ABSTRACT
Background. SARS-CoV-2 induces endothelial damage and activates the complement system. In severe COVID-19 patients, complement split factor C5a is highly elevated leading to inflammation that contributes to multiorgan failure. The anti-C5a monoclonal antibody, Vilobelimab (Vilo), which preserves the membrane attack complex (MAC), was investigated in an adaptively designed, randomized doubleblind, placebo (P)-controlled Phase 3 international multicenter study for survival in critically ill COVID-19 patients (pts). Methods. COVID-19 pneumonia pts (N=368;Vilo n=177, P n=191), mechanically ventilated within 48 hrs before treatment, received up to 6, 800 mg infusions of Vilo or P on top of standard of care. The primary and main secondary endpoints were 28-day (d) and 60-d all-cause mortality. Results. Pts enrolled in the study were on corticosteroids (97%) and anticoagulants (98%) as standard of care. A smaller proportion (20%) were either continuing or had taken immunomodulators such as tocilizumab and baricitinib prior to receiving Vilo. The 28-d all-cause mortality was 31.7% with Vilo vs 41.6% with P (Kaplan-Meier estimates;Cox regression site-stratified, HR 0.73;95% CI:0.50-1.06;P=0.094), representing a 23.8% relative mortality reduction. In predefined primary outcome analysis without site stratification, however, Vilo significantly reduced mortality at 28 (HR 0.67;95% CI:0.48-0.96;P=0.027) and 60 days (HR 0.67;95% CI:0.48-0.92;P=0.016). Vilo also significantly reduced 28-d mortality in more severe pts with baseline WHO ordinal scale score of 7 (n=237, HR 0.62;95% CI:0.40-0.95;P=0.028), severe ARDS/PaO2/FiO2 <= 100 mmHg (n=98, HR 0.55;95% CI:0.30-0.98;P=0.044) and eGFR < 60 mL/min/1.73m2 (n=108, HR 0.55;95% CI:0.31-0.96;P=0.036). Treatment-emergent AEs were 90.9% Vilo vs 91.0% P. Infections were comparable: Vilo 62.9%, P 59.3%. Infection incidence per 100 Pt days were equal. No meningococcal infections were reported. Serious AEs were 58.9% Vilo, 63.5% P. Conclusion. Vilo significantly reduced mortality at 28 and 60 days in critically ill COVID-19 pts with no increase in infections suggesting the importance of targeting C5a while preserving MAC. Vilo targets inflammation which may represent an approach to treat sepsis and ARDS caused by other respiratory viruses. (Figure Presented).
ABSTRACT
Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.