Relationship of chest CT score with clinical characteristics of 108 patients hospitalized with COVID-19 in Wuhan, China

Assessment of the Severity of COVID-19 on the Basis of Examination and Laboratory Diagnostics in Relation to Computed Tomography Imagery of Patients Hospitalised Due to COVID-19-Single-Centre Study

From the moment the SARS-CoV-2 virus was identified in December 2019, the COVID-19 disease spread around the world, causing an increase in hospitalisations and deaths. From the beginning of the pandemic, scientists tried to determine the major cause that led to patient deaths. In this paper, the background to creating a research model was diagnostic problems related to early assessment of the degree of damage to the lungs in patients with COVID-19. The study group comprised patients hospitalised in one of the temporary COVID hospitals. Patients admitted to the hospital had confirmed infection with SARS-CoV-2. At the moment of admittance, arterial blood was taken and the relevant parameters noted. The results of physical examinations, the use of oxygen therapy and later test results were compared with the condition of the patients in later computed tomography images and descriptions. The point of reference for determining the severity of the patient's condition in the computer imagery was set for a mild condition as consisting of a percentage of total lung parenchyma surface area affected no greater than 30%, an average condition of between 30% and 70%, and a severe condition as greater than 70% of the lung parenchyma surface area affected. Patients in a mild clinical condition most frequently had mild lung damage on the CT image, similarly to patients in an average clinical condition. Patients in a serious clinical condition most often had average levels of damage on the CT image. On the basis of the collected data, it can be said that at the moment of admittance, BNP, PE and HCO3- levels, selected due to the form of lung damage, on computed tomography differed from one another in a statistically significant manner (p < 0.05). Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of a physical examination and applied oxygen therapy. Patients can qualify for an appropriate group according to the severity of COVID-19 on the basis of BNP, HCO3 and BE parameters obtained from arterial blood.

Early predictors of intensive care unit admission among COVID-19 patients in Qatar

Background

COVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19.

Methods

This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender.

Results

A total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001).

Conclusion

Having cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.

The Risk Factors and Outcomes for Radiological Abnormalities in Early Convalescence of COVID-19 Patients Caused by the SARS-CoV-2 Omicron Variant: A Retrospective, Multicenter Follow-up Study

BACKGROUND

The emergence of the severe acute respiratory syndrome coronavirus 2 omicron variant has been triggering the new wave of coronavirus disease 2019 (COVID-19) globally. However, the risk factors and outcomes for radiological abnormalities in the early convalescent stage (1 month after diagnosis) of omicron infected patients are still unknown.

METHODS

Patients were retrospectively enrolled if they were admitted to the hospital due to COVID-19. The chest computed tomography (CT) images and clinical data obtained at baseline (at the time of the first CT image that showed abnormalities after diagnosis) and 1 month after diagnosis were longitudinally analyzed. Uni-/multi-variable logistic regression tests were performed to explore independent risk factors for radiological abnormalities at baseline and residual pulmonary abnormalities after 1 month.

RESULTS

We assessed 316 COVID-19 patients, including 47% with radiological abnormalities at baseline and 23% with residual pulmonary abnormalities at 1-month follow-up. In a multivariate regression analysis, age ≥ 50 years, body mass index ≥ 23.87, days after vaccination ≥ 81 days, lymphocyte count ≤ 1.21 × 10^-9/L, interleukin-6 (IL-6) ≥ 10.05 pg/mL and IgG ≤ 14.140 S/CO were independent risk factors for CT abnormalities at baseline. The age ≥ 47 years, presence of interlobular septal thickening and IL-6 ≥ 5.85 pg/mL were the independent risk factors for residual pulmonary abnormalities at 1-month follow-up. For residual abnormalities group, the patients with less consolidations and more parenchymal bands at baseline could progress on CT score after 1 month. There were no significant changes in the number of involved lung lobes and total CT score during the early convalescent stage.

CONCLUSION

The higher IL-6 level was a common independent risk factor for CT abnormalities at baseline and residual pulmonary abnormalities at 1-month follow-up. There were no obvious radiographic changes during the early convalescent stage in patients with residual pulmonary abnormalities.

Correlation of Viral Load With the Biochemical and Hematological Profiles of COVID-19 Patients in Al Buraimi Hospital, Sultanate of Oman: A Cross-Sectional Study

Background Rapid identification of COVID-19 is crucial during the pandemic for the treatment and management of patients. Thus, early diagnosis of the disease using laboratory parameters can help in the rapid management of infected patients. This study aimed to investigate the correlation of viral load with hematological and biochemical parameters. This will ultimately help physicians to better understand the dynamics of this novel virus and aid in the management of patients. Methodology Laboratory confirmation of SARS-CoV-2 was performed by reverse transcription-polymerase chain reaction (RT-PCR) at the Al-Buraimi Hospital Laboratory Department using oropharyngeal and nasopharyngeal swabs. Positive cases were collected from July 2020 to January 2021 to be enrolled in this study. Results In this study, 264 confirmed positive patients were included initially and divided into three groups according to their cycle threshold (Ct) values obtained by PCR. Out of the total 264 patients, 174 (65.9%) were male, while 90 (34.1%) were female. However, the final sample was only 253 patients who met the inclusion criteria. With regard to Ct values, the study population was divided into the following three groups: Group 1 with Ct values of 9-20 (n = 87; 34.4%), group 2 with Ct values of 21-30 (n = 122; 47.8%), and group 3 with Ct values of 31-42 (n = 44; 17.4%). Conclusions We found that the proportion of male patients infected with COVID-19 was higher compared to females. In addition, the highest incidence was among patients in the age group of 51-70 years. The ferritin and alanine transaminase levels were highest in the initial stage of the infection (group 1) and decreased at the recovery stage. However, neutrophil, lymphocyte, alkaline phosphatase, and C-reactive protein showed an increasing trend from high viral load groups to low viral load groups. The values of the rest of the parameters, such as albumin, total bilirubin, lactate dehydrogenase, and D-dimer, were slightly higher in the initial stage of the infection but the decreasing trend was low; therefore, they were not considered helpful in predicting the disease severity reflected by their Ct value in the three different groups.

Increased lactate dehydrogenase reflects the progression of COVID-19 pneumonia on chest computed tomography and predicts subsequent severe disease

Chest computed tomography (CT) is effective for assessing the severity of coronavirus disease 2019 (COVID-19). However, the clinical factors reflecting the disease progression of COVID-19 pneumonia on chest CT and predicting a subsequent exacerbation remain controversial. We conducted a retrospective cohort study of 450 COVID-19 patients. We used an automated image processing tool to quantify the COVID-19 pneumonia lesion extent on chest CT at admission. The factors associated with the lesion extent were estimated by a multiple regression analysis. After adjusting for background factors by propensity score matching, we conducted a multivariate Cox proportional hazards analysis to identify factors associated with severe disease after admission. The multiple regression analysis identified, body-mass index (BMI), lactate dehydrogenase (LDH), C-reactive protein (CRP), and albumin as continuous variables associated with the lesion extent on chest CT. The standardized partial regression coefficients for them were 1.76, 2.42, 1.54, and 0.71. The multivariate Cox proportional hazards analysis identified LDH (hazard ratio, 1.003; 95% confidence interval, 1.001-1.005) as a factor independently associated with the development of severe COVID-19 pneumonia. Increased serum LDH at admission may be useful in real-world clinical practice for the simple screening of COVID-19 patients at high risk of developing subsequent severe disease.

DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans

The recent disaster of COVID-19 has brought the whole world to the verge of devastation because of its highly transmissible nature. In this pandemic, radiographic imaging modalities, particularly, computed tomography (CT), have shown remarkable performance for the effective diagnosis of this virus. However, the diagnostic assessment of CT data is a human-dependent process that requires sufficient time by expert radiologists. Recent developments in artificial intelligence have substituted several personal diagnostic procedures with computer-aided diagnosis (CAD) methods that can make an effective diagnosis, even in real time. In response to COVID-19, various CAD methods have been developed in the literature, which can detect and localize infectious regions in chest CT images. However, most existing methods do not provide cross-data analysis, which is an essential measure for assessing the generality of a CAD method. A few studies have performed cross-data analysis in their methods. Nevertheless, these methods show limited results in real-world scenarios without addressing generality issues. Therefore, in this study, we attempt to address generality issues and propose a deep learning-based CAD solution for the diagnosis of COVID-19 lesions from chest CT images. We propose a dual multiscale dilated fusion network (DMDF-Net) for the robust segmentation of small lesions in a given CT image. The proposed network mainly utilizes the strength of multiscale deep features fusion inside the encoder and decoder modules in a mutually beneficial manner to achieve superior segmentation performance. Additional pre- and post-processing steps are introduced in the proposed method to address the generality issues and further improve the diagnostic performance. Mainly, the concept of post-region of interest (ROI) fusion is introduced in the post-processing step, which reduces the number of false-positives and provides a way to accurately quantify the infected area of lung. Consequently, the proposed framework outperforms various state-of-the-art methods by accomplishing superior infection segmentation results with an average Dice similarity coefficient of 75.7%, Intersection over Union of 67.22%, Average Precision of 69.92%, Sensitivity of 72.78%, Specificity of 99.79%, Enhance-Alignment Measure of 91.11%, and Mean Absolute Error of 0.026.

Low-Dose Whole Lung Irradiation for Treatment of COVID-19 Pneumonia: A Systematic Review and Meta-Analysis

PURPOSE

Studies dating back to a century ago have reported using low-dose radiation therapy for the treatment of viral and bacterial pneumonia. In the modern era, since the COVID-19 pandemic began, several groups worldwide have researched the applicability of whole lung irradiation (WLI) for the treatment of COVID-19. We aimed to bring together the results of these experimental studies.

METHODS AND MATERIALS

We performed a systematic review and meta-analysis searching PubMed and Scopus databases for clinical trials incorporating WLI for the treatment of patients with COVID-19. Required data were extracted from each study. Using the random-effects model, the overall pooled day 28 survival rate, survival hazard ratio, and intubation-free days within 15 days after WLI were calculated, and forest plots were produced.

RESULTS

Ten studies were identified, and eventually, 5 were included for meta-analysis. The overall survival hazard ratio was calculated to be 0.85 (0.46-1.57). The pooled mean difference of intubation-free days within 15 days after WLI was 1.87, favoring the WLI group (95% confidence interval, -0.02 to 3.76). The overall day 28 survival rate of patients receiving WLI for the 9 studies with adequate follow-up data was 74% (95% confidence interval, 61-87). Except for 2 studies, the other 8 studies were assessed to have moderate to high risk of bias, and there were many differences among the designs of the studies, included patients, primary endpoints, outcome measurement methods, and reporting of the results.

CONCLUSIONS

Despite a mild improvement in intubation-free days, WLI had no significant effect on patients' overall survival. Currently, we cannot recommend routine use of WLI for the treatment of patients with moderate-to-severe COVID-19.

Correlation of Lung Damage on CT Scan with Laboratory Inflammatory Markers in COVID-19 Patients: A Single-Center Study from Romania

(1) Background: This study aims to evaluate the association of CRP, NLR, IL-6, and Procalcitonin with lung damage observed on CT scans; (2) Methods: A cross-sectional study was performed among 106 COVID-19 patients hospitalized in Timisoara Municipal Emergency Hospital. Chest CT and laboratory analysis were performed in all patients. The rank Spearmen correlation was used to assess the association between inflammatory markers and lung involvement. In addition, ROC curve analysis was used to determine the accuracy of inflammatory markers in the diagnosis of severe lung damage; (3) Results: CRP, NLR, and IL-6 were significantly positively correlated with lung damage. All inflammatory markers had good accuracy for diagnosis of severe lung involvement. Moreover, IL-6 has the highest AUC- ROC curve; (4) Conclusions: The inflammatory markers are associated with lung damage and can be used to evaluate COVID-19 severity.

COVID-19 triajında CLUE protokolünün etkinliği

Purpose: The purpose of this study was to evaluate the effectiveness of the CLUE protocol in COVID-19 triage. Materials and Methods: Patients who presented to the emergency department due to dyspnea with oxygen saturation below 95 % and were diagnosed with COVID-19 by reverse transcription polymerase chain reaction (RT-PCR) tests were included in this prospective, observational study. Patients included in the study underwent lung ultrasound (LUS) in the light of the CLUE protocol, and were accordingly given LUS scores of between 0 and 36, also within the scope of the protocol. Patients were placed under observation, and clinical outcomes of discharge from the emergency department, admission to the ward, and admission to intensive care or discharge were recorded. ROC analysis was applied in the calculation of threshold values for LUS scores predicting discharge, admission to intensive care, and mortality. Results: Forty-five patients with a mean age of 63 ± 18 years were included in the study. Fifteen patients (33 %) were treated on an outpatient basis and discharged, while 12 (27 %) were admitted to the ward and 18 (40 %) to the intensive care unit. Mortality occurred in 15 (33 %) patients. An LUS score lower than 3 was 97 % sensitive and 80 % specific for discharge, a score greater than 10 was 94 % sensitive and 78 % specific for admission to the intensive care unit, and a score higher than 11 was 93 % sensitive and 87 % specific for mortality. Based on regression analysis, an LUS score higher than 10 emerged as an independent risk factor for intensive care requirement, a score lower than 3 for discharge, and a score over 11 for mortality. Conclusion: The CLUE protocol may be a useful bedside test in COVID-19 triage, and one that does not involve radiation or require laboratory tests.

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.

Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review

This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.

COVITALE 2020 from eastern Indian population: imageologists perspective, a learning curve

Background

High-resolution computed tomography (HRCT) chest becomes a valuable diagnostic tool for identifying patients infected with Coronavirus Disease 2019 (COVID-19) in the early stage, where patients may be asymptomatic or with non-specific pulmonary symptoms. An early diagnosis of COVID-19 is of utmost importance, so that patients can be isolated and treated in time, eventually preventing spread of the disease, improving the prognosis and reducing the mortality. In this paper, we have highlighted our radiological experience of dealing with the pandemic crisis of 2020 through the study of HRCT thorax, lung ultrasonography, chest X-rays and artificial intelligence (AI).

Results

Results of CT thorax analysis have been given in detail. We had also compared CT severity score (CTSS) with clinical and laboratory parameters. Correlation of CTSS with SpO2 values and comorbidities was also studied. We also analysed manual CTSS with the CTSS scored calculated by the AI software.

Conclusions

CTSS and use of COVID-19 Reporting and Data System (CORADS) result in accuracy and uniform percolation of information among the clinicians. Bed-side X-rays and ultrasonography have played a role where the patients could not be shifted for CT scan. The possibility of predicting impending or progression of hypoxia was not possible when SpO2 mapping was correlated with the CTSS. AI was alternatively tried with available software (CT pneumonia analysis) which was not so appropriate considering the imaging patterns in the bulk of atypical category.

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.

Differences in Dynamics of Lung Computed Tomography Patterns between Survivors and Deceased Adult Patients with COVID-19

This study’s aim was to investigate CT (computed tomography) pattern dynamics differences within surviving and deceased adult patients with COVID-19, revealing new prognostic factors and reproducing already known data with our patients’ cohort: 635 hospitalized patients (55.3% of them were men, 44.7%—women), of which 87.3% had a positive result of RT-PCR (reverse transcription-polymerase chain reaction) at admission. The number of deaths was 53 people (69.8% of them were men and 30.2% were women). In total, more than 1500 CT examinations were performed on patients, using a GE Optima CT 660 computed tomography (General Electric Healthcare, Chicago, IL, USA). The study was performed at hospital admission, the frequency of repetitive scans further varied based on clinical need. The interpretation of the imaging data was carried out by 11 radiologists with filling in individual registration cards that take into account the scale of the lesion, the location, contours, and shape of the foci, the dominating types of changes, as well as the presence of additional findings and the dynamics of the process—a total of 45 parameters. Statistical analysis was performed using the software packages SPSS Statistics version 23.0 (IBM, Armonk, NY, USA) and R software version 3.3.2. For comparisons in pattern dynamics across hospitalization we used repeated measures general linear model with outcome and disease phase as factors. The crazy paving pattern, which is more common and has a greater contribution to the overall CT picture in different phases of the disease in deceased patients, has isolated prognostic significance and is probably a reflection of faster dynamics of the process with a long phase of progression of pulmonary parenchyma damage with an identical trend of changes in the scale of the lesion (as recovered) in this group of patients. Already known data on typical pulmonological CT manifestations of infection, frequency of occurrence, and the prognostic significance of the scale of the lesion were reproduced, new differences in the dynamics of the process between recovered and deceased adult patients were also found that may have prognostic significance and can be reflected in clinical practice.

Domain-Adaptive Artificial Intelligence-Based Model for Personalized Diagnosis of Trivial Lesions Related to COVID-19 in Chest Computed Tomography Scans

BACKGROUND

Early and accurate detection of COVID-19-related findings (such as well-aerated regions, ground-glass opacity, crazy paving and linear opacities, and consolidation in lung computed tomography (CT) scan) is crucial for preventive measures and treatment. However, the visual assessment of lung CT scans is a time-consuming process particularly in case of trivial lesions and requires medical specialists.

METHOD

A recent breakthrough in deep learning methods has boosted the diagnostic capability of computer-aided diagnosis (CAD) systems and further aided health professionals in making effective diagnostic decisions. In this study, we propose a domain-adaptive CAD framework, namely the dilated aggregation-based lightweight network (DAL-Net), for effective recognition of trivial COVID-19 lesions in CT scans. Our network design achieves a fast execution speed (inference time is 43 ms on a single image) with optimal memory consumption (almost 9 MB). To evaluate the performances of the proposed and state-of-the-art models, we considered two publicly accessible datasets, namely COVID-19-CT-Seg (comprising a total of 3520 images of 20 different patients) and MosMed (including a total of 2049 images of 50 different patients).

RESULTS

Our method exhibits average area under the curve (AUC) up to 98.84%, 98.47%, and 95.51% for COVID-19-CT-Seg, MosMed, and cross-dataset, respectively, and outperforms various state-of-the-art methods.

CONCLUSIONS

These results demonstrate that deep learning-based models are an effective tool for building a robust CAD solution based on CT data in response to present disaster of COVID-19.

Time to hospitalisation, CT pulmonary involvement and in-hospital death in COVID-19 patients in an Emergency Medicine Unit

BACKGROUND

Patients with coronavirus disease 2019 (COVID-19) are often treated at home given the limited healthcare resources. Many patients may have sudden clinical worsening and may be already compromised at hospitalisation. We investigated the burden of lung involvement according to the time to hospitalisation.

METHODS

In this observational cohort study, 55 consecutive COVID-19-related pneumonia patients were admitted to the Emergency Medicine Unit. Groups of lung involvement at computed tomography were classified as follows: 0 (<5%), 1 (5%-25%), 2 (26%-50%), 3 (51%-75%) and 4 (>75%). We also investigated in-hospital death and the predictive value of Yan-XGBoost model and PREDI-CO scores for death.

RESULTS

The median age was 74 years and 34 were men. Time to admission increased from 2 days in group 0 to 8.5-9 days in groups 3 and 4. A progressive increase in LDH, CRP and d-dimer was found across groups, while a decrease of lymphocytes paO₂ /FiO₂ ratio and SpO₂ was found. Ten (18.2%) patients died during the in-hospital staying. Patients who died were older, with a trend to lower lymphocytes, a higher d-dimer, creatine phosphokinase and troponin T. The Yan-XGBoost model did not accurately predict in-hospital death with an AUC of 0.57 (95% confidence interval [CI] 0.37-0.76), which improved after the addition of the lung involvement groups (AUC 0.68, 95%CI 0.45-0.90). Conversely, a good predictive value was found for the original PREDI-CO score with an AUC of 0.76 (95% CI 0.58-0.93) which remained similar after the addition of the lung involvement (AUC 0.76, 95% CI 0.57-0.94).

CONCLUSION

We found that delayed hospital admission is associated with higher lung involvement. Hence, our data suggest that patients at risk for more severe disease, such as those with high LDH, CRP and d-dimer, should be promptly referred to hospital care.

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).

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy.

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study

Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients.

Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis.

Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = −0.535, p < 0.001).

Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

COVID-19 Imaging: What We Know Now and What Remains Unknown

Infection with SARS-CoV-2 ranges from an asymptomatic condition to a severe and sometimes fatal disease, with mortality most frequently being the result of acute lung injury. The role of imaging has evolved during the pandemic, with CT initially being an alternative and possibly superior testing method compared with reverse transcriptase-polymerase chain reaction (RT-PCR) testing and evolving to having a more limited role based on specific indications. Several classification and reporting schemes were developed for chest imaging early during the pandemic for patients suspected of having COVID-19 to aid in triage when the availability of RT-PCR testing was limited and its level of performance was unclear. Interobserver agreement for categories with findings typical of COVID-19 and those suggesting an alternative diagnosis is high across multiple studies. Furthermore, some studies looking at the extent of lung involvement on chest radiographs and CT images showed correlations with critical illness and a need for mechanical ventilation. In addition to pulmonary manifestations, cardiovascular complications such as thromboembolism and myocarditis have been ascribed to COVID-19, sometimes contributing to neurologic and abdominal manifestations. Finally, artificial intelligence has shown promise for use in determining both the diagnosis and prognosis of COVID-19 pneumonia with respect to both radiography and CT.

Diabetes is most important cause for mortality in COVID-19 hospitalized patients: Systematic review and meta-analysis

The presence of SARS-CoV-2 was officially documented in Europe at the end of February 2020. Despite many observations, the real impact of COVID-19 in the European Union (EU), its underlying factors and their contribution to mortality and morbidity outcomes were never systematically investigated. The aim of the present work is to provide an overview and a meta-analysis of main predictors and of country differences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-associated mortality rate (MR) in hospitalized patients. Out of 3714 retrieved articles, 87 studies were considered, including 35,486 patients (mean age 60.9 ± 8.2 years) and 5867 deaths. After adjustment for confounders, diabetes mellitus was the best predictors of MR in an age- and sex-dependent manner, followed by chronic pulmonary obstructive diseases and malignancies. In both the US and Europe, MR was higher than that reported in Asia (25[20;29] % and 20[17;23] % vs. 13[10;17]%; both p < 0.02). Among clinical parameters, dyspnea, fatigue and myalgia, along with respiratory rate, emerged as the best predictors of MR. Finally, reduced lymphocyte and platelet count, along with increased D-dimer levels, all significantly contributed to increased mortality. The optimization of glucose profile along with an adequate thrombotic complications preventive strategy must become routine practice in diseased SARS-CoV-2 infected patients.

Chest CT severity score and radiological patterns as predictors of disease severity, ICU admission, and viral positivity in COVID-19 patients

Background

Chest computed tomography (CT) is a useful tool for the diagnosis of coronavirus disease-2019 (COVID-19), although its exact value for predicting critical illness remains unclear. This study evaluated the efficacy of chest CT to predict disease progression, pulmonary complications, and viral positivity duration.

Methods

A single-center cohort study was conducted by consecutively including hospitalized patients with confirmed COVID-19. The chest CT patterns were described and a total severity score was calculated. The predictive accuracy of the severity score was evaluated using the receiver operating characteristic analysis, while a Cox proportional hazards regression model was implemented to identify the radiological features that are linked to prolonged duration of viral positivity.

Results

Overall, 42 patients were included with 10 of them requiring intensive care unit admission. The most common lesions were ground glass opacities (92.9%), consolidation (66.7%), and crazy-paving patterns (61.9%). The total severity score significantly correlated with inflammatory and respiratory distress markers, as well as with admission CURB-65 and PSI/PORT scores. It was estimated to predict critical illness with a sensitivity and specificity of 75% and 70%, respectively. Time-to-event analysis indicated that patients without ground-glass opacities presented significantly shorter median viral positivity (16 vs. 27 days).

Conclusions

Chest CT severity score positively correlates with markers of COVID-19 severity and presents promising efficacy in predicting critical illness. It is suggested that ground-glass opacities are linked to prolonged viral positivity. Further studies should confirm the efficacy of the severity score and elucidate the long-term pulmonary effects of COVID-19.

Independent role of CT chest scan in COVID-19 prognosis: Evidence from the machine learning classification

Background: The current coronavirus disease-19 (COVID-19) pandemic call attention to the key role informatics play in healthcare. The present study discovers an independent role of computerised tomography chest (CT) scans in prognosis of COVID-19 using classification learning algorithms. Methods: In this retrospective study, 57 RT PCR positive COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India) after approval from the Institutional Ethics Committee. A set of 21 features including clinical findings and laboratory parameters and chest CT severity score were recorded. The CT score with mild, moderate and severe categories was chosen as response variable. The dimensionality reduction of feature space was performed and classifiers including, decision trees, K-nearest neighbours, support vector machine and ensemble learning were trained with principal components. The model with highest accuracy and area under the ROC curve (AUC) was selected. Results: The median age of patients was 55 years (range: 20-99 years) with 37 males. The feature space was reduced from 21 to 7 predictors, that included fever, cough, fibrin degradation products, haemoglobin, neutrophil-lymphocyte ratio, ferritin and procalcitonin. The linear support vector machine was chosen as the best classifier with 73.7 % and 0.69 accuracy and AUC for severe CT chest score, respectively. The variance contributed by first three principal components were 97.5 %, 2.4 % and 0.0 %, respectively. Conclusion: In view of low degree of relationships between predictors and chest CT scan severity score category as interpreted from accuracy and AUC it can be concluded that chest CT scan has an independent role in the prognosis of COVID-19 patients.

The value of AI based CT severity scoring system in triage of patients with Covid-19 pneumonia as regards oxygen requirement and place of admission

CONTEXT

CT scan is a quick and effective method to triage patients in the Covid-19 pandemic to prevent the heathcare facilities from getting overwhelmed.

AIMS

To find whether an initial HRCT chest can help triage patient by determining their oxygen requirement, place of treatment, laboratory parameters and risk of mortality and to compare 3 CT scoring systems (0-20, 0-25 and percentage of involved lung models) to find if one is a better predictor of prognosis than the other.

SETTINGS AND DESIGN

This was a prospective observational study conducted at a Tertiary care hospital in Mumbai, Patients undergoing CT scan were included by complete enumeration method.

METHODS AND MATERIAL

Data collected included demographics, days from swab positivity to CT scan, comorbidities, place of treatment, laboratory parameters, oxygen requirement and mortality. We divided the patients into mild, moderate and severe based on 3 criteria - 20 point CT score (OS1), 25 point CT score (OS2) and opacity percentage (OP). CT scans were analysed using CT pneumonia analysis prototype software (Siemens Healthcare version 2.5.2, Erlangen, Germany).

STATISTICAL ANALYSIS

ROC curve and Youden's index were used to determine cut off points. Multinomial logistic regression used to study the relations with oxygen requirement and place of admission. Hosmer-Lemeshow test was done to test the goodness of fit of our models.

RESULTS

A total of 740 patients were included in our study. All the 3 scoring systems showed a significant positive correlation with oxygen requirement, place of admission and death. Based on ROC analysis a score of 4 for OS1, 9 for OS2 and 12.7% for OP was determined as the cut off for oxygen requirement.

CONCLUSIONS

CT severity scoring using an automated deep learning software programme is a boon for determining oxygen requirement and triage. As the score increases, the chances of requirement of higher oxygen and intubation increase. All the three scoring systems are predictive of oxygen requirement.

Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.

Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.

Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.

Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

Chest CT imaging features and severity scores as biomarkers for prognostic prediction in patients with COVID-19

Background

Coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have explored the role of chest computed tomography (CT) features and severity scores for prognostic prediction. In this study, we aimed to investigate the role of chest CT severity score and imaging features in the prediction of the prognosis of COVID-19 patients.

Methods

A total of 134 patients (62 recovered and 72 deceased patients) with confirmed COVID-19 were enrolled. The clinical, laboratory, and chest CT (316 scans) data were retrospectively reviewed. Demographics, symptoms, comorbidities, and temporal changes of laboratory results, CT features, and severity scores were compared between recovered and deceased groups using the Mann-Whitney U test and logistic regression to identify the risk factors for poor prognosis.

Results

Median age was 48 and 58 years for recovered and deceased patients, respectively. More patients had at least one comorbidity in the deceased group than the recovered group (60% vs. 29%). Leukocytes, neutrophil, high-sensitivity C-reactive protein (hsCRP), prothrombin, D-dimer, serum ferritin, interleukin (IL)-2, and IL-6 were significantly elevated in the deceased group than the recovered group at different stages. The total CT score at the peak stage was significantly greater in the deceased group than the recovered group (20 vs. 11 points). The optimal cutoff value of the total CT scores was 16.5 points, achieving 69.4% sensitivity and 82.2% specificity for the prognostic prediction. The crazy-paving pattern and consolidation were more common in the deceased patients than those in the recovered patients. Linear opacities significantly increased with the disease course in the recovered patients. Sex, age, neutrophil, IL-2, IL-6, and total CT scores were independent risk factors for the prognosis with odds ratios of 3.8 to 8.7.

Conclusions

Sex (male), older age (>60 years), elevated neutrophil, IL-2, IL-6 level, and total CT scores (≥16) were independent risk factors for poor prognosis in patients with COVID-19. Temporal changes of chest CT features and severity scores could be valuable for early identification of severe cases and eventually reducing the mortality rate of COVID-19.