Consent models in Canadian critical care randomized controlled trials: a scoping review

O’Hearn, Katie; Gibson, Jess; Krewulak, Karla D.; Porteous, Rebecca; Saigle, Victoria; Sampson, Margaret; Tsampalieros, Anne; Barrowman, Nick; Cameron, Shona

doi:10.1007/s12630-021-02176-y

Cited by 2 publications

(3 citation statements)

References 53 publications

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“…We searched MEDLINE, EMBASE, and CENTRAL from 11-Mar-2020 to 28-Apr-2023 using the search strategy from a related study conducted by our research team (8). We performed manual searches in medRxiv (the preprint server for health sciences) for the terms “ICU,” “PICU,” “study,” “trial,” “canada” and “canadian.” Though our intention was to use MeSH terms and keywords related to COVID-19 (per the registered protocol), we removed these two groups of search terms to capture non-pandemic related studies.…”

Section: Methodsmentioning

confidence: 99%

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Consent in Canadian-Led Critical Care Research During the COVID-19 Pandemic: A Scoping Review

Krewulak,

Albrecht,

Cameron

et al. 2024

Preprint

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Introduction: Despite the importance of critical care research during the SARS-CoV-2 pandemic, several pandemic-related factors made the process of obtaining prior written informed consent for research infeasible. To overcome these challenges, research studies utilized alternate informed consent models suggested by available guidance. Objective: To describe the consent models used in Canadian intensive care unit (ICU) and pediatric ICU (PICU) studies during the COVID-19 pandemic. Data Sources: We searched MEDLINE, EMBASE, CENTRAL, clinicaltrials.gov, and medRxiv from 01-Jan-2020 to 28-Apr-2023 using Medical Subject Headings and keywords related to the setting (ICU, PICU), study design (e.g., RCT) and study region (i.e., Canada). We included Canadian-led studies that were enrolling during the SARS-CoV-2 and reported on consent. Two independent reviewers reviewed titles/abstracts and full text articles for inclusion. Results: We included 13 studies from adult (n=12, 92.3%) and pediatric (n=1, 7.7%) populations. Some study authors reported that informed (n=3/13, 23.1%) or a priori (n=2/13, 15.4%) consent was obtained, without further details. Study authors also reported using written informed (n=4/13, 30.8%), deferred (n=3/13, 23.1%), verbal/waived/assent (each n=2/13, 15.4%), or that ethics approval was not necessary which means consent was not required (n=1/13, 7.7%). Five studies (n=5/13, 38.5%) used multiple consent models: a priori/deferred (n=2/5, 40%), written/verbal (n=2/5, 40%), or waived/assent (n=1/5, 20%). Conclusion: This scoping review underscores the importance of transparent reporting of or modifications to trial procedures during crises, such as the COVID-19 pandemic. Improved reporting practices and exploration of alternate consent models, including electronic consent, are crucial for advancing critical care trials beyond the pandemic and preparing for future health emergencies.

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Section: Methodsmentioning

confidence: 99%

Section: Information Sources and Searchmentioning

confidence: 99%

Consent in Canadian-Led Critical Care Research During the COVID-19 Pandemic: A Scoping Review

Krewulak,

Albrecht,

Cameron

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Data collection can be laborious, prone to human errors, and constrained in terms of quantity and granularity [ 14 ]. Moreover, obtaining patient consent for most randomized controlled trials in the ICU poses challenges [ 15 ], necessitating alternative consent models. These limitations have become increasingly apparent as medical complexity continues to grow exponentially [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study

Ke,

Yang,

Liu

2024

J Med Internet Res

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Background Intensive care research has predominantly relied on conventional methods like randomized controlled trials. However, the increasing popularity of open-access, free databases in the past decade has opened new avenues for research, offering fresh insights. Leveraging machine learning (ML) techniques enables the analysis of trends in a vast number of studies. Objective This study aims to conduct a comprehensive bibliometric analysis using ML to compare trends and research topics in traditional intensive care unit (ICU) studies and those done with open-access databases (OADs). Methods We used ML for the analysis of publications in the Web of Science database in this study. Articles were categorized into “OAD” and “traditional intensive care” (TIC) studies. OAD studies were included in the Medical Information Mart for Intensive Care (MIMIC), eICU Collaborative Research Database (eICU-CRD), Amsterdam University Medical Centers Database (AmsterdamUMCdb), High Time Resolution ICU Dataset (HiRID), and Pediatric Intensive Care database. TIC studies included all other intensive care studies. Uniform manifold approximation and projection was used to visualize the corpus distribution. The BERTopic technique was used to generate 30 topic-unique identification numbers and to categorize topics into 22 topic families. Results A total of 227,893 records were extracted. After exclusions, 145,426 articles were identified as TIC and 1301 articles as OAD studies. TIC studies experienced exponential growth over the last 2 decades, culminating in a peak of 16,378 articles in 2021, while OAD studies demonstrated a consistent upsurge since 2018. Sepsis, ventilation-related research, and pediatric intensive care were the most frequently discussed topics. TIC studies exhibited broader coverage than OAD studies, suggesting a more extensive research scope. Conclusions This study analyzed ICU research, providing valuable insights from a large number of publications. OAD studies complement TIC studies, focusing on predictive modeling, while TIC studies capture essential qualitative information. Integrating both approaches in a complementary manner is the future direction for ICU research. Additionally, natural language processing techniques offer a transformative alternative for literature review and bibliometric analysis.

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Consent models in Canadian critical care randomized controlled trials: a scoping review

Cited by 2 publications

References 53 publications

Consent in Canadian-Led Critical Care Research During the COVID-19 Pandemic: A Scoping Review

Consent in Canadian-Led Critical Care Research During the COVID-19 Pandemic: A Scoping Review

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study

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