Blood Ozonization in Patients With SARS-CoV-2 Respiratory Failure
Phase: NA
Conditions: SARS-CoV-2 Respiratory Failure
Sponsor: Azienda Sanitaria-Universitaria Integrata di Udine

 
 
CIG: Z7C2CA5837       
 
 
Blood Ozonization in Patients With SARS-CoV-2 Respiratory Failure 
 
 
 
NCT number: Pending. 

 
 
Flow chart of CORMOR study 
 
 
Legend: * = Darunavir/Cobicistat 800/150 mg BD; § = 400 mg BD at Day 1, after 200 mg BD for 4 days; BD = every 12 hours; ICU: 
intensive care unit. 

 
 
Schedule of assessments of CORMOR study 
 
T0 
T3 
T10 
Informed consent 
X 
 
 
Inclusionand exclusion criteria 
X 
 
 
Demography and physical examination (including height and 
weight) 
X 
X 
X 
SOFA score 
X 
 
 
Charlson Comorbidity Index 
X 
 
 
Medical history 
X 
 
 
Concomitant systemic therapy  
X 
 
 
Arterial Blood Gas analysis 
X 
X 
X 
P/F ratio and respiratory assistance assessment 
X 
X 
X 
Thoracic CT scan or Chest XR or Chest echo*  
X 
X 
X 
12-lead ECG 
X 
 
 
Laboratory assessment § 
X 
X 
X 
IL-6, HLA-DR, lymphocyte typing ° 
X 
X 
X 
2019-nCoV testing by RT-PCR by upper respiratory tract and 
expectorated sputum 
X 
X 
X 
AE review 
X 
X 
X 
Concomitant medical review 
X 
X 
X 
Survival follow-up 
 
 
X 
 
Legend: T0 – study inclusion; T3 – after the 3rd blood ozonization treatment; T10 – follow-up visit at one week after the end of blood 
ozonization; * - if it is not possible to perform a thoracic CT scan is recommended to perform a chest x-ray and/or chest ultrasound 
(POCUS - Point-of-Care Ultrasound); § - blood count, bilirubin, AST, ALT, LDH, creatinine, PCR, PCT, CPK, PT, aPTT, D-dimer, 
fibrinogen, serum electrolytes; ° - lymphocyte typing  for CD4, CD3, CD8, HLA-DR, CD45. 

 
 
Statistical analyses 
Variables are described as means ± standard deviation, median and interquartile range, or proportion 
(depending on distribution). To compare categorical variables, we used a 2-tailed paired test, Mann-Whitney 
U test, and χ2 test. The χ2 test was used through Yates continuity correction or Fisher test, as required by 
the specific case. Normality of the variables was assessed with a nonparametric Kolmogorov-Smirnov test.  
In order to identify the risk factors associations, and to adjust the results respect to the different clinical 
conditions, a multivariate regression will be calculated on the subjects enrol on the two study arms. This 
regression will be calculated with two distinct methodological approaches (multivariate Cox regression and 
survival random forest [Breiman L. Random Forests. Machine Learning. 2001; 45: 5-32]); the results of each 
method will be compared on the basis of predictive accuracy, calculated with cross-validation and ROC 
survival curve. 
Furthermore, the number of events that occurred to each patient during follow-up will be calculated, 
comparing it between the different treatment groups with a χ2 test. About the biomarkers, recorded 
longitudinally, a two-way analysis of variance for repeated measurements will be performed, followed by the 
appropriate post-hoc test. If the distributions are nonlinear, Friedman's non-parametric test will be performed. 
The parameters of adherence to therapy in the different treatment groups will be studied with one-way 
analysis of variance, followed by the appropriate post-hoc test. If the distributions are nonlinear, the non-
parametric Kruskal-Wallis or chi-square test will be performed. Persistence of treatment will be assessed 
using the classic survival analysis methods [Marubini E., Valsecchi M.G., Emmerson M., Analysing Survival 
Data from Clinical Trials and Observational Studies. John Wiley & Sons, 1995, New-York.]. 
Along with an unbiased estimation of prediction accuracy, a ranking of variable importance and a measure of 
similarity between any couple of patients were produced. The similarity measure was used for risk 
stratification, following a model-based clustering approach. Multiple comparisons were adjusted with the 
Bonferroni method.  
We used the statistical software R (R Foundation for Statistical Computing, Vienna) for all statistical analyses, 
and we considered a P value less than 0.05 statistically significant [R Core Team. R: A Language and 
Environment for Statistical Computing. Vienna, Austria. 2013. http://www.R-project.org/]. 
The comparison between the two groups will be carried out with a χ2 test with Yate correction. We calculate 
to enrol 45 patients per arm to obtain a statistical power of 80% with a significance level of 95%.