CT lung lesions as predictors of early death or ICU admission in COVID-19 patients

Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

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.

Vaccination Can Prevent Severe Pulmonary Disease in COVID-19 Positive Patients: A Case-Control Study

BACKGROUND

The COVID-19 pandemic was a global health emergency, which brought lives to a standstill. To combat this deadly virus, two vaccines were deployed widely: COVISHIELD (ChAdOx1 nCoV-19) and COVAXIN (BBV152). These were approved based on the immunological response they elicit in standardized conditions; however, the real-life scenario after deployment was completely different. Only in such situations can the true effectiveness of vaccines be assessed. The primary objective was to assess the effectiveness (VE) of COVAXIN/COVISHIELD in preventing severe pulmonary disease in RT-PCR-positive COVID-19 patients greater than 18 years of age.

MATERIALS AND METHODS

A case-control study was conducted among 260 subjects aged above 18 years, positive for COVID-19 through RT-PCR. 130 cases and 130 controls were enrolled. Radiological findings were obtained and subjects with >50% lung involvement were considered as cases. Subjects were interviewed about their vaccination status. Odds ratio was calculated, and the adjusted odds ratio was estimated for vaccine effectiveness, using the formula (1-adjusted ODDS ratio)*100.

RESULTS

The vaccine effectiveness for a single dose of vaccine was 55.2% (95% C.I. 11.0%-77.5%) and with two doses was 98.0% (95% C.I. 85.0%-99.7%). Hence two doses are highly effective than a single dose of vaccine in reducing lung involvement.

CONCLUSION

Two doses of vaccine are more effective than a single dose vaccine in reducing lung involvement. Since sporadic cases of COVID-19 still persist, it is important to emphasize the role of vaccination in preventing severe COVID-19 infections, particularly in the elderly and those with comorbidities.

COVID-19 in patients with rheumatological diseases in the Eastern Province of Saudi Arabia

The severity of the 2019 coronavirus disease (COVID-19) and its effects remain unpredictable. Certain factors, such as obesity, hypertension, and type 2 diabetes mellitus, may increase the severity of the disease. Rheumatology experts suggest that patients with active autoimmune conditions and controlled autoimmune diseases on immunosuppressive therapy may be at higher risk of developing severe COVID-19. In this retrospective observational study, we aimed to examine the patterns of COVID-19 in patients with underlying rheumatological diseases and their association with disease severity and hospital outcomes. A total of 34 patients with underlying rheumatological diseases who tested positive for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) by polymerase chain reaction (PCR) were included between March 2020 and April 2021 at King Fahd Hospital of the University. The study population consisted of 76.47% female and 23.53% male patients, with a mean age ranging from 20 to 40 years. Female gender (p=0.0001) and younger age (p=0.004) were associated with milder disease. The most frequent rheumatological disease was systemic lupus erythematosus (SLE) (38.24%), which was associated with a milder infection (p=0.045). Patients treated with mycophenolate mofetil (MMF) had a milder disease course (p=0.0037). Hypertension was significantly associated with severe COVID-19 disease (p=0.037). There was no significant relationship between SLE and the need for ICU admission. Patients on hydroxychloroquine and MMF tended to develop milder disease, and there was no association between the severity of the infection and the treatment with steroids.

Chest CT in COVID-19 patients: A clinical need

INTRODUCTION

The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 has resulted in a global healthcare crisis. The provision of computed tomography (CT) imaging services by radiology departments for COVID-19 patients poses multiple challenges. Consequently, it is important to explore the clinical need and indications for thoracic CT and whether they subsequently alter patient management.

METHODS

We report our experience in this single-centre retrospective cohort study of all confirmed COVID-19 cases admitted during the peak of the 'Delta' variant wave in Australia, and who underwent a chest CT. Clinical indication and patient management plan pre- and post-CT were ascertained.

RESULTS

A total of 92 out of 1403 patients who were admitted with COVID-19 underwent a thoracic CT (73 CT pulmonary angiogram (CTPA), 14 CT Chest and five high-resolution CT (HRCT) studies). 72.8% of studies were to evaluate for pulmonary emboli, 16.2% for assessment of COVID-19 pneumonia complications, 5.4% for tuberculosis and 6.5% for other indications. 21 (23%) of these studies resulted in a change in management with two patients having a major change in management (thrombolysis, CT-guided aspiration). Management was altered due to diagnosis of pulmonary embolism (PE), pneumonia, cryptogenic organising pneumonia and other reasons. Of 73 CTPA studies, 11 (15%) patients had evidence of PE.

CONCLUSION

In our centre, thoracic CT in COVID-19 patients were predominantly for the evaluation of PE with other indications being for COVID-19 complications and other cardiopulmonary pathologies. 23% of studies subsequently altered patient management, suggesting there is good clinical need for CT chests for these indications.

A practical scoring model to predict the occurrence of critical illness in hospitalized patients with SARS-CoV-2 omicron infection

Background

The variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged repeatedly, especially the Omicron strain which is extremely infectious, so early identification of patients who may develop critical illness will aid in delivering proper treatment and optimizing use of resources. We aimed to develop and validate a practical scoring model at hospital admission for predicting which patients with Omicron infection will develop critical illness.

Methods

A total of 2,459 patients with Omicron infection were enrolled in this retrospective study. Univariate and multivariate logistic regression analysis were performed to evaluate predictors associated with critical illness. Moreover, the area under the receiver operating characteristic curve (AUROC), continuous net reclassification improvement, and integrated discrimination index were assessed.

Results

The derivation cohort included 1721 patients and the validation cohort included 738 patients. A total of 98 patients developed critical illness. Thirteen variables were independent predictive factors and were included in the risk score: age > 65, C-reactive protein > 10 mg/L, lactate dehydrogenase > 250 U/L, lymphocyte < 0.8*10^⁹/L, white blood cell > 10*10^⁹/L, Oxygen saturation < 90%, malignancy, chronic kidney disease, chronic cardiac disease, chronic obstructive pulmonary disease, diabetes, cerebrovascular disease, and non-vaccination. AUROC in the derivation cohort and validation cohort were 0.926 (95% CI, 0.903-0.948) and 0.907 (95% CI, 0.860-0.955), respectively. Moreover, the critical illness risk scoring model had the highest AUROC compared with CURB-65, sequential organ failure assessment (SOFA) and 4C mortality scores, and always obtained more net benefit.

Conclusion

The risk scoring model based on the characteristics of patients at the time of admission to the hospital may help medical practitioners to identify critically ill patients and take prompt measures.

Prognostic accuracy of visual lung damage computed tomography score for mortality prediction in patients with COVID-19 pneumonia: a systematic review and meta-analysis

Background

Chest computed tomography (CT) findings provide great added value in characterizing the extent of disease and severity of pulmonary involvements. Chest CT severity score (CT-SS) could be considered as an appropriate prognostic factor for mortality prediction in patients with COVID-19 pneumonia. In this study, we performed a meta-analysis evaluating the prognostic accuracy of CT-SS for mortality prediction in patients with COVID-19 pneumonia.

Methods

A systematic search was conducted on Web of Science, PubMed, Embase, Scopus, and Google Scholar databases between December 2019 and September 2021. The meta-analysis was performed using the random-effects model, and sensitivity and specificity (with 95%CIs) of CT-SS were calculated using the study authors’ pre-specified threshold.

Results

Sensitivity estimates ranged from 0.32 to 1.00, and the pooled estimate of sensitivity was 0.67 [95%CI (0.59–0.75)]. Specificity estimates ranged from 0.53 to 0.95 and the pooled estimate of specificity was 0.79 [95%CI (0.74–0.84)]. Results of meta-regression analysis showed that radiologist experiences did not affect the sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients (P = 0.314 and 0.283, respectively). The test for subgroup differences suggests that study location significantly modifies sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients. The area under the summary receiver operator characteristic (ROC) curve was 0.8248.

Conclusion

Our results have shown that CT-SS has acceptable prognostic accuracy for mortality prediction in COVID-19 patients. This simple scoring method could help to improve the management of high-risk patients with COVID-19.

Characteristics and outcomes of SARS-COV 2 critically ill patients after emergence of the variant of concern 20H/501Y.V2: A comparative cohort study

There are currently no data regarding characteristics of critically ill patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant of concern (VOC) 20H/501Y.V2. We therefore aimed to describe changes of characteristics in critically ill patients with Covid-19 between the first and the second wave when viral genome sequencing indicated that VOC was largely dominant in Mayotte Island (Indian Ocean). Consecutive patients with Covid-19 and over 18 years admitted in the unique intensive care unit (ICU) of Mayotte during wave 2 were compared with an historical cohort of patients admitted during wave 1. We performed a LR comparing wave 1 and wave 2 as outcomes. To complete analysis, we built a Random Forest model (RF), that is, a machine learning classification tool- using the same variable set as that of the LR. We included 156 patients, 41 (26.3%) and 115 (73.7%) belonging to the first and second waves respectively. Univariate analysis did not find difference in demographic data or in mortality. Our multivariate LR found that patients in wave 2 had less fever (absence of fever aOR 5.23, 95% confidence interval (CI) 1.89-14.48, p = .001) and a lower simplified acute physiology score (SAPS II) (aOR 0.95, 95% CI 0.91-0.99, p = .007) at admission; at 24 hours, the need of invasive mechanical ventilation was higher (aOR 3.49, 95% CI 0.98-12.51, p = .055) and pO2/FiO2 ratio was lower (aOR 0.99, 95 % CI 0.98-0.99, p = .03). Patients in wave 2 had also an increased risk of ventilator-associated pneumonia (VAP) (aOR 4.64, 95% CI 1.54-13.93, p = .006). Occurrence of VAP was also a key variable to classify patients between wave 1 and wave 2 in the variable importance plot of the RF model. Our data suggested that VOC 20H/501Y.V2 could be associated with a higher severity of respiratory failure at admission and a higher risk for developing VAP. We hypothesized that the expected gain in survival brought by recent improvements in critical care management could have been mitigated by increased transmissibility of the new lineage leading to admission of more severe patients. The immunological role of VOC 20H/501Y.V2 in the propensity for VAP requires further investigations.

COVID-19 Severity and Mortality in Two Pandemic Waves in Poland and Predictors of Poor Outcomes of SARS-CoV-2 Infection in Hospitalized Young Adults

SARS-CoV-2 variants pose a significant threat to global public health. However, their influence on disease severity, especially among young adults who may exhibit different clinical characteristics, is debatable. In this retrospective study of 229 young adults hospitalized with COVID-19, we investigated the differences between Poland's second and third waves of the pandemic. To identify potential predictors of severe COVID-19 in young adults, we analyzed patient characteristics and laboratory findings between survivors and non-survivors and we performed logistic regression to assess the risk of death, mechanical ventilation, and intensive care unit treatment. We found no increase in COVID-19 severity comparing the third and second waves of the pandemic, indicating that the alpha variant had no influence on disease severity. In addition, we found that factors, such as obesity, comorbidities, lung involvement, leukocytosis, neutrophilia, lymphopenia, higher IG count, the neutrophil-to-lymphocyte ratio, C-reactive protein, procalcitonin, interleukin-6, D-Dimer, lactate dehydrogenase, high-sensitive troponin I, creatine kinase-myocardial band, myoglobin, N-terminal-pro-B-type natriuretic peptide, creatinine, urea and gamma-glutamyl transferase, lower estimated glomerular filtration rate, albumin, calcium and vitamin D3, possibly a decrease in red blood cell counts, hemoglobin and hematocrit, and an increase in creatine kinase during hospitalization may be associated with poor outcomes of COVID-19.

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.

COVID-19 Acute Respiratory Distress Syndrome: One Pathogen, Multiple Phenotypes

Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome arising from multiple causes with a range of clinical severity. In recent years, the potential for prognostic and predictive enrichment of clinical trials has been increased with identification of more biologically homogeneous subgroups or phenotypes within ARDS. COVID-19 ARDS also exhibits significant clinical heterogeneity despite a single causative agent. In this review the authors summarize the existing literature on COVID-19 ARDS phenotypes, including physiologic, clinical, and biological subgroups as well as the implications for improving both prognostication and precision therapy.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer “LungCTAnalyzer” and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

A Brief Analysis of a New Device to Prevent Early Intubation in Hypoxemic Patients: An Observational Study

The need for mechanical ventilation is one of the main concerns related to the care of patients with COVID-19. The aim of this study is to evaluate the efficacy of a bubble device for oxygen supplementation. This device was implemented for the selected patients hospitalized with severe COVID-19 pneumonia with persistent low oxygen saturation. Patients were selected in three major COVID-19 hospitals of Bahia state in Brazil from July to November 2020, where they remained with the device for seven days and were monitored for different factors, such as vital signs, oximetry evaluation, and arterial blood gasometry. Among the 51 patients included in the study, 68.63% successfully overcame hypoxemia without the necessity to be transferred to mechanical ventilation, whereas 31.37% required tracheal intubation (p value < 0.05). There was no difference of note on the analysis of the clinical data, chemistry, and hematological evaluation, with the exception of the SpO2 on follow-up days. Multivariate analysis revealed that the independent variable, male sex, SpO2, and non-inhaled mask, was associated with the necessity of requiring early mechanical ventilation. We concluded that this bubble device should be a prior step to be utilized before indication of mechanical ventilation in patients with persistent hypoxemia of severe COVID-19 pneumonia.

Chest CT Characteristics are Strongly Predictive of Mortality in Patients with COVID-19 Pneumonia: A Multicentric Cohort Study

Rationale and Objectives

The novel coronavirus (COVID-19) has presented a significant and urgent threat to global health and there has been a need to identify prognostic factors in COVID-19 patients. The aim of this study was to determine whether chest computed tomography (CT) characteristics had any prognostic value in patients with COVID-19.

Materials and Methods

A retrospective analysis of COVID-19 patients who underwent a chest CT-scan was performed in four medical centers. The prognostic value of chest CT results was assessed using a multivariable survival analysis with the Cox model. The characteristics included in the model were the degree of lung involvement, ground glass opacities, nodular consolidations, linear consolidations, a peripheral topography, a predominantly inferior lung involvement, pleural effusion, and crazy paving. The model was also adjusted on age, sex, and the center in which the patient was hospitalized. The primary endpoint was 30-day in-hospital mortality. A second model used a composite endpoint of admission to an intensive care unit or 30-day in-hospital mortality.

Results

A total of 515 patients with available follow-up information were included. Advanced age, a degree of pulmonary involvement ≥50% (Hazard Ratio 2.25 [95% CI: 1.378-3.671], p = 0.001), nodular consolidations and pleural effusions were associated with lower 30-day in-hospital survival rates. An exploratory subgroup analysis showed a 60.6% mortality rate in patients over 75 with ≥50% lung involvement on a CT-scan.

Conclusion

Chest CT findings such as the percentage of pulmonary involvement ≥50%, pleural effusion and nodular consolidation were strongly associated with 30-day mortality in COVID-19 patients. CT examinations are essential for the assessment of severe COVID-19 patients and their results must be considered when making care management decisions.

Caracterización y factores pronóstico de mortalidad en pacientes ingresados en UCI por COVID-19 en un hospital público de referencia en Bogotá, Colombia

Introducción

Objetivo

Materiales y métodos

Resultados

Conclusión

Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy

OBJECTIVE

We aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalisation/death, in patients with COVID-19 previously discharged from the ED. We also developed a disease progression velocity index.

DESIGN AND SETTING

Retrospective cohort study of prospectively collected data. The charts of consecutive patients with COVID-19 discharged from the Reggio Emilia (Italy) ED (2 March 2 to 31 March 2020) were retrospectively examined. Clinical, laboratory and CT findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index.

PARTICIPANTS

450 patients discharged from the ED with diagnosis of COVID-19.

MAIN OUTCOME MEASURE

ED readmission within 14 days, followed by hospitalisation/death.

RESULTS

Of the discharged patients, 84 (18.7%) were readmitted to the ED, 61 (13.6%) were hospitalised and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03 to 1.08), Charlson Comorbidity Index 3 versus 0 (OR=11.61; 95% CI 1.76 to 76.58), days from symptom onset (OR for 1-day increase=0.81; 95% CI 0.73 to 0.90) and CT extension (OR for 1% increase=1.03; 95% CI 1.01 to 1.06) were associated in a multivariable model for readmission with hospitalisation/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08 to 1.36); the model including this index resulted in less information loss.

CONCLUSIONS

A velocity index combining CT extension and days from symptom onset predicts disease progression in patients with COVID-19. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Discharge from the emergency department and early hospital revaluation in patients with COVID-19 pneumonia: a prospective study

Objective The national health systems are currently facing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. We assessed the efficacy of outpatient management for patients with SARS-CoV-2 related pneumonia at risk of progression after discharge from the emergency department.Methods This was a single-center prospective study. We enrolled patients with confirmed SARS-CoV-2 pneumonia, without hypoxemic respiratory failure, and at least one of the following: age ≥ 65 years or the presence of relevant comorbidities or pneumonia extension > 25% on high resolution computed tomography. Patients with pneumonia extension > 50% were excluded. An ambulatory visit was performed after at least 48 hours, when patients were either discharged, admitted, or deferred for a further visit. As a control, we evaluated a comparable historical cohort of hospitalized patients.Results A total of 84 patients were enrolled (51 male patients; mean age, 62.8 years). Two-thirds of the patients had at least one comorbidity and 41.6% had a lung involvement > 25% on high resolution computed tomography; the mean duration of symptoms was 8.0 ± 3.0 days, and the mean PaO2/FiO2 ratio was 357.5 ± 38.6. At the end of the follow-up period, 69 patients had been discharged, and 15 were hospitalized (mean stay of 6 days). Older age and higher National Early Warning Score 2 were significant predictors of hospitalization at the first follow-up visit. One hospitalized patient died of septic shock. In the control group, the mean hospital stay was 8 days.Conclusion Adopting a “discharge and early revaluation” strategy appears to be safe, feasible, and may optimize hospital resources during the SARS-CoV-2 pandemic.

[Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19]

To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic unhospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.

Euthyroid sick syndrome as a prognostic indicator of COVID-19 pulmonary involvement, associated with poorer disease prognosis and increased mortality

Objective

The prevalence of euthyroid sick syndrome (ESS) and its association with the prognosis of COVID-19 and mortality in patients with lung involvement in COVID-19 have not yet been elucidated.

Methods

Clinical and laboratory data of patients with COVID-19 with or without ESS were collected retrospectively and analyzed on admission. All subjects were admitted to the Department of Internal Diseases and Clinical Pharmacology at Bieganski Hospital between December 2020 and April 2021.

Results

In total, 310 medical records of patients with COVID-19 were analyzed retrospectively. Among 215 enrolled patients, 82 cases of ESS were diagnosed. The patients with ESS had higher pro-inflammatory factor levels, longer hospitalizations, and a higher risk of requiring high-flow nasal oxygen therapy or intubation than the patients without ESS. The Kaplan-Meier curve indicated that the patients with ESS had a lower probability of survival when computed tomography showed ≤50% parenchymal involvement compared with that in patients without ESS. However, no differences in mortality were noted in those with more than 50% parenchymal involvement. The survival curve showed that ESS was associated with a higher risk of mortality during hospitalization.

Conclusion

ESS is closely associated with a poor prognosis, including longer hospitalizations, more frequent intubation, transfer to the intensive care unit, and a higher mortality rate in patients with COVID-19. ESS is a potential prognostic predictor of survival, regardless of lung involvement in COVID-19.

Development and Validation of the Acute PNeumonia Early Assessment Score for Safely Discharging Low-Risk SARS-CoV-2-Infected Patients from the Emergency Department

A continuous demand for assistance and an overcrowded emergency department (ED) require early and safe discharge of low-risk severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients. We developed (n = 128) and validated (n = 330) the acute PNeumonia early assessment (aPNea) score in a tertiary hospital and preliminarily tested the score on an external secondary hospital (n = 97). The score's performance was compared to that of the National Early Warning Score 2 (NEWS2). The composite outcome of either death or oral intubation within 30 days from admission occurred in 101 and 28 patients in the two hospitals, respectively. The area under the receiver operating characteristic (AUROC) curve of the aPNea model was 0.86 (95% confidence interval (CI), 0.78-0.93) and 0.79 (95% CI, 0.73-0.89) for the development and validation cohorts, respectively. The aPNea score discriminated low-risk patients better than NEWS2 at a 10% outcome probability, corresponding to five cut-off points and one cut-off point, respectively. aPNea's cut-off reduced the number of unnecessary hospitalizations without missing outcomes by 27% (95% CI, 9-41) in the validation cohort. NEWS2 was not significant. In the external cohort, aPNea's cut-off had 93% sensitivity (95% CI, 83-102) and a 94% negative predictive value (95% CI, 87-102). In conclusion, the aPNea score appears to be appropriate for discharging low-risk SARS-CoV-2-infected patients from the ED.

Clinical implications of microvascular CT scan signs in COVID-19 patients requiring invasive mechanical ventilation

Purpose

COVID-19-related acute respiratory distress syndrome (ARDS) is characterized by the presence of signs of microvascular involvement at the CT scan, such as the vascular tree in bud (TIB) and the vascular enlargement pattern (VEP). Recent evidence suggests that TIB could be associated with an increased duration of invasive mechanical ventilation (IMV) and intensive care unit (ICU) stay. The primary objective of this study was to evaluate whether microvascular involvement signs could have a prognostic significance concerning liberation from IMV.

Material and methods

All the COVID-19 patients requiring IMV admitted to 16 Italian ICUs and having a lung CT scan recorded within 3 days from intubation were enrolled in this secondary analysis. Radiologic, clinical and biochemical data were collected.

Results

A total of 139 patients affected by COVID-19 related ARDS were enrolled. After grouping based on TIB or VEP detection, we found no differences in terms of duration of IMV and mortality. Extension of VEP and TIB was significantly correlated with ground-glass opacities (GGOs) and crazy paving pattern extension. A parenchymal extent over 50% of GGO and crazy paving pattern was more frequently observed among non-survivors, while a VEP and TIB extent involving 3 or more lobes was significantly more frequent in non-responders to prone positioning.

Conclusions

The presence of early CT scan signs of microvascular involvement in COVID-19 patients does not appear to be associated with differences in duration of IMV and mortality. However, patients with a high extension of VEP and TIB may have a reduced oxygenation response to prone positioning.

Trial Registration: NCT04411459

Supplementary Information

The online version contains supplementary material available at 10.1007/s11547-021-01444-7.

Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: A complex relationship

Introduction

Delay between symptom onset and access to care is essential to prevent clinical worsening for different infectious diseases. For COVID-19, this delay might be associated with the clinical prognosis, but also with the different characteristics of patients. The objective was to describe characteristics and symptoms of community-acquired (CA) COVID-19 patients at hospital admission according to the delay between symptom onset and hospital admission, and to identify determinants associated with delay of admission.

Methods

The present work was based on prospective NOSO-COR cohort data, and restricted to patients with laboratory confirmed CA SARS-CoV-2 infection admitted to Lyon hospitals between February 8 and June 30, 2020. Long delay of hospital admission was defined as ≥6 days between symptom onset and hospital admission. Determinants of the delay between symptom onset and hospital admission were identified by univariate and multiple logistic regression analysis.

Results

Data from 827 patients were analysed. Patients with a long delay between symptom onset and hospital admission were younger (p<0.01), had higher body mass index (p<0.01), and were more frequently admitted to intensive care unit (p<0.01). Their plasma levels of C-reactive protein were also significantly higher (p<0.01). The crude in-hospital fatality rate was lower in this group (13.3% versus 27.6%), p<0.01. Multiple analysis with correction for multiple testing showed that age ≥75 years was associated with a short delay between symptom onset and hospital admission (≤5 days) (aOR: 0.47 95% CI (0.34–0.66)) and CRP>100 mg/L at admission was associated with a long delay (aOR: 1.84 95% CI (1.32–2.55)).

Discussion

Delay between symptom onset and hospital admission is a major issue regarding prognosis of COVID-19 but can be related to multiple factors such as individual characteristics, organization of care and severe pathogenic processes. Age seems to play a key role in the delay of access to care and the disease prognosis.

COVID-view: Diagnosis of COVID-19 using Chest CT

Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data. However, comprehensive visualization systems focused on supporting the dual visual+DL diagnosis of COVID-19 are non-existent. We present COVID-view, a visualization application specially tailored for radiologists to diagnose COVID-19 from chest CT data. The system incorporates a complete pipeline of automatic lungs segmentation, localization/isolation of lung abnormalities, followed by visualization, visual and DL analysis, and measurement/quantification tools. Our system combines the traditional 2D workflow of radiologists with newer 2D and 3D visualization techniques with DL support for a more comprehensive diagnosis. COVID-view incorporates a novel DL model for classifying the patients into positive/negative COVID-19 cases, which acts as a reading aid for the radiologist using COVID-view and provides the attention heatmap as an explainable DL for the model output. We designed and evaluated COVID-view through suggestions, close feedback and conducting case studies of real-world patient data by expert radiologists who have substantial experience diagnosing chest CT scans for COVID-19, pulmonary embolism, and other forms of lung infections. We present requirements and task analysis for the diagnosis of COVID-19 that motivate our design choices and results in a practical system which is capable of handling real-world patient cases.

Imaging of COVID-19

The novel coronavirus disease 2019 (COVID-19) emerged as the source of a global pandemic in late 2019 and early 2020 and quickly spread throughout the world becoming one of the worst pandemics in recent history. This chapter reviews the most up to date radiological literature and outlines the utility of thoracic imaging in COVID-19, defining both the common and the less typical imaging appearances during the acute and subacute phases of COVID-19. The short term complications and the long term sequela will also be discussed in the context of radiology, including pulmonary emboli, acute respiratory distress syndrome, superimposed infections, barotrauma, cardiac manifestations, pulmonary parenchymal scarring and fibrosis.

Occurrence of pulmonary residuals as one of the sequelae of COVID-19 and it's predictors among moderate and severe cases

Background

Moderate and severe COVID-19 patients typically present with pneumonia. In this study we aimed to detect the occurrence of pulmonary residuals as a late sequela of COVID-19 and to identify it's predictors among moderate and severe cases.

Methods

This observational prospective study involved 85 COVID-19 patients confirmed by real time polymerase chain reaction (RT-PCR) nasopharyngeal swab, patients were recruited in the period of 1 st of June to 1 st of July. Demographic and clinical data were obtained for each patient. Chest imaging was performed initially and after 3 weeks to detect post COVID pulmonary residuals.

Results

The study population included 74 (87.1%) moderate and 11 (12.9%) severe patients. Patients with older age, male gender, high BMI and initial chest CT of consolidation/mixed consolidation and ground glass opacities (GGOs) had more frequent post COVID-19 pulmonary residuals (P 0.003, 0.026, 0.031, 0.035) respectively. There was a statistically significant difference between patients who showed complete resolution and patients who developed pulmonary residuals regarding the lymphocyte count, serum CRP and ferritin levels (P 0.0001). After logistic regression, male gender, high BMI, initial chest CT of consolidation/mixed consolidation and GGOs, lymphocytopenia, high serum CRP and ferritin levels were the predictors of pulmonary residuals. While the age wasn't statistically significant.

Conclusion

38.5% of moderate and severe COVID-19 patients tend to have pulmonary residuals. Independent predictors of pulmonary residuals as a sequela of COVID-19 are male gender, high BMI, initial chest CT of consolidation and mixed consolidation/GGOs, lymphocytopenia, high serum CRP and ferritin levels.

The role of ventilatory support for long-term outcomes after critical infection with COVID-19: A prospective cohort study

Objectives

The full range of long‐term health consequences in intensive care unit (ICU) survivors with COVID‐19 is unclear. This study aims to investigate the role of ventilatory support for long‐term pulmonary impairment in critically ill patients and further to identify risk factors for prolonged radiological recovery.

Methods

A prospective observational study from a single general hospital, including all with COVID‐19 admitted to ICU between March and August 2020, investigating the association between ventilatory support and the extent of residual parenchymal changes on chest computed tomography (CT) scan and measurement of lung volumes at follow‐up comparing high‐flow nasal oxygen (HFNO) or non‐invasive ventilation (NIV) with invasive ventilation. A semi‐quantitative score (CT involvement score) based on lobar involvement and a total score for all five lobes was used to estimate residual parenchymal changes. The association was calculated with logistic regression and adjusted for age, sex, smoking, and severity of illness.

Results

Among the 187 eligible, 86 had a chest CT scan and 76 a pulmonary function test at the follow‐up with a median time of 6 months after ICU discharge. Residual lung changes were seen in 74%. The extent of pulmonary changes was similar regardless of ventilatory support, but patients with invasive ventilation had a lower total lung capacity 84% versus 92% of predicted (p < 0.001).

Conclusions

The majority of ICU‐treated patients with COVID‐19 had residual lung changes at 6 months of follow‐up regardless of ventilator support or not, but the total lung capacity was lower in those treated with invasive ventilation.

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.

Descriptive Analysis of Chest Computed Tomography Scan in Coronavirus Disease 2019 Pneumonia: Correlation with Reverse Transcription-polymerase Chain Reaction and Clinical Features

BACKGROUND: Reverse transcriptase-polymerase chain reaction (RT-PCR) is the primary diagnostic tool to confirm coronavirus disease 2019 (COVID-2019) due to its high specificity. However, it has relatively low sensitivity and time consuming. In contrast, chest computed tomography (CT) has high sensitivity and achieves quick results. It may, therefore, play a critical role in screening and diagnosing COVID-19. A cross-sectional study was done in 212 patients with confirmed cases and patients under surveillance for COVID-19 tested for RT-PCR and chest CT scan. Statistical analysis was performed using SPSS Version 23 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY, USA). AIM: We aim to investigate the diagnostic value of chest CT in correlation to RT-PCR in Indonesia. METHODS: A cross-sectional study was done in 212 patients with confirmed cases and patients under surveillance for COVID-19 tested for RT-PCR and chest CT scan. Statistical analysis was performed using SPSS Version 23 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY, USA). RESULTS: From a total of 212 patients, 92% of them were diagnosed as confirmed cases of COVID-19. It was found that the sensitivity of CT scan for COVID-19 patients was 72.3% (65.5% and 78.5%) with positive predictive value (PPV) of 93.9% (90.9% and 96.0%) and the sensitivity and PPV improve in symptomatic patients. Typical chest CT scan lesions were 8.0 times which were more likely (3.9–16.4; p <0.001) to be detected in symptomatic patients while patients with severe CT scan findings were 4.4 times more likely (3.0–6.5; p <0.001) to be admitted to the intensive care unit. CONCLUSION: A high PPV suggests that a chest CT scan can detect COVID-19 lesions, but the absence of the lesions would not exclude the disease’s presence.

CT-Based Risk Stratification for Intensive Care Need and Survival in COVID-19 Patients-A Simple Solution

We evaluated a simple semi-quantitative (SSQ) method for determining pulmonary involvement in computed tomography (CT) scans of COVID-19 patients. The extent of lung involvement in the first available CT was assessed with the SSQ method and subjectively. We identified risk factors for the need of invasive ventilation, intensive care unit (ICU) admission and for time to death after infection. Additionally, the diagnostic performance of both methods was evaluated. With the SSQ method, a 10% increase in the affected lung area was found to significantly increase the risk for need of ICU treatment with an odds ratio (OR) of 1.68 and for invasive ventilation with an OR of 1.35. Male sex, age, and pre-existing chronic lung disease were also associated with higher risks. A larger affected lung area was associated with a higher instantaneous risk of dying (hazard ratio (HR) of 1.11) independently of other risk factors. SSQ measurement was slightly superior to the subjective approach with an AUC of 73.5% for need of ICU treatment and 72.7% for invasive ventilation. SSQ assessment of the affected lung in the first available CT scans of COVID-19 patients may support early identification of those with higher risks for need of ICU treatment, invasive ventilation, or death.

A novel CT scoring method predicts the prognosis of interstitial lung disease associated with anti-MDA5 positive dermatomyositis

Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5⁺ DM-ILD) is a life-threatening disease. This study aimed to develop a novel pulmonary CT visual scoring method for assessing the prognosis of the disease, and an artificial intelligence (AI) algorithm-based analysis and an idiopathic pulmonary fibrosis (IPF)-based scoring were conducted as comparators. A retrospective cohort of hospitalized patients with MDA5⁺ DM-ILD was analyzed. Since most fatalities occur within the first half year of the disease course, the primary outcome was the six-month all-cause mortality since the time of admission. A ground glass opacity (GGO) and consolidation-weighted CT visual scoring model for MDA5⁺ DM-ILD, namely ‘MDA5 score’, was then developed with C-index values of 0.80 (95%CI 0.75–0.86) in the derivation dataset (n = 116) and 0.84 (95%CI 0.71–0.97) in the validation dataset (n = 57), respectively. While, the AI algorithm-based analysis, namely ‘AI score’, yielded C-index 0.78 (95%CI 0.72–0.84) for the derivation dataset and 0.77 (95%CI 0.64–0.90) for the validation dataset. These findings suggest that the newly derived ‘MDA5 score’ may serve as an applicable prognostic predictor for MDA5⁺ DM-ILD and facilitate further clinical trial design. The AI based CT quantitative analysis provided a promising solution for ILD evaluation.

A quantitative analysis of extension and distribution of lung injury in COVID-19: a prospective study based on chest computed tomography

Background

Typical features differentiate COVID-19-associated lung injury from acute respiratory distress syndrome. The clinical role of chest computed tomography (CT) in describing the progression of COVID-19-associated lung injury remains to be clarified. We investigated in COVID-19 patients the regional distribution of lung injury and the influence of clinical and laboratory features on its progression.

Methods

This was a prospective study. For each CT, twenty images, evenly spaced along the cranio-caudal axis, were selected. For regional analysis, each CT image was divided into three concentric subpleural regions of interest and four quadrants. Hyper-, normally, hypo- and non-inflated lung compartments were defined. Nonparametric tests were used for hypothesis testing (α = 0.05). Spearman correlation test was used to detect correlations between lung compartments and clinical features.

Results

Twenty-three out of 111 recruited patients were eligible for further analysis. Five hundred-sixty CT images were analyzed. Lung injury, composed by hypo- and non-inflated areas, was significantly more represented in subpleural than in core lung regions. A secondary, centripetal spread of lung injury was associated with exposure to mechanical ventilation (p < 0.04), longer spontaneous breathing (more than 14 days, p < 0.05) and non-protective tidal volume (p < 0.04). Positive fluid balance (p < 0.01), high plasma D-dimers (p < 0.01) and ferritin (p < 0.04) were associated with increased lung injury.

Conclusions

In a cohort of COVID-19 patients with severe respiratory failure, a predominant subpleural distribution of lung injury is observed. Prolonged spontaneous breathing and high tidal volumes, both causes of patient self-induced lung injury, are associated to an extensive involvement of more central regions. Positive fluid balance, inflammation and thrombosis are associated with lung injury.

Trial registration Study registered a priori the 20th of March, 2020. Clinical Trials ID NCT04316884.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03685-4.

Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model

BACKGROUND

COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients.

MATERIALS AND METHODS

We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model's discriminatory ability was assessed with Harrell's C-statistic and the goodness-of-fit was evaluated with calibration plot.

RESULTS

242 patients were included [median age, 64 years (56-71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6-18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82).

CONCLUSIONS

We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

Salivary SARS-CoV-2 load reduction with mouthwash use: A randomized pilot clinical trial

The saliva of patients with COVID-19 has a high SARS-CoV-2 viral load. The risk of spreading the virus is high, and procedures for viral load reduction in the oral cavity are important. Little research to date has been performed on the effect of mouthwashes on the salivary SARS-CoV-2 viral load. This pilot randomized single-center clinical trial investigated whether three types of mouthwash with solutions containing either 0.075% cetylpyridinium chloride plus 0.28% zinc lactate (CPC + Zn), 1.5% hydrogen peroxide (HP), or 0.12% chlorhexidine gluconate (CHX) reduce the SARS-CoV-2 viral load in saliva at different time points. Sixty SARS-CoV-2-positive patients were recruited and randomly partitioned into a placebo (oral rinsing with distilled water) group and other groups according to the type of mouthwash. Saliva samples were collected from the participants before rinsing (T0), immediately after rinsing (T1), 30 min after rinsing (T2), and 60 min after rinsing (T3). The salivary SARS-CoV-2 viral load was measured by qRT-PCR assays. Rinsing with HP and CPC + Zn resulted in better reductions in viral load, with 15.8 ± 0.08- and 20.4 ± 3.7-fold reductions at T1, respectively. Although the CPC + Zn group maintained a 2.6 ± 0.1-fold reduction at T3, this trend was not observed for HP. HP mouthwash resulted in a significant reduction in the SARS-CoV-2 viral load up to 30 min after rinsing (6.5 ± 3.4). The CHX mouthwash significantly reduced the viral load at T1, T2, and T3 (2.1 ± 1.5-, 6.2 ± 3.8-, and 4.2 ± 2.4-fold reductions, respectively). In conclusion, mouthwash with CPC + Zinc and CHX resulted in significant reductions of the SARS-CoV-2 viral load in saliva up to 60 mins after rinsing, while HP mouthwash resulted in a significant reduction up to 30 mins after rinsing. Despite this transitory effect, these results encourage further studies and suggest that these products could be considered as risk-mitigation strategies for patients infected with SARS-CoV-2.

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.

Computed tomography findings of COVID-19 pneumonia in Intensive Care Unit-patients

BACKGROUND

In December 2019, a cluster of unknown etiology pneumonia cases occurred in Wuhan, China leading to identification of the responsible pathogen as SARS-coV-2. Since then, the coronavirus disease 2019 (COVID-19) has spread to the entire world. Computed Tomography (CT) is frequently used to assess severity and complications of COVID-19 pneumonia. The purpose of this study is to compare the CT patterns and clinical characteristics in intensive care unit (ICU) and non-ICU patients with COVID-19 pneumonia.

DESIGN AND METHODS

This retrospective study included 218 consecutive patients (136 males; 82 females; mean age 63±15 years) with laboratory-confirmed SARS-coV-2. Patients were categorized in two different groups: (a) ICU patients and (b) non-ICU inpatients. We assessed the type and extent of pulmonary opacities on chest CT exams and recorded the information on comorbidities and laboratory values for all patients.

RESULTS

Of the 218 patients, 23 (20 males: 3 females; mean age 60 years) required ICU admission, 195 (118 males: 77 females, mean age 64 years) were admitted to a clinical ward. Compared with non-ICU patients, ICU patients were predominantly males (60% versus 83% p=0.03), had more comorbidities, a positive CRP (p=0.04) and higher LDH values (p=0.008). ICU patients' chest CT demonstrated higher incidence of consolidation (p=0.03), mixed lesions (p=0.01), bilateral opacities (p<0.01) and overall greater lung involvement by consolidation (p=0.02) and GGO (p=0.001).

CONCLUSIONS

CT imaging features of ICU patients affected by COVID-19 are significantly different compared with non-ICU patients. Identification of CT features could assist in a stratification of the disease severity and supportive treatment.

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.

Predictors of Intensive Care Unit Admission or Death in Patients with Coronavirus Disease 2019 Pneumonia in Istanbul, Turkey

We aimed to determine the predictors of intensive care unit (ICU) admission or death in patients with Coronavirus Disease 2019 (COVID-19) pneumonia. This retrospective and single-center study includes patients aged ≥18 years who were diagnosed with COVID-19 pneumonia (laboratory and radiologically confirmed) between March 9 and April 8, 2020. Our composite endpoint was ICU admission or in-hospital death. To evaluate the factors in the composite endpoint, univariate and multivariate logistic regression analyses were performed. A total of 336 patients with COVID-19 pneumonia were recorded. The median age was 54 years [interquartile range (IQR): 21] and 187 (55.7%) were male. Fifty-one (15.2%) patients were admitted to the ICU. In-hospital death occurred in 33 (9.8%) patients. In univariate analysis, 17 parameters were associated with the composite endpoint and procalcitonin had the highest ODDs ratio (OR=36.568 CI=5.145-259.915). Our results revealed that body temperature (OR=1.489 CI=1.023-2.167, p=0.037), peripheral capillary oxygen saturation (SpO2) (OR=0.835 CI=0.773-0.901, p<0.001), and consolidation (>25%) in chest computed tomography (OR=3.170 CI=1.218-8.252, p=0.018) at admission were independent predictors. As a result, increased body temperature, decreased SpO2, a high level of procalcitonin, and degree of consolidation in chest computed tomography may predict a poor prognosis and have utility in the management of patients.

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings

Objective

To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.

Materials and Methods

All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans.

Results

While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88).

Conclusion

Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

Predictors of Intensive Care Unit Admission among Hospitalized COVID-19 Patients in a Large University Hospital in Tehran, Iran

Background: The rapid increase in the spread of COVID-19 and the numbers of infected patients worldwide has highlighted the need for intensive care unit (ICU) beds and more advanced therapy. This need is more urgent in resource-constrained settings. The present study aimed to identify the predictors of ICU admission among hospitalized COVID-19 patients.

Study design: The current study was conducted based on a retrospective cohort design.

Methods: The participants included 665 definite cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hospitalized in Imam Hossein Hospital from February 20 to May 14, 2020. The baseline characteristics of patients were assessed, and multivariate logistic regression analysis was utilized to determine the significant odds ratio (OR) for ICU admission.

Results: Participants were aged 59.52±16.72 years, and the majority (55.6%) of them were male. Compared to non-ICU patients (n=547), the ICU patients (n=118) were older, had more baseline comorbidities, and presented more often with dyspnea, convulsion, loss of consciousness, tachycardia, tachypnea, and hypoxia, and less often with myalgia. Significant OR (95% CI) of ICU admission was observed for the 60-80 age group (2.42, 95%CI: 1.01; 5.79), ≥80 age group (3.73, 95%CI: 1.44; 9.42), ≥3 comorbidities (2.07, 95%CI: 1.31; 3.80), loss of consciousness (6.70, 95%CI: 2.94, 15.24), tachypnea (1.79, 95%CI: 1.03, 3.11), and SpO2<90 (5.83, 95%CI: 2.74; 12.4). Abnormal laboratory results were more common among ICU-admitted patients; in this regard, leukocytosis (4.45, 95%CI: 1.49, 13.31), lymphopenia (2.39, 95%CI: 1.30; 4.39), elevated creatine phosphokinase (CPK) (1.99, 95%CI: 1.04; 3.83), and increased aspartate aminotransferase (AST) (2.25, 95%CI: 1.18-4.30) had a significant OR of ICU admission. Chest computer tomography (CT) revealed that consolidation (1.82, 95%CI: 1.02, 3.24), pleural effusion (3.19, 95%CI: 1.71, 5.95), and crazy paving pattern (8.36, 95%CI: 1.92, 36.48) had a significant OR of ICU admission.

Conclusion: As evidenced by the obtained results, the predictors of ICU admission were identified among epidemiological characteristics, presenting symptoms and signs, laboratory tests, and chest CT findings.

Mediastinal lymphadenopathy may predict 30-day mortality in patients with COVID-19

PURPOSE

There is scarce data on the impact of the presence of mediastinal lymphadenopathy on the prognosis of coronavirus-disease 2019 (COVID-19). We aimed to investigate whether its presence is associated with increased risk for 30-day mortality in a large group of patients with COVID-19.

METHOD

In this retrospective cross-sectional study, 650 adult laboratory-confirmed hospitalized COVID-19 patients were included. Patients with comorbidities that may cause enlarged mediastinal lymphadenopathy were excluded. Demographics, clinical characteristics, vital and laboratory findings, and outcome were obtained from electronic medical records. Computed tomography scans were evaluated by two blinded radiologists. Univariate and multivariate logistic regression analyses were performed to determine independent predictive factors of 30-day mortality.

RESULTS

Patients with enlarged mediastinal lymphadenopathy (n = 60, 9.2%) were older and more likely to have at least one comorbidity than patients without enlarged mediastinal lymphadenopathy (p = 0.03, p = 0.003). There were more deaths in patients with enlarged mediastinal lymphadenopathy than in those without (11/60 vs 45/590, p = 0.01). Older age (OR:3.74, 95% CI: 2.06-6.79; p < 0.001), presence of consolidation pattern (OR:1.93, 95% CI: 1.09-3.40; p = 0.02) and enlarged mediastinal lymphadenopathy (OR:2.38, 95% CI:1.13-4.98; p = 0.02) were independently associated with 30-day mortality.

CONCLUSION

In this large group of hospitalized patients with COVID-19, we found that in addition to older age and consolidation pattern on CT scan, enlarged mediastinal lymphadenopathy were independently associated with increased mortality. Mediastinal evaluation should be performed in all patients with COVID-19.

Post-discharge critical COVID-19 lung function related to severity of radiologic lung involvement at admission

Lung function impairment persists in 55% of critical COVID-19 patients three months after ICU discharge. Patient lung function, exercise capacity, radiologic, and quality of life data suggest impairment is related to radiologic lung involvement at admission.

Respiratory failure among patients with COVID-19 in Jiangsu province, China: a multicentre retrospective cohort study

This study was a retrospective multicentre cohort study of patients with coronavirus disease 2019 (COVID-19) diagnosed at 24 hospitals in Jiangsu province, China as of 15 March 2020. The primary outcome was the occurrence of acute respiratory failure during hospital stay. Of 625 patients, 56 (9%) had respiratory failure. Some selected demographic, epidemiologic, clinical and laboratory features as well as radiologic features at admission and treatment during hospitalisation were significantly different in patients with and without respiratory failure. The multivariate logistic analysis indicated that age (in years) (odds ratio [OR], 1.07; 95% confidence interval [CI]: 1.03–1.10; P = 0.0002), respiratory rate (breaths/minute) (OR, 1.23; 95% CI: 1.08–1.40; P = 0.0020), lymphocyte count (10⁹/l) (OR, 0.18; 95% CI: 0.05–0.69; P = 0.0157) and pulmonary opacity score (per 5%) (OR, 1.38; 95% CI: 1.19–1.61; P < 0.0001) at admission were associated with the occurrence of respiratory failure. Older age, increased respiratory rate, decreased lymphocyte count and greater pulmonary opacity score at admission were independent risk factors of respiratory failure in patients with COVID-19. Patients having these risk factors need to be intensively managed during hospitalisation.

Normal chest CT in 1091 symptomatic patients with confirmed Covid-19: frequency, characteristics and outcome

OBJECTIVE

Frequency of normal chest CT in symptomatic COVID-19 patients as well as the outcome of these patients remains unknown. The objectives of this work were to assess the incidence of initially normal chest CT in a cohort of consecutive confirmed COVID-19 patients with respiratory symptoms and to compare their clinical characteristics and their outcome to matched patients with typical COVID-19 lesions at initial CT.

METHODS

From March 6^th to April 22^nd, all consecutive adult patients referred to the COVID-19 clinic of our Emergency Department were retrospectively analyzed. Each patient with a positive SARS-CoV-2 RT-PCR and a normal initial chest CT after second reading was 1:1 matched based on sex, age and date of CT acquisition to a patient with positive RT-PCR and initial chest CT with typical COVID-19 lesions. Clinical data, laboratory results and outcomes (major being mechanical ventilation and/or death) were compared between both groups, using Wilcoxon signed-rank test, McNemar's chi-squared test and/or exact McNemar's test where appropriate.

RESULTS

Fifty-seven chest CT out of 1091 (5.2%, 95% CI 4.0-6.7) in symptomatic patients with positive RT-PCR were normal, with a median onset of symptoms of 4.5 days (IQR [1.25-10.25]). After a median follow-up of 43 days, death and/or mechanical ventilation occurred in 3 patients (5.3%) in the study group, versus 11 (19.3%) in the control group (p = 0.011).

CONCLUSIONS

Normal initial chest CT occurred in 5.2% of symptomatic confirmed COVID-19 cases in our cohort. While better than those with abnormal chest CT, outcome was not entirely benign with 5.3% death and/or mechanical ventilation.

KEY POINTS

• In a cohort of 1091 symptomatic COVID-19 patients, initial chest CT was normal in 5.2% of cases. • Normal chest CT in confirmed COVID-19 is frequent even when onset of symptoms is greater than 3 days. • The outcome of COVID-19 patients with initial normal chest CT, while better than those with abnormal CT, was not entirely benign with 5.3% death and/or mechanical ventilation.

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.

Risk factors associated with COVID-19-associated pulmonary aspergillosis in ICU patients: a French multicentric retrospective cohort

OBJECTIVES

The main objective of this study was to determine the incidence of invasive pulmonary aspergillosis (IPA) in patients with coronavirus disease 2019 (COVID-19) admitted to the intensive care unit (ICU), and to describe the patient characteristics associated with IPA occurrence and to evaluate its impact on prognosis.

METHODS

We conducted a retrospective cohort study including all successive COVID-19 patients, hospitalized in four ICUs, with secondary deterioration and one or more respiratory samples sent to the mycology department. We used a strengthened IPA testing strategy including seven mycological criteria. Patients were classified as probable IPA according to the European Organization for Research and Treatment of Cancer (EORTC)/Mycoses Study Group Education and Research Consortium (MSGERC) classification if immunocompromised, and according to the recent COVID-19-associated IPA classification otherwise.

RESULTS

Probable IPA was diagnosed in 21 out of the 366 COVID-19 patients (5.7%) admitted to the ICU and in the 108 patients (19.4%) who underwent respiratory sampling for deterioration. No significant differences were observed between patients with and without IPA regarding age, gender, medical history and severity on admission and during hospitalization. Treatment with azithromycin for ≥3 days was associated with the diagnosis of probable IPA (odds ratio 3.1, 95% confidence interval 1.1-8.5, p = 0.02). A trend was observed with high-dose dexamethasone and the occurrence of IPA. Overall mortality was higher in the IPA patients (15/21, 71.4% versus 32/87, 36.8%, p < 0.01).

CONCLUSION

IPA is a relatively frequent complication in severe COVID-19 patients and is responsible for increased mortality. Azithromycin, known to have immunomodulatory properties, may contribute to increase COVID-19 patient's susceptibility to IPA.