Dataset Information

How Are Emerging Data Translated Into Clinical Practice? A Mixed Methods Investigation of Coronavirus Disease 2019 Institutional Treatment Protocols.

ABSTRACT:

Background

Early in the coronavirus disease 2019 (COVID-19) pandemic, there was minimal data to guide treatment, and we lacked understanding of how clinicians translated this limited evidence base for potential therapeutics to bedside care. Our objective was to systematically determine how emerging data about COVID-19 treatments was implemented by analyzing institutional treatment protocols.

Methods

Treatment protocols from North American healthcare facilities and recommendations from guideline-issuing bodies were collected. Qualitative data on treatment regimens and their applications were extracted using an adapted National Institutes of Health/US Food and Drug Administration experimental therapeutics framework. Structured data on risk factor and severity of illness scoring systems were extracted and analyzed using descriptive statistics.

Results

We extracted data from 105 independent protocols. Guideline-issuing organizations published recommendations after the initial peak of the pandemic in many regions and generally recommended clinical trial referral, with limited additional guidance. Facility-specific protocols favored offering some treatment (96.8%, N = 92 of 95), most commonly, hydroxychloroquine (90.5%), followed by remdesivir and interleukin-6 inhibitors. Recommendation for clinical trial enrollment was limited largely to academic medical centers (19 of 52 vs 9 of 43 community/Veterans Affairs [VA]), which were more likely to have access to research studies. Other themes identified included urgent protocol development, plans for rapid updates, contradictory statements, and entirely missing sections, with section headings but no content other than "in process."

Conclusions

In the COVID-19 pandemic, emerging information was rapidly implemented by institutions into clinical practice and, unlike recommendations from guideline-issuing bodies, heavily favored administering some form of therapy. Understanding how and why evidence is translated into clinical care is critical to improve processes for other emerging diseases.

SUBMITTER: Asundi A

PROVIDER: S-EPMC8026152 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundWith the global spread of coronavirus disease 2019 (COVID-19), an increasing number of clinical trials are being designed and executed to evaluate the efficacy and safety of various therapies for COVID-19. We conducted this survey to assess the methodological quality of registry protocols on potential treatments for COVID-19.MethodsClinical trial protocols were identified on the ClinicalTrials.gov and the Chinese Clinical Trial Registry. Protocols were screened by two investigators independently against pre-defined eligibility criteria. Quality of the included protocols was assessed according to the modified 14-item SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement.ResultsWe included 82 randomized controlled trial (RCT) protocols investigating treatment modalities for COVID-19. These ongoing trials are being conducted in 16 provinces, autonomous regions, and municipalities of China, and study interventions were either Western medicines (n = 56) or traditional Chinese medicine (n = 26). Findings of our quality assessment indicated that the existing trial protocols could be further improved on several aspects, including selection and definition of outcome measures, descriptions of study interventions and comparators, study subject recruitment time, definition of study inclusion and exclusion criteria, and allocation concealment methods. Descriptions of random sequence generation methodologies were accurate for the majority of included trial protocols (n = 64; 78.05%); however, reporting of allocation concealment remained unclear in 63 (76.83%) protocols. Therefore, the overall risk of selection bias across these RCTs was judged to be unclear. A total of 52 (63.41%) included RCT protocols were open-label trials and are thus associated with a high risk of performance bias and detection bias.ConclusionQuality of currently available RCT protocols on the treatments for COVID-19 could be further improved. For transparency and effective knowledge translation in real-world clinically settings, it is important for trial investigators to standardize baseline treatments for patients with COVID-19 and assess clinically important core outcome measures. Despite eager anticipation from the public on the results of effectiveness trials in COVID-19, robust design, execution, and reporting of these trials should be regarded as high priority.