Leveraging analytics to assure quality during the Covid-19 pandemic - The COVACTA clinical study example

Ménard, Timothé; Bowling, Rich; Mehta, Pooja; Koneswarakantha, Björn; Magruder, Eileen

doi:10.1016/j.conctc.2020.100662

Cited by 15 publications

(17 citation statements)

References 7 publications

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“…The treatments proposed in various studies have included corticosteroid therapies (commonly used to suppress inflammation), Janus kinase (JAK) inhibitors (IL-1 family antagonists) or IL-6 receptor blockade (IL-6 antagonists), and TNF blockers. The randomized controlled COVACTA trial NCT04320615 (A Study to Evaluate the Safety and Efficacy of Tocilizumab in Patients With Severe COVID-19 Pneumonia)failed to meet its primary end point of improved clinical status [ 43 ], but evidence in the literature, instead, reports efficacy with tocilizumab [ 44 , 45 , 46 ]. Care should be taken in interpreting data from the COVACTA study, one consideration is that something must be done about the timing of the administration, because the immunosuppression (with glucocorticoids or IL-6 blockade) needs to intervene when the immune response makes the patient sick.…”

Section: Discussionmentioning

confidence: 99%

When Does the Cytokine Storm Begin in COVID-19 Patients? A Quick Score to Recognize It

Cappanera

Palumbo

Kwan

et al. 2021

JCM

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that is responsible for coronavirus disease 2019 (COVID-19), which has rapidly spread across the world, becoming a pandemic. The “cytokine storm” (CS) in COVID-19 leads to the worst stage of illness, and its timely control through immunomodulators, corticosteroids, and cytokine antagonists may be the key to reducing mortality. After reviewing published studies, we proposed a Cytokine Storm Score (CSs) to identify patients who were in this hyperinflammation state, and at risk of progression and poorer outcomes. We retrospectively analyzed 31 patients admitted to Infectious Disease Department in “St. Maria” Hospital in Terni with confirmed SARS-CoV-2 infections, and analyzed the “CS score” (CSs) and the severity of COVID-19. Then we conducted a prospective study of COVID-19 patients admitted after the definition of the CSscore. This is the first study that proposes and applies a new score to quickly identify COVID-19 patients who are in a hyperinflammation stage, to rapidly treat them in order to reduce the risk of intubation. CSs can accurately identify COVID-19 patients in the early stages of a CS, to conduct timely, safe, and effect administration of immunomodulators, corticosteroids, and cytokine antagonists, to prevent progression and reduce mortality.

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Section: Discussionmentioning

confidence: 99%

When Does the Cytokine Storm Begin in COVID-19 Patients? A Quick Score to Recognize It

Cappanera

Palumbo

Kwan

et al. 2021

JCM

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“…Despite data from the RECOVERY trial on dexamethasone [ 110 ], no additional immunomodulating drug could conclusively demonstrate mortality benefits in randomized controlled trials [ 105 ]. For example, as persistently high IL-6 levels are associated with reduced survival in ARDS patients [ 116 ], IL-6 blockade (e.g., using tocilizumab) was advocated, but the results (e.g., the COVACTA trial) did not indicate clinical benefits [ 117 , 118 ].…”

Section: Clinical Presentationmentioning

confidence: 99%

COVID-19-associated acute respiratory distress syndrome (CARDS): Current knowledge on pathophysiology and ICU treatment – A narrative review

Pfortmueller

Spinetti

Urman

et al. 2021

Best Practice & Research Clinical Anaesthesiology

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“…An added benefit of the proposed Bayesian approach and the selected risk metrics is that the This model was developed during the Covid-19 pandemic where on-site audits could not be performed [17]. Having a data product enabling remote monitoring of safety reporting from investigator sites was essential to ensure business continuity for clinical quality assurance activities [18]. Our approach has the potential to reduce the need for on-site audits and thereby shift the focus away from source data validation and verification towards pre-identified, higher risk areas.…”

Section: Discussionmentioning

confidence: 99%

Bayesian modeling for the detection of adverse events underreporting in clinical trials

Barmaz

Ménard

2020

Preprint

Self Cite

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IntroductionSafety underreporting is a recurrent issue in clinical trials that can impact patient safety and data integrity. Clinical Quality Assurance (QA) practices used to detect underreporting rely on on-site audits, however adverse events underreporting remains a recurrent issue. In a recent project, we developed a predictive model that enables oversight of Adverse Event (AE) reporting for clinical Quality Program Leads (QPL). However, there were limitations to using solely a machine learning model.ObjectiveOur primary objective was to propose a robust method to compute the probability of AE underreporting that could complement our machine learning model. Our model was developed to enhance patients safety while reducing the need for on-site and manual QA activities in clinical trials.MethodsWe used a Bayesian hierarchical model to estimate the site reporting rates and assess the risk of underreporting. We designed the model with Project Data Sphere clinical trial data that is public and anonymized.OutcomeWith the resulting reporting rates and metrics, we could focus quality assurance strategic activities for clinical trials on the investigator sites with the highest risk of underreporting.ConclusionThe new model will be integrated into the current dashboard designed for clinical Quality Program Leads. This approach reduces the need for on-site audits, shifting focus from source data verification (SDV) to pre-identified, higher risk areas. It will enhance further quality assurance activities for safety reporting from clinical trials and generate quality evidence during pre-approval inspections.

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Leveraging analytics to assure quality during the Covid-19 pandemic - The COVACTA clinical study example

Cited by 15 publications

References 7 publications

When Does the Cytokine Storm Begin in COVID-19 Patients? A Quick Score to Recognize It

When Does the Cytokine Storm Begin in COVID-19 Patients? A Quick Score to Recognize It

COVID-19-associated acute respiratory distress syndrome (CARDS): Current knowledge on pathophysiology and ICU treatment – A narrative review

Bayesian modeling for the detection of adverse events underreporting in clinical trials

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