Predictive value of initial CT scan for various adverse outcomes in patients with COVID-19 pneumonia

Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19

PURPOSE

 The prevalent coronavirus disease 2019 (COVID-19) pandemic has spread throughout the world and is considered a serious threat to global health. The prognostic role of thoracic lymphadenopathy in COVID-19 is unclear. The aim of the present meta-analysis was to analyze the prognostic role of thoracic lymphadenopathy for the prediction of 30-day mortality in patients with COVID-19.

MATERIALS AND METHODS

 The MEDLINE library, Cochrane, and SCOPUS databases were screened for associations between CT-defined features and mortality in COVID-19 patients up to June 2021. In total, 21 studies were included in the present analysis. The quality of the included studies was assessed by the Newcastle-Ottawa Scale. The meta-analysis was performed using RevMan 5.3. Heterogeneity was calculated by means of the inconsistency index I2. DerSimonian and Laird random-effect models with inverse variance weights were performed without any further correction.

RESULTS

 The included studies comprised 4621 patients. The prevalence of thoracic lymphadenopathy varied between 1 % and 73.4 %. The pooled prevalence was 16.7 %, 95 % CI = (15.6 %; 17.8 %). The hospital mortality was higher in patients with thoracic lymphadenopathy (34.7 %) than in patients without (20.0 %). The pooled odds ratio for the influence of thoracic lymphadenopathy on mortality was 2.13 (95 % CI = [1.80-2.52], p < 0.001).

CONCLUSION

 The prevalence of thoracic lymphadenopathy in COVID-19 is 16.7 %. The presence of thoracic lymphadenopathy is associated with an approximately twofold increase in the risk for hospital mortality in COVID-19.

KEY POINTS

  · The prevalence of lymphadenopathy in COVID-19 is 16.7 %.. · Patients with lymphadenopathy in COVID-19 have a higher risk of mortality during hospitalization.. · Lymphadenopathy nearly doubles mortality and plays an important prognostic role..

CITATION FORMAT

· Bucher AM, Sieren M, Meinel F et al. Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2293-8132.

Pericardial Effusion Predicts Clinical Outcomes in Patients with COVID-19: A Nationwide Multicenter Study

RATIONALE AND OBJECTIVES

The prognostic role of pericardial effusion (PE) in Covid 19 is unclear. The aim of the present study was to estimate the prognostic role of PE in patients with Covid 19 in a large multicentre setting.

MATERIALS AND METHODS

This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the Covid 19 pandemic). The acquired sample comprises 1197 patients, 363 (30.3%) women and 834 (69.7%) men. In every case, chest computed tomography was analyzed for PE. Data about 30-day mortality, need for mechanical ventilation and need for intensive care unit (ICU) admission were collected. Data were evaluated by means of descriptive statistics. Group differences were calculated with Mann-Whitney test and Fisher exact test. Uni-and multivariable regression analyses were performed.

RESULTS

Overall, 46.4% of the patients were admitted to ICU, mechanical lung ventilation was performed in 26.6% and 30-day mortality was 24%. PE was identified in 159 patients (13.3%). The presence of PE was associated with 30-day mortality: HR= 1.54, CI 95% (1.05; 2.23), p = 0.02 (univariable analysis), and HR= 1.60, CI 95% (1.03; 2.48), p = 0.03 (multivariable analysis). Furthermore, density of PE was associated with the need for intubation (OR=1.02, CI 95% (1.003; 1.05), p = 0.03) and the need for ICU admission (OR=1.03, CI 95% (1.005; 1.05), p = 0.01) in univariable regression analysis. The presence of PE was associated with 30-day mortality in male patients, HR= 1.56, CI 95%(1.01-2.43), p = 0.04 (multivariable analysis). In female patients, none of PE values predicted clinical outcomes.

CONCLUSION

The prevalence of PE in Covid 19 is 13.3%. PE is an independent predictor of 30-day mortality in male patients with Covid 19. In female patients, PE plays no predictive role.

Discovering associations between radiological features and COVID-19 patients' deterioration

Background and Aims

Data mining methods are effective and well-known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID-19 by applying the rule mining method using characteristics of medical images.

Methods

This retrospective study has analyzed the radiological data from 104 COVID-19 hospitalized patients diagnosed with COVID-19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes.

Results

Ten rules were extracted with only X-ray-related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan-related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13.

Conclusion

This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID-19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.

Extension of Lung Damage at Chest Computed Tomography in Severely Ill COVID-19 Patients Treated with Interleukin-6 Receptor Blockers Correlates with Inflammatory Cytokines Production and Prognosis

Elevated inflammatory markers are associated with severe coronavirus disease 2019 (COVID-19), and some patients benefit from Interleukin (IL)-6 pathway inhibitors. Different chest computed tomography (CT) scoring systems have shown a prognostic value in COVID-19, but not specifically in anti-IL-6-treated patients at high risk of respiratory failure. We aimed to explore the relationship between baseline CT findings and inflammatory conditions and to evaluate the prognostic value of chest CT scores and laboratory findings in COVID-19 patients specifically treated with anti-IL-6. Baseline CT lung involvement was assessed in 51 hospitalized COVID-19 patients naive to glucocorticoids and other immunosuppressants using four CT scoring systems. CT data were correlated with systemic inflammation and 30-day prognosis after anti-IL-6 treatment. All the considered CT scores showed a negative correlation with pulmonary function and a positive one with C-reactive protein (CRP), IL-6, IL-8, and Tumor Necrosis Factor α (TNF-α) serum levels. All the performed scores were prognostic factors, but the disease extension assessed by the six-lung-zone CT score (S24) was the only independently associated with intensive care unit (ICU) admission (p = 0.04). In conclusion, CT involvement correlates with laboratory inflammation markers and is an independent prognostic factor in COVID-19 patients representing a further tool to implement prognostic stratification in hospitalized patients.

The Role of Computed Tomography in the Management of Hospitalized Patients With COVID-19

The emergence of SARS-CoV-2 at the end of 2019 sparked the beginning of the COVID-19 pandemic. Even though it was a novel virus, the workup of suspected COVID-19 included standard protocols used for the investigation of similar respiratory infections and pneumonia. One of the most important diagnostic tests in this regard is computed tomography (CT). CT scans have a high sensitivity in diagnosing COVID-19, and many of the characteristic imaging findings of COVID-19 are used in its diagnosis. The role of CT in COVID-19 management is expanding as more and more hospital practices adopt regular CT use in both the initial workup and continued care of COVID-19 patients. CT has helped hospitalists diagnose complications such as pulmonary embolism, subcutaneous emphysema, pneumomediastinum, pneumothoraces, and nosocomial pneumonia. Although mainly used as a diagnostic tool, the prognostic role of CT in COVID-19 patients is developing. In this review, we explore the role of CT in the management of hospitalized patients with COVID-19, specifically elucidating its use as a diagnostic and prognostic modality, as well as its ability to guide hospital decision-making regarding complex cases. We will highlight important time points when CT scans are used: the initial encounter, the time at admission, and during hospitalization.

COVID-19 in Patients with Rheumatic Disease Using Immunomodulatory Drugs: Imaging Findings and Predictors of Hospitalization

Introduction

SARS-CoV-2 causes more severe symptoms in most chronic diseases, and rheumatic disease is no exception. This study aims to investigate whether there is an association between the use of immunomodulatory medications, including conventional disease-modifying agents (csDMARDs), glucocorticoids, and biologic DMARDs, and outcomes such as hospitalization and lung involvement in patients with rheumatic disease with COVID-19.

Methods

We performed a cross-sectional study on 177 COVID-19 cases with rheumatologic diseases using immunomodulatory drugs as their regular treatment. All patients were evaluated regarding their initial chest computed tomography (CT) scan, COVID-19 symptoms, and comorbidities. We ran predictive models to find variables associated with chest CT-scan involvement and hospitalization status.

Results

CT findings showed lung involvement in 87 patients with chest CT-scan severity score (C-ss) of less than 8 in 59 (33%) and more than 8 in 28 (16%) of our patients. Of all patients, 76 (43%) were hospitalized. Hospitalized patients were significantly older and had more comorbidities (P = 0.02). On multivariate analysis, older age [odds ratio (OR) 1.90, 95% confidence interval (CI) 1.31-3.08] and comorbidity (OR 2.75, 95% CI 1.06-3.66) were significantly associated with higher odds of hospitalization (P = 0.03). On multivariate analysis, older age (OR 1.15, 95% CI 0.94-2.01), pulmonary diseases (OR 2.05, 95% CI 1.18-3.32), and treatment with csDMARDs (OR 1.88, 95% CI 0.37-1.93) were associated with higher C-ss (P = 0.039).

Conclusions

This study found that advanced age and comorbidities, similar to the general population, are risk factors for hospitalization in patients with COVID-19 with rheumatic disorders. Administration of csDMARDs, older age, and pulmonary disorders were linked to increased risk of COVID-19 pneumonia in these individuals.

Predictive Factors of Death and the Clinical Profile of Hospitalized Covid-19 Patients in Morocco: A One-Year Mixed Cohort Study

Background Since the onset of the Covid-19 pandemic, several studies have been conducted around the world in an attempt to understand this heterogeneous and unpredictable disease and to prevent related death. It was therefore necessary to study the associated risk factors of Covid-19-related mortality. Objectives The aim of this study was to describe the clinical profile and to identify the factors associated with mortality of patients with Covid-19 in Morocco. Methods We performed a mixed cohort study (retrospective and prospective) of 615 in-patients with Covid-19 disease, enrolled between August 2020 and October 2021. We followed the cohort throughout the hospitalization until discharge and 30 days thereafter. Results The median age was 64 years old; 62.1% of the patients were male. The mean time from symptom onset to hospitalization was 8.5 days (±4.67), and 68.1% of patients had comorbidities. On admission, the most common symptoms were dyspnea (82.2%), cough (80.3%), and fever (76.8%). The main follow-up complication was secondary infection (56.9%). Based on univariate analysis, male gender (p<0.008 and brut relative risk {_bRR}=1.57), advanced age (p<0.001), lung involvement (p<0.001), lymphopenia (p<0.001 and _bRR=2.32), D-dimers of >500 µg/l (p<0.007 and _bRR=2.47), C-reactive protein (CRP) of >130 mg/l (p<0.001 and _bRR=2.45), elevated creatinine (p<0.013 and _bRR=1.61), lactate dehydrogenase (LDH) of >500 U/l (p<0.001 and _bRR=7.16), receiving corticosteroids (p<0.001 and _bRR=5.08), invasive ventilation (p<0.001 and _bRR=30.10), the stay in the resuscitation unit (p<0.001 and _bRR=13.37), and acute respiratory distress syndrome (ARDS) (p<0.001 and _bRR=10.98) were associated with a higher risk of death. In the opposite, receiving azithromycin and hydroxychloroquine (p<0.001 and _bRR=0.28) and pre-admission anticoagulants (p<0.005 and _bRR=0.46) was associated with a lower risk of mortality. Multivariate regression analysis showed that age of >60 years (p<0.001 and adjusted odds ratio {aOR}=4.90), the use of invasive ventilation (p<0.001 and aOR=9.60), the stay in the resuscitation unit (p<0.001 and aOR=5.09), and acute respiratory distress syndrome (p<0.001 and aOR=6.49) were independent predictors of Covid-19 mortality. Conclusion In this cohort study focusing on Covid-19 in-patient's mortality, we found that age of >60 years, the use of invasive ventilation, the stay in the resuscitation unit, and acute respiratory distress syndrome were independent predictors of Covid-19 mortality. The results of this study can be used to improve knowledge for better clinical management of Covid-19 in-patients.

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine-Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.

Significant association between anemia and higher risk for COVID-19 mortality: A meta-analysis of adjusted effect estimates

OBJECTIVE

This study aimed to evaluate whether there was a significant relationship between anemia and the risk for mortality among coronavirus disease 2019 (COVID-19) patients by a quantitative meta-analysis based on the adjusted effect estimates.

METHODS

A systematic search was conducted in electronic databases to identify all published literature. A random-effects meta-analysis model was used to estimate the pooled effect size and 95% confidence interval (CI). Heterogeneity test, Begg's test, subgroup analysis and meta-regression were performed.

RESULTS

Twenty-three articles with 573,928 COVID-19 patients were included in the quantitative meta-analysis. There was a significant association between anemia and an elevated risk of COVID-19 mortality (pooled effect size = 1.47, 95% CI [1.30-1.67]). We observed this significant association in the further subgroup analyses by age, proportion of males, sample size, study design, region and setting. Sensitivity analysis exhibited that our results were reliable. Begg's test showed that there was no publication bias. Meta-regression indicated that the tested variables might not be the source of heterogeneity.

CONCLUSION

Our meta-analysis based on risk factors-adjusted effect estimates indicated that anemia was independently associated with a significantly elevated risk for mortality among COVID-19 patients.

Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis

Purpose

Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis.

Methods

Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes.

Results

This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157–1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307–9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both P > 0.2). The shapes of Begg’s funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg’s test P = 0.945 and 0.356, respectively).

Conclusions

The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement.

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.

Relationship of Computed Tomography Severity Score With Patient Characteristics and Survival in Hypoxemic COVID-19 Patients

Background

Computed tomography (CT) scans and CT severity scores (CTSS) are widely used to assess the severity and prognosis in coronavirus disease 2019 (COVID-19). CTSS has performed well as a predictor in differentiating severe from non-severe cases. However, it is not known if CTSS performs similarly in hospitalized severe cases with hypoxia at admission.

Methods

We conducted a retrospective comparative study at a COVID-care center from Western India between 25th April and 31st May 2021, enrolling all consecutive severe COVID-19 patients with hypoxemia (peripheral oxygen saturation < 94%). Neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), interleukin-6 (IL-6), lactate dehydrogenase (LDH), D-dimer, ferritin, and CT thorax were done within 24h of admission before being initiated on any anti-COVID-19 therapy. CTSS was calculated by visual assessment and categorized into three severity categories and was correlated with laboratory markers and overall survival (OS). Statistical analysis was done using John's Macintosh Project (JMP) 15.0.0 ver. 3.0.0 (Cary, North Carolina).

Results

The median age of the study population (n-298) was 59 years (24-95) with a male preponderance (61.41%, n=183). The 15 and 30-day survivals were 67.64% and 59.90%, respectively. CTSS did not correlate with age, gender, time from vaccination, symptoms, or comorbidities but had a significant though weak correlation with LDH (p=0.009), D-dimer (p=0.006), and NLR (p=0.019). Comparing demographic and laboratory aspects using CT severity categories, only NLR (p=0.0146) and D-dimer (p=0.0006) had significant differences. The 15d-OS of mild, moderate, and severe CT categories were 88.62%, 70.39%, and 52.62%, respectively, while 30d-OS of three categories were 59.08%, 63.96%, and 49.12%, respectively.

Conclusion

Among hospitalized severe COVID-19 patients with hypoxemia at admission, CT severity categories correlate well with outcomes but not inflammatory markers at admission.

Associations between CT pulmonary opacity score on admission and clinical characteristics and outcomes in patients with COVID-19

This study investigated associations between chest computed tomography (CT) pulmonary opacity score on admission and clinical features and outcomes in COVID-19 patients. The retrospective multi-center cohort study included 496 COVID-19 patients in Jiangsu province, China diagnosed as of March 15, 2020. Patients were divided into four groups based on the quartile of pulmonary opacity score: ≤ 5%, 6-20%, 21-40% and 41% +. CT pulmonary opacity score was independently associated with age, single onset, fever, cough, peripheral capillary oxygen saturation, lymphocyte count, platelet count, albumin level, C-reactive protein (CRP) level and fibrinogen level on admission. Patients with score ≥ 41% had a dramatic increased risk of severe or critical illness [odds ratio (OR), 15.58, 95% confidence interval (CI) 3.82-63.53), intensive care unit (ICU)] admission (OR, 6.26, 95% CI 2.15-18.23), respiratory failure (OR, 19.49, 95% CI 4.55-83.40), and a prolonged hospital stay (coefficient, 2.59, 95% CI 0.46-4.72) compared to those with score ≤ 5%. CT pulmonary opacity score on admission, especially when ≥ 41%, was closely related to some clinical characteristics and was an independent predictor of disease severity, ICU admission, respiratory failure and long hospital stay in patients with COVID-19.

Extrapulmonary CT Findings Predict In-Hospital Mortality in COVID-19. A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVES

Several prognostic factors have been identified for COVID-19 disease. Our aim was to elucidate the influence of non-pulmonary findings of thoracic computed tomography (CT) on unfavorable outcomes and in-hospital mortality in COVID-19 patients based on a large patient sample.

MATERIALS AND METHODS

MEDLINE library, Cochrane and SCOPUS databases were screened for the associations between CT-defined features and mortality in COVID-19 patients up to June 2021. In total, 22 studies were suitable for the analysis, and included into the present analysis. Overall, data regarding 4 extrapulmonary findings could be pooled: pleural effusion, pericardial effusion, mediastinal lymphadenopathy, and coronary calcification.

RESULTS

The included studies comprised 7859 patients. The pooled odds ratios for the effect of the identified extrapulmonary findings on in-hospital mortality are as follows: pleural effusion, 4.60 (95% CI 2.97-7.12); pericardial effusion, 1.29 (95% CI 0.83-1.98); coronary calcification, 2.68 (95% CI 1.78-4.04); mediastinal lymphadenopathy, 2.02 (95% CI 1.18-3.45).

CONCLUSION

Pleural effusion, mediastinal lymphadenopathy and coronary calcification have a relevant association with in-hospital mortality in COVID-19 patients and should be included as prognostic biomarker into clinical routine.

Predicting Severe/Critical Outcomes in Patients With SARS-CoV2 Pneumonia: Development of the prediCtion seveRe/crItical ouTcome in COVID-19 (CRITIC) Model

Objective: To create a prediction model of the risk of severe/critical disease in patients with Coronavirus disease (COVID-19). Methods: Clinical, laboratory, and lung computed tomography (CT) severity score were collected from patients admitted for COVID-19 pneumonia and considered as independent variables for the risk of severe/critical disease in a logistic regression analysis. The discriminative properties of the variables were analyzed through the area under the receiver operating characteristic curve analysis and included in a prediction model based on Fagan's nomogram to calculate the post-test probability of severe/critical disease. All analyses were conducted using Medcalc (version 19.0, MedCalc Software, Ostend, Belgium). Results: One hundred seventy-one patients with COVID-19 pneumonia, including 37 severe/critical cases (21.6%) and 134 mild/moderate cases were evaluated. Among all the analyzed variables, Charlson Comorbidity Index (CCI) was that with the highest relative importance (p = 0.0001), followed by CT severity score (p = 0.0002), and age (p = 0.0009). The optimal cut-off points for the predictive variables resulted: 3 for CCI [sensitivity 83.8%, specificity 69.6%, positive likelihood ratio (+LR) 2.76], 69.9 for age (sensitivity 94.6%, specificity 68.1, +LR 2.97), and 53 for CT severity score (sensitivity 64.9%, specificity 84.4%, +LR 4.17). Conclusion: The nomogram including CCI, age, and CT severity score, may be used to stratify patients with COVID-19 pneumonia.

Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets

In the epidemiological COVID-19 research, artificial intelligence is a unique approach to make predictions about disease severity to manage COVID-19 patients. A limitation of artificial intelligence is, however, the high risk of bias. We investigated the skill of data mining and machine learning, two advanced forms of artificial intelligence, to predict severe COVID-19 pneumonia based on routine laboratory tests. A sample of 4009 COVID-19 patients was divided into Severe (PaO₂< 60 mmHg, 489 cases) and Non-Severe (PaO₂ ≥ 60 mmHg, 3520 cases) groups according to blood hypoxemia on admission and their laboratory datasets analyzed by the R software and WEKA workbench. After curation, data were processed for the selection of the most influential features including hemogram, pCO₂, blood acid-base balance, prothrombin time, inflammation biomarkers, and glucose. The best fit of variables was successfully confirmed by either the Multilayer Perceptron, a feedforward neural network algorithm that performed machine recognition of severe COVID-19 with 96.5% precision, or by the C4.5 software, a supervised learning algorithm based on an objective-predefined variable (severity) that generated a decision tree with 89.4% precision. Finally, a complex bivariate Pearson's correlation matrix combined with advanced hierarchical clustering (dendrograms) were conducted for knowledge discovery. The hidden structure of the datasets revealed shift patterns related to the development of COVID-19-induced pneumonia that involved the lymphocyte-to-C-reactive protein and leukocyte-to-C-protein ratios, neutrophil %, pH and pCO₂. The data mining approaches to the hematological fluctuations associated with severe COVID-19 pneumonia could not only anticipate adverse clinical outcomes, but also reveal putative therapeutic targets.

Role of Lung Ultrasound in Predicting Clinical Severity and Fatality in COVID-19 Pneumonia

Background: Lung ultrasound (LUS) is a useful imaging method for identifying COVID-19 pneumonia. The aim of this study was to explore the role of LUS in predicting the severity of the disease and fatality in patients with COVID-19. Methods: This was a single-center, follow-up study, conducted from 1 November 2020, to 22 March 2021. The LUS protocol was based on the assessment of 14 lung zones with a total score up to 42, which was compared to the disease severity and fatality. Results: A total of 133 patients with COVID-19 pneumonia confirmed by RT-PCR were enrolled, with a median time from hospital admission to lung ultrasound of one day. The LUS score was correlated with clinical severity at hospital admission (Spearman’s rho 0.40, 95% CI 0.24 to 0.53, p < 0.001). Patients with higher LUS scores were experiencing greater disease severity; a high flow nasal cannula had an odds ratio of 1.43 (5% CI 1.17–1.74) in patients with LUS score > 29; the same score also predicted the need for mechanical ventilation (1.25, [1.07–1.48]). An LUS score > 30 (1.41 [1.18–1.68]) and age over 68 (1.26 [1.11–1.43]) were significant predictors of fatality. Conclusions: LUS at hospital admission is shown to have a high predictive power of the severity and fatality of COVID-19 pneumonia.

A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images

Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray (CXR) images, and rapid antigen test (RAnT) is proposed. Transfer Learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false-negatives. This model reports a preliminary accuracy of 80% for the breathing sound analysis, and 99.66% Covid-19 detection accuracy for the curated CXR image dataset. Based on Ai-CovScan, a smartphone app is conceptualised as a mass-deployable screening tool, which could alter the course of this pandemic. This app’s deployment could minimise the number of people accessing the limited and expensive confirmatory tests, thereby reducing the burden on the severely stressed healthcare infrastructure.

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

OBJECTIVE

To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images.

METHODS

Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients' history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets.

RESULTS

For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)).

CONCLUSION

Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.

A Pictorial Review of the Role of Imaging in the Detection, Management, Histopathological Correlations, and Complications of COVID-19 Pneumonia

Imaging plays an important role in the detection of coronavirus (COVID-19) pneumonia in both managing the disease and evaluating the complications. Imaging with chest computed tomography (CT) can also have a potential predictive and prognostic role in COVID-19 patient outcomes. The aim of this pictorial review is to describe the role of imaging with chest X-ray (CXR), lung ultrasound (LUS), and CT in the diagnosis and management of COVID-19 pneumonia, the current indications, the scores proposed for each modality, the advantages/limitations of each modality and their role in detecting complications, and the histopathological correlations.

Lung Lesion Burden found on Chest CT as a Prognostic Marker in Hospitalized Patients with High Clinical Suspicion of COVID-19 Pneumonia: a Brazilian experience

OBJECTIVE

To investigate the relationship between lung lesion burden (LLB) found on chest computed tomography (CT) and 30-day mortality in hospitalized patients with high clinical suspicion of coronavirus disease 2019 (COVID-19), accounting for tomographic dynamic changes.

METHODS

Patients hospitalized with high clinical suspicion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a dedicated and reference hospital for COVID-19, having undergone at least one RT-PCR test, regardless of the result, and with one CT compatible with COVID-19, were retrospectively studied. Clinical and laboratory data upon admission were assessed, and LLB found on CT was semi-quantitatively evaluated through visual analysis. The primary outcome was 30-day mortality after admission. Secondary outcomes, including the intensive care unit (ICU) admission, mechanical ventilation used, and length of stay (LOS), were assessed.

RESULTS

A total of 457 patients with a mean age of 57±15 years were included. Among these, 58% presented with positive RT-PCR result for COVID-19. The median time from symptom onset to RT-PCR was 8 days [interquartile range 6-11 days]. An initial LLB of ≥50% using CT was found in 201 patients (44%), which was associated with an increased crude at 30-day mortality (31% vs. 15% in patients with LLB of <50%, p<0.001). An LLB of ≥50% was also associated with an increase in the ICU admission, the need for mechanical ventilation, and a prolonged LOS after adjusting for baseline covariates and accounting for the CT findings as a time-varying covariate; hence, patients with an LLB of ≥50% remained at a higher risk at 30-day mortality (adjusted hazard ratio 2.17, 95% confidence interval 1.47-3.18, p<0.001).

CONCLUSION

Even after accounting for dynamic CT changes in patients with both clinical and imaging findings consistent with COVID-19, an LLB of ≥50% might be associated with a higher risk of mortality.

Chest Computed Tomography Severity Score to Predict Adverse Outcomes of Patients with COVID-19

Background

The novel coronavirus disease 2019 (COVID-19) continues to wreak havoc worldwide. This study assessed the ability of chest computed tomography (CT) severity score (CSS) to predict intensive care unit (ICU) admission and mortality in patients with COVID-19 pneumonia.

Materials and Methods

A total of 192 consecutive patients with COVID-19 pneumonia aged more than 20 years and typical CT findings and reverse-transcription polymerase chain reaction positive admitted in a tertiary hospital were included. Clinical symptoms at admission and short-term outcome were obtained. A semi-quantitative scoring system was used to evaluate the parenchymal involvement. The association between CSS, disease severity, and outcomes were evaluated. Prediction of CSS was assessed with the area under the receiver-operating characteristic (ROC) curves.

Results

The incidence of admission to ICU was 22.8% in men and 14.1% in women. CSS was related to ICU admission and mortality. Areas under the ROC curves were 0.764 for total CSS. Using a stepwise binary logistic regression model, gender, age, oxygen saturation, and CSS had a significant independent relationship with ICU admission and death. Patients with CSS ≥12.5 had about four-time risk of ICU admission and death (odds ratio 1.66, 95% confidence interval 1.66 – 9.25). The multivariate regression analysis showed the superiority of CSS over other clinical information and co-morbidities.

Conclusion

CSS was a strong predictor of progression to ICU admission and death and there was a substantial role of non-contrast chest CT imaging in the presence of typical features for COVID-19 pneumonia as a reliable predictor of clinical severity and patient’s outcome.