On-admission and dynamic trend of laboratory profiles as prognostic biomarkers in COVID-19 inpatients.

This large-scale study aimed to investigate the trend of laboratory tests of patients with COVID-19. Hospitalized confirmed and probable COVID-19 patients in three general hospitals were examined from March 20, 2020, to June 18, 2021. The confirmed and probable COVID-19 patients with known outcomes and valid laboratory results were included. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to select admittance prognostic features. Parallel Pairwise Comparison of mortality versus survival was used to examine the trend of markers. In the final cohort, 11,944 patients were enrolled, with an in-hospital mortality rate of 21.8%, mean age of 59.4 ± 18.0, and a male-to-female ratio of 1.3. Abnormal admittance level of white blood cells, neutrophils, lymphocytes, mean cellular volume, urea, creatinine, bilirubin, creatine kinase-myoglobin binding, lactate dehydrogenase (LDH), Troponin, c-reactive protein (CRP), potassium, and creatinine phosphokinase reduced the survival of COVID-19 inpatients. Moreover, the trend analysis showed lymphocytes, platelet, urea, CRP, alanine transaminase (ALT), and LDH have a dissimilar trend in non-survivors compared to survived patients. This study proposed a novel approach to find serial laboratory markers. Serial examination of platelet count, creatinine, CRP, LDH, and ALT can guide healthcare professionals in finding patients at risk of deterioration.

Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features.

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.

Efficacy of Hemoperfusion in Severe and Critical Cases of COVID-19.

INTRODUCTION: Uncontrolled overproduction of inflammatory mediators is predominantly observed in patients with severe COVID-19. The excessive immune response gives rise to multiple organ dysfunction. Implementing extracorporeal therapies may be useful in omitting inflammatory mediators and supporting different organ systems. We aimed to investigate the effectiveness of hemoperfusion in combination with standard therapy in critically ill COVID-19 patients. METHOD: We conducted a single-center, matched control retrospective study on patients with confirmed SARS-CoV-2 infection. Patients were treated with hemoperfusion in combination with standard therapy (hemoperfusion group) or standard treatment (matched group). Hemoperfusion or hemoperfusion and continuous renal replacement therapies were initiated in the hemoperfusion group. The patients in the matched group were matched one by one with the hemoperfusion group for age, sex, oxygen saturation (SPO2) at the admission, and the frequency of using invasive mechanical ventilation during hospitalization. Two types of hemoperfusion cartridges used in this study were Jafron© (HA330) and CytoSorb® 300. RESULT: A total of 128 COVID-19-confirmed patients were enrolled in this study; 73 patients were allotted to the matched group and 55 patients received hemoperfusion. The median SPO2 at the admission day in the control and hemoperfusion groups was 80% and 75%, respectively (p value = 0.113). The mortality rate was significantly lower in the hemoperfusion group compared to the matched group (67.3% vs. 89%; p value = 0.002). The median length of ICU stay was statistically different in studied groups (median, 12 days for hemoperfusion group vs. 8 days for the matched group; p < 0.001). The median final SPO2 was statistically higher in the hemoperfusion group than in the matched group, and the median PaCO2 was lower. CONCLUSION: Among critically ill COVID-19 patients, based on our study, the use of hemoperfusion may reduce the mortality rate and improve SPO2 and PaCO2.

Effect of Electrolyte Imbalance on Mortality and Late Acute Kidney Injury in Hospitalized COVID-19 Patients.

INTRODUCTION: As a multisystem illness, Coronavirus disease 2019 (COVID-19) can damage different organs. This study investigated the effect of electrolyte imbalance (EI), with or without concomitant renal dysfunction, on the prognosis of COVID-19 in hospitalized patients. METHODS: We evaluated 499 hospitalized patients with confirmed COVID-19, without a history of chronic kidney disease. The patients' demographic data, laboratory values, and outcomes were retrospectively collected from the hospital information system. Serumelectrolytes including sodium, potassium, magnesium, calcium, and phosphorus abnormalities were analyzed on admission and during the hospitalization period. The outcomes of this study were the occurrence of acute kidney injury (AKI) after the first week of hospitalization and in-hospital mortality rate. Multivariate analyses were carried out to obtain the independent risk of each EI on mortality, by adjusting for age, gender, and AKI occurrence. RESULTS: Among the 499 COVID-19 patients (60.9% male), AKI occurred in 168 (33.7%) and mortality in 92 (18.4%) cases. Hypocalcemia (38%) and hyponatremia (22.6%) were the most prevalent EIs, and all EIs were more common in the AKI group than in the non-AKI group. Hyponatremia (Adjusted Odds ratio [AOR] = 2.34, 95% CI: 1.30 to 4.18), hypernatremia (AOR = 8.52, 95% CI: 1.95 to 37.32), and hyperkalemia (AOR = 4.63, 95% CI: 1.65 to 13) on admission were associated with poor prognosis. Moreover, hyponatremia (AOR = 3.02, 95% CI: 1.28 to 7.15) and hyperphosphatemia (AOR = 5.12, 95% CI: 1.24 to 21.09) on admission were associated with late AKI occurrence. CONCLUSION: This study highlights the role of hyponatremia, hypernatremia, hyperkalemia, and hyperphosphatemia in poor prognosis of COVID-19. According to the independent effect of EI on late AKI and mortality, we recommend physicians to raise awareness to closely monitor and correct EI during hospitalization.  DOI: 10.52547/ijkd.6904.

A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes.

There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.

Is there any association between plasma lipid profile and severity of COVID-19?

BACKGROUND: COVID-19 is an infectious disease which caused a pandemic with many diseases and fatalities. This new variant of coronavirus called SARS-CoV-2 and is primarily characterized by respiratory symptoms. There are some data indicating that LDL-cholesterol (LDL-C) as well as HDL-cholesterol (HDL-C) levels are inversely correlated to disease severity and could act as a predictor for disease progression and unfavorable prognosis. However, the results of some other studies do not confirm this. This current study aimed to provide an answer to this question. METHODS: This prospective, single-center study analyzed 367 confirmed COVID-19 patients to find whether there are any differences in plasma lipoproteins between survivors and non-survivors patients or between the patients with a "duration of ≤10 days intensive unit care (ICU) stay" and patients with a "duration of >10 days ICU stay". RESULTS: No association between any lipid/lipoprotein parameter and the severity of COVID-19 could be found but survivors and non-survivors did differ concerning total cholesterol and LDL-C levels. CONCLUSION: Multivariate cox regression analysis could not prove any association between lipids/lipoproteins and severe events in COVID-19 patients. Significantly less non-survivors with COVID-19 were taking atorvastatin than survivors which is consistent with the majority of previous findings.

High prevalence of SARS-Coronavirus-2 in patients with inflammatory bowel disease and the role of soluble angiotensin converting Enzyme2.

Context: Patients with inflammatory bowel disease (IBD) were found to have the higher intestinal expression of Angiotensin-Converting Enzyme2 (ACE2) that could consequently increase susceptibility to COVID-19 infection.Objective: This study reports the outcomes of COVID-19 infection in a large cohort of IBD patients. We compare levels of serum ACE and IFN-α between COVID19 patients with and without IBD. We performed a cross-sectional retrospective multicenter study.Methods: We enrolled patients with IBD screened for SARS-COV-2 in six medical centres in Iran from June to November 2020. The blood samples were drawn to measure COVID-19 IgM and IgG, and serum levels of sACE2, sACE1, and interferon-α, regardless of suspicious symptoms have done the molecular test.Results: A total of 534 IBD patients were included in the study. Of these, 109 (20.0%) cases had detectable IgG and IgM against SARS-CoV-2. sACE2 levels were higher in IBD patients than controls, whereas ACE1and IFN-α levels were similar among groups.

Epidemiology of COVID-19 in Tehran, Iran: A Cohort Study of Clinical Profile, Risk Factors, and Outcomes.

Background: The outbreak of coronavirus disease 2019 (COVID-19) dates back to December 2019 in China. Iran has been among the most prone countries to the virus. The aim of this study was to report demographics, clinical data, and their association with death and CFR. Methods: This observational cohort study was performed from 20th March 2020 to 18th March 2021 in three tertiary educational hospitals in Tehran, Iran. All patients were admitted based on the WHO, CDC, and Iran's National Guidelines. Their information was recorded in their medical files. Multivariable analysis was performed to assess demographics, clinical profile, outcomes of disease, and finding the predictors of death due to COVID-19. Results: Of all 5318 participants, the median age was 60.0 years, and 57.2% of patients were male. The most significant comorbidities were hypertension and diabetes mellitus. Cough, dyspnea, and fever were the most dominant symptoms. Results showed that ICU admission, elderly age, decreased consciousness, low BMI, HTN, IHD, CVA, dialysis, intubation, Alzheimer disease, blood injection, injection of platelets or FFP, and high number of comorbidities were associated with a higher risk of death related to COVID-19. The trend of CFR was increasing (WPC: 1.86) during weeks 25 to 51. Conclusions: Accurate detection of predictors of poor outcomes helps healthcare providers in stratifying patients, based on their risk factors and healthcare requirements to improve their survival chance.

Early estimation of the epidemiological parameters of novel coronavirus disease (COVID-2019) outbreak in Iran: 19 Feb-15 March, 2020.

AIM: To estimate the epidemiological parameters related to the Covid-19 outbreak in Iran. BACKGROUND: Estimating the epidemiological parameters of new public health threat (COVID-19) is essential to support and inform public health decision-making in different communities including Iran. METHODS: We established a mathematical model to estimate the epidemiological parameters from 19 Feb to 15 March based on daily COVID-19 confirmed cases in Iran. Then, we estimated the effect of early traffic restriction on our estimation. RESULTS: We estimated the R0 at 2.11 (95% CI, 1.87-2.50) and the infected number at 92,260 (95% CI: 59,263 -152,212) by 15 March. Our estimate for the ascertainment rate was about 1.2% (95% CI: 1.1-1.4). The latent period estimation was 4.24 (95% CI: 2.84-6.65). We observed a decline in our estimate after considering the traffic restriction. CONCLUSION: Our results suggest that health authorities in Iran must take impactful strategies to control the COVID-19 outbreak to reach R0<1. Therefore, the establishment of complementary, multilateral, and cost-effective measures for the treatment of symptomatic and early diagnosis and isolation of asymptomatic cases/contacts are strongly recommended because of low ascertainment rate and large number of infected cases. We additionally recommend that traffic restriction be combined with other controlling measures.

Excess all-cause mortality and COVID-19 reported fatality in Iran (April 2013-September 2021): age and sex disaggregated time series analysis.

OBJECTIVE: The actual impact of the pandemic on COVID-19 specific mortality is still unclear due to the variability in access to diagnostic tools. This study aimed to estimate the excess all-cause mortality in Iran until September 2021 based on the national death statistics. RESULTS: The autoregressive integrated moving average was used to predict seasonal all-cause death in Iran (R-squared = 0.45). We observed a 38.8% (95% confidence interval (CI) 29.7%-40.1%) rise in the all-cause mortality from 22 June 2020 to 21 June 2021. The excess all-cause mortality per 100,000 population were 178.86 (95% CI 137.2-220.5, M:F ratio = 1.3) with 49.1% of these excess deaths due to COVID-19. Comparison of spring 2019 and spring 2021 revealed that the highest percent increase in mortality was among men aged 65-69 years old (77%) and women aged 60-64 years old (86.8%). Moreover, the excess mortality among 31 provinces of Iran ranged from 109.7 (Hormozgan) to 273.2 (East-Azerbaijan) per 100,000 population. In conclusion, there was a significant rise in all-cause mortality during the pandemic. Since COVID-19 fatality explains about half of this rise, the increase in other causes of death and underestimation in reported data should be concerned by further studies.

Associastion of In-hospital Use of Statins, Aspirin, and Renin-Angiotensin-Aldosterone Inhibitors with Mortality and ICU Admission Due to COVID-19.

The exaggerated host response to Sars-CoV-2 plays an important role in COVID-19 pathology but provides a therapeutic opportunity until definitive virus targeted therapies and vaccines become available. Given a central role of endothelial dysfunction and systemic inflammation, repurposing ACE inhibitors (ACEIs), angiotensin receptor blockers (ARBs), statins, and aspirin has been of interest. In this retrospective, single-center study, we evaluated the primary outcomes of mortality and ICU admission in 587 hospitalized patients with documented COVID-19 with or without ACEIs, ARBs, statins, and aspirin. Atorvastatin was associated with reduced mortality, which persisted after adjusting for age, lockdown status, and other medications (OR: 0.18. 95% CI: 0.06-0.49, P = 0.001). ACEIs were also associated with reduced mortality in the crude model (OR: 0.20, CI: 0.06-0.66, P = 0.008), as ACEIs and ARBs were combined as a single group (OR: 0.35, CI: 0.16-0.75, P = 0.007), although ARBs alone did not reach statistical significance. There was no association between any medications and risk of ICU admission. Aspirin only achieved a significant association of reduced mortality in a subgroup of patients with diabetes in the crude model (OR: 0.17, CI: 0.04-0.80, P = 0.02). The reduced mortality observed with atorvastatin is consistent with other literature, and consideration should be given to atorvastatin as a COVID-19 treatment. While there was suggested benefit of ACEIs and ARBs in the present study, other studies are varied and further studies are warranted to recommend employing these medications as a treatment strategy. Nevertheless, this study combined with others continues to give credibility that ACEIs and ARBs are safe to continue in the setting of COVID-19.

One-step and sequential SARSCOV-2 polymerase chain reaction tests would not work every time.

INTRODUCTION: RT-PCR is widely used as a diagnostic test for the detection of SARS-CoV-2. In this study, we aim to describe the clinical utility of serial PCR testing in the final detection of COVID-19. METHOD: We collected multiple nasopharyngeal swab samples from patients who had negative RT-PCR test on the first day after hospitalization. RT-PCR tests were performed on the second day for all patients with initial negative result. For the patients with secondary negative results on day 2, tertiary RT-PCR tests were performed on day 3 after hospitalization. RESULT: Among 68 patients with initial negative test results, at the end of follow-up, the mortality number was 20 (29.4%). About 33.8% of patients had subsequent positive PCR test results for the second time and 17.4% of the patients who performed third PCR test had positive result. CONCLUSION: Based on this study, serial RT-PCR testing is unlikely to yield additional information.

Assessing sex differential in COVID-19 mortality rate by age and polymerase chain reaction test results: an Iranian multi-center study.

BACKGROUND: The aim of this study is to evaluate the sex differential effect in the COVID-19 mortality by different age groups and polymerase chain reaction (PCR) test results. RESEARCH DESIGN: In a multicenter cross-sectional study from 55 hospitals in Tehran, Iran, patients were categorized as positive, negative, and suspected cases. RESULTS: A total of 25,481 cases (14,791 males) were included in the study with a mortality rate of 12.0%. The mortality rates in positive, negative, and suspected cases were 20.55%, 9.97%, and 7.31%, respectively. Using a Cox regression model, sex had a significant effect on the hazard of death due to COVID-19 in adult and senior male patients having positive and suspected PCR test results. However, sex was not found as significant factor for mortality in patients with a negative PCR test in different age groups. CONCLUSIONS: Regardless of other risk factors, we found that the effect of sex on COVID-19 mortality varied significantly in different age groups. Therefore, appropriate strategies should be designed to protect adult and senior males from this deadly infectious disease. Furthermore, owing to the considerable death rate of COVID-19 patients with negative test results, new policies should be launched to increase the accuracy of diagnosis tests.

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases.

Umifenovir in hospitalized moderate to severe COVID-19 patients: A randomized clinical trial.

INTRODUCTION: The effectiveness of umifenovir against COVID-19 is controversial; therefore, clinical trials are crucial to evaluate its efficacy. METHODS: The study was conducted as a single-center, randomized, open-label clinical trial. Eligible moderate-severe hospitalized patients with confirmed SARS-Cov-2 infection were randomly segregated into intervention and control groups. The intervention group were treated with lopinavir/ritonavir (400 mg/100 mg bid for 10-14 days) + hydroxychloroquine (400 mg single dose) + interferon-ß1a (Subcutaneous injections of 44 µg (12,000 IU) on days 1, 3, 5) + umifenovir (200 mg trice daily for 10 days), and the control group received lopinavir/ritonavir (same dose) + hydroxychloroquine (same dose) + interferon-ß1a (same dose). RESULTS: Of 1180 patients with positive RT-PCRs and positive chest CT scans, 101 patients were finally included in the trial; 50 were assigned to receive IFNß1a + hydroxychloroquine + lopinavir/ritonavir group and 51 were managed to treat with IFNß1a + hydroxychloroquine + lopinavir/ritonavir + umifenovir. Since all patients received the intended treatment as scheduled, the analysis just included as the ITT population. Time to clinical improvement (TTCI) did not hold a statistically significant difference between intervention and control groups (median, 9 days for intervention group versus 7 days for the control group; P: 0.22). Besides, Hazard Ratio for TTCI in the Cox regression model was 0.75 (95% CI: 0.45-1.23, P:0.25) which also confirmed that there was no statistically significant difference between the treatment group and the control group. The mortality was not statistically significant between the two groups (38% in controls vs 33.3% treatment group). CONCLUSIONS: Our findings shed new lights on the facts that additional umifenovir has not been found to be effective in shortening the duration of SARS-CoV-2 in severe patients and improving the prognosis in non-ICU patients and mortality. TRIAL REGISTRATION: The trial was confirmed by the Ethics in Medical Research Committee of the Shahid Beheshti University of Medical Sciences. signed informed consents were obtained from all the participants or their legally authorized representatives. This trial has been registered as ClinicalTrials.gov, NCT04350684.

Role of interferon therapy in severe COVID-19: the COVIFERON randomized controlled trial.

Type 1 Interferons (IFNs) have been associated with positive effects on Coronaviruses. Previous studies point towards the superior potency of IFNß compared to IFNα against viral infections. We conducted a three-armed, individually-randomized, open-label, controlled trial of IFNß1a and IFNß1b, comparing them against each other and a control group. Patients were randomly assigned in a 1:1:1 ratio to IFNß1a (subcutaneous injections of 12,000 IU on days 1, 3, 6), IFNß1b (subcutaneous injections of 8,000,000 IU on days 1, 3, 6), or the control group. All three arms orally received Lopinavir/Ritonavir (400 mg/100 mg twice a day for ten days) and a single dose of Hydroxychloroquine 400 mg on the first day. Our utilized primary outcome measure was Time To Clinical Improvement (TTCI) defined as the time from enrollment to discharge or a decline of two steps on the clinical seven-step ordinal scale, whichsoever came first. A total of 60 severely ill patients with positive RT-PCR and Chest CT scans underwent randomization (20 patients to each arm). In the Intention-To-Treat population, IFNß1a was associated with a significant difference against the control group, in the TTCI; (HR; 2.36, 95% CI 1.10-5.17, P-value = 0.031) while the IFNß1b indicated no significant difference compared with the control; HR; 1.42, (95% CI 0.63-3.16, P-value = 0.395). The median TTCI for both of the intervention groups was five days vs. seven days for the control group. The mortality was numerically lower in both of the intervention groups (20% in the IFNß1a group and 30% in the IFNß1b group vs. 45% in the control group). There were no significant differences between the three arms regarding the adverse events. In patients with laboratory-confirmed SARS-CoV-2 infection, as compared with the base therapeutic regiment, the benefit of a significant reduction in TTCI was observed in the IFNß1a arm. This finding needs further confirmation in larger studies.Trial Registration Number: ClinicalTrials.gov, NCT04343768. (Submitted: 08/04/2020; First Online: 13/04/2020) (Registration Number: NCT04343768).

A cluster analysis of epidemiological and clinical factors associated with the accumulation process of the burden of COVID-19 in European countries.

Background and aim of the work European COVID-19 statistics showed differentiation between mortality and new cases. Some studies suggested several factors including migration, cancer incidence, life expectancy and health system capacity maybe associated with differentiations. Up to now, impact of those factors in different European societies is not discussed and compared. Aim of the present study was to perform the cluster analysis in European countries in attention to clinical and epidemiological factors due to covid-19. Methods We collected some appropriate extreme data of COVID-19 to access the situations by ANOVA post-hoc test in 3 scenarios, as well as to estimate regression coefficients in simple linear regression, and a cluster analysis using average linkage. Covid-19 Statistics were considered in all analyses until April 24, 2020. Results Among 39 European countries, several countries reported highest rate of confirmed cases included of Italy (current statues=2270.52) and Spain (current status=2616.24). The highest rate of mortality was seen in France (current status=242.16), Italy (current status=305.52). Life expectancy (female) (P=0.01, 95%Cl=1521.27,15264.58), migration (P<0.001, 95%Cl=41.42,96.72) had significant association with confirmed cases and death. Overall cancer death (P<0.001, 95%Cl=0.36,0.68; P<0.001, 95%Cl=0.01,0.07) and lung cancer death (P<0.001, 95%Cl=1.97,3.56; P<0.001, 95%Cl=0.09,0.37) associated with confirmed cases and mortality, too. We were also determined 5 clusters which more than 30 countries were categorized in the first cluster. Conclusions Demographic factors, including population, life expectancy and migration, underlying disorders, such as several types of cancers, especially lung cancers lead to various distribution of COVID-19 in terms of prevalence and mortality, across European counties.

Identification, Monitoring, and Prediction of Disease Severity in Patients with COVID-19 Pneumonia Based on Chest Computed Tomography Scans: A Retrospective Study.

Background and Aims Non-contrast chest computed tomography (CT) scans can accurately evaluate the type and extent of lung lesions. The aim of this study was to investigate the chest CT features associated with critical and non-critical patients with coronavirus disease 2019 (COVID-19). Methods A total of 1078 patients with COVID-19 pneumonia who underwent chest CT scans, including 169 critical cases and 909 non-critical cases, were enrolled in this retrospective study. The scans of all participants were reviewed and compared in two groups of study. In addition, the risk factors associated with disease in critical and non-critical patients were analyzed. Results Chest CT scans showed bilateral and multifocal involvement in most (86.4%) of the participants, with 97.6 and 84.3% reported in critical and non-critical patients, respectively. The incidences of pure consolidation (p = 0.019), mixed ground-glass opacities (GGOs) and consolidation (p < 0.001), pleural effusion (p < 0.001), and intralesional traction bronchiectasis (p = 0.007) were significantly higher in critical compared to non-critical patients. However, non-critical patients showed higher incidence of pure GGOs than the critical patients (p < 0.001). Finally, the total opacity scores of the critical patients were significantly higher than those of non-critical patients (13.71 ± 6.26 versus 4.86 ± 3.52, p < 0.001), with an area under the curve of 0.91 (0.88-0.94) for COVID-19 detection. Conclusions Our results revealed that the chest CT examination was an effective means of detecting pulmonary parenchymal abnormalities in the natural course of COVID-19. It can distinguish the critical patients from the non-critical patients (AUC = 0.91), which is helpful for the judgment of clinical condition and has important clinical value for the diagnosis and follow-up of COVID-19 pneumonia.