SARS-CoV-2 and lung injury: Dysregulation of immune response but not hyperimmune response as in "cytokine storm syndrome".

SARS-CoV-2 infection can present either an asymptomatic or symptomatic; the spectrum of symptomatic infection ranges from mild to critical. A majority of patients have experienced mild symptoms with a good prognosis. But approximately 14% of them have severe infection presenting with hypoxemia and extensive lung involvement. The current mini-review describes the dysregulation of immune response for SARS-CoV-2 viral pneumonia and virus-induced lung injury. Also, many confounding factors can increase lung injury, in addition to virus-induced lung injury. Especially in critically ill patients, confounding factors can cause the inflammatory cascade, acute respiratory distress syndrome (ARDS), and mortality.

AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia.

OBJECTIVE: To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course. METHODS: From March 11 to April 15, 2020, 51 patients (age, 18-84 years; 28 men) diagnosed and hospitalized with COVID-19 pneumonia with a total of 116 CT scans were enrolled in the study. Patients were divided into mild (n = 12), moderate (n = 31), and severe (n = 8) groups based on clinical severity. An AI-based quantitative CT analysis, including lung volume, opacity score, opacity volume, percentage of opacity, and mean lung density, was performed in initial and follow-up CTs obtained at different time points. Receiver operating characteristic analysis was performed to find the diagnostic ability of quantitative CT parameters for discriminating severe from nonsevere pneumonia. RESULTS: In baseline assessment, the severe group had significantly higher opacity score, opacity volume, higher percentage of opacity, and higher mean lung density than the moderate group (all P ≤ 0.001). Through consecutive time points, the severe group had a significant decrease in lung volume (P = 0.006), a significant increase in total opacity score (P = 0.003), and percentage of opacity (P = 0.007). A significant increase in total opacity score was also observed for the mild group (P = 0.011). Residual opacities were observed in all groups. The involvement of more than 4 lobes (sensitivity, 100%; specificity, 65.26%), total opacity score greater than 4 (sensitivity, 100%; specificity, 64.21), total opacity volume greater than 337.4 mL (sensitivity, 80.95%; specificity, 84.21%), percentage of opacity greater than 11% (sensitivity, 80.95%; specificity, 88.42%), total high opacity volume greater than 10.5 mL (sensitivity, 95.24%; specificity, 66.32%), percentage of high opacity greater than 0.8% (sensitivity, 85.71%; specificity, 80.00%) and mean lung density HU greater than -705 HU (sensitivity, 57.14%; specificity, 90.53%) were related to severe pneumonia. CONCLUSIONS: An AI-based quantitative CT analysis is an objective tool in demonstrating disease severity and can also assist the clinician in follow-up by providing information about the disease course and prognosis according to different clinical severity groups.

Clinical Outcomes and Independent Risk Factors for 90-Day Mortality in Critically Ill Patients with Respiratory Failure Infected with SARS-CoV-2: A Multicenter Study in Turkish Intensive Care Units.

BACKGROUND: There are limited data on the long-term outcomes of COVID-19 from different parts of the world. AIMS: To determine risk factors of 90-day mortality in critically ill patients in Turkish intensive care units (ICUs), with respiratory failure. STUDY DESIGN: Retrospective, observational cohort. METHODS: Patients with laboratory-confirmed COVID-19 and who had been followed up in the ICUs with respiratory failure for more than 24 hours were included in the study. Their demographics, clinical characteristics, laboratory variables, treatment protocols, and survival data were recorded. RESULTS: A total of 421 patients were included. The median age was 67 (IQR: 57-76) years, and 251 patients (59.6%) were men. The 90-day mortality rate was 55.1%. The factors independently associated with 90-day mortality were invasive mechanical ventilation (IMV) (HR 4.09 [95% CI: [2.20-7.63], P < .001), lactate level >2 mmol/L (2.78 [1.93-4.01], P < .001), age ≥60 years (2.45 [1.48-4.06)], P < .001), cardiac arrhythmia during ICU stay (2.01 [1.27-3.20], P = .003), vasopressor treatment (1.94 [1.32-2.84], P = .001), positive fluid balance of ≥600 mL/day (1.68 [1.21-2.34], P = .002), PaO2/FiO2 ratio of ≤150 mmHg (1.66 [1.18-2.32], P = .003), and ECOG score ≥1 (1.42 [1.00-2.02], P = .050). CONCLUSION: Long-term mortality was high in critically ill patients with COVID-19 hospitalized in intensive care units in Turkey. Invasive mechanical ventilation, lactate level, age, cardiac arrhythmia, vasopressor therapy, positive fluid balance, severe hypoxemia and ECOG score were the independent risk factors for 90-day mortality.

Influenza or other respiratory viruses: does it matter as the cause of acute respiratory failure in the critically-ill patients?

INTRODUCTION: Respiratory virus infections may cause serious respiratory failure requiring intensive care unit (ICU) admission. The objective of this study was to evaluate the clinical features and the outcome in patients with acute respiratory failure (ARF) due to viral infections comparing etiological agents. MATERIALS AND METHODS: ARF patients with positive viral serology were retrospectively recruited. Cohort was evaluated with regard to subgroups as influenza and other respiratory viruses (ORV), as well as survivors and nonsurvivors. RESULT: Out of 938 admitted patients, 319 were followed as ARF and only 149 patients had viral respiratory panel results. In 49 patients with ARF, 52 positive viral results were detected and 47 patients with single positive viral isolates of either influenza or ORV were included. Among them, 62% had ORV with quite similar characteristics with influenza group apart from diabetes mellitus which was encountered more in influenza group (p= 0.02). Overall ICU mortality was 32% and there was no difference between the two groups (p= 0.42). Acute Physiology and Chronic Health Evaluation (APACHE) II score was independently associated with ICU mortality (OR: 1.25; 95% CI: 1.04-1.51; p= 0.02). CONCLUSIONS: This study emphasizes to consider the possibility of other respiratory viruses for the cause of ARF with similar characteristics and mortality as influenza species.

Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings.

PURPOSE: The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data. METHODS: Patients with COVID-19 who underwent chest computed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU). RESULTS: A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP <82.45% (sensitivity, 83.1%; specificity, 84.2%) were related to severe pneumonia, VQAS >9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia. CONCLUSION: Both SQNLP and VQAS were significantly related to the clinical findings, highlighting their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.

Convalescent (immune) plasma treatment in a myelodysplastic COVID-19 patient with disseminated tuberculosis.

During the ongoing COVID-19 pandemic due to the SARS-CoV-2 virus of which evidence-based medical paradigms cannot be easily applied; difficult clinical decisions shall be required particularly in the 'difficult-to-treat' cases of high risk group with associated comorbidities. Convalescent immune plasma therapy is a promising option as a sort of 'rescue' treatment in COVID-19 immune syndrome, where miraculous antiviral drugs are not available yet. In this report, we aim to convey our experience of multi-task treatment approach with convalescent immune plasma and anti-cytokine drug combination in a COVID-19 patient with extremely challenging comorbidities including active myeloid malignancy, disseminated tuberculosis and kidney failure.