The investigators decided to conduct a longitudinal study that compares the pulmonary tomographic patterns found in patients with viral pneumonia (i.e. influenza H1N1 and SARS-CoV-2) at a regional hospital. The primary aim of this study is to compare the radiological patterns found in patients with COVID-19 and influenza H1N1. The secondary aims of this study will assess the association between the radiological CT pattern and the need for invasive mechanical ventilation and mortality within the first 28 days of intensive care unit admission.

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. There are few studies that 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. Are the 28-day survival distributions different for SARS-CoV-2 and H1N1 influenza?
4. 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?
5. Is there a difference in the 28-day survival distribution and the pulmonary tomographic radiological patterns in patients with pneumonia secondary to H1N1 influenza?
6. What factors are associated with the survival differences in 28-day mortality in both groups and between groups?

Inclusion Criteria:

• Patients with signed informed consent.
• Patients with a positive PCR test for SARS-CoV-2 or influenza H1N1 test upon emergency department admission.
• Patients with lung CT within 24hrs of specimen collection for PCR test.
• Patients with complete 30-day follow-up information.

Exclusion Criteria:

• Patients who are unwilling to undergo a lung CT.
• Negative PCR test for SARS-CoV-2 or influenza H1N1 test upon emergency department admission.
• Patients with a tumor or tumor metastasis on the pulmonary CT.
• Patients with a previous or de novo autoimmune disease diagnosis.
• Patients with a previous or de novo interstitial lung disease.
• Pregnancy.

Elimination Criteria:

• Patients with loss of information on the variables of interest.
• Patients without 30-day follow-up information.
• Patients who chose to withdraw their participation at any time of the study.

Convenience sample from a Mexican urban, suburban, and rural population. The patients come from all demographical and socio-economic strata from a country with a high (0.767) Human Development Index. Our hospital is open to the entire population and primarily assists people with limited to very limited resources.

A database will be generated and available upon request. All patients will de unidentified.