ABSTRACT
Background: The discovery and development of SARS-CoV-2 therapies remains a priority. SAB-185 is a Transchromosomic, bovine-derived, fully human polyclonal immunoglobulin product for SARS-CoV-2 being studied in ACTIV-2, randomized controlled platform trial evaluating the safety and efficacy of investigational agents for non-hospitalized adults with mild-moderate COVID-19 Methods: This Phase II trial was a superiority comparison of SAB-185 vs. placebo. Participants with confirmed SAR-CoV-2 infection received intravenous infusion of SAB-185 (3,840 Units/kg) or placebo. Primary outcome measures were proportion of participants with SARS-CoV-2 RNA < lower limit of quantification (LLoQ) in nasopharyngeal (NP) swab, time to improvement in targeted symptoms for 2 consecutive days after Day 0, and safety through Day 28. Secondary outcomes included quantitative NP RNA levels and all-cause hospitalizations and deaths. Antiviral or clinical efficacy and safety criteria for graduation to Phase III were pre-specified. Results: From April to August 2021, randomized participants from 42 sites in the US received SAB-185 (N=107) or placebo (N=106). Median age was 38 years (quartiles: 30,48), 54% female, >98% cis-gender, 7% Black/African-American, 50% Hispanic, and 11% were classified as high-risk for COVID-19 progression, with median 4 days (3,6) from symptom onset. Day 0 NP SARS-CoV-2 RNA levels were similar between SAB-185 and placebo: 4.80 vs 4.80 log10 copies/ml. No differences were observed in the proportion with NP SARS-CoV-2 RNA< lower limit of quantification (LLoQ) in nasopharyngeal (NP) swab, time to improvement in targeted symptoms for 2 consecutive days after Day 0, and safety through Day 28. Secondary outcomes included quantitative NP RNA levels and all-cause hospitalizations and deaths. Antiviral or clinical efficacy and safety criteria for graduation to phase 3 were pre-specified. Conclusion: SAB-185 was safe in this Phase II study. While no significant differences to placebo were seen in symptom duration and proportion of participants with NP SARS-CoV-2 RNA< lower limit of quantification (LLoQ) in nasopharyngeal (NP) swab, time to improvement in targeted symptoms for 2 consecutive days after Day 0, and safety through Day 28. Secondary outcomes included quantitative NP RNA levels and all-cause hospitalizations and deaths. Antiviral or clinical efficacy and safety criteria for graduation to phase 3 were pre-specified.
ABSTRACT
Introduction: Standard, un-gated chest CT can be used as the basis of detailed segmentation of the atrial and ventricular cardiac chambers. In conditions such as COVID19 where dedicated cardiac imaging may be hazardous or unavailable atlas-based machine learning tools allow automatic quantification of cardiac morphology and may allow early detection of abnormalities. Purpose: To develop an automated screening tool to detect cardiac changes associated with COVID19 on chest/lung CT to allow early treatment and appropriate selection of patients for dedicated cardiac imaging. Methods: A previously validated atlas-based cardiac contouring algorithm was modified to work within the setting of variable and severe lung pathology. The modified technique was used to segment the left and right atria and ventricles from non-contrast CT scans. We applied the developed algorithm to the Moscow University COVID19 CT dataset. 1110 scans were available. COVID19 severity was graded 0 to 4. Grade 4 was not used in analysis due to insufficient numbers. Cardiac chamber sizes were compared according to COVID19 severity status. In a limited cohort of repeat studies, the feasibility of polar mapping to demonstrated serial morphological change was tested. Results: A statistically significant increase of average cardiac chamber volumes was noted relative to mild Grade 0 COVID19 at every incremental severity grade (Figure 1). Changes in average ventricular volumes were greater (up to 15.2% and 16.9% for left and right ventricles) than changes in atrial volumes (up 12.1% and 7.6% for left and right atria). Automated quantification was successful in the large majority of cases and interpatient polar mapping of sequential data to detect progressive chamber enlargement appears feasible (Figure 2). Conclusion: Machine learning methods permit automatic quantification of cardiac chamber size from standard lung CT scans. Cardiac changes on lung CT examinations may be used to identify cardiac abnormalities at an early stage and could be useful to triage individuals for dedicated cardiac investigations. With further refinement, this method may be useful to detect and track temporal cardiac changes in COVID19, as well as in other pulmonary pathology and conditions in which chest CT is routinely used. (Figure Presented).