Sharing Clinical Trial Data--A Proposal from the International Committee of Medical Journal Editors

Raw data were not disclosed in 95% of PubMed-indexed heart failure meta-analyses in 2021: A systematic analysis of transparency

BACKGROUND

The rising concern of irreproducible and non-transparent studies poses a significant challenge in modern medical literature. The impact of this issue on cardiology, particularly in the subfield of heart failure, remains poorly understood. To address this knowledge gap, we assessed the quality of evidence presented in recent heart failure meta-analyses by exploring several crucial transparency indicators.

METHODS

We conducted a cross-sectional study and searched PubMed for meta - analyses themed around heart failure. We included the 100 most recent publications from 2021 and investigated the presence of several indices that are associated with transparency and reproducibility.

RESULTS

The vast majority of the papers did not include their raw data (95/100, 95%) nor their analytic code (99/100, 99%). Less than half (42/100, 42%) preregistered their protocol, while only 65/100 (65%) adhered to a reporting guidelines method. Bias calculation for the respective studies included in each meta - analysis was present in 83/100 (83%) papers and publication bias was measured in approximately half (56/100, 56%).

CONCLUSIONS

Our study indicates that meta-analyses in the field of heart failure present important information of transparency infrequently. Therefore, reproduction and validation of their findings seems to be practically impossible.

Health data sharing attitudes towards primary and secondary use of data: a systematic review

Background

To receive the best care, people share their health data (HD) with their health practitioners (known as sharing HD for primary purposes). However, during the past two decades, sharing for other (i.e., secondary) purposes has become of great importance in numerous fields, including public health, personalized medicine, research, and development. We aimed to conduct the first comprehensive overview of all studies that investigated people's HD sharing attitudes-along with associated barriers/motivators and significant influencing factors-for all data types and across both primary and secondary uses.

Methods

We searched PubMed, MEDLINE, PsycINFO, Web of Science, EMBASE, and CINAHL for relevant studies published in English between database inception and February 28, 2023, using a predefined set of keywords. Studies were included, regardless of their design, if they reported outcomes related to attitudes towards sharing HD. We extracted key data from the included studies, including the type of HD involved and findings related to: HD sharing attitudes (either in general or depending on type of data/user); barriers/motivators/benefits/concerns of the study participants; and sociodemographic and other variables that could impact HD sharing behaviour. The qualitative synthesis was conducted by dividing the studies according to the data type (resulting in five subgroups) as well as the purpose the data sharing was focused on (primary, secondary or both). The Newcastle-Ottawa Scale (NOS) was used to assess the quality of non-randomised studies. This work was registered with PROSPERO, CRD42023413822.

Findings

Of 2109 studies identified through our search, 116 were included in the qualitative synthesis, yielding a total of 228,501 participants and various types of HD represented: person-generated HD (n = 17 studies and 10,771 participants), personal HD in general (n = 69 studies and 117,054 participants), Biobank data (n = 7 studies and 27,073 participants), genomic data (n = 13 studies and 54,716 participants), and miscellaneous data (n = 10 studies and 18,887 participants). The majority of studies had a moderate level of quality (83 [71.6%] of 116 studies), but varying levels of quality were observed across the included studies. Overall, studies suggest that sharing intentions for primary purposes were observed to be high regardless of data type, and it was higher than sharing intentions for secondary purposes. Sharing for secondary purposes yielded variable findings, where both the highest and the lowest intention rates were observed in the case of studies that explored sharing biobank data (98% and 10%, respectively). Several influencing factors on sharing intentions were identified, such as the type of data recipient, data, consent. Further, concerns related to data sharing that were found to be mutual for all data types included privacy, security, and data access/control, while the perceived benefits included those related to improvements in healthcare. Findings regarding attitudes towards sharing varied significantly across sociodemographic factors and depended on data type and type of use. In most cases, these findings were derived from single studies and therefore warrant confirmations from additional studies.

Interpretation

Sharing health data is a complex issue that is influenced by various factors (the type of health data, the intended use, the data recipient, among others) and these insights could be used to overcome barriers, address people's concerns, and focus on spreading awareness about the data sharing process and benefits.

Funding

None.

Stakeholder perspectives on data sharing from pragmatic clinical trials: Unanticipated challenges for meeting emerging requirements

Introduction

Numerous arguments have been advanced for broadly sharing de-identified, participant-level clinical trial data. However, data sharing in pragmatic clinical trials (PCTs) presents ethical challenges. While prior scholarship has described aspects of PCTs that raise distinct considerations for data sharing, there have been no reports of the experiences of those at the leading edge of data-sharing efforts for PCTs, including how these particular challenges have been navigated. To address this gap, we conducted interviews with key stakeholders, with a focus on the ethical issues presented by sharing data from PCTs.

Methods

We recruited respondents using purposive sampling to reflect the range of stakeholder groups affected by efforts to expand PCT data sharing. Through semi-structured interviews, we explored respondents' experiences and perceptions about sharing de-identified, individual-level data from PCTs. An integrated approach was used to identify and describe key themes.

Results

We conducted 40 interviews between April and September 2022. Five overarching themes emerged through analysis: (1) challenges in sharing data collected under a waiver or alteration of consent; (2) conflicting views regarding PCT patient-subject preferences for data sharing; (3) identification of respect-promoting practices beyond consent; (4) concerns about elevated risks or burdens from sharing PCT data; and (5) diverse views about the likely benefits resulting from sharing PCT data.

Conclusion

Our data indicate unresolved tensions in how to fulfill the expectation to broadly share de-identified, individual-level data from PCTs, and suggest that those promulgating and implementing data-sharing policies must be sensitive to PCT-specific considerations. Future work could inform efforts to tailor data-sharing policy and practice to reflect the challenges presented by PCTs, including sharing experiences from trials that have successfully navigated these tensions.

Sustainability and Climate Protection in Radiology - An Overview

BACKGROUND

Sustainability is becoming increasingly important in radiology. Besides climate protection - economic, ecological, and social aspects are integral elements of sustainability. An overview of the scientific background of the sustainability and environmental impact of radiology as well as possibilities for future concepts for more sustainable diagnostic and interventional radiology are presented below.The three elements of sustainability:1. EcologyWith an annually increasing number of tomographic images, Germany is in one of the leading positions worldwide in a per capita comparison. The energy consumption of an MRI system is comparable to 26 four-person households annually. CT and MRI together make a significant contribution to the overall energy consumption of a hospital. In particular, the energy consumption in the idle or inactive state is responsible for a relevant proportion.2. EconomyA critical assessment of the indications for radiological imaging is important not only because of radiation protection, but also in terms of sustainability and "value-based radiology". As part of the "Choosing Wisely" initiative, a total of 600 recommendations for avoiding unnecessary examinations were compiled from various medical societies, including specific indications in radiological diagnostics.3. Social SustainabilityThe alignment of radiology to the needs of patients and referring physicians is a core aspect of the social component of sustainability. Likewise, ensuring employee loyalty by supporting and maintaining motivation, well-being, and job satisfaction is an essential aspect of social sustainability. In addition, sustainable concepts are of relevance in teaching and research, such as the educational curriculum for residents in radiology, RADUCATION or the recommendations of the International Committee of Medical Journal Editors.

KEY POINTS

· Sustainability comprises three pillars: economy, ecology and the social component.. · Radiologies have a high optimization potential due to a significant demand of these resources.. · A dialogue between medicine, politics and industry is necessary for a sustainable radiology.. · The discourse, knowledge transfer and public communication of recommendations are part of the sustainability network of the German Roentgen Society (DRG)..

CITATION FORMAT

· Palm V, Heye T, Molwitz I et al. Sustainability and Climate Protection in Radiology - An Overview. Fortschr Röntgenstr 2023; 195: 981 - 988.

Data sharing in neurodegenerative disease research: challenges and learnings from the innovative medicines initiative public-private partnership model

Efficient data sharing is hampered by an array of organizational, ethical, behavioral, and technical challenges, slowing research progress and reducing the utility of data generated by clinical research studies on neurodegenerative diseases. There is a particular need to address differences between public and private sector environments for research and data sharing, which have varying standards, expectations, motivations, and interests. The Neuronet data sharing Working Group was set up to understand the existing barriers to data sharing in public-private partnership projects, and to provide guidance to overcome these barriers, by convening data sharing experts from diverse projects in the IMI neurodegeneration portfolio. In this policy and practice review, we outline the challenges and learnings of the WG, providing the neurodegeneration community with examples of good practices and recommendations on how to overcome obstacles to data sharing. These obstacles span organizational issues linked to the unique structure of cross-sectoral, collaborative research initiatives, to technical issues that affect the storage, structure and annotations of individual datasets. We also identify sociotechnical hurdles, such as academic recognition and reward systems that disincentivise data sharing, and legal challenges linked to heightened perceptions of data privacy risk, compounded by a lack of clear guidance on GDPR compliance mechanisms for public-private research. Focusing on real-world, neuroimaging and digital biomarker data, we highlight particular challenges and learnings for data sharing, such as data management planning, development of ethical codes of conduct, and harmonization of protocols and curation processes. Cross-cutting solutions and enablers include the principles of transparency, standardization and co-design - from open, accessible metadata catalogs that enhance findability of data, to measures that increase visibility and trust in data reuse.

COVID-19 trials were not more likely to report intent to share individual data than non-COVID-19 trials in ClinicalTrials.gov

OBJECTIVES

To compare intent to share individual participant data (IPD) between COVID-19 and non-COVID-19 trials registered at ClinicalTrials.gov between 01/09/2020, and 01/03/2021. We also evaluated factors independently associated with intent to share IPD and whether intent to share IPD has improved as compared with the prepandemic period.

METHODS

We searched ClinicalTrials.gov for all interventional phase 3 studies registered between 01/09/2020, and 01/03/2021. Then, we identified COVID-19 trials and selected a random sample of non-COVID-19 trials with a ratio 2:1. We compared the intent to share IPD between these trials and with 292 trials registered between 01/12/2019, and 01/03/2020 (prepandemic period).

RESULTS

We included 148 COVID-19 trials and 296 non-COVID-19 trials. Intent to share IPD did not significantly differ between COVID-19 and non-COVID-19 trials (22.3% vs. 27.0%, P = 0.3). Intent to share IPD was independently associated with industry-sponsorship (odds ratio [OR] = 2.92; 95% confidence interval [CI]: 1.65-5.27) and location in the United States (OR = 2.93; 95% CI: 1.64-5.41) or the European Union (OR = 2.06; 95% CI: 1.03-4.19). The intent to share IPD has not significantly improved compared with the prepandemic period (P = 0.16).

CONCLUSION

Data-sharing intent at registration does not seem better for COVID-19 trials.

Development of Smart Clothing to Prevent Pressure Injuries in Bedridden Persons and/or with Severely Impaired Mobility: 4NoPressure Research Protocol

Pressure injuries (PIs) are a major public health problem and can be used as quality-of-care indicators. An incipient development in the field of medical devices takes the form of Smart Health Textiles, which can possess innovative properties such as thermoregulation, sensing, and antibacterial control. This protocol aims to describe the process for the development of a new type of smart clothing for individuals with reduced mobility and/or who are bedridden in order to prevent PIs. This paper's main purpose is to present the eight phases of the project, each consisting of tasks in specific phases: (i) product and process requirements and specifications; (ii and iii) study of the fibrous structure technology, textiles, and design; (iv and v) investigation of the sensor technology with respect to pressure, temperature, humidity, and bioactive properties; (vi and vii) production layout and adaptations in the manufacturing process; (viii) clinical trial. This project will introduce a new structural system and design for smart clothing to prevent PIs. New materials and architectures will be studied that provide better pressure relief, thermo-physiological control of the cutaneous microclimate, and personalisation of care.

Elbow Injuries Among MLB Pitchers Increased During Covid-19 Disrupted Season, But Not Other Baseball Injuries

Background

The 2020 Major League Baseball Season (MLB) demonstrated season disruptions due to the COVID-19 pandemic. Changes in training and seasonal time frames may be associated with higher rates of injury.

Purpose

To use publicly available data to compare injury rates during the 2015-2019 seasons, COVID-19 shortened season (2020), and the 2021 season stratified by body region and position (pitchers versus position players).

Study Design

A retrospective cohort study utilizing publicly available data.

Methods

MLB players who competed in 1+ seasons between 2015-2021 were included and stratified by position (pitcher, position player). Incidence rate (IR), reported by 1000 x Athlete-Game Exposures (AGEs), was calculated for each season, and stratified by position and body region. Poisson regressions were performed for all injuries and stratified by position to determine association between season and injury incidence. Subgroup analyses were performed on the elbow, groin/hip/thigh, shoulder.

Results

Four thousand, two hundred and seventy-four injuries and 796,502 AGEs across 15,152 players were documented. Overall IR was similar across seasons (2015-2019:5.39; 2020:5.85; 2021:5.04 per 1000 AGEs). IR remained high for the groin/hip/thigh for position players (2015-2019:1.7; 2020:2.0; 2021:1.7 per 1000 AGEs). There was no difference in injury rates between 2015-2019 and 2020 seasons [1.1 (0.9-1.2), p=0.310]. The 2020 season demonstrated a significant increase in elbow injuries [2.7 (1.8-4.0), p<0.001]; when stratified by position, this increase remained significant for pitchers [pitchers: 3.5 (2.1-5.9), p<0.001; position players: 1.8 (0.9-3.6), p=0.073]. No other differences were observed.

Conclusion

The groin/hip/thigh demonstrated the highest IR in 2020 among position players across all season time frames, indicating that continued injury mitigation for this region is necessary. When stratified by body region, elbow injury rates among pitchers demonstrated 3.5 times the rate of injury in 2020 compared to previous seasons, impacting injury burden for the most vulnerable body region among pitchers.

Level of Evidence

Level III.

Efficacy of traditional Chinese exercise for sarcopenia: A systematic review and meta-analysis of randomized controlled trials

Objective

To conduct a systematic review and meta-analysis to evaluate the effectiveness of Traditional Chinese Exercise (TCE) for sarcopenia.

Methods

A literature search was conducted in eight online databases from inception until September 2022. Based on the Cochrane risk of bias tool, randomized controlled trials (RCTs) with RoB score ≥ 4 were included for further analyses. The primary outcome was muscle strength and physical function, and the secondary outcomes were adverse events. Data collection and analyses were conducted by RevMan 5.4 Software. GRADE system was used to evaluate the certainty of evidence.

Results

A total of 13 eligible RCTs with 718 subjects were identified and included in this study. Among them, 10 RCTs involved Yijinjing; 2 involved Tai Chi; and 1 involved Baduanjin. Meta-analyses showed that TCE had better clinical effects than control measures in the chair stand test (P < 0.00001, I2 = 38%; Certainty of evidence: Moderate), squatting-to-standing test (P < 0.00001, I2 = 0%; Certainty of evidence: Moderate), 6-m gait speed (P < 0.00001, I2 = 13%; Certainty of evidence: Moderate), Time Up and Go Test (P = 0.03, I2 = 81%; Certainty of evidence: Low), peak torque of the extensors (P = 0.03, I2 = 0%; Certainty of evidence: Moderate), total work of the extensors (P = 0.03, I2 = 35%; Certainty of evidence: Moderate), peak torque of the flexors (P = 0.03, I2 = 47%; Certainty of evidence: Low), total work of the flexors (P = 0.02, I2 = 42%; Certainty of evidence: Low), the average power of the flexors (P = 0.03, I2 = 30%; Certainty of evidence: Moderate), and balance function (P < 0.00001, I2 = 53%; Certainty of evidence: Low). In additional, no adverse events were reported in participants who receive TCE.

Conclusion

The findings of the present systematic review, at least to a certain extent, provided supporting evidence for the routine use of TCE for sarcopenia.

EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation

BACKGROUND

Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability.

RESULTS

To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor.

CONCLUSIONS

We have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.

Ethics challenges in sharing data from pragmatic clinical trials

Numerous arguments have been advanced for broadly sharing de-identified, participant-level clinical trials data, and trial sponsors and journals are increasingly requiring it. However, data sharing in pragmatic clinical trials presents ethical challenges related to the use of waivers or alterations of informed consent for some pragmatic clinical trials and corresponding limitations of informed consent to guide sharing decisions; the potential for data sharing in pragmatic clinical trials to present risks not only for individual patient-subjects, but also for health systems and the clinicians within them; sharing of data from electronic health records instead of data newly collected for research purposes; and researchers' limited capacity to control sensitive data within an electronic health record and potential implications of such limits for meeting obligations inherent to Certificates of Confidentiality. These challenges raise questions about the extent to which traditional research ethics governance structures are capable of guiding decisions about pragmatic clinical trial data sharing. This article identifies and examines these ethical challenges for pragmatic clinical trial data sharing. We suggest several areas for future empirical scholarship, including the need to identify patient and public attitudes regarding pragmatic clinical trial data sharing as well as to assess the demand for pragmatic clinical trial data and the correspondingly likely benefit of such sharing. Further conceptual work is also needed to explore how requirements to respect patient-subjects about whom data are shared in the context of pragmatic clinical trials should be understood, particularly in the absence of informed consent for initial research activities, and the appropriate balance between promoting the generation of socially valuable knowledge and respecting autonomy.

Reaching a Tipping Point for Neurorehabilitation Research: Obstacles and Opportunities in Trial Design, Description, and Pooled Analysis

The record-breaking pace of COVID-19 vaccine development and implementation depended heavily on collaboration among academic, government, and commercial stakeholders, especially through data-sharing and robust multicenter trials. Collaborative efforts have not been as fruitful in fields such as neurorehabilitation, where non-pharmacological interventions play a much larger role. Barriers to translating scientific advancements into clinical practice in neurorehabilitation include pervasively small study sizes, exacerbated by limited funding for non-pharmacological multicenter clinical trials; difficulty standardizing—and adequately describing—non-pharmacological interventions; and a lack of incentives for individual patient-level data-sharing. These barriers prevent reliable meta-analysis of non-pharmacological clinical studies in neurorehabilitation. This point-of-view will highlight these challenges as well as suggest practical steps that may be taken to improve the neurorehabilitation pipeline between evidence and implementation.

Regulatory Frameworks for Clinical Trial Data Sharing: Scoping Review

Background

Although well recognized for its scientific value, data sharing from clinical trials remains limited. Steps toward harmonization and standardization are increasing in various pockets of the global scientific community. This issue has gained salience during the COVID-19 pandemic. Even for agencies willing to share data, data exclusivity practices complicate matters; strict regulations by funders affect this even further. Finally, many low- and middle-income countries (LMICs) have weaker institutional mechanisms. This complex of factors hampers research and rapid response during public health emergencies. This drew our attention to the need for a review of the regulatory landscape governing clinical trial data sharing.

Objective

This review seeks to identify regulatory frameworks and policies that govern clinical trial data sharing and explore key elements of data-sharing mechanisms as outlined in existing regulatory documents. Following from, and based on, this empirical analysis of gaps in existing policy frameworks, we aimed to suggest focal areas for policy interventions on a systematic basis to facilitate clinical trial data sharing.

Methods

We followed the JBI scoping review approach. Our review covered electronic databases and relevant gray literature through a targeted web search. We included records (all publication types, except for conference abstracts) available in English that describe clinical trial data–sharing policies, guidelines, or standard operating procedures. Data extraction was performed independently by 2 authors, and findings were summarized using a narrative synthesis approach.

Results

We identified 4 articles and 13 policy documents; none originated from LMICs. Most (11/17, 65%) of the clinical trial agencies mandated a data-sharing agreement; 47% (8/17) of these policies required informed consent by trial participants; and 71% (12/17) outlined requirements for a data-sharing proposal review committee. Data-sharing policies have, a priori, milestone-based timelines when clinical trial data can be shared. We classify clinical trial agencies as following either controlled- or open-access data-sharing models. Incentives to promote data sharing and distinctions between mandated requirements and supportive requirements for informed consent during the data-sharing process remain gray areas, needing explication. To augment participant privacy and confidentiality, a neutral institutional mechanism to oversee dissemination of information from the appropriate data sets and more policy interventions led by LMICs to facilitate data sharing are strongly recommended.

Conclusions

Our review outlines the immediate need for developing a pragmatic data-sharing mechanism that aims to improve research and innovations as well as facilitate cross-border collaborations. Although a one-policy-fits-all approach would not account for regional and subnational legislation, we suggest that a focus on key elements of data-sharing mechanisms can be used to inform the development of flexible yet comprehensive data-sharing policies so that institutional mechanisms rather than disparate efforts guide data generation, which is the foundation of all scientific endeavor.

A deterministic approach for protecting privacy in sensitive personal data

BACKGROUND

Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge.

METHODS

In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm.

RESULTS

We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort.

CONCLUSIONS

The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.

Clinical pharmacology: Current innovations and future challenges

Clinical pharmacology is the study of drugs in humans, from first-in-human studies to randomized controlled trials (RCTs) and benefit-risk ratio assessment in large populations. The objective of this review is to present the recent innovations that may revolutionize the development of drugs in the future. On behalf of the French Society of Pharmacology and Therapeutics, we provide recommendations to address those future challenges in clinical pharmacology. Whatever the future will be, robust preliminary data on drug mechanism of action and rigorous study design will remain crucial prior to the start of pharmacological studies in human. At the present time, RCTs remains the gold standard to evaluate the efficacy of human drugs, although alternative designs (pragmatic trials, platform trials, etc.) are emerging. Innovations in healthy volunteers' studies and the contribution of new technologies such as artificial intelligence, machine learning and internet-based trials have the potential to improve drug development. In the field of precision medicine, new disease phenotypes and endotypes will probably help to identify new pharmacological targets, responders to therapies and patients at risk for drug adverse events. In such a moving landscape, the development of translational research through academic and private partnership, transparent sharing of clinical trial data and enhanced interactions between drug experts, patients and the general public are priority areas for action.

Frequency of Publication of Dermoscopic Images in Inter-observer Studies: A Systematic Review

Research interest in dermoscopy is increasing, but the complete dermoscopic image sets used in inter-observer studies of skin tumours are not often shared in research publications. The aim of this systematic review was to analyse what proportion of images depicting skin tumours are published in studies investigating inter-observer variations in the assessment of dermoscopic features and/or patterns. Embase, MEDLINE and Scopus databases were screened for eligible studies published from inception to 2 July 2020. For included studies the proportion of lesion images presented in the papers and/or supplements was extracted. A total of 61 studies (53 original studies and 8 shorter reports (i.e. research letters or concise reports)). published in the period 1997 to 2020 were included. These studies combined included 14,124 skin tumours, of which 373 (3%) images were published. This systematic review highlights that the vast majority of images included in dermoscopy research are not published. Data sharing should be a requirement for future studies, and must be enabled and standardized by the dermatology research community and editorial offices.

Individual notions of fair data sharing from the perspectives of Swiss stakeholders

Background

The meaningful sharing of health data between different stakeholders is central to the advancement of science and to improve care offered to individual patients. However, it is important that the interests of individual stakeholders involved in this data sharing ecosystem are taken into account to ensure fair data sharing practices. In this regard, this qualitative study investigates such practices from the perspectives of a subset of relevant Swiss expert stakeholders, using a distributive justice lens.

Methods

Using purposive and snowball sampling methodologies, 48 expert stakeholders from the Swiss healthcare and research domains were recruited for semi-structured interviews. After the experts had consented, the interviews were audio-recorded and transcribed verbatim, but omitting identifying information to ensure confidentiality and anonymity. A thematic analysis using a deductive approach was conducted to identify fair data sharing practices for secondary research purposes. Themes and subthemes were then identified and developed during the analysis.

Results

Three distributive justice themes were identified in the data sharing negotiation processes, and these are: (i) effort, which was subcategorized into two subthemes (i.e. a claim to data reciprocity and other reciprocal advantages, and a claim to transparency on data re-use), (ii) compensation, which was subcategorized into two subthemes (i.e. a claim to an academic compensation and a claim to a financial compensation), and lastly, (iii) contribution, i.e. the significance of data contributions should be matched with a corresponding reward.

Conclusions

This qualitative study provides insights, which could inform policy-making on claims and incentives that encourage Swiss expert stakeholders to share their datasets. Importantly, several claims have been identified and justified under the basis of distributive justice principles, whilst some are more debatable and likely insufficient in justifying data sharing activities. Nonetheless, these claims should be taken seriously and discussed more broadly. Indeed, promoting health research while ensuring that healthcare systems guarantee better services, it is paramount to ensure that solutions developed are sustainable, provide fair criteria for academic careers and promote the sharing of high quality data to advance science.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06906-2.

Cardiovascular Safety of Biologics Targeting Interleukin (IL)-12 and/or IL-23: What Does the Evidence Say?

There is substantial evidence regarding the association between psoriasis and the elevated risk of cardiovascular (CV) disease. Many patients with psoriasis may also be concerned that their treatments may be associated with a further increase in the risk of CV disease. In this article, we summarize the data regarding the biological role of interleukin (IL)-12/23 in atherogenesis. We performed a literature search for currently known CV safety data from trials and observational studies of treatments targeting IL-12/23 in psoriasis, i.e. the p40 inhibitors ustekinumab and briakinumab, and the p19 inhibitors guselkumab, risankizumab, and tildrakizumab. On balance, extensive evidence supports the CV safety of ustekinumab, with over 14 years of follow-up data in multiple cohort studies and randomized controlled trials (RCTs). One self-controlled study concluded ustekinumab may precipitate short-term raised CV risk, but the study had limitations hindering interpretation. The safety evidence from RCTs on the p19 inhibitors are reassuring thus far, but these studies may not detect rare CV events in real-world patients. We concluded that the overall evidence does not show that ustekinumab is associated with an increase in the risk of CV disease in patients with psoriasis, but further data are awaited to assess the CV safety of p19 inhibitors for the treatment of psoriasis.

The Chagas disease study landscape: A systematic review of clinical and observational antiparasitic treatment studies to assess the potential for establishing an individual participant-level data platform

Background

Chagas disease (CD), caused by the parasite Trypanosoma cruzi, affects ~6–7 million people worldwide. Significant limitations still exist in our understanding of CD. Harnessing individual participant data (IPD) from studies could support more in-depth analyses to address the many outstanding research questions. This systematic review aims to describe the characteristics and treatment practices of clinical studies in CD and assess the breadth and availability of research data for the potential establishment of a data-sharing platform.

Methodology/Principal findings

This review includes prospective CD clinical studies published after 1997 with patients receiving a trypanocidal treatment. The following electronic databases and clinical trial registry platforms were searched: Cochrane Library, PubMed, Embase, LILACS, Scielo, Clintrials.gov, and WHO ICTRP.

Of the 11,966 unique citations screened, 109 (0.9%) studies (31 observational and 78 interventional) representing 23,116 patients were included. Diagnosis for patient enrolment required 1 positive test result in 5 (4.6%) studies (2 used molecular method, 1 used molecular and serology, 2 used serology and parasitological methods), 2 in 60 (55.0%), 3 in 14 (12.8%) and 4 or more in 4 (3.7%) studies. A description of treatment regimen was available for 19,199 (83.1%) patients, of whom 14,605 (76.1%) received an active treatment and 4,594 (23.9%) were assigned to a placebo/no-treatment. Of the 14,605 patients who received an active treatment, benznidazole was administered in 12,467 (85.4%), nifurtimox in 825 (5.6%), itraconazole in 284 (1.9%), allopurinol in 251 (1.7%) and other drugs in 286 (1.9%). Assessment of efficacy varied largely and was based primarily on biological outcome; parasitological efficacy relied on serology in 67/85 (78.8%) studies, molecular methods in 52/85 (61.2%), parasitological in 34/85 (40.0%), microscopy in 3/85 (3.5%) and immunohistochemistry in 1/85 (1.2%). The median time at which parasitological assessment was carried out was 79 days [interquartile range (IQR): 30–180] for the first assessment, 180 days [IQR: 60–500] for second, and 270 days [IQR: 18–545] for the third assessment.

Conclusions/Significance

This review demonstrates the heterogeneity of clinical practice in CD treatment and in the conduct of clinical studies. The sheer volume of potential IPD identified demonstrates the potential for development of an IPD platform for CD and that such efforts would enable in-depth analyses to optimise the limited pharmacopoeia of CD and inform prospective data collection.

Chagas disease, also known as American trypanosomiasis, is a neglected tropical disease transmitted by triatomine insects, first identified in 1909. Chagas disease affects approximately 6–7 million people globally and is highly prevalent in Latin America where most cases are reported. However, there is increasing evidence that Chagas disease is now an important public health issue outside the “classical” endemic countries due to population migration. Our understanding of Chagas disease, including its pathologies and factors relating to progression, remains to date limited, and is also challenged by lack of diagnosis and highly effective treatment. This systematic review aims to describe studies with Chagas patients receiving antiparasitic treatment. Databases were searched for relevant studies published after 1997, and the results of these searches were screened.

Although a large volume of studies was identified in the review, heterogeneity was observed in study design, diagnostic methods, outcome assessment, and treatment regimens. While this aspect will be a limitation in pooling individual patient data, the volume of data available should allow sufficient comparison to form the basis of guidelines for future studies. The results of this review demonstrate that development of a Chagas disease data platform for clinical research would enable optimisation of existing data to strengthen evidence for the treatment and diagnosis of Chagas disease.

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

BACKGROUND

Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches.

METHODS

The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes.

RESULTS

Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility.

CONCLUSIONS

There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

European Heart Journal quality standards

The aim of the European Heart Journal (EHJ) is to attract innovative, methodologically sound, and clinically relevant research manuscripts able to change clinical practice and/or substantially advance knowledge on cardiovascular diseases. As the reference journal in cardiovascular medicine, the EHJ is committed to publishing only the best cardiovascular science adhering to the highest ethical principles. EHJ uses highly rigorous peer-review, critical statistical review and the highest quality editorial process, to ensure the novelty, accuracy, quality, and relevance of all accepted manuscripts with the aim of inspiring the clinical practice of EHJ readers and reducing the global burden of cardiovascular diseases. This review article summarizes the quality standards pursued by the EHJ to fulfill its mission.

Systemic Fairness for Sharing Health Data: Perspectives From Swiss Stakeholders

Introduction: Health research is gradually embracing a more collectivist approach, fueled by a new movement of open science, data sharing and collaborative partnerships. However, the existence of systemic contradictions hinders the sharing of health data and such collectivist endeavor. Therefore, this qualitative study explores these systemic barriers to a fair sharing of health data from the perspectives of Swiss stakeholders.

Methods: Purposive and snowball sampling were used to recruit 48 experts active in the Swiss healthcare domain, from the research/policy-making field and those having a high position in a health data enterprise (e.g., health register, hospital IT data infrastructure or a national health data initiative). Semi-structured interviews were then conducted, audio-recorded, verbatim transcribed with identifying information removed to guarantee the anonymity of participants. A theoretical thematic analysis was then carried out to identify themes and subthemes related to the topic of systemic fairness for sharing health data.

Results: Two themes related to the topic of systemic fairness for sharing health data were identified, namely (i) the hypercompetitive environment and (ii) the legal uncertainty blocking data sharing. The theme, hypercompetitive environment was further divided into two subthemes, (i) systemic contradictions to fair data sharing and the (ii) need of fair systemic attribution mechanisms.

Discussion: From the perspectives of Swiss stakeholders, hypercompetition in the Swiss academic system is hindering the sharing of health data for secondary research purposes, with the downside effect of influencing researchers to embrace individualism for career opportunities, thereby opposing the data sharing movement. In addition, there was a perceived sense of legal uncertainty from legislations governing the sharing of health data, which adds unreasonable burdens on individual researchers, who are often unequipped to deal with such facets of their data sharing activities.

Variation in Calculating and Reporting Antimalarial Efficacy against Plasmodium falciparum in Sub-Saharan Africa: A Systematic Review of Published Reports

Antimalarials, in particular artemisinin-based combination therapies (ACTs), are critical tools in reducing the global burden of malaria, which is concentrated in sub-Saharan Africa. Performing and reporting antimalarial efficacy studies in a transparent and standardized fashion permit comparison of efficacy outcomes across countries and time periods. This systematic review summarizes study compliance with WHO laboratory and reporting guidance pertaining to antimalarial therapeutic efficacy studies and evaluates how well studies from sub-Saharan Africa adhered to these guidelines. We included all published studies (January 2020 or before) performed in sub-Saharan Africa where ACT efficacy for treatment of uncomplicated Plasmodium falciparum infection was reported. The primary outcome was a composite indicator for study methodology consistent with WHO guidelines for statistical analysis of corrected efficacy, defined as an article presenting a Kaplan-Meier survival analysis of corrected efficacy or reporting a per-protocol analysis where new infections were excluded from the numerator and denominator. Of 581 articles screened, we identified 279 for the review. Molecular correction was used in 83% (232/279) to distinguish new infections from recrudescences in subjects experiencing recurrent parasitemia. Only 45% (99/221) of articles with therapeutic efficacy as a primary outcome and performing molecular correction reported corrected efficacy outcomes calculated in a way consistent with WHO recommendations. These results indicate a widespread lack of compliance with WHO-recommended methods of analysis, which may result in biases in how antimalarial effectiveness is being measured and reported from sub-Saharan Africa.

Evaluation of Data Sharing After Implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement

Importance

The benefits of responsible sharing of individual-participant data (IPD) from clinical studies are well recognized, but stakeholders often disagree on how to align those benefits with privacy risks, costs, and incentives for clinical trialists and sponsors. The International Committee of Medical Journal Editors (ICMJE) required a data sharing statement (DSS) from submissions reporting clinical trials effective July 1, 2018. The required DSSs provide a window into current data sharing rates, practices, and norms among trialists and sponsors.

Objective

To evaluate the implementation of the ICMJE DSS requirement in 3 leading medical journals: JAMA, Lancet, and New England Journal of Medicine (NEJM).

Design, Setting, and Participants

This is a cross-sectional study of clinical trial reports published as articles in JAMA, Lancet, and NEJM between July 1, 2018, and April 4, 2020. Articles not eligible for DSS, including observational studies and letters or correspondence, were excluded. A MEDLINE/PubMed search identified 487 eligible clinical trials in JAMA (112 trials), Lancet (147 trials), and NEJM (228 trials). Two reviewers evaluated each of the 487 articles independently.

Exposure

Publication of clinical trial reports in an ICMJE medical journal requiring a DSS.

Main Outcomes and Measures

The primary outcomes of the study were declared data availability and actual data availability in repositories. Other captured outcomes were data type, access, and conditions and reasons for data availability or unavailability. Associations with funding sources were examined.

Results

A total of 334 of 487 articles (68.6%; 95% CI, 64%-73%) declared data sharing, with nonindustry NIH-funded trials exhibiting the highest rates of declared data sharing (89%; 95% CI, 80%-98%) and industry-funded trials the lowest (61%; 95% CI, 54%-68%). However, only 2 IPD sets (0.6%; 95% CI, 0.0%-1.5%) were actually deidentified and publicly available as of April 10, 2020. The remaining were supposedly accessible via request to authors (143 of 334 articles [42.8%]), repository (89 of 334 articles [26.6%]), and company (78 of 334 articles [23.4%]). Among the 89 articles declaring that IPD would be stored in repositories, only 17 (19.1%) deposited data, mostly because of embargo and regulatory approval. Embargo was set in 47.3% of data-sharing articles (158 of 334), and in half of them the period exceeded 1 year or was unspecified.

Conclusions and Relevance

Most trials published in JAMA, Lancet, and NEJM after the implementation of the ICMJE policy declared their intent to make clinical data available. However, a wide gap between declared and actual data sharing exists. To improve transparency and data reuse, journals should promote the use of unique pointers to data set location and standardized choices for embargo periods and access requirements.

Model-to-Data Approach for Deep Learning in Optical Coherence Tomography Intraretinal Fluid Segmentation

Importance

Amid an explosion of interest in deep learning in medicine, including within ophthalmology, concerns regarding data privacy, security, and sharing are of increasing importance. A model-to-data approach, in which the model itself is transferred rather than data, can circumvent many of these challenges but has not been previously demonstrated in ophthalmology.

Objective

To determine whether a model-to-data deep learning approach (ie, validation of the algorithm without any data transfer) can be applied in ophthalmology.

Design, Setting, and Participants

This single-center cross-sectional study included patients with active exudative age-related macular degeneration undergoing optical coherence tomography (OCT) at the New England Eye Center from August 1, 2018, to February 28, 2019. Data were primarily analyzed from March 1 to June 20, 2019.

Main Outcomes and Measures

Training of the deep learning model, using a model-to-data approach, in recognizing intraretinal fluid (IRF) on OCT B-scans.

Results

The model was trained (learning curve Dice coefficient, >80%) using 400 OCT B-scans from 128 participants (69 female [54%] and 59 male [46%]; mean [SD] age, 77.5 [9.1] years). In comparing the model with manual human grading of IRF pockets, no statistically significant difference in Dice coefficients or intersection over union scores was found (P > .05).

Conclusions and Relevance

A model-to-data approach to deep learning applied in ophthalmology avoided many of the traditional hurdles in large-scale deep learning, including data sharing, security, and privacy concerns. Although the clinical relevance of these results is limited at this time, this proof-of-concept study suggests that such a paradigm should be further examined in larger-scale, multicenter deep learning studies.

A Field Test of Major Value Frameworks in Chemotherapy of Nasopharyngeal Carcinoma—To Know, Then to Measure

Background: The European Society for Medical Oncology (ESMO) and the American Society of Clinical Oncology (ASCO) have independently developed their own frameworks to assess the benefits of different cancer treatment options, which have significant implications in health science and policy. We aimed to compare these frameworks in nasopharyngeal carcinoma.

Methods: We identified all randomized controlled trials of systemic chemotherapies for nasopharyngeal carcinoma until April 5th, 2020. Trials were eligible if significant differences favoring the experimental group in a prespecified primary or secondary outcome were reported. Two assessors independently scored the trials and the final scores were determined by consensus.

Results: Fifteen trials were included in the analysis. Five different toxicity grading criteria were applied to the 15 trials. Ten (66.7%) trials did not report grade 1–2 toxicities and eight (53.3%) did not report late toxicities. The number of acute toxicities reported was strikingly different (17 vs. 8) in two trials using the same regimen. All trials met the ESMO criteria for a high level of benefit. However, significant variations in ASCO scores between trials were observed (mean [standard deviation]: 38.9 [20.0]).

Conclusions: The underreporting and inconsistent reporting of toxicities would significantly impair the assessment of value using any framework. Moreover, there is a concern that the ASCO framework generated highly inconsistent scoring for treatments that met the ESMO criteria for a high level of benefit. The anomalies identified in the frameworks function would be helpful in their future improvement.

Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.

Big data in systemic sclerosis: Great potential for the future

Since it was first used in 1997, the term “big data” has been popularized; however, the concept of big data is relatively new to medicine. Big data refers to a method and technique to systematically retrieve, collect, manage, and analyze very large and complex sets of structured and unstructured data that cannot be sufficiently processed using traditional methods of processing data. Integrating big data in rare diseases with low prevalence and incidence, like systemic sclerosis is of particular importance. We conducted a literature review of use of big data in systemic sclerosis. The volume of data on systemic sclerosis has grown steadily in the recent years; however, big data methods have not been readily used. This inexhaustible source of data needs to be used more to unleash its full potential.

Sharing Is Caring-Data Sharing Initiatives in Healthcare

In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these 'big data' put together can be utilized to optimize treatments for each unique patient ('precision medicine'). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the 'valley of death' of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing, such as medical crowdsourcing and data generalists.

Data Sharing in the Context of Health-Related Citizen Science

As citizen science expands, questions arise regarding the applicability of norms and policies created in the context of conventional science. This article focuses on data sharing in the conduct of health-related citizen science, asking whether citizen scientists have obligations to share data and publish findings on par with the obligations of professional scientists. We conclude that there are good reasons for supporting citizen scientists in sharing data and publishing findings, and we applaud recent efforts to facilitate data sharing. At the same time, we believe it is problematic to treat data sharing and publication as ethical requirements for citizen scientists, especially where there is the potential for burden and harm without compensating benefit.

Did awarding badges increase data sharing in BMJ Open? A randomized controlled trial

Sharing data and code are important components of reproducible research. Data sharing in research is widely discussed in the literature; however, there are no well-established evidence-based incentives that reward data sharing, nor randomized studies that demonstrate the effectiveness of data sharing policies at increasing data sharing. A simple incentive, such as an Open Data Badge, might provide the change needed to increase data sharing in health and medical research. This study was a parallel group randomized controlled trial (protocol registration: doi:10.17605/OSF.IO/PXWZQ) with two groups, control and intervention, with 80 research articles published in BMJ Open per group, with a total of 160 research articles. The intervention group received an email offer for an Open Data Badge if they shared their data along with their final publication and the control group received an email with no offer of a badge if they shared their data with their final publication. The primary outcome was the data sharing rate. Badges did not noticeably motivate researchers who published in BMJ Open to share their data; the odds of awarding badges were nearly equal in the intervention and control groups (odds ratio = 0.9, 95% CI [0.1, 9.0]). Data sharing rates were low in both groups, with just two datasets shared in each of the intervention and control groups. The global movement towards open science has made significant gains with the development of numerous data sharing policies and tools. What remains to be established is an effective incentive that motivates researchers to take up such tools to share their data.

Primed to comply: Individual participant data sharing statements on ClinicalTrials.gov

In June 2017, the International Committee of Medical Journal Editors (ICMJE) announced a requirement that authors reporting the results of clinical trials to journals that follow ICMJE recommendations must include an individual participant data (IPD) sharing statement with manuscripts submitted after 01 July 2018. Additionally, all new clinical trials for which enrollment began on or after 01 January 2019 must include a data sharing statement in the trial’s publicly posted registration. This study sought to understand whether IPD sharing statements of clinical trials first registered on ClinicalTrials.gov before 01 January 2019 reflected comprehension of the expectations and a willingness to share. To establish baseline characteristics for the prevalence and quality of IPD sharing statements, we examined IPD sharing statements among 2,040 clinical trials first posted on ClinicalTrials.gov between 01 January 2018 and 06 June 2018. Two independent coders further analyzed the quality of the IPD sharing statements of trials whose registration records indicated the intent to share IPD. The vast majority of trials included in this study did not indicate an intent to share IPD (n = 1,928; 94.5%). Among the trials that did commit to sharing IPD (n = 112, 5.5%), significant variability existed in the content and structure of IPD sharing statements. The results of this study suggest that successful compliance with the IPD sharing statement requirements of the ICMJE will require further clarification, enhanced education, and outreach to investigators.

Results dissemination of registered clinical trials across Polish academic institutions: a cross-sectional analysis

OBJECTIVES

To establish the rates of publication and reporting of results for interventional clinical trials across Polish academic medical centres (AMCs) completed between 2009 and 2013. We aim also to compare the publication and reporting success between adult and paediatric trials.

DESIGN

Cross-sectional study.

SETTING

AMCs in Poland.

PARTICIPANTS

AMCs with interventional trials registered on ClinicalTrials.gov.

MAIN OUTCOME MEASURE

Results reporting on ClinicalTrials.gov and publishing via journal publication.

RESULTS

We identified 305 interventional clinical trials registered on ClinicalTrials.gov, completed between 2009 and 2013 and affiliated with at least one AMC. Overall, 243 of the 305 trials (79.7%) had been published as articles or posted their summary results on ClinicalTrials.gov. Results were posted within a year of study completion and/or published within 2 years of study completion for 131 trials (43.0%). Dissemination by both posting and publishing results in a timely manner was achieved by four trials (1.3%).

CONCLUSIONS

Our cross-sectional analysis revealed that Polish AMCs fail to meet the expectation for timely disseminating the findings of all interventional clinical trials. Delayed dissemination and non-dissemination of trial results negatively affects decisions in healthcare.

Responsible data sharing in a big data-driven translational research platform: lessons learned

BACKGROUND

To foster responsible data sharing in health research, ethical governance complementary to the EU General Data Protection Regulation is necessary. A governance framework for Big Data-driven research platforms will at least need to consider the conditions as specified a priori for individual datasets. We aim to identify and analyze these conditions for the Innovative Medicines Initiative's (IMI) BigData@Heart platform.

METHODS

We performed a unique descriptive case study into the conditions for data sharing as specified for datasets participating in BigData@Heart. Principle investigators of 56 participating databases were contacted via e-mail with the request to send any kind of documentation that possibly specified the conditions for data sharing. Documents were qualitatively reviewed for conditions pertaining to data sharing and data access.

RESULTS

Qualitative content analysis of 55 relevant documents revealed overlap on the conditions: (1) only to share health data for scientific research, (2) in anonymized/coded form, (3) after approval from a designated review committee, and while (4) observing all appropriate measures for data security and in compliance with the applicable laws and regulations.

CONCLUSIONS

Despite considerable overlap, prespecified conditions give rise to challenges for data sharing. At the same time, these challenges inform our thinking about the design of an ethical governance framework for data sharing platforms. We urge current data sharing initiatives to concentrate on: (1) the scope of the research questions that may be addressed, (2) how to deal with varying levels of de-identification, (3) determining when and how review committees should come into play, (4) align what policies and regulations mean by "data sharing" and (5) how to deal with datasets that have no system in place for data sharing.

Protocol for the Effectiveness of an Anesthesiology Control Tower System in Improving Perioperative Quality Metrics and Clinical Outcomes: the TECTONICS randomized, pragmatic trial

Introduction: Perioperative morbidity is a public health priority, and surgical volume is increasing rapidly. With advances in technology, there is an opportunity to research the utility of a telemedicine-based control center for anesthesia clinicians that assess risk, diagnoses negative patient trajectories, and implements evidence-based practices. Objectives: The primary objective of this trial is to determine whether an anesthesiology control tower (ACT) prevents clinically relevant adverse postoperative outcomes including 30-day mortality, delirium, respiratory failure, and acute kidney injury. Secondary objectives are to determine whether the ACT improves perioperative quality of care metrics including management of temperature, mean arterial pressure, mean airway pressure with mechanical ventilation, blood glucose, anesthetic concentration, antibiotic redosing, and efficient fresh gas flow. Methods and analysis: We are conducting a single center, randomized, controlled, phase 3 pragmatic clinical trial. A total of 58 operating rooms are randomized daily to receive support from the ACT or not. All adults (eighteen years and older) undergoing surgical procedures in these operating rooms are included and followed until 30 days after their surgery. Clinicians in operating rooms randomized to ACT support receive decision support from clinicians in the ACT. In operating rooms randomized to no intervention, the current standard of anesthesia care is delivered. The intention-to-treat principle will be followed for all analyses. Differences between groups will be presented with 99% confidence intervals; p-values <0.005 will be reported as providing compelling evidence, and p-values between 0.05 and 0.005 will be reported as providing suggestive evidence. Registration: TECTONICS is registered on ClinicalTrials.gov, NCT03923699; registered on 23 April 2019.

Role of industry funders in oncology RCTs published in high-impact journals and its association with trial conclusions and time to publication

Background

Previous studies have shown that industry funded trials are associated with pro-industry conclusions and publication bias. Less is known about the role of industry funders and their influence on trial conclusions and time to publication.

Methods

We identified all industry funded RCTs published in six high-impact clinical journals between 2014 and 2016 to estimate the prevalence of the role of industry funders in trial design, data collection, data analyses, data interpretation and manuscript writing. Ordinal logistic regression was used to assess the association between the role of industry funders and trial conclusions, which was classified on a five-point scale. Cox proportional-hazards were used to examine the effect of role of funder on time to publication.

Results

Of the 255 eligible RCTs, industry funders had a role in trial design in 179 (70.2%) trials, data collection in 160 (62.7%) trials, data analyses in 173 (67.8%) trials, data interpretation in 135 (52.9%) trials and manuscript writing in 168 (65.9%) trials. Trials with any role of industry funders had 3.6 times (95% CI 2.0-6.6) higher odds of having positive conclusions compared with those without role of industry funders. In trials with any role of industry funders, positive trials were published more rapidly than negative trials (hazard ratio = 4.3; 95% CI 2.7-6.7, P < 0.001), while for trials without role of industry funders, there was no association (hazard ratio = 1.07; 95% CI 0.57-1.99, P = 0.84).

Conclusion

The involvement of industry funders is common in all stages of clinical trials and was associated with more positive conclusions and more rapid publication of RCTs with positive results.

Pragmatic Home-Based Exercise after Total Hip Arthroplasty - Silkeborg: Protocol for a prospective cohort study (PHETHAS-1)

Introduction: Rehabilitation exercises are offered to patients after total hip arthroplasty (THA); however, the effectiveness and optimal type and dose of exercise remains unknown. The primary objective of this trial is to indicate the preliminary efficacy of home-based rehabilitation using elastic band exercise on performance-based function after THA, based on the relationship between the performed exercise dose and the change in performance-based function (gait speed) from 3 (start of intervention) to 10 weeks (end of intervention) after surgery. The secondary objective is to investigate if a dose-response relationship exists between the performed exercise dose and changes in: hip-related disability, lower-extremity functional performance, and hip muscle strength Methods: In this prospective cohort study, patients scheduled for THA will be consecutively included until 88 have completed the intervention period from 3 to 10 weeks postoperatively. Participants perform the standard rehabilitation program with elastic band exercises. Exercise dose (exposure) will be objectively quantified using a sensor attached to the elastic band. The primary outcome is gait speed measured by the 40-m fast-paced walk test. Secondary outcomes include: patient reported hip disability (Hip disability and Osteoarthritis Outcome Score (HOOS)), hip muscle strength (hand-held dynamometry) and lower extremity function (30-s chair stand test). Discussion: This trial will add knowledge concerning the relationship between performed exercise dose and post-operative outcomes after THA. The protocol paper describes the study design and methods in detail, including the statistical analysis plan. Trial registration: Pre-registered on March 27, 2017 at ClinicalTrails.gov (ID: NCT03109821).

Openness in Big Data and Data Repositories: The Application of an Ethics Framework for Big Data in Health and Research

There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

Clinical trial data sharing: here's the challenge

OBJECTIVE

Anonymised patient-level data from clinical research are increasingly recognised as a fundamental and valuable resource. It has value beyond the original research project and can help drive scientific research and innovations and improve patient care. To support responsible data sharing, we need to develop systems that work for all stakeholders. The members of the Independent Review Panel (IRP) for the data sharing platform Clinical Study Data Request (CSDR) describe here some summary metrics from the platform and challenge the research community on why the promised demand for data has not been observed.

SUMMARY OF DATA

From 2014 to the end of January 2019, there were a total of 473 research proposals (RPs) submitted to CSDR. Of these, 364 met initial administrative and data availability checks, and the IRP approved 291. Of the 90 research teams that had completed their analyses by January 2018, 41 reported at least one resulting publication to CSDR. Less than half of the studies ever listed on CSDR have been requested.

CONCLUSION

While acknowledging there are areas for improvement in speed of access and promotion of the platform, the total number of applications for access and the resulting publications have been low and challenge the sustainability of this model. What are the barriers for data contributors and secondary analysis researchers? If this model does not work for all, what needs to be changed? One thing is clear: that data access can realise new and unforeseen contributions to knowledge and improve patient health, but this will not be achieved unless we build sustainable models together that work for all.

Big data management challenges in health research-a literature review

Big data management for information centralization (i.e. making data of interest findable) and integration (i.e. making related data connectable) in health research is a defining challenge in biomedical informatics. While essential to create a foundation for knowledge discovery, optimized solutions to deliver high-quality and easy-to-use information resources are not thoroughly explored. In this review, we identify the gaps between current data management approaches and the need for new capacity to manage big data generated in advanced health research. Focusing on these unmet needs and well-recognized problems, we introduce state-of-the-art concepts, approaches and technologies for data management from computing academia and industry to explore improvement solutions. We explain the potential and significance of these advances for biomedical informatics. In addition, we discuss specific issues that have a great impact on technical solutions for developing the next generation of digital products (tools and data) to facilitate the raw-data-to-knowledge process in health research.

Pragmatic Home-Based Exercise after Total Hip Arthroplasty - Silkeborg: Protocol for a prospective cohort study (PHETHAS-1)

Introduction: Rehabilitation exercises are offered to patients after total hip arthroplasty (THA); however, the effectiveness and optimal type and dose of exercise remains unknown. The primary objective of this trial is to indicate the preliminary efficacy of home-based rehabilitation using elastic band exercise on performance-based function after THA, based on the relationship between the performed exercise dose and the change in performance-based function (gait speed) from 3 (start of intervention) to 10 weeks (end of intervention) after surgery. The secondary objective is to investigate if a dose-response relationship exists between the performed exercise dose and changes in: hip-related disability, lower-extremity functional performance, and hip muscle strength Methods: In this prospective cohort study, patients scheduled for THA will be consecutively included until 88 have completed the intervention period from 3 to 10 weeks postoperatively. Participants perform the standard rehabilitation program with elastic band exercises. Exercise dose (exposure) will be objectively quantified using a sensor attached to the elastic band. The primary outcome is gait speed measured by the 40-m fast-paced walk test. Secondary outcomes include: patient reported hip disability (Hip disability and Osteoarthritis Outcome Score (HOOS)), hip muscle strength (hand-held dynamometry) and lower extremity function (30-s chair stand test). Discussion: This trial will add knowledge concerning the relationship between performed exercise dose and post-operative outcomes after THA. The protocol paper describes the study design and methods in detail, including the statistical analysis plan. Trial registration: Pre-registered on March 27, 2017 at ClinicalTrails.gov (ID: NCT03109821).

Big data in IBD: a look into the future

Big data methodologies, made possible with the increasing generation and availability of digital data and enhanced analytical capabilities, have produced new insights to improve outcomes in many disciplines. Application of big data in the health-care sector is in its early stages, although the potential for leveraging underutilized data to gain a better understanding of disease and improve quality of care is enormous. Owing to the intrinsic characteristics of inflammatory bowel disease (IBD) and the management dilemmas that it imposes, the implementation of big data research strategies not only can complement current research efforts but also could represent the only way to disentangle the complexity of the disease. In this Review, we explore important potential applications of big data in IBD research, including predictive models of disease course and response to therapy, characterization of disease heterogeneity, drug safety and development, precision medicine and cost-effectiveness of care. We also discuss the strengths and limitations of potential data sources that big data analytics could draw from in the field of IBD, including electronic health records, clinical trial data, e-health applications and genomic, transcriptomic, proteomic, metabolomic and microbiomic data.

Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board

Background

Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer.

Patients and methods

Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in 2016. A blinded second review was carried out by the center's tumor board in 2016 for all cases in which there was not agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered concordant if the tumor board recommendations were designated 'recommended' or 'for consideration' by WFO.

Results

Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases. Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P ≤ 0.02; P < 0.001) in all age groups compared with patients <45 years of age, except for the age group 55-64 years. Receptor status was not found to affect concordance.

Conclusion

Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not. This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment decision making, especially at centers where expert breast cancer resources are limited.

Quality assessment of clinical trial registration with traditional Chinese medicine in WHO registries

OBJECTIVE

This study aimed to assess the registration quality of clinical trials (CTs) with traditional Chinese medicine (TCM) in the WHO International Clinical Trials Registry Platform (ICTRP) and identify the common problems if any.

METHODS

The ICTRP database was searched for all TCM CTs that were registered up to 31 December 2017. Registered information of each trial was collected from specific registry involved in ICTRP through hyperlink. The primary analysis was to assess the reporting quality of registered trials with TCM interventions, which is based on the minimum 20 items of WHO Trial Registration Data Set (TRDS, V.1.2.1) plus optional additional three items recommended by ICTRP, and some specific items for TCM information (including TCM intervention, diagnosis, outcome and rationale). Descriptive statistics were additionally used to analyse the baseline characteristics of TCM trial registrations.

RESULTS

A total of 3339 records in 15 registries were examined. The number of TCM registered trials has increased rapidly after the requirement of mandatory trial registration proposed by International Committee of Medical Journal Editors on 1 July 2005, and the top two registries were Chinese Clinical Trial Registry and ClincialTrials.gov. Of 3339 trials, 61% were prospective registration and 12.8% shared resultant publications. There were 2955 interventional trials but none of them had a 100% reporting rate of the minimum 20 items and additional three items. The reporting quality of these 23 items was not optimal due to 11 of them had a lower reporting rate (<65%). For TCM details, 49.2% lacked information on description of TCM intervention(s), 85.9% did not contain TCM diagnosis criteria, 92.6% did not use TCM outcome(s) and 67.1% lacked information on TCM background and rationale.

CONCLUSION

The registration quality of TCM CTs should be improved by prospective registration, full completion of WHO TRDS, full reporting of TCM information and results sharing. Further full set of trial registration items for TCM trials should be developed thus to standardise the content of TCM trial registration.

Authors of trials from high-ranking anesthesiology journals were not willing to share raw data

OBJECTIVES

To analyze data sharing practices among authors of randomized controlled trials (RCTs) published in seven high-ranking anesthesiology journals from 2014 to 2016.

STUDY DESIGN AND SETTING

We analyzed data sharing statements in 619 included RCTs and contacted their corresponding authors, asking them to share de-identified raw data from trial.

RESULTS

Of the 86 (14%) authors who responded to our query for data sharing, only 24 (4%) provided the requested data. Only one of those 24 had a data sharing statement in the published manuscript. Only 24 (4%) of manuscripts contained statements suggesting a willingness to share trial data; only one of those authors actually shared data. There was no difference in proportion of data sharing between studies with commercial and nonprofit funding. Among the 62 authors who refused to provide data, reasons were seldom provided. When reasons were provided, common themes included issues regarding data ownership and participant privacy. Only one of the seven analyzed journals encouraged authors toward data sharing.

CONCLUSION

Willingness to share data among anesthesiology RCTs is very low. To achieve widespread availability of de-identified trial data, journals should request their publication, as opposed to only encouraging authors to do so.