability and burden, administration of PRO questionnaires for participants who are unable to self-report PRO data, input on PRO strategy by patient partners or members of the public, avoiding missing data, and dissemination plans. Conclusions and Relevance: The PRO ethics guidelines provide recommendations for ethical issues that should be addressed in PRO clinical research. Addressing ethical issues of PRO clinical research has the potential to ensure high-quality PRO data while minimizing participant risk, burden, and harm and protecting participant and researcher welfare.
Assuntos
Pesquisa Biomédica/ética , Ética Clínica , Medidas de Resultados Relatados pelo Paciente , Consenso , Técnica Delphi , Humanos , Princípios Morais , Guias de Prática Clínica como Assunto , Projetos de Pesquisa , Relatório de PesquisaRESUMO
PURPOSE: To develop an agreed upon set of outcomes known as a "core outcome set" (COS) for noninfectious uveitis of the posterior segment (NIU-PS) clinical trials. DESIGN: Mixed-methods study design comprising a systematic review and qualitative study followed by a 2-round Delphi exercise and face-to-face consensus meeting. PARTICIPANTS: Key stakeholders including patients diagnosed with NIU-PS, their caregivers, and healthcare professionals involved in decision-making for patients with NIU-PS, including ophthalmologists, nurse practitioners, and policymakers/commissioners. METHODS: A long list of outcomes was developed based on the results of (1) a systematic review of clinical trials of NIU-PS and (2) a qualitative study of key stakeholders including focus groups and interviews. The long list was used to generate a 2-round Delphi exercise of stakeholders rating the importance of outcomes on a 9-point Likert scale. The proportion of respondents rating each item was calculated, leading to recommendations of "include," "exclude," or "for discussion" that were taken to a face-to-face consensus meeting of key stakeholders at which they agreed on the final COS. MAIN OUTCOME MEASURE: Items recommended for inclusion in the COS for NIU-PS. RESULTS: A total of 57 outcomes grouped in 11 outcome domains were presented for evaluation in the Delphi exercise, resulting in 9 outcomes directly qualifying for inclusion and 15 outcomes being carried forward to the consensus meeting, of which 7 of 15 were agreed on for inclusion. The final COS contained 16 outcomes organized into 4 outcome domains comprising visual function, health-related quality of life, treatment side effects, and disease control. CONCLUSIONS: This study builds on international work across the clinical trials community and our qualitative research to construct the world's first COS for NIU-PS. The COS provides a list of outcomes that represent the priorities of key stakeholders and provides a minimum set of outcomes for use in all future NIU-PS clinical trials. Adoption of this COS can improve the value of future uveitis clinical trials and reduce noninformative research. Some of the outcomes identified do not yet have internationally agreed upon methods for measurement and should be the subject of future international consensus development.
Assuntos
Ensaios Clínicos como Assunto/métodos , Determinação de Ponto Final/métodos , Avaliação de Resultados em Cuidados de Saúde/métodos , Uveíte Posterior/terapia , Adulto , Idoso , Cuidadores/psicologia , Consenso , Técnica Delphi , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Oftalmologistas/psicologia , Pacientes/psicologia , Qualidade de Vida , Projetos de Pesquisa , Revisões Sistemáticas como Assunto , Uveíte Posterior/diagnóstico , Uveíte Posterior/psicologia , Acuidade Visual/fisiologiaRESUMO
PURPOSE: Patient-reported outcomes (PROs) are increasingly used in clinical trials to provide patients' perspectives regarding symptoms, health-related quality of life, and satisfaction with treatments. A range of guidance documents exist for the selection of patient-reported outcome measures (PROMs) in clinical trials, and it is unclear to what extent these documents present consistent recommendations. METHODS: We conducted a targeted review of publications and regulatory guidance documents that advise on the selection of PROMs for use in clinical trials. A total of seven guidance documents from the US Food and Drug Administration, European Medicines Agency, and scientific consortia from professional societies were included in the final review. Guidance documents were analyzed using a content analysis approach comparing them with minimum standards recommended by the International Society for Quality of Life Research. RESULTS: Overall there was substantial agreement between guidance regarding the appropriate considerations for PROM selection within a clinical trial. Variations among the guidance primarily related to differences in their format and differences in the perspectives and mandates of their respective organizations. Whereas scientific consortia tended to produce checklist or rating-type guidance, regulatory groups tended to use more narrative-based approaches sometimes supplemented with lists of criteria. CONCLUSION: The consistency in recommendations suggests an emerging consensus in the field and supports use of any of the major guidance documents available to guide PROM selection for clinical trials without concern of conflicting recommendations. This work represents an important first step in the international PROTEUS Consortium's ongoing efforts to optimize the use of PROs in clinical trials.
Assuntos
Medidas de Resultados Relatados pelo Paciente , Qualidade de Vida/psicologia , Ensaios Clínicos como Assunto , HumanosRESUMO
AIMS: To establish the extent and impact of symptoms in patients with atrial fibrillation (AF), the importance of different aspects of quality of life (QoL), and how we should assess wellbeing. METHODS: Focus groups of patients with symptomatic permanent AF in a trial of heart rate control; the RATE-AF trial randomised 160 patients aged ≥60 years with permanent AF and at least NYHA class II dyspnoea to either digoxin or beta-blockers. Patient and public representatives led the focus groups and performed all data acquisition and analysis, using thematic approaches to interpret patient views about QoL and its measurement. RESULTS: Substantial impairment of health-related QoL was noted in 160 trial patients, with impact on all domains apart from mental health. Eight women and 11 men aged 61-87 years participated in the focus groups. Common themes were a lack of information from healthcare professionals about AF, a lack of focus on QoL in consultations, and a sense of frustration, isolation, and reduced confidence. There was marked variability in symptoms in individual patients, with some describing severe impact on activities of daily living, and profound interaction with comorbidities such as arthritis. Day-to-day variation in QoL and difficulty in attributing symptom burden to AF or other comorbidities led to challenges in questionnaire completion. Consensus was reached that collecting both general and AF-specific QoL would be useful in routine practice, along with participation in peer support, which was empowering for the patients. CONCLUSIONS: The impact of comorbidities is poorly appreciated in the context of AF, with considerable variability in QoL that requires both generic and AF-specific assessment. Improvement in QoL should direct the appraisal, and reappraisal, of treatment decisions for patients with permanent AF.
Assuntos
Fibrilação Atrial , Qualidade de Vida , Atividades Cotidianas , Feminino , Grupos Focais , Humanos , Masculino , Inquéritos e QuestionáriosRESUMO
Importance: There is little evidence to support selection of heart rate control therapy in patients with permanent atrial fibrillation, in particular those with coexisting heart failure. Objective: To compare low-dose digoxin with bisoprolol (a ß-blocker). Design, Setting, and Participants: Randomized, open-label, blinded end-point clinical trial including 160 patients aged 60 years or older with permanent atrial fibrillation (defined as no plan to restore sinus rhythm) and dyspnea classified as New York Heart Association class II or higher. Patients were recruited from 3 hospitals and primary care practices in England from 2016 through 2018; last follow-up occurred in October 2019. Interventions: Digoxin (n = 80; dose range, 62.5-250 µg/d; mean dose, 161 µg/d) or bisoprolol (n = 80; dose range, 1.25-15 mg/d; mean dose, 3.2 mg/d). Main Outcomes and Measures: The primary end point was patient-reported quality of life using the 36-Item Short Form Health Survey physical component summary score (SF-36 PCS) at 6 months (higher scores are better; range, 0-100), with a minimal clinically important difference of 0.5 SD. There were 17 secondary end points (including resting heart rate, modified European Heart Rhythm Association [EHRA] symptom classification, and N-terminal pro-brain natriuretic peptide [NT-proBNP] level) at 6 months, 20 end points at 12 months, and adverse event (AE) reporting. Results: Among 160 patients (mean age, 76 [SD, 8] years; 74 [46%] women; mean baseline heart rate, 100/min [SD, 18/min]), 145 (91%) completed the trial and 150 (94%) were included in the analysis for the primary outcome. There was no significant difference in the primary outcome of normalized SF-36 PCS at 6 months (mean, 31.9 [SD, 11.7] for digoxin vs 29.7 [11.4] for bisoprolol; adjusted mean difference, 1.4 [95% CI, -1.1 to 3.8]; P = .28). Of the 17 secondary outcomes at 6 months, there were no significant between-group differences for 16 outcomes, including resting heart rate (a mean of 76.9/min [SD, 12.1/min] with digoxin vs a mean of 74.8/min [SD, 11.6/min] with bisoprolol; difference, 1.5/min [95% CI, -2.0 to 5.1/min]; P = .40). The modified EHRA class was significantly different between groups at 6 months; 53% of patients in the digoxin group reported a 2-class improvement vs 9% of patients in the bisoprolol group (adjusted odds ratio, 10.3 [95% CI, 4.0 to 26.6]; P < .001). At 12 months, 8 of 20 outcomes were significantly different (all favoring digoxin), with a median NT-proBNP level of 960 pg/mL (interquartile range, 626 to 1531 pg/mL) in the digoxin group vs 1250 pg/mL (interquartile range, 847 to 1890 pg/mL) in the bisoprolol group (ratio of geometric means, 0.77 [95% CI, 0.64 to 0.92]; P = .005). Adverse events were less common with digoxin; 20 patients (25%) in the digoxin group had at least 1 AE vs 51 patients (64%) in the bisoprolol group (P < .001). There were 29 treatment-related AEs and 16 serious AEs in the digoxin group vs 142 and 37, respectively, in the bisoprolol group. Conclusions and Relevance: Among patients with permanent atrial fibrillation and symptoms of heart failure treated with low-dose digoxin or bisoprolol, there was no statistically significant difference in quality of life at 6 months. These findings support potentially basing decisions about treatment on other end points. Trial Registration: ClinicalTrials.gov Identifier: NCT02391337 and clinicaltrialsregister.eu Identifier: 2015-005043-13.
Assuntos
Antiarrítmicos/uso terapêutico , Fibrilação Atrial/tratamento farmacológico , Bisoprolol/uso terapêutico , Digoxina/uso terapêutico , Frequência Cardíaca/efeitos dos fármacos , Qualidade de Vida , Antagonistas de Receptores Adrenérgicos beta 1/uso terapêutico , Idoso , Idoso de 80 Anos ou mais , Antiarrítmicos/efeitos adversos , Antiarrítmicos/farmacologia , Fibrilação Atrial/complicações , Fibrilação Atrial/fisiopatologia , Bisoprolol/efeitos adversos , Bisoprolol/farmacologia , Digoxina/efeitos adversos , Digoxina/farmacologia , Feminino , Insuficiência Cardíaca/complicações , Insuficiência Cardíaca/tratamento farmacológico , Humanos , Masculino , Pessoa de Meia-Idade , Método Simples-Cego , Volume SistólicoRESUMO
BACKGROUND: Patient-reported outcomes (PROs) are commonly collected in clinical trials and should provide impactful evidence on the effect of interventions on patient symptoms and quality of life. However, it is unclear how PRO impact is currently realised in practice. In addition, the different types of impact associated with PRO trial results, their barriers and facilitators, and appropriate impact metrics are not well defined. Therefore, our objectives were: i) to determine the range of potential impacts from PRO clinical trial data, ii) identify potential PRO impact metrics and iii) identify barriers/facilitators to maximising PRO impact; and iv) to examine real-world evidence of PRO trial data impact based on Research Excellence Framework (REF) impact case studies. METHODS: Two independent investigators searched MEDLINE, EMBASE, CINAHL+, HMIC databases from inception until December 2018. Articles were eligible if they discussed research impact in the context of PRO clinical trial data. In addition, the REF 2014 database was systematically searched. REF impact case studies were included if they incorporated PRO data in a clinical trial. RESULTS: Thirty-nine publications of eleven thousand four hundred eighty screened met the inclusion criteria. Nine types of PRO trial impact were identified; the most frequent of which centred around PRO data informing clinical decision-making. The included publications identified several barriers and facilitators around PRO trial design, conduct, analysis and report that can hinder or promote the impact of PRO trial data. Sixty-nine out of two hundred nine screened REF 2014 case studies were included. 12 (17%) REF case studies led to demonstrable impact including changes to international guidelines; national guidelines; influencing cost-effectiveness analysis; and influencing drug approvals. CONCLUSIONS: PRO trial data may potentially lead to a range of benefits for patients and society, which can be measured through appropriate impact metrics. However, in practice there is relatively limited evidence demonstrating directly attributable and indirect real world PRO-related research impact. In part, this is due to the wider challenges of measuring the impact of research and PRO-specific issues around design, conduct, analysis and reporting. Adherence to guidelines and multi-stakeholder collaboration is essential to maximise the use of PRO trial data, facilitate impact and minimise research waste. TRIAL REGISTRATION: Systematic Review registration PROSPERO CRD42017067799.
Assuntos
Ensaios Clínicos como Assunto/métodos , Medidas de Resultados Relatados pelo Paciente , Qualidade de Vida , Ensaios Clínicos como Assunto/economia , Ensaios Clínicos como Assunto/psicologia , Análise Custo-Benefício , Humanos , Projetos de Pesquisa/normasRESUMO
BACKGROUND: Non-infectious uveitis describes a heterogenous group of ocular disorders characterised by intraocular inflammation in the absence of infection. Uveitis is a leading cause of visual loss, most commonly due to uveitic macular oedema (UMO). Treatment is aimed at reducing disease activity by suppression of the intraocular inflammatory response. In the case of macular oedema, the aim is to restore macular architecture as quickly as possible, in order to prevent irreversible photoreceptor damage in this area. Acute exacerbations are typically managed with corticosteroids, which may be administered topically, locally or systemically. Whilst these are often rapidly effective in achieving disease control, long-term use is associated with significant local and systemic side effects, and 'steroid sparing agents' are typically used to achieve prolonged control in severe or recalcitrant disease. Anti-tumour necrosis factor (TNF) drugs block a critical cytokine in the inflammatory signalling process, and have emerged as effective steroid-sparing immunomodulatory agents in a wide range of non-ocular conditions. There is mechanistic data to suggest that they may provide a more targeted approach to disease control in UMO than other agents, but to date, these agents have predominantly been used 'off label' as the majority are not licensed for ocular use. This review aims to summarise the available literature reporting the use of anti-TNF therapy in UMO, thus developing the evidence-base on which to make future treatment decisions and develop clinical guidelines in this area. OBJECTIVES: To assess the efficacy of anti-TNF therapy in treatment of UMO. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL; 2018, Issue 2), which contains the Cochrane Eyes and Vision Trials Register; Ovid MEDLINE; Ovid Embase; LILACS; Web of Science Conference Proceedings Citation Index- Science (CPCI-S); System for Information on Grey Literature in Europe (OpenGrey); the ISRCTN registry; ClinicalTrials.gov and the WHO ICTRP. The date of the search was 29 March 2018. SELECTION CRITERIA: We planned to include all relevant randomised controlled trials assessing the use of anti-TNF agents in treatment of UMO. No limits were applied to participant age, gender or ethnicity. The primary comparisons of this review were: anti-TNF versus no treatment or placebo; anti-TNF versus another pharmacological agent; comparison of different anti-TNF drugs; comparison of different doses and routes of administration of the same anti-TNF drug. The primary outcome measure that we assessed for this review was best-corrected visual acuity (BCVA) in the treated eye. Secondary outcome measures were anatomical macular change, clinical estimation of vitreous haze and health-related quality of life. DATA COLLECTION AND ANALYSIS: Two review authors independently screened titles and abstracts retrieved through the database searches. We retrieved full-text reports of studies categorised as 'unsure' or 'include' after we had reviewed the abstracts. Two review authors independently reviewed each full-text report for eligibility. We resolved discrepancies through discussion. MAIN RESULTS: We identified no completed or ongoing trial that was eligible for this Cochrane Review. AUTHORS' CONCLUSIONS: Our review did not identify any evidence from randomised controlled trials for or against the role of anti-TNF agents in the management of UMO. Although there are a number of high-quality randomised controlled trials that demonstrate the efficacy of anti-TNF agents in preventing recurrence of inflammation in uveitis, the reported study outcomes do not include changes in UMO. As a result, there were insufficient data to conclude whether there was a significant treatment effect specifically for UMO. Future trials should be designed to include quantitative measures of UMO as primary study outcomes, for example by reporting the presence or absence of UMO, or by measuring central macular thickness for study participants. Furthermore, whilst UMO is an important complication of uveitis, we acknowledge that uveitis is associated with many significant structural and functional complications. It is not possible to determine treatment efficacy based on a single outcome measure. We recommend that future reviews of therapeutic interventions in uveitis should use composite measures of treatment response comprising a range of potential complications of disease.
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Edema Macular/tratamento farmacológico , Fator de Necrose Tumoral alfa/antagonistas & inibidores , Uveíte/complicações , Humanos , Edema Macular/etiologia , Uso Off-LabelRESUMO
BACKGROUND: Increasingly, researchers need to demonstrate the impact of their research to their sponsors, funders, and fellow academics. However, the most appropriate way of measuring the impact of healthcare research is subject to debate. We aimed to identify the existing methodological frameworks used to measure healthcare research impact and to summarise the common themes and metrics in an impact matrix. METHODS AND FINDINGS: Two independent investigators systematically searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), the Excerpta Medica Database (EMBASE), the Cumulative Index to Nursing and Allied Health Literature (CINAHL+), the Health Management Information Consortium, and the Journal of Research Evaluation from inception until May 2017 for publications that presented a methodological framework for research impact. We then summarised the common concepts and themes across methodological frameworks and identified the metrics used to evaluate differing forms of impact. Twenty-four unique methodological frameworks were identified, addressing 5 broad categories of impact: (1) 'primary research-related impact', (2) 'influence on policy making', (3) 'health and health systems impact', (4) 'health-related and societal impact', and (5) 'broader economic impact'. These categories were subdivided into 16 common impact subgroups. Authors of the included publications proposed 80 different metrics aimed at measuring impact in these areas. The main limitation of the study was the potential exclusion of relevant articles, as a consequence of the poor indexing of the databases searched. CONCLUSIONS: The measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise research benefit, and to help minimise research waste. This review provides a collective summary of existing methodological frameworks for research impact, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.
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Pesquisa sobre Serviços de Saúde/métodos , Bases de Dados Factuais/estatística & dados numéricos , Pesquisa sobre Serviços de Saúde/normas , HumanosAssuntos
Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/terapia , Medidas de Resultados Relatados pelo Paciente , Pneumonia Viral/diagnóstico , Pneumonia Viral/terapia , Betacoronavirus/imunologia , COVID-19 , Vacinas contra COVID-19 , Infecções por Coronavirus/prevenção & controle , Humanos , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Consulta Remota , SARS-CoV-2 , Vacinas Virais/efeitos adversosRESUMO
BACKGROUND: Left ventricular assist device (LVAD) recipients report symptom improvement but find adjusting to life with the LVAD challenging. These challenges are unique, and existing patient-reported outcome measures (PROMs) do not reflect their experiences. This study aimed to develop a culturally relevant quality of life PROM for use with LVAD recipients in future research, design evolutions and clinical practice. METHODS: A three-stage mixed-methods approach was used to develop a PROM: stage 1 included group concept mapping (GCM); stage 2 semi-structured qualitative interviews were conducted with 11 LVAD recipients and 10 clinicians, and a questionnaire was developed using a conceptual framework; and stage 3 used exploratory psychometric analysis of the PROM data using Rasch measurement theory. This paper presents stages 2 and 3. RESULTS: The conceptual framework consisted of four key concepts, including general health, life with the LVAD, equipment and clothing and emotional impact. Statements from interviews and GCM were used to create items for the LVAD quality of life (LVAD-QoL). Cognitive interviews tested face validity and participant comprehension. Forty-nine participants were recruited from three UK transplant centres. PROM data were collected and analysed using Rasch analysis. Four items displayed misfit; dependency between item sets was the biggest issue (57/485 pairwise differences). After restructuring and dealing with item misfit, the LVAD-QoL conformed to the Rasch model, supporting the psychometric properties and quality of the LVAD-QoL. CONCLUSIONS: Using a mixed-methods approach ensured the development of a robust and psychometrically sound tool for research, design evolution and clinical practice with LVAD recipients.
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Patient-reported outcomes (PROs) are increasingly used in healthcare research to provide evidence of the benefits and risks of interventions from the patient perspective and to inform regulatory decisions and health policy. The use of PROs in clinical practice can facilitate symptom monitoring, tailor care to individual needs, aid clinical decision-making and inform value-based healthcare initiatives. Despite their benefits, there are concerns that the potential burden on respondents may reduce their willingness to complete PROs, with potential impact on the completeness and quality of the data for decision-making. We therefore conducted an initial literature review to generate a list of candidate recommendations aimed at reducing respondent burden. This was followed by a two-stage Delphi survey by an international multi-stakeholder group. A consensus meeting was held to finalize the recommendations. The final consensus statement includes 19 recommendations to address PRO respondent burden in healthcare research and clinical practice. If implemented, these recommendations may reduce PRO respondent burden.
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Avaliação de Resultados da Assistência ao Paciente , Medidas de Resultados Relatados pelo Paciente , Humanos , Consenso , Tomada de Decisão ClínicaRESUMO
BACKGROUND: Advanced therapy medicinal products such as Chimeric antigen receptor T-cell therapy offer ground-breaking opportunities for the treatment of various cancers, inherited diseases, and chronic conditions. With development of these novel therapies continuing to increase it's important to learn from the experiences of patients who were among the first recipients of ATMPs. In this way we can improve the clinical and psychosocial support offered to early patient recipients in the future to support the successful completion of treatments and trials. STUDY DESIGN: We conducted a qualitative investigation informed by the principles of the key informant technique to capture the experience of some of the first patients to experience CAR-T therapy in the UK. A directed content analysis was used to populate a theoretical framework informed by Burden of Treatment Theory to determine the lessons that can be learnt in supporting their care, support, and ongoing self-management. RESULTS: A total of five key informants were interviewed. Their experiences were described within the three domains of the burden of treatment framework; (1) The health care tasks delegated to patients, Participants described the frequency of follow-up and the resources involved, the esoteric nature of the information provided by clinicians; (2) Exacerbating factors of the treatment, which notably included the lack of understanding of the clinical impacts of the treatment in the broader health service, and the lack of a peer network to support patient understanding; (3) Consequences of the treatment, in which they described the anxiety induced by the process surrounding their selection for treatment, and the feeling of loneliness and isolation at being amongst the very first recipients. CONCLUSIONS: If ATMPs are to be successfully introduced at the rates forecast, then it is important that the burden placed on early recipients is minimised. We have discovered how they can feel emotionally isolated, clinically vulnerable, and structurally unsupported by a disparate and pressured health service. We recommend that where possible, structured peer support be put in place alongside signposting to additional information that includes the planned pattern of follow-up, and the management of discharged patients would ideally accommodate individual circumstances and preferences to minimize the burden of treatment.
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Transtornos de Ansiedade , Atenção à Saúde , Humanos , AnsiedadeRESUMO
The extent to which patient-reported outcome measures (PROMs) are used in clinical trials for artificial intelligence (AI) technologies is unknown. In this systematic evaluation, we aim to establish how PROMs are being used to assess AI health technologies. We searched ClinicalTrials.gov for interventional trials registered from inception to Sept 20, 2022, and included trials that tested an AI health technology. We excluded observational studies, patient registries, and expanded access reports. We extracted data regarding the form, function, and intended use population of the AI health technology, in addition to the PROMs used and whether PROMs were incorporated as an input or output in the AI model. The search identified 2958 trials, of which 627 were included in the analysis. 152 (24%) of the included trials used one or more PROM, visual analogue scale, patient-reported experience measure, or usability measure as a trial endpoint. The type of AI health technologies used by these trials included AI-enabled smart devices, clinical decision support systems, and chatbots. The number of clinical trials of AI health technologies registered on ClinicalTrials.gov and the proportion of trials that used PROMs increased from registry inception to 2022. The most common clinical areas AI health technologies were designed for were digestive system health for non-PROM trials and musculoskeletal health (followed by mental and behavioural health) for PROM trials, with PROMs commonly used in clinical areas for which assessment of health-related quality of life and symptom burden is particularly important. Additionally, AI-enabled smart devices were the most common applications tested in trials that used at least one PROM. 24 trials tested AI models that captured PROM data as an input for the AI model. PROM use in clinical trials of AI health technologies falls behind PROM use in all clinical trials. Trial records having inadequate detail regarding the PROMs used or the type of AI health technology tested was a limitation of this systematic evaluation and might have contributed to inaccuracies in the data synthesised. Overall, the use of PROMs in the function and assessment of AI health technologies is not only possible, but is a powerful way of showing that, even in the most technologically advanced health-care systems, patients' perspectives remain central.
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Medidas de Resultados Relatados pelo Paciente , Qualidade de Vida , Humanos , Inteligência Artificial , Tecnologia Biomédica , Sistema de Registros , Ensaios Clínicos como AssuntoRESUMO
Integration of patient-reported outcome measures (PROMs) in artificial intelligence (AI) studies is a critical part of the humanisation of AI for health. It allows AI technologies to incorporate patients' own views of their symptoms and predict outcomes, reflecting a more holistic picture of health and wellbeing and ultimately helping patients and clinicians to make the best health-care decisions together. By positioning patient-reported outcomes (PROs) as a model input or output we propose a framework to embed PROMs within the function and evaluation of AI health care. However, the integration of PROs in AI systems presents several challenges. These challenges include (1) fragmentation of PRO data collection; (2) validation of AI systems trained and validated against clinician performance, rather than outcome data; (3) scarcity of large-scale PRO datasets; (4) inadequate selection of PROMs for the target population and inadequate infrastructure for collecting PROs; and (5) clinicians might not recognise the value of PROs and therefore not prioritise their adoption; and (6) studies involving PRO or AI frequently present suboptimal design. Notwithstanding these challenges, we propose considerations for the inclusion of PROs in AI health-care technologies to avoid promoting survival at the expense of wellbeing.
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Inteligência Artificial , Medidas de Resultados Relatados pelo Paciente , Humanos , Coleta de Dados , Atenção à Saúde , CoraçãoRESUMO
The global demand for hospital treatment exceeds capacity.The COVID-19 pandemic has exacerbated this issue, leading to increased backlogs and longer wait times for patients. The amount of outpatient attendances undertaken in many settings is still below pre-pandemic levels and this, combined with delayed referrals, means that patients are facing delays in treatment and poorer health outcomes. Use of digital health technologies, notably the use of remote symptom monitoring systems based on patient-reported outcomes (PROs), may offer a solution to reduce outpatient waiting lists and tailor care to those in greatest need. Drawing on international examples, the authors explore the use of electronic PRO systems to triage clinical care. We summarise the key benefits of the approach and also highlight the challenges for implementation, which need to be addressed to promote equitable healthcare delivery.