Rapid Translation of COVID-19 Preprint Data into Critical Care Practice

National ICU Registries as Enablers of Clinical Research and Quality Improvement

OBJECTIVES

Clinical quality registries (CQRs) have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. This narrative review describes the challenges, proposed solutions, and evidence generated by National ICU registries as facilitators for research and quality improvement.

DATA SOURCES

English language articles were identified in PubMed using phrases related to ICU registries, CQRs, outcomes, and case-mix.

STUDY SELECTION

Original research, review articles, letters, and commentaries, were considered.

DATA EXTRACTION

Data from relevant literature were identified, reviewed, and integrated into a concise narrative review.

DATA SYNTHESIS

CQRs have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. The initial experience in European countries and in Oceania ensured that through locally generated data, ICUs could assess their performances by using risk-adjusted measures and compare their results through fair and validated benchmarking metrics with other ICUs contributing to the CQR. The accomplishment of these initiatives, coupled with the increasing adoption of information technology, resulted in a broad geographic expansion of CQRs as well as their use in quality improvement studies, clinical trials as well as international comparisons, and benchmarking for ICUs.

CONCLUSIONS

ICU registries have provided increased knowledge of case-mix and outcomes of ICU patients based on real-world data and contributed to improve care delivery through quality improvement initiatives and trials. Recent increases in adoption of new technologies (i.e., cloud-based structures, artificial intelligence, machine learning) will ensure a broader and better use of data for epidemiology, healthcare policies, quality improvement, and clinical trials.

Comparison of Preprint Postings of Randomized Clinical Trials on COVID-19 and Corresponding Published Journal Articles: A Systematic Review

IMPORTANCE

Randomized clinical trials (RCTs) on COVID-19 are increasingly being posted as preprints before publication in a scientific, peer-reviewed journal.

OBJECTIVE

To assess time to journal publication for COVID-19 RCT preprints and to compare differences between pairs of preprints and corresponding journal articles.

EVIDENCE REVIEW

This systematic review used a meta-epidemiologic approach to conduct a literature search using the World Health Organization COVID-19 database and Embase to identify preprints published between January 1 and December 31, 2021. This review included RCTs with human participants and research questions regarding the treatment or prevention of COVID-19. For each preprint, a literature search was done to locate the corresponding journal article. Two independent reviewers read the full text, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2 tool. Time to publication was analyzed using a Cox proportional hazards regression model. Differences between preprint and journal article pairs in terms of outcomes, analyses, results, or conclusions were described. Statistical analysis was performed on October 17, 2022.

FINDINGS

This study included 152 preprints. As of October 1, 2022, 119 of 152 preprints (78.3%) had been published in journals. The median time to publication was 186 days (range, 17-407 days). In a multivariable model, larger sample size and low risk of bias were associated with journal publication. With a sample size of less than 200 as the reference, sample sizes of 201 to 1000 and greater than 1000 had hazard ratios (HRs) of 1.23 (95% CI, 0.80-1.91) and 2.19 (95% CI, 1.36-3.53) for publication, respectively. With high risk of bias as the reference, medium-risk articles with some concerns for bias had an HR of 1.77 (95% CI, 1.02-3.09); those with a low risk of bias had an HR of 3.01 (95% CI, 1.71-5.30). Of the 119 published preprints, there were differences in terms of outcomes, analyses, results, or conclusions in 65 studies (54.6%). The main conclusion in the preprint contradicted the conclusion in the journal article for 2 studies (1.7%).

CONCLUSIONS AND RELEVANCE

These findings suggest that there is a substantial time lag from preprint posting to journal publication. Preprints with smaller sample sizes and high risk of bias were less likely to be published. Finally, although differences in terms of outcomes, analyses, results, or conclusions were observed for preprint and journal article pairs in most studies, the main conclusion remained consistent for the majority of studies.

A randomized, double-blind, placebo-controlled trial of intravenous alpha-1 antitrypsin for acute respiratory distress syndrome secondary to COVID-19

Background

Patients with severe coronavirus disease 2019 (COVID-19) develop a febrile pro-inflammatory cytokinemia with accelerated progression to acute respiratory distress syndrome (ARDS). Here we report the results of a phase 2, multicenter, randomized, double-blind, placebo-controlled trial of intravenous (IV) plasma-purified alpha-1 antitrypsin (AAT) for moderate to severe ARDS secondary to COVID-19 (EudraCT 2020-001391-15).

Methods

Patients (n = 36) were randomized to receive weekly placebo, weekly AAT (Prolastin, Grifols, S.A.; 120 mg/kg), or AAT once followed by weekly placebo. The primary endpoint was the change in plasma interleukin (IL)-6 concentration at 1 week. In addition to assessing safety and tolerability, changes in plasma levels of IL-1β, IL-8, IL-10, and soluble tumor necrosis factor receptor 1 (sTNFR1) and clinical outcomes were assessed as secondary endpoints.

Findings

Treatment with IV AAT resulted in decreased inflammation and was safe and well tolerated. The study met its primary endpoint, with decreased circulating IL-6 concentrations at 1 week in the treatment group. This was in contrast to the placebo group, where IL-6 was increased. Similarly, plasma sTNFR1 was substantially decreased in the treatment group while remaining unchanged in patients receiving placebo. IV AAT did not definitively reduce levels of IL-1β, IL-8, and IL-10. No difference in mortality or ventilator-free days was observed between groups, although a trend toward decreased time on ventilator was observed in AAT-treated patients.

Conclusions

In patients with COVID-19 and moderate to severe ARDS, treatment with IV AAT was safe, feasible, and biochemically efficacious. The data support progression to a phase 3 trial and prompt further investigation of AAT as an anti-inflammatory therapeutic.

Funding

ECSA-2020-009; Elaine Galwey Research Bursary.

ISARIC-4C Mortality Score overestimates risk of death due to COVID-19 in Australian ICU patients: a validation cohort study

Objective: To assess the performance of the UK International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) Coronavirus Clinical Characterisation Consortium (4C) Mortality Score for predicting mortality in Australian patients with coronavirus disease 2019 (COVID-19) requiring intensive care unit (ICU) admission. Design: Multicentre, prospective, observational cohort study. Setting: 78 Australian ICUs participating in the SPRINT-SARI (Short Period Incidence Study of Severe Acute Respiratory Infection) Australia study of COVID-19. Participants: Patients aged 16 years or older admitted to participating Australian ICUs with polymerase chain reaction (PCR)-confirmed COVID-19 between 27 February and 10 October 2020. Main outcome measures: ISARIC-4C Mortality Score, calculated at the time of ICU admission. The primary outcome was observed versus predicted in-hospital mortality (by 4C Mortality and APACHE II). Results: 461 patients admitted to a participating ICU were included. 149 (32%) had complete data to calculate a 4C Mortality Score without imputation. Overall, 61/461 patients (13.2%) died, 16.9% lower than the comparable ISARIC-4C cohort in the United Kingdom. In patients with complete data, the median (interquartile range [IQR]) 4C Mortality Score was 10.0 (IQR, 8.0-13.0) and the observed mortality was 16.1% (24/149) versus 22.9% median predicted risk of death. The 4C Mortality Score discriminatory performance measured by the area under the receiver operating characteristic curve (AUROC) was 0.79 (95% CI, 0.68-0.90), similar to its performance in the original ISARIC-4C UK cohort (0.77) and not superior to APACHE II (AUROC, 0.81; 95% CI, 0.75-0.87). Conclusions: When calculated at the time of ICU admission, the 4C Mortality Score consistently overestimated the risk of death for Australian ICU patients with COVID-19. The 4C Mortality Score may need to be individually recalibrated for use outside the UK and in different hospital settings.