Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China

Carvalho A, S. F. Serrano J, A. T. Dall-Aglio P, Radermacher P, Silva JM. Association of chest computed tomography severity score at ICU admission and respiratory outcomes in critically ill COVID-19 patients

OBJECTIVE

To evaluate the association of a validated chest computed tomography (Chest-CT) severity score in COVID-19 patients with their respiratory outcome in the Intensive Care Unit.

METHODS

A single-center, prospective study evaluated patients with positive RT-PCR for COVID-19, who underwent Chest-CT and had a final COVID-19 clinical diagnosis needing invasive mechanical ventilation in the ICU. The admission chest-CT was evaluated according to a validated Chest-CT Severity Score in COVID-19 (Chest-CTSS) divided into low ≤50% (<14 points) and >50% high (≥14 points) lung parenchyma involvement. The association between the initial score and their pulmonary clinical outcomes was evaluated.

RESULTS

121 patients were clustered into the > 50% lung involvement group and 105 patients into the ≤ 50% lung involvement group. Patients ≤ 50% lung involvement (<14 points) group presented lower PEEP levels and FiO2 values, respectively GEE P = 0.09 and P = 0.04. The adjusted COX model found higher hazard to stay longer on invasive mechanical ventilation HR: 1.69, 95% CI, 1.02-2.80, P = 0.042 and the adjusted logistic regression model showed increased risk ventilator-associated pneumonia OR = 1.85 95% CI 1.01-3.39 for COVID-19 patients with > 50% lung involvement (≥14 points) on Chest-CT at ICU admission.

CONCLUSION

COVID-19 patients with >50% lung involvement on Chest-CT admission presented higher chances to stay longer on invasive mechanical ventilation and more chances to developed ventilator-associated pneumonia.

Predicting survival of Iranian COVID-19 patients infected by various variants including omicron from CT Scan images and clinical data using deep neural networks

Purpose: The rapid spread of the COVID-19 omicron variant virus has resulted in an overload of hospitals around the globe. As a result, many patients are deprived of hospital facilities, increasing mortality rates. Therefore, mortality rates can be reduced by efficiently assigning facilities to higher-risk patients. Therefore, it is crucial to estimate patients' survival probability based on their conditions at the time of admission so that the minimum required facilities can be provided, allowing more opportunities to be available for those who need them. Although radiologic findings in chest computerized tomography scans show various patterns, considering the individual risk factors and other underlying diseases, it is difficult to predict patient prognosis through routine clinical or statistical analysis. Method: In this study, a deep neural network model is proposed for predicting survival based on simple clinical features, blood tests, axial computerized tomography scan images of lungs, and the patients' planned treatment. The model's architecture combines a Convolutional Neural Network and a Long Short Term Memory network. The model was trained using 390 survivors and 108 deceased patients from the Rasoul Akram Hospital and evaluated 109 surviving and 36 deceased patients infected by the omicron variant. Results: The proposed model reached an accuracy of 87.5% on the test data, indicating survival prediction possibility. The accuracy was significantly higher than the accuracy achieved by classical machine learning methods without considering computerized tomography scan images (p-value <= 4E-5). The images were also replaced with hand-crafted features related to the ratio of infected lung lobes used in classical machine-learning models. The highest-performing model reached an accuracy of 84.5%, which was considerably higher than the models trained on mere clinical information (p-value <= 0.006). However, the performance was still significantly less than the deep model (p-value <= 0.016). Conclusion: The proposed deep model achieved a higher accuracy than classical machine learning methods trained on features other than computerized tomography scan images. This proves the images contain extra information. Meanwhile, Artificial Intelligence methods with multimodal inputs can be more reliable and accurate than computerized tomography severity scores.

Promising role of Vitamin D and plant metabolites against COVID-19: Clinical trials review

Vitamin D possesses immunomodulatory qualities and is protective against respiratory infections. Additionally, it strengthens adaptive and cellular immunity and boosts the expression of genes involved in oxidation. Experts suggested taking vitamin D supplements to avoid and treat viral infection and also COVID-19, on the other hand, since the beginning of time, the use of plants as medicines have been vital to human wellbeing. The WHO estimates that 80 % of people worldwide use plants or herbs for therapeutic purposes. Secondary metabolites from medicinal plants are thought to be useful in lowering infections from pathogenic microorganisms due to their ability to inhibit viral protein and enzyme activity by binding with them. As a result, this manuscript seeks to describe the role of vitamin D and probable plant metabolites that have antiviral activities and may be complementary to the alternative strategy against COVID-19 in a single manuscript through reviewing various case studies.

Clinical characteristics and factors associated with severe COVID-19 in hospitalized children during the SARS-CoV-2 Omicron pandemic in Taiwan

BACKGROUND

Since April 2022, a notable increase in COVID-19 cases with the rapid spread of the SARS-CoV-2 Omicron variant has been reported in Taiwan. In the epidemic, children were one of the most vulnerable groups, so we analyzed their clinical presentations and factors associated with severe complications of COVID-19 in children.

METHODS

We included hospitalized patients under 18 years old with lab-confirmed SARS-CoV-2 infection from March 1, 2022, to July 31, 2022. We collected the demographic and clinical characteristics of the patients. Patients requiring intensive care were defined as severe cases.

RESULTS

Among the 339 enrolled patients, the median age was 31 months (interquartile range (IQR), 8-79.0 months); and 96 patients (28.3%) had underlying diseases. Fever was noted in 319 patients (94.1%) with a median duration of two days (IQR 2-3 days). Twenty-two patients (6.5%) were severe cases, including 10 patients (2.9%) with encephalopathy with abnormal neuroimaging and ten patients (2.9%) with shock. Two patients (0.6%) died. Patients with congenital cardiovascular disease (aOR: 21.689), duration of fever up to four days or more (aOR: 6.466), desaturation (aOR: 16.081), seizure (aOR: 20.92), and procalcitonin >0.5 ng/mL (aOR: 7.886) had a higher risk of severe COVID-19.

CONCLUSIONS

Vital signs need close monitoring, early management and/or intensive care may be applied in COVID-19 patients with congenital cardiovascular diseases, fever lasting ≥4 days, seizures, desaturation and/or elevated procalcition since they are at higher risks of severe diseases.

COVID-19 fatality rates in hospitalized patients: A new systematic review and meta-analysis

BACKGROUND

SARS-COV2 or COVID-19 disease is an infectious illness that emerged for the first time at the end of 2019, in Wuhan, China and rapidly turned out to be an international pandemic with deleterious effects all over the world. In March 2021, A. Macedo et al., has published the first meta-analysis of hospital mortality, so the authors decided to update those data at a time of emergence of new therapies and increasing vaccination rates.

METHODS

As the outcome of interest was the mortality in hospitalized general patients, the authors looked for articles evaluating the clinical characteristics of those patients, consulting PUBMED (The US National Library of Medicine) and EMBASE (Medical database) in an independent selection using predefined terms of search. A meta-analysis random-effect model was estimated using Mantel-Haenszel method. Heterogeneity among studies was tested using Tau2 statistics and Chi2 statistics.

RESULTS

In a first instance 25 articles were included for final analysis with a total of 103,840 patients, but as the goal was to update the anterior data, these studies were analysed together with the 21 studies of the previous meta-analysis, with a total of 114609 patients. The mortality rate of COVID-19 general patients admitted to the hospital was 16% (95% CI 12; 21, I2 =100%).

CONCLUSION

Global hospital mortality of COVID-19 of general patients was 16%, with quite different rates according to the different geographic areas analysed.

Validation of a risk prediction model for COVID-19: the PERIL prospective cohort study

Aim: This study aims to perform an external validation of a recently developed prognostic model for early prediction of the risk of progression to severe COVID-19. Patients & methods/materials: Patients were recruited at their initial diagnosis at two facilities within Hamad Medical Corporation in Qatar. 356 adults were included for analysis. Predictors for progression of COVID-19 were all measured at disease onset and first contact with the health system. Results: The C statistic was 83% (95% CI: 78%-87%) and the calibration plot showed that the model was well-calibrated. Conclusion: The published prognostic model for the progression of COVID-19 infection showed satisfactory discrimination and calibration and the model is easy to apply in clinical practice.d.

The Association of Hypertension with Increased Mortality Rate During the COVID-19 Pandemic: An Update with Meta-analysis

BACKGROUND AND AIM

The impact of multiple risk factors on COVID-19 mortality has been previously reported in multiple systematic reviews and meta-analyses. The aim of this review is to provide a comprehensive update on the association between hypertension (HTN) and mortality in patients with COVID-19.

METHODS

A systematic review and meta-analysis were performed and followed the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines. A search was achieved using PubMed, Scopus, and Cochrane Databases for research publications on hypertension, COVID-19, and mortality published between December 2019 and August 2022.

RESULTS

A total of 23 observational studies involving 611,522 patients from 5 countries (China, Korea, the UK, Australia, and the USA) were included in our study. The confirmed number of COVID-19 with HTN cases in each study ranged from 5 to 9964. The mortality ranged from 0.17% to 31% in different studies. Pooled results show that the mortality rate of COVID-19 among the included studies ranges from a minimum of 0.39 (95% CI 0.13-1.12) to a maximum of 5.74 (95% CI 3.77-8.74). Out of the 611,522 patients, 3119 died which resulted in an overall mortality prevalence of 0.5%. Subgroup analyses indicated that patients with COVID-19 who have hypertension and male patients had slightly less risk of mortality than female patients [the percentage of men > 50%; OR 1.33: 95% CI (1.01, 1.76); the percentage of men ≤ 50%: OR 2.26; and 95% CI (1.15, 4.48)]. Meta-regression analysis results also showed a statistically significant association between hypertension and COVID-19 mortality.

CONCLUSION

This systematic review and meta-analysis suggest that hypertension may not be the only risk factor associated with the increased mortality rate during the COVID-19 pandemic. In addition, a combination of other comorbidities and old age appears to increase the risk of mortality from COVID-19. The impact of hypertension on mortality rate among COVID-19 patients.

A Retrospective Study to Evaluate the Perioperative Clinical Characteristics and Outcomes of Rhino-Orbital Cerebral Mucormycosis in COVID-19 Patients at a Tertiary Care Hospital in India

Background and aims A descriptive analysis of patients who underwent surgical debridement for coronavirus disease 2019 (COVID-19) related mucormycosis was described, which aimed at the evaluation of perioperative clinical characteristics, perioperative complications, and outcomes. Methods We conducted a retrospective study on patients who underwent surgical intervention for mucormycosis during the COVID-19 pandemic at a tertiary care institute in India from March 1, 2021, to June 30, 2021. The medical records of 92 patients were reviewed and analyzed. Results There was a male predominance with a mean age of 50.86 years. The most common comorbidity was diabetes mellitus (DM) (98.9%). Intra-operative complications included hypotension, hyperglycemia, and hypokalemia. Most of the patients (88%) were extubated inside the operation theater, and 48% of patients had mortality. Serum ferritin levels, computed tomography severity score (CTSS), and D-dimers were significantly high in the patient who had mortality. Conclusion The perioperative mortality in patients with COVID-19 associated mucormycosis was very high. DM was the most common comorbidity followed by hypertension. Pre-operative elevated serum ferritin, D-dimer, and high CTSS were associated with higher mortality; hypokalemia, followed by hypocalcemia, was the most common perioperative and post-operative electrolyte imbalance. Thorough pre-operative optimization, multidisciplinary involvement, and perioperative care are of the utmost importance to decrease mortality and improve outcomes.

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Background

In December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.

Methods

Our study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.

Results

The receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome.

Conclusion

Our study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.

The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study

Introduction

Chest computed tomography (CT) is suitable to assess morphological changes in the lungs. Chest CT scoring systems (CCTS) have been developed and use in order to quantify the severity of pulmonary involvement in COVID-19. CCTS has also been correlated with clinical outcomes. Here we wished to use a validated, relatively simple CTSS to assess chest CT patterns and to correlate CTSS with clinical outcomes in COVID-19.

Patients and methods

Altogether 227 COVID-19 cases underwent chest CT scanning using a 128 multi-detector CT scanner (SOMATOM Go Top, Siemens Healthineers, Germany). Specific pathological features, such as ground-glass opacity (GGO), crazy-paving pattern, consolidation, fibrosis, subpleural lines, pleural effusion, lymphadenopathy and pulmonary embolism were evaluated. CTSS developed by Pan et al. (CTSS-Pan) was applied. CTSS and specific pathologies were correlated with demographic, clinical and laboratory data, A-DROP scores, as well as outcome measures. We compared CTSS-Pan to two other CT scoring systems.

Results

The mean CTSS-Pan in the 227 COVID-19 patients was 14.6 ± 6.7. The need for ICU admission (p < 0.001) and death (p < 0.001) were significantly associated with higher CTSS. With respect to chest CT patterns, crazy-paving pattern was significantly associated with ICU admission. Subpleural lines exerted significant inverse associations with ICU admission and ventilation. Lymphadenopathy was associated with all three outcome parameters. Pulmonary embolism led to ICU admission. In the ROC analysis, CTSS>18.5 significantly predicted admission to ICU (p = 0.026) and CTSS>19.5 was the cutoff for increased mortality (p < 0.001). CTSS-Pan and the two other CTSS systems exerted similar performance. With respect to clinical outcomes, CTSS-Pan might have the best performance.

Conclusion

CTSS may be suitable to assess severity and prognosis of COVID-19-associated pneumonia. CTSS and specific chest CT patterns may predict the need for ventilation, as well as mortality in COVID-19. This can help the physician to guide treatment strategies in COVID-19, as well as other pulmonary infections.

A Theoretical Death Map of Patients with COVID-19: A Single Center Experience

Background

Given the increase in mortality from COVID-19 disease, understanding the causal chain leading to death in patients with this disease will be of particular importance. This study aimed to draw the death map of patients with COVID-19 in BESAT hospital (West of Iran), based on investigating the underlying, intermediate, and terminal causes of death in this group of patients.

Materials and Methods

To draw the death map of patients with COVID-19 in this cross-sectional study, the death certificate and medical records of 183 COVID-19 patients who died at BESAT Hospital in Hamadan (West of Iran) in 2020 were reviewed. The cases in which the underlying cause of death was COVID-19 were reviewed. A checklist was used to collect the data. It was designed based on the international form of medical certificate of cause of death (issued by WHO). The collected data were analyzed by SPSS software version 23.

Results

The most prevalent underlying causes of death were COVID-19 (60.7%), COVID-19-related pneumonia (19.1%), acute respiratory distress syndrome (10.9%), and severe sepsis (9.8%). Hypertension (8.2%), diabetes (6.0%), seizures (3.8%), and ischemic heart disease (2.2%) were the most influential conditions affecting death. The number of deaths due to the terminal cause of acute respiratory distress syndrome in women (22.5%) was much higher than in men (7.1%) (P-value=0.041). Findings indicated that most patients died from four main pathways originating from COVID-19, leading to causes such as sepsis, ARDS, myocarditis, MI, and PTE.

Conclusion

The results indicate that health officials and healthcare providers should be able to identify and monitor patients with chronic diseases and implement effective plans to prevent COVID-19. Physicians should also take important steps in offices, clinics, and hospitals, such as conducting early echocardiography in children, providing respiratory support, and preventing deep vein thrombosis in adults during hospitalization. It is also essential to inform the public through audio and video media, including radio and television.

A Review of Crucial Radiological Investigations in the Management of COVID-19 Cases

Chest X-ray, chest CT, and lung ultrasound are the most common radiological interventions used in the diagnosis and management of coronavirus disease 2019 (COVID-19) patients. The purpose of this literature review, which was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, is to determine which radiological investigation is crucial for that purpose. PubMed, Medline, American Journal of Radiology (AJR), Public Library of Science (PLOS), Elsevier, National Center for Biotechnology Information (NCBI), and ScienceDirect were explored. Seventy-two articles were reviewed for potential inclusion, including 50 discussing chest CT, 15 discussing chest X-ray, five discussing lung ultrasound, and two discussing COVID-19 epidemiology. The reported sensitivities and specificities for chest CT ranged from 64 to 98% and 25 to 88%, respectively. The reported sensitivities and specificities for chest X-rays ranged from 33 to 89% and 11.1 to 88.9%, respectively. The reported sensitivities and specificities for lung ultrasound ranged from 93 to 96.8% and 21.3 to 95%, respectively. The most common findings on chest CT include ground glass opacities and consolidation. The most common findings on chest X-rays include opacities, consolidation, and pleural effusion. The data indicate that chest CT is the most effective radiological tool for the diagnosis and management of COVID-19 patients. The authors support the continued use of reverse transcription polymerase chain reaction (RT-PCR), along with physical examination and contact history, for such diagnosis. Chest CT could be more appropriate in emergency situations when quick triage of patients is necessary before RT-PCR results are available. CT can also be used to visualize the progression of COVID-19 pneumonia and to identify potential false positive RT-PCR results. Chest X-ray and lung ultrasound are acceptable in situations where chest CT is unavailable or contraindicated.

Vertical Artifacts as Lung Ultrasound Signs: Trick or Trap? Part 2- An Accademia di Ecografia Toracica Position Paper on B-Lines and Sonographic Interstitial Syndrome

Although during the last few years the lung ultrasound (LUS) technique has progressed substantially, several artifacts, which are currently observed in clinical practice, still need a solid explanation of the physical phenomena involved in their origin. This is particularly true for vertical artifacts, conventionally known as B-lines, and for their use in clinical practice. A wider consensus and a deeper understanding of the nature of these artifactual phenomena will lead to a better classification and a shared nomenclature, and, ultimately, result in a more objective correlation between anatomo-pathological data and clinical scenarios. The objective of this review is to collect and document the different signs and artifacts described in the history of chest ultrasound, with a particular focus on vertical artifacts (B-lines) and sonographic interstitial syndrome (SIS). By reviewing the possible physical and anatomical interpretation of the signs and artifacts proposed in the literature, this work also aims to bring order to the available studies and to present the AdET (Accademia di Ecografia Toracica) viewpoint in terms of nomenclature and clinical approach to the SIS.

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms

Background

The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality.

Objective

The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms.

Methods

This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting.

Results

This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath.

Conclusion

The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions.

Evaluating the characteristics of patients with SARS-CoV-2 infection admitted during COVID-19 peaks: A single-center study

Background

Nowadays, the world is facing a coronavirus disease (COVID-19) pandemic, elicited by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). At the time of studying, five COVID-19 waves occurred in Iran. We aimed to evaluate the characteristics of patients with SARS-CoV-2 infection admitted to Vasei Hospital of Sabzevar, Iran during COVID-19 peaks.

Methods

Clinical manifestations, laboratory findings, radiological findings, and underlying diseases of patients with COVID-19 were obtained from electronic medical records. Then, this information was compared in patients with SARS-CoV-2 infection to the peaks of COVID-19.

Results

The highest and lowest respiratory involvements were observed in the third (74.6%) and fourth (38.8%) peaks, respectively. The most common radiological finding in all peaks was ground-glass opacity (28.98%), followed by consolidation, which was the highest (14.6%) in peak three. The lymphocyte count decreased in all peaks. Its highest reduction (16.12) occurred in the third peak. The SpO2 was lower than normal range in all peaks, except for the second (90.77%) and fifth (91.06%) peaks. Dyspnea (52.36%) was the most and dizziness (1.26%) and sore throat (0.6%) were the least frequent symptoms. The mortality rates were 14. 4%, 18.2%, 23%, 9.02%, and 9.4% in the first to fifth peaks, respectively.

Conclusion

As different variants of the SARS-CoV-2 virus were predominant in each wave, COVID-19 patients had different features in various peaks. The fifth wave of COVID-19 had the highest number of hospitalized patients, while the first peak had the lowest number. Perhaps, the significant increase in testing capacity in the fifth wave and its long time period are the reasons for this growth. Most of the clinical symptoms were similar in all peaks, but the incidence was different. As patients hospitalized in the third peak had the highest rate of underlying disease, it can be a reason for the increase in the death rate of patients. We did not observe any significant differences in laboratory tests among the patients during different peaks. Thus, we should be vigilant in continuously studying the characteristics of the disease, and be able to modify treatments rapidly if necessary.

Evaluating the characteristics of patients with SARS-CoV-2 infection admitted during COVID-19 peaks: A single-center study

Background

Nowadays, the world is facing a coronavirus disease (COVID-19) pandemic, elicited by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). At the time of studying, five COVID-19 waves occurred in Iran. We aimed to evaluate the characteristics of patients with SARS-CoV-2 infection admitted to Vasei Hospital of Sabzevar, Iran during COVID-19 peaks.

Methods

Clinical manifestations, laboratory findings, radiological findings, and underlying diseases of patients with COVID-19 were obtained from electronic medical records. Then, this information was compared in patients with SARS-CoV-2 infection to the peaks of COVID-19.

Results

The highest and lowest respiratory involvements were observed in the third (74.6%) and fourth (38.8%) peaks, respectively. The most common radiological finding in all peaks was ground-glass opacity (28.98%), followed by consolidation, which was the highest (14.6%) in peak three. The lymphocyte count decreased in all peaks. Its highest reduction (16.12) occurred in the third peak. The SpO2 was lower than normal range in all peaks, except for the second (90.77%) and fifth (91.06%) peaks. Dyspnea (52.36%) was the most and dizziness (1.26%) and sore throat (0.6%) were the least frequent symptoms. The mortality rates were 14. 4%, 18.2%, 23%, 9.02%, and 9.4% in the first to fifth peaks, respectively.

Conclusion

As different variants of the SARS-CoV-2 virus were predominant in each wave, COVID-19 patients had different features in various peaks. The fifth wave of COVID-19 had the highest number of hospitalized patients, while the first peak had the lowest number. Perhaps, the significant increase in testing capacity in the fifth wave and its long time period are the reasons for this growth. Most of the clinical symptoms were similar in all peaks, but the incidence was different. As patients hospitalized in the third peak had the highest rate of underlying disease, it can be a reason for the increase in the death rate of patients. We did not observe any significant differences in laboratory tests among the patients during different peaks. Thus, we should be vigilant in continuously studying the characteristics of the disease, and be able to modify treatments rapidly if necessary.

A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws

A critical operations management problem in the ongoing COVID-19 pandemic is cognizance of (a) the number of all carriers at large (CaL) conveying the SARS-CoV-2, including asymptomatic ones and (b) the infection rate (IR). Both are random and unobservable, affecting the spread of the disease, patient arrivals to health care units (HCUs) and the number of deaths. A novel, inventory perspective of COVID-19 is proposed, with random inflow, random losses and retrials (recurrent cases) and delayed/distributed exit, with randomly varying fractions of the exit distribution. A minimal construal, it enables representation of COVID-19 evolution with close fit of national incidence profiles, including single and multiple pattern outbreaks, oscillatory, periodic or non-periodic evolution, followed by retraction, leveling off, or strong resurgence. Furthermore, based on asymptotic laws, the minimum number of variables that must be monitored for identifying CaL and IR is determined and a real-time identification method is presented. The method is data-driven, utilizing the entry rate to HCUs and scaled, or dimensionless variables, including the mean residence time of symptomatic carriers in CaL and the mean residence time in CaL of patients entering HCUs. As manifested by several robust case studies of national COVID-19 incidence profiles, it provides efficient identification in real-time under unbiased monitoring error, without relying on any model. The propagation factor, a stochastic process, is reconstructed from the identified trajectories of CaL and IR, enabling evaluation of control measures. The results are useful towards the design of policies restricting COVID-19 and encumbrance to HCUs and mitigating economic contraction.

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

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

COVID-19 detection and classification for machine learning methods using human genomic data

Coronavirus is a disease connected to coronavirus. World Health Organization has declared COVID-19 a pandemic. It has an impact on 212 nations and territories worldwide. Examining and identifying patterns in X-Ray pictures of the lungs is still necessary. Early diagnosis may help to lessen a person's virus exposure and prevent it. Manual diagnosis is a time- and labor-intensive process. Since the COVID-19 virus has the potential to infect individuals all around the world, its finding is extremely concerning. The purpose of this study is to apply machine learning to identify and classify coronaviruses. The COVID-19 is anticipated to be discriminated and categorized in CT-Lung screening and computer-aided diagnosis (CAD). Several machine learning methods, including Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function, were utilised in conjunction with clinical samples from patients who had contracted corona. While some medical professionals think an RT-PCR test is the most reliable and economical way to detect Covid-19 patients, others think a lung CT scan is more precise and less expensive. Serum samples, respiratory secretions, and whole blood samples are examples of clinical specimens. As a result of the earlier clinical evaluations, these tissues are used to assess 15 different parameters. As part of the proposed four-phase CAD system, the CT lungs screening collection is followed by a pre-processing step that enhances the appearance of ground-glass opacities (GGOs) nodules, which are initially extremely fuzzy and poorly contrasting due to the absence of contrast. These zones will be found and segmented using a modified K-means technique. Support vector machines (SVM) and radial basis functions (RBF) will be used as the input and target data for machine learning classifiers with a 50x50 pixel resolution to categorise the contaminated zones found during the detection phase (RBF). The 15 input items gathered from clinical specimens may be entered into a graphical user interface (GUI) tool that has been created to help doctors receive accurate findings.

Usefulness of High-Resolution Computed Tomography in Early Diagnosis of Patients with Suspected COVID-19

BACKGROUND

Diagnosis of coronavirus disease 2019 (COVID-19) is mainly based on molecular testing. General population studies have shown that chest Computed Tomography (CT) can also be useful.

OBJECTIVE

The study aims to examine the usefulness of high-resolution chest CT for early diagnosis of patients with suspected COVID-19.

DESIGN AND SETTING

This is a cross-sectional study from May 1, 2020, to August 31, 2021, at the COVID Hospital, Mexico City.

METHODS

This study examined the clinical, high-resolution chest CT imaging, and laboratory data of 160 patients who were suspected to have COVID-19. Patients with positive Reverse Transcription- Polymerase Chain Reaction (RT-PCR) testing and those with negative RT-PCR testing but clinical data compatible with COVID-19 and positive antibody testing were considered to have COVID-19 (positive). Sensitivity and specificity of CT for diagnosis of COVID-19 were calculated. p < 0.05 was considered significant.

RESULTS

Median age of 160 study patients was 58 years. The proportion of patients with groundglass pattern was significantly higher in patients with COVID-19 than in those without COVID (65.1% versus 0%; P = 0.005). COVID-19 was ruled out in sixteen (11.1%). Only four of the 132 patients diagnosed with COVID-19 (3.0%) did not show CT alterations (p < 0.001). Sensitivity and specificity of CT for COVID-19 diagnosis were 96.7% and 42.8%, respectively.

CONCLUSIONS

Chest CT can identify patients with COVID-19, as characteristic disease patterns are observed on CT in the early disease stage.

Role of biological markers and CT severity score in predicting mortality in patients with COVID‑19: An observational retrospective study

COVID-19 pandemic is a continuing ongoing emergency of public concern. Early identification of markers associated with disease severity and mortality can lead to a prompter therapeutic approach. The present study conducted a multivariate analysis of different markers associated with mortality in order to establish their predictive role. Confirmed cases of 697 patients were examined. Demographic data, clinical symptoms and comorbidities were evaluated. Laboratory and imaging severity scores were reviewed. A total of 133 (19.1%) out of 697 patients succumbed during hospitalization. Obesity was the most common comorbidity, followed by hypertension, diabetes, coronary heart disease and chronic kidney disease. Compared with the survivor patients, non-survivors had a higher prevalence of diabetes, chronic kidney disease and coronary heart disease, as well as higher values of laboratory markers such as neutrophil-lymphocyte ratio (NLR), D-dimer, procalcitonin, IL-6 and C Reactive protein (CRP) and respectively high values of imaging severity scores. Multivariate regression analysis showed that high values of the proposed markers and chest computerized tomography (CT) severity imaging score were predictive for in hospital death: NLR [hazard ratio (HR): 3.127 confidence interval (CI) 95: 2.137-4.576]; D-dimer [HR: 6.223 (CI 95:3.809-10.167)]; procalcitonin [HR: 4.414 (CI 95:2.804-6.948)]; IL-6 [HR: 3.344 (CI 95:1.423-7.855)]; CRP [HR:2.997 (CI 95:1.940-4.630)]; and CT severity score [HR: 3.068 (CI 95:1.777-5.299)]. Laboratory markers and imaging severity scores could be used to stratify mortality risk in COVID-19 patients.

Radiographic Findings and Association With Clinical Severity and Outcomes in Critically Ill Patients With COVID-19

PURPOSE

To describe evolution and severity of radiographic findings and assess association with disease severity and outcomes in critically ill COVID-19 patients.

MATERIALS AND METHODS

This retrospective study included 62 COVID-19 patients admitted to the intensive care unit (ICU). Clinical data was obtained from electronic medical records. A total of 270 chest radiographs were reviewed and qualitatively scored (CXR score) using a severity scale of 0-30. Radiographic findings were correlated with clinical severity and outcome.

RESULTS

The CXR score increases from a median initial score of 10 at hospital presentation to the median peak CXR score of 18 within a median time of 4 days after hospitalization, and then slowly decreases to a median last CXR score of 15 in a median time of 12 days after hospitalization. The initial and peak CXR score was independently associated with invasive MV after adjusting for age, gender, body mass index, smoking, and comorbidities (Initial, odds ratio [OR]: 2.11 per 5-point increase, confidence interval [CI] 1.35-3.32, P= 0.001; Peak, OR: 2.50 per 5-point increase, CI 1.48-4.22, P= 0.001). Peak CXR scores were also independently associated with vasopressor usage (OR: 2.28 per 5-point increase, CI 1.30-3.98, P= 0.004). Peak CXR scores strongly correlated with the duration of invasive MV (Rho = 0.62, P< 0.001), while the initial CXR score (Rho = 0.26) and the peak CXR score (Rho = 0.27) correlated weakly with the sequential organ failure assessment score. No statistically significant associations were found between radiographic findings and mortality.

CONCLUSIONS

Evolution of radiographic features indicates rapid disease progression and correlate with requirement for invasive MV or vasopressors but not mortality, which suggests potential nonpulmonary pathways to death in COVID-19.

The mortality predictors in non-vaccinated COVID-19 patients

Aim: The novel coronavirus (SARS-CoV-2) causes COVID-19 disease. From December 31, 2019, to the present (July 2022), 545 million new cases have been detected, while the number of deaths due to the disease has reached 6.3 million. This study aims to reveal mortality-related risk factors, including comorbid conditions, clinical course, imaging, and laboratory parameters in COVID-19 patients hospitalized in a tertiary hospital. Material and Method: An observational, retrospective study was conducted among hospitalized COVID-19 patients at the tertiary health center in Turkey between November 2020 and April 2021. A total of 601 patients were treated in this period and vaccinated 85 patients were excluded. The remaining 516 patients’ demographical data, clinical severity, laboratory parameters, thorax computed tomography (CT) involvement, and mortalities were recorded. Results: In evaluating the factors affecting COVID-19 mortality, it was observed that male gender and advanced age were significantly associated with mortality, and the mortality rate was higher in patients with involvement in thorax CT and patients with a clinically severe course. In the evaluation of the patients in terms of comorbidities, DM, HT, and CAD were significantly higher in the patients who died. It was determined that patients who died during hospitalization needed respiratory support at a higher rate. Conclusion: Identifying predicting factors is essential for the early recognition the vulnerable patients for hospitalization decisions and early aggressive treatment. In this study, male gender, advanced age, comorbidities (DM, HT, CAD), pulmonary involvement, and severe clinical presentation were identified as significantly related factors associated with mortality.

Lung Ultrasound in COVID-19 and Post-COVID-19 Patients, an Evidence-Based Approach

OBJECTIVES

Worldwide, lung ultrasound (LUS) was utilized to assess coronavirus disease 2019 (COVID-19) patients. Often, imaging protocols were however defined arbitrarily and not following an evidence-based approach. Moreover, extensive studies on LUS in post-COVID-19 patients are currently lacking. This study analyses the impact of different LUS imaging protocols on the evaluation of COVID-19 and post-COVID-19 LUS data.

METHODS

LUS data from 220 patients were collected, 100 COVID-19 positive and 120 post-COVID-19. A validated and standardized imaging protocol based on 14 scanning areas and a 4-level scoring system was implemented. We utilized this dataset to compare the capability of 5 imaging protocols, respectively based on 4, 8, 10, 12, and 14 scanning areas, to intercept the most important LUS findings. This to evaluate the optimal trade-off between a time-efficient imaging protocol and an accurate LUS examination. We also performed a longitudinal study, aimed at investigating how to eventually simplify the protocol during follow-up. Additionally, we present results on the agreement between AI models and LUS experts with respect to LUS data evaluation.

RESULTS

A 12-areas protocol emerges as the optimal trade-off, for both COVID-19 and post-COVID-19 patients. For what concerns follow-up studies, it appears not to be possible to reduce the number of scanning areas. Finally, COVID-19 and post-COVID-19 LUS data seem to show differences capable to confuse AI models that were not trained on post-COVID-19 data, supporting the hypothesis of the existence of LUS patterns specific to post-COVID-19 patients.

CONCLUSIONS

A 12-areas acquisition protocol is recommended for both COVID-19 and post-COVID-19 patients, also during follow-up.

Abrupt transition of the efficient vaccination strategy in a population with heterogeneous fatality rates

An insufficient supply of an effective SARS-CoV-2 vaccine in most countries demands an effective vaccination strategy to minimize the damage caused by the disease. Currently, many countries vaccinate their population in descending order of age (i.e., descending order of fatality rate) to minimize the deaths caused by the disease; however, the effectiveness of this strategy needs to be quantitatively assessed. We employ the susceptible-infected-recovered-dead model to investigate various vaccination strategies. We constructed a metapopulation model with heterogeneous contact and fatality rates and investigated the effectiveness of vaccination strategies to reduce epidemic mortality. We found that the fatality-based strategy, which is currently employed in many countries, is more effective when the contagion rate is high and vaccine supply is low, but the contact-based method outperforms the fatality-based strategy when there is a sufficiently high supply of the vaccine. We identified a discontinuous transition of the optimal vaccination strategy and path-dependency analogous to hysteresis. This transition and path-dependency imply that combining the fatality-based and contact-based strategies is ineffective in reducing the number of deaths. Furthermore, we demonstrate that such phenomena occur in real-world epidemic diseases, such as tuberculosis and COVID-19. We also show that the conclusions of this research are valid even when the complex epidemic stages, efficacy of the vaccine, and reinfection are considered.

Estimated Atherosclerotic Cardiovascular Disease Risk: Disparities and Severe COVID-19 Outcomes (from the National COVID Cohort Collaborative)

Although cardiovascular disease risk factors relate to COVID-19, the association of estimated atherosclerotic cardiovascular disease (ASCVD) risk with severe COVID-19 is not established. We examined the relation of the pooled-cohort ASCVD risk score to severe COVID-19 among 28,646 subjects from the National COVID Cohort Collaborative database who had positive SARS-CoV-2 test results from April 1, 2020 to April 1, 2021. In addition, 10-year ASCVD risk scores were calculated, and subjects were stratified into low-risk (<5%), borderline-risk (5% to <7.5%), intermediate-risk (7.5% to <20%), and high-risk (>=20%) groups. Severe COVID-19 outcomes (including death, remdesivir treatment, COVID-19 pneumonia, acute respiratory distress syndrome, and mechanical ventilation) occurring during follow-up were examined individually and as a composite in relation to ASCVD risk group across race and gender. Multiple logistic regression, adjusted for age, gender, and race, examined the relation of ASCVD risk group to the odds of severe COVID-19 outcomes. Our subjects had a mean age of 59.4 years; 14% were black and 57% were female. ASCVD risk group was directly related to severe COVID-19 prevalence. The adjusted odds ratio of the severe composite COVID-19 outcome by risk group (vs the low-risk group) was 1.8 (95% confidence interval 1.5 to 2.2) for the borderline-risk, 2.7 (2.3 to 3.2) for the intermediate-risk, and 4.6 (3.7 to 5.6) for the high-risk group. Black men and black women in the high-risk group showed higher severe COVID-19 prevalence compared with nonblack men and nonblack women. Prevalence of severe COVID-19 outcomes was similar in intermediate-risk black men and high-risk nonblack men (approximately 12%). In conclusion, although further research is needed, the 10-year ASCVD risk score in adults ages 40 to 79 years may be used to identify those who are at highest risk for COVID-19 complications and for whom more intensive treatment may be warranted.

Modeling the Transmission Dynamics of COVID-19 Among Five High Burden African Countries

Background

Today, coronavirus disease-19 has left a permanent dark mark on the history of human beings. The ongoing global pandemic outbreak of COVID-19 has spread to 58 African countries, with over 6.07 million confirmed cases and over 151,412 deaths. The five high burden African countries are South Africa, Morocco, Tunisia, Ethiopia, and Libya, with case fatality rates (CFR) of nearly 0.15%, 0.042%, 0.22%, 0.006%, and 0.086%, respectively. This is why the research aims to adequately understand the transmission dynamics of the virus and its variants in five high-burden African countries.

Methods

Our study is a deterministic model, where the population is partitioned into five components on the epidemiological state of the individuals. We presented a year-structured susceptible, infected, mild severs, critical severe, and recover (SIMCR) compartmental model of COVID-19 disease transmission with incidence rate during the pandemic period.

Results

The number of susceptible individuals increased by 30,711,930 in South Africa, 5,919,837 in Morocco, 3,485,020 in Tunisia, 7,833,642 in Ethiopia, and 2,145,404 in Libya in the next 3 decades with compare to the unvaccinated population and the number of infected individuals decreased by 30,479,271 in South Africa, 19,809,751 in Morocco, 3,456,406 in Tunisia, 7,761,993 in Ethiopia, and 2,125,038 in Libya.

Conclusion

SIMCR model is used to describe the transmission of COVID-19 among five high-burden African countries. For the next 30 years, we will have around 86 million infected individuals and millions of death only in those five African countries. To reduce those problems, vaccination is the best and most effective mechanism. So vaccinating half of the populations in those countries helps to control and reduce the transmission rate of COVID-19 in Africa for the next 30 years. This leads to preventing 17,212,405 people from becoming infected and millions of deaths being reduced in those five high-burden African countries.

Prognostic Value of Chest-Computed Tomography in Patients with COVID-19

Background: The diagnostic value for chest CT has been widely established in patients with COVID-19. However, there is a lack of satisfactory data about the prognostic value of chest CTs. This study investigated the prognostic value of chest CTs in COVID-19 patients. Materials and Methods: A total of 521 symptomatic patients hospitalized with COVID-19 were included retrospectively. Clinical, laboratory, and chest CT characteristics were compared between survivors and non-survivors. Concerning chest CT, for each subject, a semi-quantitative CT severity scoring system was applied. Results: Most patients showed typical CT features based on the likelihood of COVID-19. The global CT score was significantly higher in non-survivors (median (IQR), 1 (0−6) vs. 10 (5−13), p < 0.001). A cut-off value of 5.5 for the global CT score predicted in-hospital mortality with 74% sensitivity and 73% specificity. Global CT score, age, C-reactive protein, and diabetes were independent predictors of in-hospital mortality. The global CT score was significantly correlated with the C-reactive protein, D-dimer, pro-brain natriuretic peptide, and procalcitonin levels. Conclusion: The global CT score could provide valuable prognostic data in symptomatic patients with COVID-19.

Predictors of mortality among hospitalized patients with COVID-19: A single-centre retrospective analysis

BACKGROUND

The severity of disease and mortality due to coronavirus disease (COVID-19) was found to be high among patients with concurrent medical illnesses. Serum biomarkers can be used to predict the course of COVID-19 pneumonia. Data from India are very scarce about predictors of mortality among COVID-19 patients.

METHODOLOGY

In the present retrospective study of 65 RT-PCR confirmed COVID-19 patients, we retrieved data regarding clinical symptoms, laboratory parameters, and radiological grading of severity. Further, we also collected data about their hospital course, duration of stay, treatment, and outcome. Data analysis was done to compare the patient characteristics between survivor and non-survivor groups and to assess the predictors of mortality.

RESULTS

The mean age of the study population was 56.23 years (SD, 12.91) and most of them were males (63%); 81.5% of patients survived and were discharged, whereas 18.5% of patients succumbed to the disease. Univariate analysis across both groups showed that older age, diabetes mellitus, higher computed tomogram (CT) severity score, and raised levels of laboratory parameters viz, D-dimer, CPK-MB (creatine kinase), and lactate dehydrogenase (LDH) were associated with increased mortality among hospitalized patients. On multivariate analysis, elevated levels of serum D-dimer (odds ratio, 95% CI: 10.98, 1.13-106.62, p = 0.04) and LDH (odds ratio, 95% CI: 19.15, 3.28-111.87, p = 0.001) were independently associated with mortality.

CONCLUSION

Older patients, diabetics, and patients with high CT severity scores at admission are at increased risk of death from COVID-19. Serum biomarkers such as D-dimer and LDH help in predicting mortality in COVID-19 patients.

Machine Learning Approach for Autonomous Detection and Classification of COVID-19 Virus

As people all over the world are vulnerable to be affected by the COVID-19 virus, the automatic detection of such a virus is an important concern. The paper aims to detect and classify corona virus using machine learning. To spot and identify corona virus in CT-Lung screening and Computer-Aided diagnosis (CAD) system is projected to distinguish and classifies the COVID-19. By utilizing the clinical specimens obtained from the corona-infected patients with the help of some machine learning techniques like Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function. While some specialists believe that the RT-PCR test is the best option for diagnosing Covid-19 patients, others believe that CT scans of the lungs can be more accurate in diagnosing corona virus infection, as well as being less expensive than the PCR test. The clinical specimens include serum specimens, respiratory secretions, and whole blood specimens. Overall, 15 factors are measured from these specimens as the result of the previous clinical examinations. The proposed CAD system consists of four phases starting with the CT lungs screening collection, followed by a pre-processing stage to enhance the appearance of the ground glass opacities (GGOs) nodules as they originally lock hazy with fainting contrast. A modified K-means algorithm will be used to detect and segment these regions. Finally, the use of detected, infected areas that obtained in the detection phase with a scale of 50×50 and perform segmentation of the solid false positives that seem to be GGOs as inputs and targets for the machine learning classifiers, here a support vector machine (SVM) and Radial basis function (RBF) has been utilized. Moreover, a GUI application is developed which avoids the confusion of the doctors for getting the exact results by giving the 15 input factors obtained from the clinical specimens.

The Disconnect Between Development and Intended Use of Clinical Prediction Models for Covid-19: A Systematic Review and Real-World Data Illustration

Background

The SARS-CoV-2 pandemic has boosted the appearance of clinical predictions models in medical literature. Many of these models aim to provide guidance for decision making on treatment initiation. Special consideration on how to account for post-baseline treatments is needed when developing such models. We examined how post-baseline treatment was handled in published Covid-19 clinical prediction models and we illustrated how much estimated risks may differ according to how treatment is handled.

Methods

Firstly, we reviewed 33 Covid-19 prognostic models published in literature in the period up to 5 May 2020. We extracted: (1) the reported intended use of the model; (2) how treatment was incorporated during model development and (3) whether the chosen analysis strategy was in agreement with the intended use. Secondly, we used nationwide Dutch data on hospitalized patients who tested positive for SARS-CoV-2 in 2020 to illustrate how estimated mortality risks will differ when using four different analysis strategies to model ICU treatment.

Results

Of the 33 papers, 21 (64%) had misalignment between intended use and analysis strategy, 7 (21%) were unclear about the estimated risk and only 5 (15%) had clear alignment between intended use and analysis strategy. We showed with real data how different approaches to post-baseline treatment yield different estimated mortality risks, ranging between 33 and 46% for a 75 year-old patient with two medical conditions.

Conclusions

Misalignment between intended use and analysis strategy is common in reported Covid-19 clinical prediction models. This can lead to considerable under or overestimation of intended risks.

COVID-19 detection with X-ray images by using transfer learning

Coronavirus is an infectious disease induced by extreme acute respiratory syndrome coronavirus 2. Novel coronaviruses can lead to mild to serious symptoms, like tiredness, nausea, fever, dry cough and breathlessness. Coronavirus symptoms are close to influenza, pneumonia and common cold. So Coronavirus can only be confirmed with a diagnostic test. 218 countries and territories worldwide have reported a total of 59.6 million active cases of the COVID-19 and 1.4 million deaths as of November 24, 2020. Rapid, accurate and early medical diagnosis of the disease is vital at this stage. Researchers analyzed the CT and X-ray findings from a large number of patients with coronavirus pneumonia to draw their conclusions. In this paper, we applied Support Vector Machine (SVM) classifier. After that we moved on to deep transfer learning models such as VGG16 and Xception which are implemented using Keras and Tensor flow to detect positive coronavirus patient using X-ray images. VGG16 and Xception show better performances as compared to SVM. In our work, Xception gained an accuracy of 97.46% with 98% f-score.

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BACKGROUND

The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission.

METHODS

Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set.

RESULTS

No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set.

CONCLUSION

By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.

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.

When to operate after SARS-CoV-2 infection? A review on the recent consensus recommendation of the DGC/BDC and the DGAI/BDA

Since the eruption of the worldwide SARS-CoV-2 pandemic in late 2019/early 2020, multiple elective surgical interventions were postponed. Through pandemic measures, elective operation capacities were reduced in favour of intensive care treatment for critically ill SARS-CoV-2 patients. Although intermittent low-incidence infection rates allowed an increase in elective surgery, surgeons have to include long-term pulmonary and extrapulmonary complications of SARS-CoV-2 infections (especially "Long Covid") in their perioperative management considerations and risk assessment procedures. This review summarizes recent consensus statements and recommendations regarding the timepoint for surgical intervention after SARS-CoV-2 infection released by respective German societies and professional representatives including DGC/BDC (Germany Society of Surgery/Professional Association of German Surgeons e.V.) and DGAI/BDA (Germany Society of Anesthesiology and Intensive Care Medicine/Professional Association of German Anesthesiologists e.V.) within the scope of the recent literature. The current literature reveals that patients with pre- and perioperative SARS-CoV-2 infection have a dramatically deteriorated postoperative outcome. Thereby, perioperative mortality is mainly caused by pulmonary and thromboembolic complications. Notably, perioperative mortality decreases to normal values over time depending on the duration of SARS-CoV-2 infection.

Factors Predicting Progression to Severe COVID-19: A Competing Risk Survival Analysis of 1753 Patients in Community Isolation in Wuhan, China

Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19-rather than to recovery-among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients (n = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04-1.58, p = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24-2.01, p < 0.001), and CT signs of ground-glass opacity (HR = 1.39, 95% CI 1.04-1.86, p = 0.024) and infiltrating shadows (HR = 1.84, 95% CI 1.22-2.78, p = 0.004). The risk of progression was found to be lower among patients with nausea or vomiting (HR = 0.53, 95% CI 0.30-0.96, p = 0.036) and headaches (HR = 0.54, 95% CI 0.29-0.99, p = 0.046). Our results suggest that several factors that can be easily measured even in resource-poor settings (dyspnea, sex, and age) can be used to identify mild COVID-19 patients who are at increased risk of disease progression. Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings. Common and unspecific symptoms (headaches, nausea, and vomiting) are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate. Future public health and clinical guidelines should build on this evidence to improve the screening, triage, and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms.

Clinical Implication of the Effect of the Production of Neutralizing Antibodies Against SARS-Cov-2 for Chronic Immune Thrombocytopenia Flare-Up Associated with COVID-19 Infection: A Case Report and the Review of Literature

Previous studies have demonstrated that the appropriate production of serum anti-severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) neutralizing antibody (nAb) plays a critical role in the recovery from coronavirus disease 2019 (COVID-19); however, the role of nAb production in the recovery from a flare-up of chronic immune thrombocytopenia (ITP) has been unknown. We here report the first retrospectively investigated case of serum anti-SARS-Cov-2 nAb production during chronic ITP flare-up triggered by COVID-19. A 79-year-old woman with a history of corticosteroid-refractory ITP visited our hospital complaining of fever, cough, and sore throat for 4 days. Although chronic ITP was controlled by 12.5 mg of eltrombopag (EPAG) every other day, laboratory tests showed a decreased peripheral blood platelet count of 15.0 × 109/L, which indicated worsening thrombocytopenia. Meanwhile, PCR testing of a nasopharyngeal swab revealed that the patient was positive for SARS-Cov-2, and a computed tomography scan revealed bilateral pneumonia. On the basis of the flare-up of chronic ITP associated with COVID-19 pneumonia which was determined as a moderately severe status according to the WHO clinical progression scale, intravenous immunoglobulin therapy for 5 days (days 0-4) and antiviral therapy were added on top of EPAG, which only resulted in a transient increase in the platelet count for several days. After decreasing to 8.0 × 109/L on day 13, the platelet count increased from day 16, coinciding with a positive detection for serum nAb against SARS-Cov-2. Although the increased dose up to 50 mg/day of EPAG was challenged during the clinical course, rapid dose reduction did not cause another relapse. In addition, no thrombotic or bleeding event was seen. These collectively suggest the vital role of the production of anti-SARS-Cov-2 nAb and improvement of clinical symptoms for recovery from a flare-up of chronic ITP in our case.

Prognostic factors of worse outcome for hospitalized COVID-19 patients, with emphasis on chest computed tomography data: a retrospective study

OBJECTIVE

To evaluate anthropometric and clinical data, muscle mass, subcutaneous fat, spine bone mineral density, extent of acute pulmonary disease related to COVID-19, quantification of pulmonary emphysema, coronary calcium, and hepatic steatosis using chest computed tomography of hospitalized patients with confirmed diagnosis of COVID-19 pneumonia and verify its association with disease severity.

METHODS

A total of 123 adults hospitalized due to COVID-19 pneumonia were enrolled in the present study, which evaluated the anthropometric, clinical and chest computed tomography data (pectoral and paravertebral muscle area and density, subcutaneous fat, thoracic vertebral bodies density, degree of pulmonary involvement by disease, coronary calcium quantification, liver attenuation measurement) and their association with poorer prognosis characterized through a combined outcome of intubation and mechanical ventilation, need of intensive care unit, and death.

RESULTS

Age (p=0.013), body mass index (p=0.009), lymphopenia (p=0.034), and degree of pulmonary involvement of COVID-19 pneumonia (p<0.001) were associated with poor prognosis. Extent of pulmonary involvement by COVID-19 pneumonia had an odds ratio of 1,329 for a poor prognosis and a cutoff value of 6.5 for increased risk, with a sensitivity of 64.9% and specificity of 67.1%.

CONCLUSION

The present study found an association of high body mass index, older age, extent of pulmonary involvement by COVID-19, and lymphopenia with severity of COVID-19 pneumonia in hospitalized patients.

Comparison of Pneumonia Severity Indices, qCSI, 4C-Mortality Score and qSOFA in Predicting Mortality in Hospitalized Patients with COVID-19 Pneumonia

This is a retrospective and observational study on 1511 patients with SARS-CoV-2, who were diagnosed with COVID-19 by real-time PCR testing and hospitalized due to COVID-19 pneumonia. 1511 patients, 879 male (58.17%) and 632 female (41.83%) with a mean age of 60.1 ± 14.7 were included in the study. Survivors and non-survivors groups were statistically compared with respect to survival, discharge, ICU admission and in-hospital death. Although gender was not statistically significant different between two groups, 80 (60.15%) of the patients who died were male. Mean age was 72.8 ± 11.8 in non-survivors vs. 59.9 ± 14.7 in survivors (p < 0.001). Overall in-hospital mortality was found to be 8.8% (133/1511 cases), and overall ICU admission was 10.85% (164/1511 cases). The PSI/PORT score of the non-survivors group was higher than that of the survivors group (144.38 ± 28.64 versus 67.17 ± 25.63, p < 0.001). The PSI/PORT yielding the highest performance was the best predictor for in-hospital mortality, since it incorporates the factors as advanced age and comorbidity (AUROC 0.971; % 95 CI 0.961−0.981). The use of A-DROP may also be preferred as an easier alternative to PSI/PORT, which is a time-consuming evaluation although it is more comprehensive.

Thoracic imaging tests for the diagnosis of COVID-19

BACKGROUND

Our March 2021 edition of this review showed thoracic imaging computed tomography (CT) to be sensitive and moderately specific in diagnosing COVID-19 pneumonia. This new edition is an update of the review.

OBJECTIVES

Our objectives were to evaluate the diagnostic accuracy of thoracic imaging in people with suspected COVID-19; assess the rate of positive imaging in people who had an initial reverse transcriptase polymerase chain reaction (RT-PCR) negative result and a positive RT-PCR result on follow-up; and evaluate the accuracy of thoracic imaging for screening COVID-19 in asymptomatic individuals. The secondary objective was to assess threshold effects of index test positivity on accuracy.

SEARCH METHODS

We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 17 February 2021. We did not apply any language restrictions.

SELECTION CRITERIA

We included diagnostic accuracy studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19. Studies had to assess chest CT, chest X-ray, or ultrasound of the lungs for the diagnosis of COVID-19, use a reference standard that included RT-PCR, and report estimates of test accuracy or provide data from which we could compute estimates. We excluded studies that used imaging as part of the reference standard and studies that excluded participants with normal index test results.

DATA COLLECTION AND ANALYSIS

The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using QUADAS-2. We presented sensitivity and specificity per study on paired forest plots, and summarized pooled estimates in tables. We used a bivariate meta-analysis model where appropriate.

MAIN RESULTS

We included 98 studies in this review. Of these, 94 were included for evaluating the diagnostic accuracy of thoracic imaging in the evaluation of people with suspected COVID-19. Eight studies were included for assessing the rate of positive imaging in individuals with initial RT-PCR negative results and positive RT-PCR results on follow-up, and 10 studies were included for evaluating the accuracy of thoracic imaging for imagining asymptomatic individuals. For all 98 included studies, risk of bias was high or unclear in 52 (53%) studies with respect to participant selection, in 64 (65%) studies with respect to reference standard, in 46 (47%) studies with respect to index test, and in 48 (49%) studies with respect to flow and timing. Concerns about the applicability of the evidence to: participants were high or unclear in eight (8%) studies; index test were high or unclear in seven (7%) studies; and reference standard were high or unclear in seven (7%) studies. Imaging in people with suspected COVID-19 We included 94 studies. Eighty-seven studies evaluated one imaging modality, and seven studies evaluated two imaging modalities. All studies used RT-PCR alone or in combination with other criteria (for example, clinical signs and symptoms, positive contacts) as the reference standard for the diagnosis of COVID-19. For chest CT (69 studies, 28285 participants, 14,342 (51%) cases), sensitivities ranged from 45% to 100%, and specificities from 10% to 99%. The pooled sensitivity of chest CT was 86.9% (95% confidence interval (CI) 83.6 to 89.6), and pooled specificity was 78.3% (95% CI 73.7 to 82.3). Definition for index test positivity was a source of heterogeneity for sensitivity, but not specificity. Reference standard was not a source of heterogeneity. For chest X-ray (17 studies, 8529 participants, 5303 (62%) cases), the sensitivity ranged from 44% to 94% and specificity from 24 to 93%. The pooled sensitivity of chest X-ray was 73.1% (95% CI 64. to -80.5), and pooled specificity was 73.3% (95% CI 61.9 to 82.2). Definition for index test positivity was not found to be a source of heterogeneity. Definition for index test positivity and reference standard were not found to be sources of heterogeneity. For ultrasound of the lungs (15 studies, 2410 participants, 1158 (48%) cases), the sensitivity ranged from 73% to 94% and the specificity ranged from 21% to 98%. The pooled sensitivity of ultrasound was 88.9% (95% CI 84.9 to 92.0), and the pooled specificity was 72.2% (95% CI 58.8 to 82.5). Definition for index test positivity and reference standard were not found to be sources of heterogeneity. Indirect comparisons of modalities evaluated across all 94 studies indicated that chest CT and ultrasound gave higher sensitivity estimates than X-ray (P = 0.0003 and P = 0.001, respectively). Chest CT and ultrasound gave similar sensitivities (P=0.42). All modalities had similar specificities (CT versus X-ray P = 0.36; CT versus ultrasound P = 0.32; X-ray versus ultrasound P = 0.89). Imaging in PCR-negative people who subsequently became positive For rate of positive imaging in individuals with initial RT-PCR negative results, we included 8 studies (7 CT, 1 ultrasound) with a total of 198 participants suspected of having COVID-19, all of whom had a final diagnosis of COVID-19. Most studies (7/8) evaluated CT. Of 177 participants with initially negative RT-PCR who had positive RT-PCR results on follow-up testing, 75.8% (95% CI 45.3 to 92.2) had positive CT findings. Imaging in asymptomatic PCR-positive people For imaging asymptomatic individuals, we included 10 studies (7 CT, 1 X-ray, 2 ultrasound) with a total of 3548 asymptomatic participants, of whom 364 (10%) had a final diagnosis of COVID-19. For chest CT (7 studies, 3134 participants, 315 (10%) cases), the pooled sensitivity was 55.7% (95% CI 35.4 to 74.3) and the pooled specificity was 91.1% (95% CI 82.6 to 95.7).

AUTHORS' CONCLUSIONS

Chest CT and ultrasound of the lungs are sensitive and moderately specific in diagnosing COVID-19. Chest X-ray is moderately sensitive and moderately specific in diagnosing COVID-19. Thus, chest CT and ultrasound may have more utility for ruling out COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. The uncertainty resulting from high or unclear risk of bias and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results.

Usage of compromised lung volume in monitoring steroid therapy on severe COVID-19

BACKGROUND

Quantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of coronavirus disease 2019 (COVID-19) and for monitoring its progress. The present study aimed to assess the association between steroid therapy and quantitative CT parameters in a longitudinal cohort with COVID-19.

METHODS

Between February 7 and February 17, 2020, 72 patients with severe COVID-19 were retrospectively enrolled. All 300 chest CT scans from these patients were collected and classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (at admission); Stage 2 (3-7 days); Stage 3 (8-14 days); Stage 4 (15-21 days); and Stage 5 (22-31 days). QCT was performed using a threshold-based quantitative analysis to segment the lung according to different Hounsfield unit (HU) intervals. The primary outcomes were changes in percentage of compromised lung volume (%CL, - 500 to 100 HU) at different stages. Multivariate Generalized Estimating Equations were performed after adjusting for potential confounders.

RESULTS

Of 72 patients, 31 patients (43.1%) received steroid therapy. Steroid therapy was associated with a decrease in %CL (- 3.27% [95% CI, - 5.86 to - 0.68, P = 0.01]) after adjusting for duration and baseline %CL. Associations between steroid therapy and changes in %CL varied between different stages or baseline %CL (all interactions, P < 0.01). Steroid therapy was associated with decrease in %CL after stage 3 (all P < 0.05), but not at stage 2. Similarly, steroid therapy was associated with a more significant decrease in %CL in the high CL group (P < 0.05), but not in the low CL group.

CONCLUSIONS

Steroid administration was independently associated with a decrease in %CL, with interaction by duration or disease severity in a longitudinal cohort. The quantitative CT parameters, particularly compromised lung volume, may provide a useful tool to monitor COVID-19 progression during the treatment process. Trial registration Clinicaltrials.gov, NCT04953247. Registered July 7, 2021, https://clinicaltrials.gov/ct2/show/NCT04953247.

Usage of compromised lung volume in monitoring steroid therapy on severe COVID-19

BACKGROUND

Quantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of coronavirus disease 2019 (COVID-19) and for monitoring its progress. The present study aimed to assess the association between steroid therapy and quantitative CT parameters in a longitudinal cohort with COVID-19.

METHODS

Between February 7 and February 17, 2020, 72 patients with severe COVID-19 were retrospectively enrolled. All 300 chest CT scans from these patients were collected and classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (at admission); Stage 2 (3-7 days); Stage 3 (8-14 days); Stage 4 (15-21 days); and Stage 5 (22-31 days). QCT was performed using a threshold-based quantitative analysis to segment the lung according to different Hounsfield unit (HU) intervals. The primary outcomes were changes in percentage of compromised lung volume (%CL, - 500 to 100 HU) at different stages. Multivariate Generalized Estimating Equations were performed after adjusting for potential confounders.

RESULTS

Of 72 patients, 31 patients (43.1%) received steroid therapy. Steroid therapy was associated with a decrease in %CL (- 3.27% [95% CI, - 5.86 to - 0.68, P = 0.01]) after adjusting for duration and baseline %CL. Associations between steroid therapy and changes in %CL varied between different stages or baseline %CL (all interactions, P < 0.01). Steroid therapy was associated with decrease in %CL after stage 3 (all P < 0.05), but not at stage 2. Similarly, steroid therapy was associated with a more significant decrease in %CL in the high CL group (P < 0.05), but not in the low CL group.

CONCLUSIONS

Steroid administration was independently associated with a decrease in %CL, with interaction by duration or disease severity in a longitudinal cohort. The quantitative CT parameters, particularly compromised lung volume, may provide a useful tool to monitor COVID-19 progression during the treatment process. Trial registration Clinicaltrials.gov, NCT04953247. Registered July 7, 2021, https://clinicaltrials.gov/ct2/show/NCT04953247.

Progression of severity in coronavirus disease 2019 patients before treatment and a self-assessment scale to predict disease severity

OBJECTIVES

This study aims to further investigate the association of COVID-19 disease severity with numerous patient characteristics, and to develop a convenient severity prediction scale for use in self-assessment at home or in preliminary screening in community healthcare settings.

SETTING AND PARTICIPANTS

Data from 45,450 patients infected with COVID-19 from January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System in Wuhan, China.

PRIMARY AND SECONDARY OUTCOME MEASURES

We categorized COVID-19 disease severity, based on The Chinese Diagnosis and Treatment Protocol for COVID-19, as "nonsevere" (which grouped asymptomatic, mild, and ordinary disease) versus "severe" (grouping severe and critical illness).

RESULTS

Twelve scale items-age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage-were identified and showed good predictive ability (area under the curve = 0·72). After excluding the community healthcare laboratory parameters, the remaining model (the final self-assessment scale) showed similar area under the curve (= 0·71).

CONCLUSIONS

Our COVID-19 severity self-assessment scale can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings. Our study constructed a COVID-19 severity self-assessment scale that can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance.

Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients

Clinicians urgently need reliable and stable tools to predict the severity of COVID-19 infection for hospitalized patients to enhance the utilization of hospital resources and supplies. Published COVID-19 related guidelines are frequently being updated, which impacts its utilization as a stable go-to resource for informing clinical and operational decision-making processes. In addition, many COVID-19 patient-level severity prediction tools that were developed during the early stages of the pandemic failed to perform well in the hospital setting due to many challenges including data availability, model generalization, and clinical validation. This study describes the experience of a large tertiary hospital system network in the Middle East in developing a real-time severity prediction tool that can assist clinicians in matching patients with appropriate levels of needed care for better management of limited health care resources during COVID-19 surges. It also provides a new perspective for predicting patients’ COVID-19 severity levels at the time of hospital admission using comprehensive data collected during the first year of the pandemic in the hospital. Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April 2021. The study uses comprehensive COVID-19 patient-level clinical data from the hospital electronic medical records (EMR), vital sign monitoring devices, and Polymerase Chain Reaction (PCR) machines. The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time. Finally, this study provides results from a prospective validation test conducted by clinical experts in the hospital. The proposed prediction framework shows excellent performance in concurrent validation (n=462 patients, March 2020–April 2021) with highest discrimination obtained with the random forest classification model, achieving a macro- and micro-average area under receiver operating characteristics curve (AUC) of 0.83 and 0.87, respectively. The prospective validation conducted by clinical experts (n=185 patients, April–May 2021) showed a promising overall prediction performance with a recall of 78.4–90.0% and a precision of 75.0–97.8% for different severity classes.

[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.

Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT

Objectives

1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification.

Methods

This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy.

Results

The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; p<0.0001).

Conclusions

A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.