Tomographic Findings in COVID-19 and Influenza H1N1

Background In late 2019, a new coronavirus was linked to several cases of pneumonia in the city of Wuhan, Hubei province, China. On February 11, 2020, the World Health Organization (WHO) designated COVID-19 a pandemic disease. The mortality associated with COVID-19 patients that required management in a critical care unit is approximately 4.3%. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Diagnosis of COVID-19 is made with a positive test (i.e. reverse transcriptase-polymerase chain reaction, RT-PCR) from a person with clinical signs and symptoms of a respiratory tract infection. Viral pneumonia is currently a challenge worldwide as it is associated with high morbidity and mortality. In June of 2009, the WHO declared influenza A H1N1 a pandemic disease. Worldwide, influenza H1N1 had a mortality of 11%, with a higher mortality rate among people older than 50 years of age (i.e. 18-20%). Influenza diagnosis can be established using RT-PCR. Around 200 million cases of community-acquired viral pneumonia occur each year worldwide, 100 million in children, and 100 million in adults. Imaging findings in viral pneumonia are diverse and overlap with findings associated with non-viral infections and inflammatory conditions. However, identifying the underlying viral pathogens may not always be easy. Several imaging patterns have been described in association with these viruses. Although a definitive diagnosis cannot be achieved based on imaging studies, imaging pattern recognition of viral pneumonia can help differentiate between viral and bacterial pathogens; thus, reducing the use of indiscriminate antibiotics. Few studies correlate tomographic findings in patients with viral infections in the lower respiratory tract.

The use of computed tomography (CT) should be considered as the first option for diagnostic imaging in patients with suspected pneumonia. Peripheral multifocal ground glass patterns with irregular consolidation images found in the lower lobes or posteriorly in pulmonary CT scans have been described in patients with viral pneumonia due to SARS-CoV-2. Furthermore, complicating the diagnosis of atypical viral pneumonia, 17.9% of mild COVID-19 and 2.9% of moderate-severe COVID-19 patients did not have CT evidence of pneumonia upon hospital admission. One recent study compared the CT radiological patterns found in COVID-19 pneumonia to other viral pneumonias (i.e. influenza, parainfluenza, adenovirus, and respiratory syncytial virus) reporting higher peripheral distribution (i.e. 80% vs. 57%, p<0.001), more ground-glass opacities (i.e. 91% vs 68%, p<0.001), greater frequency of fine reticular opacities (i.e. 56% vs. 22%, p<0.001), and vascular thickening in COVID-19 patients; meanwhile, other viral pneumonias were more likely to have a mixed distribution pattern(i.e. 35% vs. 14%, p<0.001), have pleural effusion (i.e. 39% vs. 4.1%, p<0.001), and present visible lymph nodes (10.2% vs. 2.7%, p<0.001). Another study compared the pulmonary radiological patterns associated with COVID-19 compared to influenza (A and B) reporting higher round opacities (i.e. 35% vs. 17%, p=0.048) and greater frequency of interlobular septal thickening (i.e. 66% vs. 43%, p=0.014) in patients with COVID-19; conversely, influenza patients had a higher frequency of nodular lesions (i.e. 71% vs. 28%, p<0.001), higher frequency of small dense nodular lesions (i.e. 40% vs. 9%, p<0.001), and more likely to have pleural effusion (i.e. 31% vs. 6%, p<0.001).

Research questions

1. What are the pulmonary tomographic findings in patients diagnosed with community-acquired pneumonia secondary to SARS-CoV-2?
2. What are the pulmonary tomographic findings in patients diagnosed with community-acquired pneumonia secondary to H1N1 influenza?
3. Is there a difference among lung CT radiological patterns in patients with pneumonia secondary to SARS-CoV-2 and its association with the need for invasive mechanical ventilation?
4. Is there a difference among lung CT radiological patterns in patients with pneumonia secondary to H1N1 influenza and its association with the need for invasive mechanical ventilation?
5. Is there a difference between groups (i.e. SARS-CoV-2 versus H1N1 influenza) and its association with the need for the use of invasive mechanical ventilation?
6. Are the 28-day survival distributions different for SARS-CoV-2 and H1N1 influenza?
7. Is there a difference in the 28-day survival distribution and the pulmonary tomographic radiological patterns in patients with pneumonia secondary to SARS-CoV-2?
8. Is there a difference in the 28-day survival distribution and the pulmonary tomographic radiological patterns in patients with pneumonia secondary to H1N1 influenza?
9. What factors are associated with the survival differences in 28-day mortality in both groups and between groups?

Aims Primary aim: Compare pulmonary tomographic findings of patients diagnosed with SARS-CoV-2 and H1N1 influenza pneumonia patients at Hospital General Regional Leon IMSS no. 58.

Secondary aims

• Identify pulmonary CT radiological patterns in patients diagnosed with SARS-CoV-2 pneumonia.
• Identify pulmonary CT radiological patterns in patients diagnosed with influenza H1N1 pneumonia.
• Identify the association between CT patterns of patients diagnosed with SARS-CoV-2 or H1N1 influenza pneumonia who require invasive mechanical ventilation.
• Identify the CT patterns of SARS-CoV-2 and H1N1 influenza patients that are associated with 28-day mortality.

Statistical analysis No literature is available to calculate the primary objective (i.e. measure the frequency of radiological patterns associated with viral pneumonia secondary to SARS-CoV-2 and H1N1 influenza) or for the secondary objective (risk of intubation) this study will serve as a pilot study for the calculation of samples in future research projects. A sample calculation was performed to determine statistically significant differences in the outcome of radiological patterns secondary to H1N1 influenza. The sample size was calculated based on the proportions reported by Jartti et al. (2011). The sample size was calculated to detect statistically significant differences taking as parameters an α = 0.05 and statistical power of 0.8 (i.e. 1-β). The calculator was used to calculate sample sizes based on proportions of two samples considering the equality of the two extremes (i.e. one Gaussian distribution tail), available on the website: http://powerandsamplesize.com/. The parameters are as follows: nA, number of patients with H1N1 influenza in the reference study (i.e. 159); nB, number of patients with H1N1 influenza pneumonia; pA, frequency of patients with radiological consolidation data, in percentage, for previously reported H1N1 influenza patients (i.e. 93%); pB, frequency of patients with radiological consolidation syndrome, in percentage, in patients with H1N1 influenza pneumonia in our population (i.e. H0: probability = 0.5); k, sampling ratio (i.e. 1: 1). Considering the results, we consider that a sample greater than 23 patients with H1N1 influenza pneumonia is necessary to detect differences in radiological patterns in patients with viral pneumonia.