Reporting Guidelines for Clinical Trial Protocols and Reports of Implantable Neurostimulation Devices: Protocol for the SPIRIT-iNeurostim and CONSORT-iNeurostim Extensions

Author(s):  
Rui V. Duarte ◽  
Rebecca Bresnahan ◽  
Sue Copley ◽  
Sam Eldabe ◽  
Simon Thomson ◽  
...  
Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hussein Ibrahim ◽  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
An-Wen Chan ◽  
...  

Abstract Background The application of artificial intelligence (AI) in healthcare is an area of immense interest. The high profile of ‘AI in health’ means that there are unusually strong drivers to accelerate the introduction and implementation of innovative AI interventions, which may not be supported by the available evidence, and for which the usual systems of appraisal may not yet be sufficient. Main text We are beginning to see the emergence of randomised clinical trials evaluating AI interventions in real-world settings. It is imperative that these studies are conducted and reported to the highest standards to enable effective evaluation because they will potentially be a key part of the evidence that is used when deciding whether an AI intervention is sufficiently safe and effective to be approved and commissioned. Minimum reporting guidelines for clinical trial protocols and reports have been instrumental in improving the quality of clinical trials and promoting completeness and transparency of reporting for the evaluation of new health interventions. The current guidelines—SPIRIT and CONSORT—are suited to traditional health interventions but research has revealed that they do not adequately address potential sources of bias specific to AI systems. Examples of elements that require specific reporting include algorithm version and the procedure for acquiring input data. In response, the SPIRIT-AI and CONSORT-AI guidelines were developed by a multidisciplinary group of international experts using a consensus building methodological process. The extensions include a number of new items that should be reported in addition to the core items. Each item, where possible, was informed by challenges identified in existing studies of AI systems in health settings. Conclusion The SPIRIT-AI and CONSORT-AI guidelines provide the first international standards for clinical trials of AI systems. The guidelines are designed to ensure complete and transparent reporting of clinical trial protocols and reports involving AI interventions and have the potential to improve the quality of these clinical trials through improvements in their design and delivery. Their use will help to efficiently identify the safest and most effective AI interventions and commission them with confidence for the benefit of patients and the public.


BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e046450
Author(s):  
Samantha Cruz Rivera ◽  
Richard Stephens ◽  
Rebecca Mercieca-Bebber ◽  
Ameeta Retzer ◽  
Claudia Rutherford ◽  
...  

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.DesignA 1-day patient and public involvement session.ParticipantsSeven patient partners.MethodsA 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.ResultsTwo 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.ConclusionsThese 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.


2015 ◽  
Vol 54 (02) ◽  
pp. 164-170 ◽  
Author(s):  
T. Hao ◽  
C. Weng

SummaryObjectives: To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts.Methods: We develop a “plug-n-play” framework that integrates replaceable un-supervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach’s recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach’s adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts.Results: Our approach increased the average recall and speed by 12.8% and 47.02% respectively upon the baseline when mining FSTs from ClinicalTrials.gov, and maintained an overlap in relevant FSTs with the baseline ranging between 76.9% and 100% for varying FST frequency thresholds. The FSTs saturated when the data size reached 200 documents. Consistent trends in the prevalence of FST were observed across the three texts as the data size or frequency threshold changed.Conclusions: This paper contributes an adaptive tag-mining framework that is scalable and adaptable without sacrificing its recall. This component-based architectural design can be potentially generalizable to improve the adaptability of other clinical text mining methods.


Author(s):  
Cemal Cingi ◽  
Nuray Bayar Muluk

Author(s):  
Robert A. Greenes ◽  
Samson Tu ◽  
Aziz A. Boxwala ◽  
Mor Peleg ◽  
Edward H. Shortliffe

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5864-5864
Author(s):  
Amany R. Keruakous ◽  
Adam S. Asch

Background: Clinical trials, key elements of the processes that account for many of the recent advances in cancer care, are becoming more complex and challenging to conduct. The Stephenson Cancer Center (SCC) has been the lead accruer to NCI-LAP trials over the past three years, and in addition, fields investigator initiated and industry sponsored trials. To identify opportunities for continued improvement in clinical trial enrolment, we sought to identify the obstacles encountered by our clinical trial staff in these activities. Method: We conducted a survey of our research staff including all research nurses and disease site coordinators who participate in recruitment, screening, consenting, data collection and compliance. The survey, sent by email to the clinical trial list-serve at SCC (90 staff member), invited respondents to enumerate obstacles to patient participation in clinical trials. We then performed a follow up meeting with our research coordinators to clarify responses. A total of 26 responses from 90 respondents were received and tabulated by disease site. Results: The most commonly reported obstacles to enrolment were, in descending order: communication/language barriers, cultural bias, time/procedure commitment, and complexity of the trial protocol, financial logistics, comorbidities, and stringent trial criteria. Respondents identified 83 obstacles as frequently encountered obstacles to enrolment. The 83 reported obstacles were classified into 9 categories and organized by disease site as presented in tabular format (below). The most commonly identified obstacles to patient enrolment were communication and language barriers. In patients for whom Spanish is the primary language this was a universal obstacle, as there is a lack of consistent Spanish consents across the clinical trial portfolio. Cultural bias, as an obstacle was manifested as a general mistrust by prospective trial participants of experimental therapies and clinical trials. After communication and cultural bias as barriers, travel requirements and the associated expenses playing a role in patients from rural areas were identified as the most commonly encountered barrier. The complexity of trial protocols and the associated large number of clinic visits, frequent laboratory and imaging tests were also identified as common obstacles. Clinical trial complexity with strict inclusion and exclusion criteria and trial-specified biopsies were frequently cited. Implications: In this descriptive study, common barriers to patient enrolment in clinical trials were identified by clinical trial staff. Assessing barriers encountered by clinical trial staff is infrequently used as a metric for improving clinical trial enrolment, but provides important perspective. In our study, some obstacles are inherent in our patient populations, others appear to be actionable. Development of Spanish language consents and specific programs to overcome negative bias regarding clinical trials are potential areas for improvement. The complexity of clinical trial protocols and the increasingly strict inclusion/exclusion criteria, are issues that will require consideration and action at the level of the cooperative groups and industry. Disclosures No relevant conflicts of interest to declare.


Abstracts ◽  
1978 ◽  
pp. 496
Author(s):  
T B Binns ◽  
DM Chaput de Saintonge ◽  
A Herxheimer ◽  
D W Vere

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