Analysis of the laboratory and CT radiological findings in covid-19 patients on admission in comparison with their clinical severity status

Purpose: To retrospectively analyse the clinical status of COVID 19 patients with CT and laboratory findings Method: A total of 250 RT PCR test positive patients data were collected during the period of March 2020 to June 2021. Results: Present study showed that patients who did not need oxygen on admission, 9% of patients had a severe CT score, 41% with moderate CT score and 51% with mild CT score. Those who needed oxygen 17% of patients had a severe CT score, 40% with moderate CT score and 43% with mild CT score. Study also showed that in those who needed ICU admission, 27% of patients had a severe CT score, 56% with moderate CT score and 17% with mild CT score. Laboratory parameters such as Lactate Dehydrogenase (LDH), Ferritin, C Reactive Protein (CRP) and D-Dimer showed a significant p value between severity categories. Conclusion: This study has shown that the CT severity score alone does not always have quantifiable relation to the clinical severity of the patient. In present study found laboratory parameters has no positive correlation between mild CT score, but with moderate and severe CT score scans, a significant correlation was found. [ FROM AUTHOR] Copyright of Journal of Cardiovascular Disease Research (Journal of Cardiovascular Disease Research) is the property of Journal of Cardiovascular Disease Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Association between lung opacities and visceral fat in COVID-19 patients.

Purpose: To assess the relationship between the amount of the epigastric visceral fat area and the severity of pneumonia in the course of COVID-19 using chest computed tomography (CT) examinations. Material and methods: 177 patients (54 female), with COVID-19 infection were included. A routine chest CT was performed to assess the severity of pneumonia. The affected lung tissue as well as semi-quantitative scales such as the Chest CT Score and Total Opacity Score were calculated using SyngoVia VB30A CT Pneumonia Analysis software. The epigastric region area of visceral fat (L1) was also determined. Results: The mean value of the visceral adipose tissue area was 196.23 ± 101.36 cm2. The area of adipose tissue significantly correlated with the percentage of the affected lung tissue (r = 0.1476; p = 0.050), the Chest CT Score (r = 0.2086; p = 0.005), and the Total Opacity Score (r = 0.1744; p = 0.200). The mean area of adipose tissue in the age group ≥ 65 years was 216.13 ± 105.19 cm2, while in the group < 65 years, it was 169.18 ± 89.69 cm2. This difference was statistically significant (p = 0.002). Conclusions: The study showed a relationship between the area of visceral adipose tissue and the degree of lung inflammation in COVID-19 disease in patients under 65 years of age.

De Novo Lower Urinary Tract Symptoms in COVID-19 Patients.

Background and objective Since early 2020, the novel coronavirus disease 2019 (COVID-19) has turned into a global healthcare concern. The usual clinical presentation of COVID-19 infection includes myalgia, headache associated with pyrexia, and sore throat. Our study aimed to assess the severity of lower urinary tract symptoms (LUTS) in COVID-19 patients and determine its correlation with the prognosis of the disease. Methods We conducted an observational study in the COVID-19 care unit at a tertiary care teaching center in Rajasthan on patients diagnosed as COVID-19-positive. The overactive bladder (OAB) symptom scoring system for LUTS and the CT scoring system for lung involvement in COVID-19 patients were used to evaluate the sample population. Results While our findings showed a non-significant association between OAB and CT score (p>0.05), correlation analysis revealed that the length of hospital stay was significantly longer and oxygen needs were significantly more frequent with severe LUTS. Conclusions Based on our findings, de novo LUTS, particularly storage symptoms, may be present in COVID-19-positive cases, and the severity of these symptoms may have an impact on the patient's length of stay in the hospital. Hence, doctors and other medical professionals should consider COVID-19-related bladder dysfunctions such as de novo LUTS as part of COVID-19 symptomatology.

Studying the correlation of cycle threshold value of RT-PCR and computed tomography score of CT-Scan in covid-19 infection

Background: The COVID-19 pandemic has unfolded as one of the world's worst health crisis. Viral RT-PCR, CRP, and CT scan thorax are the most common tools used for its diagnosis, prognosis and severity assessment. Hence, a parallel between these parameters can aid in better understanding and management of COVID-19 infection. Methodology: Demographic data, history, cycle threshold values of RT-PCR from nasopharyngeal and oropharyngeal swabs, CRP and computed tomography score were obtained from 108 adult participants. Statistical analysis was performed using python programming (python 3.7) and inbuilt libraries. Result(s): Mean age of the study group was 51.05 years. 63.89% were males. The mean CT score was 15.417 indicating severe disease. Men had a higher CRP. Cycle threshold value of N gene was directly proportional to CT score. Lower cycle threshold values were associated with higher CRP. Of the 37 deaths, 62.16% were males. Cycle threshold in non-survivors was significantly higher than survivors indicating lower nasopharyngeal viral load in non-survivors. Diabetes was the most common comorbidity associated with mortality. Conclusion(s): Nasopharyngeal load can be low even with severe radiological CT findings probably due to migration of the virus to lower respiratory tract in later stages of the disease. Low nasopharyngeal viral loads cannot negate the possibility of a severe pulmonary infection. CRP values may not always correlate with CT findings in recovering stages of disease. Comorbidities adversely affect the disease outcome. These parameters should be used in conjunction to assess and veer the progression, management and outcome of patients with COVID-19 infection. Copyright © 2023 Ubiquity Press. All rights reserved.

Lactate dehydrogenase and PaO2/FiO2 ratio at admission helps to predict CT score in patients with COVID-19: An observational study.

INTRODUCTION: Since the beginning of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic an important tool for patients with Coronavirus Disease 2019 (COVID-19) has been the computed tomography (CT) scan, but not always available in some settings The aim was to find a cut-off that can predict worsening in patients with COVID-19 assessed with a computed tomography (CT) scan and to find laboratory, clinical or demographic parameters that may correlate with a higher CT score. METHODS: We performed a multi-center, observational, retrospective study involving seventeen COVID-19 Units in southern Italy, including all 321 adult patients hospitalized with a diagnosis of COVID-19 who underwent at admission a CT evaluated using Pan score. RESULTS: Considering the clinical outcome and Pan score, the best cut-off point to discriminate a severe outcome was 12.5. High lactate dehydrogenase (LDH) serum value and low PaO2/FiO2 ratio (P/F) resulted independently associated with a high CT score. The Area Under Curve (AUC) analysis showed that the best cut-off point for LDH was 367.5 U/L and for P/F 164.5. Moreover, the patients with LDH> 367.5 U/L and P/F < 164.5 showed more frequently a severe CT score than those with LDH< 367.5 U/L and P/F> 164.5, 83.4%, vs 20%, respectively. CONCLUSIONS: A direct correlation was observed between CT score value and outcome of COVID-19, such as CT score and high LDH levels and low P/F ratio at admission. Clinical or laboratory tools that predict the outcome at admission to hospital are useful to avoiding the overload of hospital facilities.

Cardiopulmonary predictors of mortality in patients with COVID-19: What are the findings?

BACKGROUND: Since 2019, coronavirus disease 2019 (COVID-19) has been the leading cause of mortality worldwide. AIMS: To determine independent predictors of mortality in COVID-19, and identify any associations between pulmonary disease severity and cardiac involvement. METHODS: Clinical, laboratory, electrocardiography and computed tomography (CT) imaging data were collected from 389 consecutive patients with COVID-19. Patients were divided into alive and deceased groups. Independent predictors of mortality were identified. Kaplan-Meier analysis was performed, based on patients having a troponin concentration>99th percentile (cardiac injury) and a CT severity score ≥18. RESULTS: The mortality rate was 29.3%. Cardiac injury (odds ratio [OR] 2.19, 95% confidence interval [CI] 1.14-4.18; P=0.018), CT score ≥18 (OR 2.24, 95% CI 1.15-4.34; P=0.017), localized ST depression (OR 3.77, 95% CI 1.33-10.67; P=0.012), hemiblocks (OR 3.09, 95% CI 1.47-6.48; P=0.003) and history of leukaemia/lymphoma (OR 3.76, 95% CI 1.37-10.29; P=0.010) were identified as independent predictors of mortality. Additionally, patients with cardiac injury and CT score ≥ 18 were identified to have a significantly shorter survival time (mean 14.21 days, 95% CI 10.45-17.98 days) than all other subgroups. There were no associations between CT severity score and electrocardiogram or cardiac injury in our results. CONCLUSIONS: Our findings suggest that using CT imaging and electrocardiogram characteristics together can provide a better means of predicting mortality in patients with COVID-19. We identified cardiac injury, CT score ≥18, presence of left or right hemiblocks on initial electrocardiogram, localized ST depression and history of haematological malignancies as independent predictors of mortality in patients with COVID-19.

Investigation of the relationship of CO-RADS and CT patterns with laboratory parameters in COVID-19 patients and a new perspective on the total CT scoring system.

BACKGROUND: It is important to determine the correlation of the CO-RADS classification and computed tomography (CT) patterns of the lung with laboratory data. To investigate the relationship of CO-RADS categories and CT patterns with laboratory data in patients with a positive RT-PCR test. We also developed a structured total CT scoring system and investigated its correlation with the total CT scoring system. METHOD: The CT examinations of the patients were evaluated in terms of the CO-RADS classification, pattern groups and total CT score. Structured total CT score values were obtained by including the total CT score values and pattern values in a regression analysis. The CT data were compared according to the laboratory data. RESULTS: A total of 198 patients were evaluated. There were significant differences between the CO-RADS groups in terms of age, ICU transfer, oxygen saturation, creatinine, LDH, D-dimer, high-sensitivity cardiac troponin-T (hs-TnT), CRP, structured total CT score values, and total CT score values. A significant difference was also observed between the CT pattern groups and oxygen saturation, creatinine and CRP values. When the structured total CT score values and total CT score values were compared they were observed to be correlated. CONCLUSIONS: Creatinine can be considered as an important marker for the CO-RADS and pattern classifications in lung involvement. LDH can be considered as an important marker of parenchymal involvement, especially bilateral and diffuse involvement. The structured total CT scoring system is a new system that can be used as an alternative.

Comparison of Chest CT and RT-PCR Assay for Indication of Disease Course of Coronavirus Disease 2019 (COVID-19) Pneumonia.

BACKGROUND: COVID-19 patients' courses vary in length, indicating a variable prognosis. The disease duration revealed by different examination methods may differ. OBJECTIVE: The study aims to compare the differences in the disease course of patients with COVID-19 by chest computed tomography (CT) and reverse-transcription polymerase chain reaction (RT-PCR) assay and explore the factors that affect the course of the illness. METHODS: 106 patients confirmed with COVID-19 were enrolled and divided into two groups (age <60 years and age ≥60 years). The clinical characteristics of the two groups were analyzed. The intervals from symptoms onset to initial positive time point (ISIP), symptoms onset to the initial negative time point (ISIN), and initial positive to initial negative time point (IIPN) indicated by chest CT and RTPCR assay were analyzed. Multiple regression analysis was performed to assess the correlations between independent factors and the intervals. RESULTS: Chest CT showed an earlier positive time point, a later negative time point, and a longer disease duration than the RT-PCR assay (P<.001, respectively). Older patients over 60 years old showed a later negative time point and a longer disease duration by chest CT than younger patients (P<.01 vs. P<.05, respectively). The CT score and clinical grades of older patients were greater than those of younger patients (P<.001, respectively). Age and clinical grades were significantly correlated with the disease course shown by chest CT (P<.05, respectively), and CT score was positively correlated with the illness course shown by chest CT and RT-PCR assay (P<.01, respectively). CONCLUSION: The disease course revealed by chest CT and RT-PCR assay was asynchronous. Chest CT showed a 17-day longer period compared to the RT-PCR assay. Older patients had a longer duration than younger ones. A prolonged course is predicted by increasing age, CT score, and clinical grades.

Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model.

BACKGROUND: Most severe, critical, or mortal COVID-19 cases often had a relatively stable period before their status worsened. We developed a deterioration risk model of COVID-19 (DRM-COVID-19) to predict exacerbation risk and optimize disease management on admission. METHOD: We conducted a multicenter retrospective cohort study with 239 confirmed symptomatic COVID-19 patients. A combination of the least absolute shrinkage and selection operator (LASSO), change-in-estimate (CIE) screened out independent risk factors for the multivariate logistic regression model (DRM-COVID-19) from 44 variables, including epidemiological, demographic, clinical, and lung CT features. The compound study endpoint was progression to severe, critical, or mortal status. Additionally, the model's performance was evaluated for discrimination, accuracy, calibration, and clinical utility, through internal validation using bootstrap resampling (1000 times). We used a nomogram and a network platform for model visualization. RESULTS: In the cohort study, 62 cases reached the compound endpoint, including 42 severe, 18 critical, and two mortal cases. DRM-COVID-19 included six factors: dyspnea [odds ratio (OR) 4.89;confidence interval (95% CI) 1.53-15.80], incubation period (OR 0.83; 95% CI 0.68-0.99), number of comorbidities (OR 1.76; 95% CI 1.03-3.05), D-dimer (OR 7.05; 95% CI, 1.35-45.7), C-reactive protein (OR 1.06; 95% CI 1.02-1.1), and semi-quantitative CT score (OR 1.50; 95% CI 1.27-1.82). The model showed good fitting (Hosmer-Lemeshow goodness, X2(8) = 7.0194, P = 0.53), high discrimination (the area under the receiver operating characteristic curve, AUROC, 0.971; 95% CI, 0.949-0.992), precision (Brier score = 0.051) as well as excellent calibration and clinical benefits. The precision-recall (PR) curve showed excellent classification performance of the model (AUCPR = 0.934). We prepared a nomogram and a freely available online prediction platform ( https://deterioration-risk-model-of-covid-19.shinyapps.io/DRMapp/ ). CONCLUSION: We developed a predictive model, which includes the including incubation period along with clinical and lung CT features. The model presented satisfactory prediction and discrimination performance for COVID-19 patients who might progress from mild or moderate to severe or critical on admission, improving the clinical prognosis and optimizing the medical resources.

Mortality estimation using APACHE and CT scores with stepwise linear regression method in COVID-19 intensive care unit: A retrospective study.

BACKGROUND: COVID-19 is a disease with high mortality worldwide, and which parameters that affect mortality in intensive care are still being investigated. This study aimed to show the factors affecting mortality in COVID-19 intensive care patients and write a model that can predict mortality. METHODS: The data of 229 patients in the COVID-19 intensive care unit were scanned. Laboratory tests, APACHE, SOFA, and GCS values were recorded. CT scores were calculated with chest CTs. The effects of these data on mortality were examined. The effects of the variables were modeled using the stepwise regression method. RESULTS: While the mean age of female (30.14%) patients was 69.1 ± 12.2, the mean age of male (69.86%) patients was 66.9 ± 11.5. The mortality rate was 69.86%. Age, CRP, D-dimer, creatinine, procalcitonin, APACHE, SOFA, GCS, and CT score were significantly different in the deceased patients than the survival group. When we attempted to create a model using stepwise linear regression analysis, the appropriate model was achieved at the fourth step. Age, CRP, APACHE, and CT score were included in the model, which has the power to predict mortality with 89.9% accuracy. CONCLUSION: Although, when viewed individually, there is a significant difference in parameters such as creatinine, procalcitonin, D-dimer, GCS, and SOFA score, the probability of mortality can be estimated by knowing only the age, CRP, APACHE, and CT scores. These four simple parameters will help clinicians effectively use resources in treatment.

Prognostic value of semi-quantitative CT-based score integrated with cardiovascular risk factors during the first peak of the COVID-19 pandemic: A new score to predict poor outcome.

PURPOSE: Predicting the clinical course of COVID-19 pneumonia is of high clinical importance and may change treatment strategies. This study aimed to compare the semi-quantitative CT score (radiological score), mCHA2DS2-VASc score (clinical score), and R-mCHA2DS2-VASc score (clinical and radiological score) to predict the risk of ICU admission and mortality in COVID 19 pneumonia. METHODS: This study retrospectively evaluated 901 COVID-19 pneumonia cases with positive PCR results. The mCHA2DS2-VASc score was calculated based on clinical risk factors. CT images were evaluated, and the semi-quantitative CT scores were obtained. A new scoring method (R-mCHA2DS2-VASc score) was developed by combining these scores. The performance of the mCHA2DS2-VASc score, semi-quantitative CT score, and a combination of these scores (R-mCHA2DS2-VASc score) was evaluated using ROC analysis. RESULTS: The ROC curves of the semi-quantitative CT, mCHA2DS2-VASc, and R-mCHA2DS2-VASc scores were examined. The semi-quantitative CT, mCHA2DS2-VASc, and R-mCHA2DS2-VASc scores were significant in predicting intensive care unit (ICU) admission and mortality (p < 0.001). The R-mCHA2DS2-VASc score performed best in predicting a severe clinical course, and the cut-off value of 8 for the R-mCHA2DS2-VASc score had 83.9% sensitivity and 91.6% specificity for mortality. CONCLUSIONS: The R-mCHA2DS2-VASc score includes both clinical and radiological parameters. It is a feasible scoring method for predicting a severe clinical course at an early stage with high sensitivity and specificity values. However, prospective studies with larger sample sizes are warranted.

CT findings in survivors and non-survivors of COVID-19 and clinical usefulness of a CT scoring system.

BACKGROUND: Many patients with coronavirus disease 2019 (COVID-19) have been diagnosed with computed tomography (CT). A prognostic tool based on CT findings could be useful for predicting death from COVID-19. OBJECTIVES: To compare the chest CT findings of patients who survived COVID-19 versus those of patients who died of COVID-19 and to determine the usefulness the clinical usefulness of a CT scoring system for COVID-19. METHODS: We included 124 patients with confirmed SARS-CoV-2 infections who were hospitalized between April 1, 2020 and July 25, 2020. RESULTS: Whereas ground-glass opacities were the most common characteristic finding in survivors (75%), crazy paving was the most characteristic finding in non-survivors (65%). Atypical findings were present in 46% of patients. The chest CT score was directly proportional to mortality; a score ≥18 was the best cutoff for predicting death, yielding 70% sensitivity (95%CI: 47%-87%). CONCLUSIONS: Our results suggest that atypical lesions are more prevalent in this cohort. The chest CT score had high sensitivity for predicting hospital mortality.

[CT findings in survivors and non-survivors of COVID-19 and clinical usefulness of a CT scoring system]. / Hallazgos tomográficos entre sobrevivientes y no-sobrevivientes con COVID-19 y utilidad clínica de una puntuación de tomografía torácica.

Background: Many patients with coronavirus disease 2019 (COVID-19) have been diagnosed with computed tomography (CT). A prognostic tool based on CT findings could be useful for predicting death from COVID-19. Objectives: To compare the chest CT findings of patients who survived COVID-19 versus those of patients who died of COVID-19 and to determine the usefulness the clinical usefulness of a CT scoring system for COVID-19. Methods: We included 124 patients with confirmed SARS-CoV-2 infections who were hospitalized between April 1, 2020 and July 25, 2020. Results: Whereas ground-glass opacities were the most common characteristic finding in survivors (75%), crazy paving was the most characteristic finding in non-survivors (65%). Atypical findings were present in 46% of patients. The chest CT score was directly proportional to mortality; a score ≥ 18 was the best cutoff for predicting death, yielding 70% sensitivity (95%CI: 47%-87%). Conclusions: Our results suggest that atypical lesions are more prevalent in this cohort. The chest CT score had high sensitivity for predicting hospital mortality.

Original Article Combined CT score, blood mononuclear cell count, LDH, and plasma D-dimer for viral pneumonia diagnosis: a retrospective study

Objective: Due to a continued increase in viral pneumonia incidence and resulting high mortality, fast and accurate diagnosis is important for effective management. This investigation examined the significance of blood biomarkers and the CT score in the early diagnosis of viral pneumonia. Methods: Patients who were hospitalized due to radiologically-confirmed pneumonia and underwent virus antigen rapid test were enrolled. Their clinical information was compared. Blood mononuclear cell count, LDH, and plasma D-dimer were obtained. To evaluate the utility of biomarker levels in differentiating viral pneumonia from other pneumonia, ROC curves were developed to analyze the AUC. The optimal cut-off thresholds, specificity, sensitivity, and predictive values were assessed using the Youden index. The added value of the multi-marker approach was delineated using IDI and Reclassification analyses using NRI;IDI and NRI values were examined with 95% CI. Results: Overall, 1163 inpatients were recruited between January 2017 and January 2021. They were sub-divided into the viral pneumonia (n = 563) and non-viral pneumonia (n = 600) categories. We found that the CT score, blood mononuclear cell count, LDH, and plasma D-dimer were markedly elevated in viral pneumonia patients. At an LDH threshold of 693.595 U/L, an AUC of ROC was 0.805 in differentiating viral pneumonia. The combination of CT score and blood biomarkers had an ROC AUC value of 0.908. Conclusions: Combining elevated biomarkers with CT assessments outperformed the CT score alone in identifying viral pneumonia. It is crucial to better characterize the significance of biomarkers in combination with CT assessments in the diagnosis of viral pneumonia.

Predictors of the chest CT score in COVID-19 patients: a cross-sectional study.

BACKGROUND: Since the COVID-19 outbreak, pulmonary involvement was one of the most significant concerns in assessing patients. In the current study, we evaluated patient's signs, symptoms, and laboratory data on the first visit to predict the severity of pulmonary involvement and their outcome regarding their initial findings. METHODS: All referred patients to the COVID-19 clinic of a tertiary referral university hospital were evaluated from April to August 2020. Four hundred seventy-eight COVID-19 patients with positive real-time reverse-transcriptase-polymerase chain reaction (RT-PCR) or highly suggestive symptoms with computed tomography (CT) imaging results with typical findings of COVID-19 were enrolled in the study. The clinical features, initial laboratory, CT findings, and short-term outcomes (ICU admission, mortality, length of hospitalization, and recovery time) were recorded. In addition, the severity of pulmonary involvement was assessed using a semi-quantitative scoring system (0-25). RESULTS: Among 478 participants in this study, 353 (73.6%) were admitted to the hospital, and 42 (8.7%) patients were admitted to the ICU. Myalgia (60.4%), fever (59.4%), and dyspnea (57.9%) were the most common symptoms of participants at the first visit. A review of chest CT scans showed that Ground Glass Opacity (GGO) (58.5%) and consolidation (20.7%) were the most patterns of lung lesions. Among initial clinical and laboratory findings, anosmia (P = 0.01), respiratory rate (RR) with a cut point of 25 (P = 0.001), C-reactive protein (CRP) with a cut point of 90 (P = 0.002), white Blood Cell (WBC) with a cut point of 10,000 (P = 0.009), and SpO2 with a cut point of 93 (P = 0.04) was associated with higher chest CT score. Lung involvement and consolidation lesions on chest CT scans were also associated with a more extended hospitalization and recovery period. CONCLUSIONS: Initial assessment of COVID-19 patients, including symptoms, vital signs, and routine laboratory tests, can predict the severity of lung involvement and unfavorable outcomes.

Risk factors associated with mortality in intensive care COVID-19 patients: the importance of chest CT score and intubation timing as risk factors

Background/aim: Coronavirus disease 2019 (COVID-19) is a disease with a high rate of progression to critical illness. However, the predictors of mortality in critically ill patients admitted to the intensive care unit (ICU) are not yet well understood. In this study, we aimed to investigate the risk factors associated with ICU mortality in our hospital. Materials and methods: In this single-centered retrospective study, we enrolled 86 critically ill adult patients with COVID-19 admitted to ICU of Dokuz Eylül University Hospital (Izmir, Turkey) between 18 March 2020 and 31 October 2020. Data on demographic information, preexisting comorbidities, treatments, the laboratory findings at ICU admission, and clinical outcomes were collected. The chest computerized tomography (CT) of the patients were evaluated specifically for COVID-19 and CT score was calculated. Data of the survivors and nonsurvivors were compared with survival analysis to identify risk factors of mortality in the ICU. Results: The mean age of the patients was 71.1 ± 14.1 years. The patients were predominantly male. The most common comorbidity in patients was hypertension. ICU mortality was 62.8%. Being over 60 years old, CT score > 15, acute physiology and chronic health evaluation (APACHE) II score ≥ 15, having dementia, treatment without favipiravir, base excess in blood gas analysis ≤ ­2.0, WBC > 10,000/mm3, D-dimer > 1.6 µg/mL, troponin > 24 ng/L, Na ≥ 145 mmol/L were considered to link with ICU mortality according to Kaplan­Meier curves (log-rank test, p < 0.05). The APACHE II score (HR: 1.055, 95% CI: 1.021­1.090) and chest CT score (HR: 2.411, 95% CI:1.193­4.875) were associated with ICU mortality in the cox proportional-hazard regression model adjusted for age, dementia, favipiravir treatment and troponin. Howewer, no difference was found between survivors and nonsurvivors in terms of intubation timing. Conclusions: COVID-19 patients have a high ICU admission and mortality rate. Studies in the ICU are also crucial in this respect. In our study, we investigated the ICU mortality risk factors of COVID-19 patients. We determined a predictive mortality model consisting of APACHE II score and chest CT score. It was thought that this feasible and practical model would assist in making clinical decisions.

The Ichikado CT score as a prognostic tool for coronavirus disease 2019 pneumonia: a retrospective cohort study.

BACKGROUND: The relationship between computed tomography (CT) and prognosis of patients with COVID-19 pneumonia remains unclear. We hypothesized that the Ichikado CT score, obtained in the first 24 h of hospital admission, is an independent predictor for all-cause mortality during hospitalization in patients with COVID-19 pneumonia. METHODS: Single-center retrospective cohort study of patients with confirmed COVID-19 pneumonia admitted at our institution between March 20th, 2020 and October 31st, 2020. Patients were enrolled if, within 24 h of admission, a chest CT scan, an arterial blood gas, a complete blood count, and a basic metabolic panel were performed. Two independent radiologists, who were blinded to clinical data, retrospectively evaluated the chest CT scans following a previously described qualitative and quantitative CT scoring system. The primary outcome was all-cause in-hospital mortality or survival to hospital discharge. Secondary outcomes were new requirements for invasive mechanical ventilation and hospital length of stay. Cox regression models were used to test the association between potential independent predictors and all-cause mortality. RESULTS: Two hundred thirty-five patients, 197 survivors and 38 nonsurvivors, were studied. The median Ichikado CT score for nonsurvivors was significantly higher than survivors (P < 0.001). An Ichikado CT score of more than 172 enabled prediction of mortality, with a sensitivity of 84.2% and a specificity of 79.7%. Multivariate analysis identified Ichikado CT score (HR, 7.772; 95% CI, 3.164-19.095; P < 0.001), together with age (HR, 1.030; 95% CI, 1.030-1.060; P = 0.043), as independent predictors of all-cause in-hospital mortality. CONCLUSIONS: Ichikado CT score is an independent predictor of both requiring invasive mechanical ventilation and all-cause mortality in patients hospitalized with COVID-19 pneumonia. Further prospective evaluation is necessary to confirm these findings. TRIAL REGISTRATION: The WCG institutional review board approved this retrospective study and patient consent was waived due to its non-interventional nature (Identifier: 20210799).

Prognostic value of neutrophil-to-lymphocyte ratio, lactate dehydrogenase, D-dimer, and computed tomography score in patients with coronavirus disease 2019.

BACKGROUND: This study aimed to explore the significance of neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), D-dimer, and CT score in evaluating the severity and prognosis of coronavirus disease 2019 (COVID-19). METHODS: Patients with laboratory-confirmed COVID-19 were retrospectively enrolled. The baseline data, laboratory findings, chest computed tomography (CT) results evaluated by CT score on admission, and clinical outcomes were collected and compared. Logistic regression was used to assess the independent relationship between the baseline level of the four indicators (NLR, LDH, D-dimer, and CT score) and the severity of COVID-19. RESULTS: Among the 432 patients, 125 (28.94%) and 307 (71.06%) were placed in the severe and non-severe groups, respectively. As per the multivariate logistic regression, high levels of NLR and LDH were independent predictors of severe COVID-19 (OR=2.163; 95% CI=1.162-4.026; p=0.015 for NLR>3.82; OR=2.298; 95% CI=1.327-3.979; p=0.003 for LDH>246 U/L). Combined NLR>3.82 and LDH>246 U/L increased the sensitivity of diagnosis in patients with severe disease (NLR>3.82 [50.40%] vs. combined diagnosis [72.80%]; p=0.0007; LDH>246 [59.2%] vs. combined diagnosis [72.80%]; p<0.0001). CONCLUSIONS: High levels of serum NLR and LDH have potential value in the early identification of patients with severe COVID-19. Moreover, the combination of LDH and NLR can improve the sensitivity of diagnosis.

Three-Dimensional CT for Quantification of Longitudinal Lung and Pneumonia Variations in COVID-19 Patients.

Objectives: Visual chest CT is subjective with interobserver variability. We aimed to quantify the dynamic changes of lung and pneumonia on three-dimensional CT (3D-CT) images in coronavirus disease 2019 (COVID-19) patients during hospitalization. Methods: A total of 110 laboratory-confirmed COVID-19 patients who underwent chest CT from January 3 to February 29, 2020 were retrospectively reviewed. Pneumonia lesions were classified as four stages: early, progressive, peak, and absorption stages on chest CT. A computer-aided diagnostic (CAD) system calculated the total lung volume (TLV), the percentage of low attenuation areas (LAA%), the volume of pneumonia, the volume of ground-glass opacities (GGO), the volume of consolidation plus the GGO/consolidation ratio. The CT score was visually assessed by radiologists. Comparisons of lung and pneumonia parameters among the four stages were performed by one-way ANOVA with post-hoc tests. The relationship between the CT score and the volume of pneumonia, and between LAA% and the volume of pneumonia in four stages was assessed by Spearman's rank correlation analysis. Results: A total of 534 chest CT scans were performed with a median interval of 4 days. TLV, LAA%, and the GGO/consolidation ratio were significantly decreased, while the volume of pneumonia, GGO, and consolidation were significantly increased in the progressive and peak stages (for all, P < 0.05). The CT score was significantly correlated with the pneumonia volume in the four stages (r = 0.731, 0.761, 0.715, and 0.669, respectively, P < 0.001). Conclusion: 3D-CT could be used as a useful quantification method in monitoring the dynamic changes of COVID-19 pneumonia.

Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality.

PURPOSE: To evaluate longitudinal computed tomography (CT) features and the predictive value of the initial CT and clinical characteristics for mortality in patients with severe/critical coronavirus disease 2019 (COVID-19) pneumonia. METHODS: A retrospective analysis was performed on patients with COVID-19 pneumonia confirmed by laboratory. By excluding mild and common patients, 155 severe/critical patients with definite outcome were finally enrolled. A total of 516 CTs of 147 patients were divided into four stages according to the time after onset (stage 1, 1-7 days; stage 2, 8-14 days; stage 3, 15-21 days, and stage 4, >21 days). The evolving imaging features between the survival and non-survival groups were compared by using Chi-square, Fisher's exact test, student's t-test or Mann-Whitney U-test, as appropriate. The predictive value of clinical and CT features at admission for mortality was analysed through logistic regression analysis. To avoid overfitting caused by CT scores, CT scores were divided into two parts, which were combined with clinical variables, respectively, to construct the models. RESULTS: Ground-glass opacities (GGO) patterns were predominant for stages 1 and 2 for both groups (both P>0.05). The numbers of consolidation lesions increased in stage 3 in both groups (P=0.857), whereas the linear opacity increased in the survival group but decreased in the non-survival group (P=0.0049). In stage 4, the survival group predominantly presented linear opacity patterns, whereas the non-survival group mainly showed consolidation patterns (P=0.007). Clinical and imaging characteristics correlated with mortality; multivariate analyses revealed age >71 years, neutrophil count >6.38 × 109/L, aspartate aminotransferase (AST) >58 IU/L, and CT score (total lesions score >17 in model 1, GGO score >14 and consolidation score >2 in model 2) as independent risk factors (all P<0.05). The areas under the curve of the six independent risk factors alone ranged from 0.65 to 0.75 and were 0.87 for model 2, 0.89 for model 1, and 0.92 for the six variables combined. Statistical differences were observed between Kaplan Meier curves of groups separated by cut-off values of these six variables (all P<0.01). CONCLUSION: Longitudinal imaging features demonstrated differences between the two groups, which may help determine the patient's prognosis. The initial CT score combined with age, AST, and neutrophil count is an excellent predictor for mortality in COVID-19 patients.