Coronavirus Disease 2019 Pneumonia Patients Research Articles

A Fatal Co-infections of Aspergillus latus and Pneumocystis jirovecii in COVID-19 Pneumonia Patient: Case Report

Introduction: For coronavirus disease 2019 (COVID-19) positive patients, co-infection with fungi has previously been reported. Owing to non-specific clinical manifestations and radiographic evidence, it is difficult to distinguish fungal infection from COVID-19 alone, resulting in delayed diagnosis and lack of appropriate treatment. Aspergillus latus infection, a newly rare species with life-threatening characteristics, requires rapid detection with accurate diagnosis to reduce mortality. However, there is little literature that provides a reference for a better understanding of this rare pathogen and guides medical practitioners in choosing the right diagnostics and therapies. Case Presentation: We reported a 67-year-old female complaining of fever, cough, and chest tightness, who had previously been hospitalized for a week due to initial COVID-19 pneumonia. On admission, clinical manifestations and radiological evidence were consistent with COVID-19 pneumonia, and there was no obvious improvement after anti-infective therapy. In terms of etiology for the patient, Galactomannan test was positive for Aspergillus genus, and Metagenomic next-generation sequencing further determined co-infections of A. latus and Pneumocystis jirovecii. Subsequently, isolation of A. latus was obtained through microbial culture. Despite targeted antifungal therapy, no clinical improvement was observed, possibly due to delayed diagnosis and treatment. Finally, the patient died of respiratory failure. Conclusions: This case report demonstrated that metagenomic next-generation sequencing is a fast, unbiased, culture-free approach that can increase the detection of rare pathogens, helping clinicians improve the accuracy of clinical diagnosis and take therapeutic action as quickly as possible. Clinicians should be aware of the risk of COVID-19-associated pulmonary Aspergillosis in COVID-19 patients, and we hope to optimize diagnosis and improve outcomes of COVID-19-associated pulmonary Aspergillosis.

Comparison of post-COVID-19 pulmonary complications in individuals vaccinated with BioNTech or Sinovac

Aims: Although several research have been undertaken to investigate the impact of the vaccination on long Coronavirus Disease 2019 (COVID-19) syndrome or post-acute sequelae, there is a lack of published evidence on the long-term effects of vaccines on lung-sequelae-related disease. Considering the limited global COVID-19 vaccine distribution, it is essential to establish the impact of vaccination in reducing pulmonary complications. Turkey has been offering COVID-19 vaccines from two platforms, including BNT162b2 (Pfizer-BioNTech, mRNA vaccine) and CoronaVac (Sinovac, inactivated vaccine). This study aimed to evaluate the efficacy of BioNTech and Sinovac vaccines in reducing post-COVID-19 pulmonary complications in individuals. Methods: A total of 94 patients COVID-19 pneumonia patients who were categorized based on the quantity of BioNTech or Sinovac vaccines they received before their first COVID-19 infection were included. The inclusion criteria consisted of a confirmed diagnosis of COVID-19 pneumonia through polymerase chain reaction testing, availability of the mentioned before and follow-up computed tomography scans, and administration of at least one dose of vaccine. Results: The number of complications in patients fully vaccinated with Sinovac and who experienced post-COVID lung complications was significantly greater than in those vaccinated with BioNTech. The C-reactive protein and D-Dimer measurements of individuals who experienced complications in the Sinovac vaccinated group were significantly elevated on the index date. Conclusion: The quantity of lung sequelae after COVID and laboratory parameters indicating this result were found to be higher in inactivated virus vaccines than in mRNA vaccines. This suggests that the protection of inactivated vaccines may be insufficient in severe cases.

Impact of Immunoglobulin M-enriched Immunoglobulins on the Outcomes of Severe Coronavirus Disease 2019.

Coronavirus disease 2019 (COVID-19) led to a major global health crisis, leading to a worldwide pandemic. Several therapeutic interventions have been tried with varied results. The purpose of this academic work was to assess the efficacy of immunoglobulin M (IgM)-enriched Ig in the management of patients with severe COVID-19 pneumonia. In this retrospective cohort study, severe COVID-19 pneumonia patients who received IgM-enriched immunoglobulin, in addition to standard-of-care treatment, were retrospectively enrolled. Levels of inflammatory biomarkers, oxygenation status, and organ dysfunction were evaluated, and differences were noted after giving IgM-supplemented IgM. Data from 32 consecutive severe COVID-19 patients admitted to medical intensive care units (ICUs) were analyzed. After giving IgM-enriched Ig, there was an improvement in oxygenation indices as shown by saturation of oxygen/fraction of inspired oxygen (SpO2/FiO2) on days 3 and 7, but it was not statistically significant. Oxygen support could be de-escalated in 13 (40.6%) patients on day 3 and in 8 (25%) patients on day 7, after giving IgM-enriched Ig. After giving IgM-enriched Ig, there was a reduction in the levels of all the studied inflammatory markers [interleukin-6 (IL-6), D-dimer, and ferritin) on days 3 and 7, but it was statistically significant only for IL-6. The overall ICU mortality was 53.1%. Outcomes of patients with severe COVID-19 requiring ICU care remain dismal. IgM-enriched Ig may be helpful in improving oxygenation and combating cytokine storm in these patients. However, in the present study, the improvement in oxygenation indices (SpO2/FiO2) and reduction in inflammatory markers like D-dimer and ferritin were not statistically significant. Hence, larger randomized controlled trials are required to get more definitive evidence to support this therapy and show significant clinical and mortality benefits.

Comparative analysis of prognostic scoring systems in predicting severity and outcomes of Omicron variant COVID-19 pneumonia.

The global spread of Coronavirus Disease 2019 (COVID-19) underscores the urgent need for reliable methods to forecast the disease's severity and outcome, thereby facilitating timely interventions and reducing mortality rates. This study focuses on evaluating the clinical and laboratory profiles of patients with Omicron variant-induced COVID-19 pneumonia and assessing the efficacy of various scoring systems in prognosticating disease severity and mortality. In this retrospective analysis, we examined the clinical records of 409 individuals diagnosed with Omicron variant COVID-19 pneumonia. We documented the Pneumonia Severity Index, CURB-65, and MuLBSTA scores within the first 24 h and analyzed the sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve for each scoring system to ascertain their predictive accuracy for disease severity and fatality risk. The cohort's median age was 78 years, predominantly presenting with fever, cough, expectoration, fatigue, and gastrointestinal symptoms. Factors such as expectoration, fatigue, Glasgow Coma Scale score, lactate dehydrogenase levels, procalcitonin, creatinine levels, and co-occurrence of acute respiratory distress syndrome were identified as independent predictors of disease severity. Furthermore, age, oxygenation index, glucose levels, lactate dehydrogenase, and septic shock were independently associated with mortality. For severe disease prediction, the CURB-65, PSI, and MuLBSTA scores demonstrated sensitivities of 65.9%, 63.8%, and 79.7%, respectively, with specificities of 63.8%, 76.8%, and 60.9%, and AUROCs of 0.707, 0.750, and 0.728. To predict mortality risk, these scores at cutoffs of 1.5, 102.5, and 12.5 exhibited sensitivities of 83.3%, 96.3%, and 70.4%, specificities of 59.4%, 60.8%, and 65.4%, and AUROCs of 0.787, 0.850, and 0.736, respectively. The study cohort predominantly comprised elderly individuals with pre-existing health conditions. Elevated lactate dehydrogenase emerged as a significant marker for both disease severity and prognosis, sputum production, gastrointestinal symptoms, GCS score, creatinine, PCT, and ARDS as independent predictors of disease severity, and age, oxygenation index, glucose levels, and septic shock as independent mortality predictors in COVID-19 pneumonia patients. Among the scoring systems evaluated, Pneumonia Severity Index demonstrated superior predictive capability for both disease severity and mortality, suggesting its utility in forecasting the clinical outcomes of Omicron variant COVID-19 pneumonia.

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

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

Vital signs and work of breathing assessment in the emergency department as predictor for acute respiratory failure in COVID-19 pneumonia

Coronavirus disease 2019 (COVID-19) with acute respiratory failure (ARF) has a high mortality rate. This study aimed to investigate the vital signs and work of breathing parameters in COVID-19 pneumonia patients to predict ARF. We predicted the risk of acute respiratory distress syndrome (ARDS) in COVID-19 patients within 72 hours of admission to the emergency department (ED) and determined cut-off values. We performed an observational prospective cohort study at the tertiary referral Persahabatan Hospital in Jakarta, Indonesia, from July to December 2020. The vital signs were as follows: of respiratory rate (RR), heart rate (HR), pulse oxygen saturation (SpO2), mean arterial pressure (MAP), and axillary body temperature. The work of breathing, which was indicated by nasal flaring and the contraction of the sternocleidomastoid and abdominal muscles, was assessed one hour after a triage examination. The ARF was monitored within 72 hours. The cut-off values of vital signs were determined using the Youden index. In total, 71 (13.65%) of the 520 patients had ARF within 72 hours of admission. The mean values of RR, HR, MAP and SpO2 in the ARF group were 26 breaths/minute, and 102 pulses/minute, at 100 and 92%, respectively. All ARF patients had nasal flaring, 86.4% had a contraction of the sternocleidomastoid, and 67.6%had a contraction of the abdominal muscle. The cut-off values for predicting ARF were as follows: RR >23 breaths/minute (sensitivity 83.1%; specificity 86%), SpO2 <93% (sensitivity 80.5%; specificity 75.2%), HR = 92 pulses/minute (sensitivity 71.8%; sensitivity 75.2%), and MAP = 93.5 (specificity 64.8%; sensitivity 60.4%). Our results indicate that vital signs and work of breathing within the first hour in the emergency department can predict ARF in COVID-19 pneumonia patients within 72 hours.

MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis

The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnosis on different prevalence scenarios. With the objective of improving and accelerating the diagnosis of COVID-19, a multi modal prediction algorithm (MultiCOVID) based on CXR and blood test was developed, to discriminate between COVID-19, Heart Failure and Non-COVID Pneumonia and healthy (Control) patients. This retrospective single-center study includes CXR and blood test obtained between January 2017 and May 2020. Multi modal prediction models were generated using opensource DL algorithms. Performance of the MultiCOVID algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar–Bowker test. A total of 8578 samples from 6123 patients (mean age 66 ± 18 years of standard deviation, 3523 men) were evaluated across datasets. For the entire test set, the overall accuracy of MultiCOVID was 84%, with a mean AUC of 0.92 (0.89–0.94). For 300 random test images, overall accuracy of MultiCOVID was significantly higher (69.6%) compared with individual radiologists (range, 43.7–58.7%) and the consensus of all five radiologists (59.3%, P < .001). Overall, we have developed a multimodal deep learning algorithm, MultiCOVID, that discriminates among COVID-19, heart failure, non-COVID pneumonia and healthy patients using both CXR and blood test with a significantly better performance than experienced thoracic radiologists.

Changes of clinical and echocardiographic parameters within a year after COVID-19 pneumonia in patients without cardiovascular diseases, depending on the obesity presence

Aim. To study the changes of clinical and echocardiographic parameters in people without cardiovascular diseases (CVDs) within a year after coronavirus disease 2019 (COVID-19) depending on the obesity presence.Material and methods. During the year after COVID-19 pneumonia, the clinical and echocardiographic parameters of 21 patients with obesity and without CVD were compared. The group both without CVDs and obesity consisted of 52 patients comparable by sex and age.Results. Newly developed CVDs were registered in 55% and 17% of the group with and without obesity, respectively. Left ventricular (LV) global longitudinal strain 3 months (-18,4±2,3 vs -20,6±2,2%, p=0,010) and one year after COVID-19 pneumonia (-18,8±1,8 vs -20,8±2,5%, p=0,021) was lower in the group with obesity. Lateral (e' later) (10,8±3,3 vs 13,1±2,9 cm/s (p=0,007)) and septal (e' sept) (8,5±2,7 vs 10,1±2,0 cm/s (p=0,011)) early diastolic mitral annulus velocity, as well as the ratio of early and late diastolic LV filling (E/A) (1,1±0,3 vs 1,2±0,3 (p=0,019)) at the end of the follow-up were lower in the group with obesity.Conclusion. In people without CVDs during a year after COVID-19 pneumonia, a high prevalence of newly developed CVD was noted in 55% and in 17% of people with and without obesity, which was accompanied by worse LV systolic and diastolic function in the group with obesity.

Incidence of Air Leak Syndrome in Pediatric Patients With SARS-COV-2 Pneumonia and Respiratory Failure: A Single-Center Retrospective Study.

Air leak syndrome (ALS) is defined as the extrusion of air from an aerated compartment into an unaerated compartment with associated symptoms of respiratory distress. This syndrome can occur as a consequence of trauma, iatrogenic causes, or spontaneously. Retrospective investigations conducted in the adult population have demonstrated an elevated risk of spontaneous ALS development in patients with coronavirus disease 2019 (COVID-19) pneumonia, along with its correlation with mortality. However, no studies have yet explored this phenomenon within the pediatric population. In light of this knowledge gap, we conducted a retrospective chart review comprising 128 pediatric patients ranging in age from one month to 18 years. The primary objective was to assess the incidence of ALS in two distinct groups: patients diagnosed with COVID-19 pneumonia and those with non-COVID-19 viral pneumonia. The groups were compared using Fisher's exact test for sex, the presence of ALS, the requirement of extracorporeal membrane oxygenation (ECMO), and death. The modified Wald method was used to calculate the 95% confidence interval for the mortality rate in patients with COVID-19 pneumonia in the presence of ALS. Our findings revealed a higher prevalence of ALS in patients with COVID-19 pneumonia compared to the non-COVID-19 viral pneumonia group, with a statistically significant P-value of 0.02 and an odds ratio (OR) of 6.72. In terms of mortality rates, there was a statistically significant difference between the two groups (P = 0.025, OR = 1.083). In addition, in patients with ALS in the presence of COVID-19 pneumonia, the mortality rate was 37.5%. However, the requirement of ECMO was not statistically significant (P = 0.16, OR = 1.04). These results suggest that patients with COVID-19 pneumonia have an increased mortality rate and a heightened risk of developing ALS compared to individuals with other viral pneumonias. Furthermore, the presence of ALS was associated with a high mortality rate in COVID-19 pneumonia patients. However, it is crucial to note that obtaining a larger patient sample and involving multiple institutions would be necessary to obtain more consistent and robust data.

Exploration of the optimal GS-441524 trough concentration for treating COVID-19

Remdesivir (RDV) is the cornerstone for treating coronavirus disease 2019 (COVID-19). The active metabolite of RDV, GS-441524 (a nucleoside analog), has high inter-individual variability in plasma concentration; however, its concentration-response relationship is still unclear. This study investigated the target GS-441524 trough concentration for symptom improvement in COVID-19 pneumonia. This single-center, retrospective, observational study included COVID-19 pneumonia patients (≥15 years) who were administered RDV for ≥3 days from May 2020 to August 2021 at Saiseikai Futsukaichi Hospital. To determine the cut-off value of GS-441524 trough concentration on day 3, the achievement of the National Institute of Allergy and Infectious Disease Ordinal Scale (NIAID-OS) ≤3 after RDV administration was evaluated using the cumulative incidence function (CIF) with the Gray test and time-dependent receiver operating characteristic (ROC) analysis. Multivariate logistic regression analysis was performed to determine the factors influencing GS-441524 target trough concentrations. Our analysis comprised 59 patients. The CIF revealed that GS-441524 trough concentration ≥70 ng/mL was associated with the achievement of NIAID-OS ≤3 (p = 0.047), which was significant based on the time-dependent ROC analysis. Factors influencing GS-441524 trough concentration ≥70 ng/mL included a decrease in eGFR (adjusted odds ratio [aOR] 0.96, 95% confidence interval [95% CI] 0.92-0.99, p = 0.027) and BMI ≥25 kg/m2 (aOR 0.26, 95% CI 0.07-0.86, p = 0.031). GS-441524 trough concentration ≥70 ng/mL is a predictor of efficacy in COVID-19 pneumonia. The presence of lower eGFR or BMI ≥25 kg/m2 was associated with achieving GS-441524 trough concentration ≥70 ng/mL.

The impact of daily troponin I and D-dimer serum levels on mortality in COVID-19 pneumonia patients.

Coronavirus disease 2019 (COVID-19) is an infection resulting in very high morbidity and mortality rates globally. Limited data are available on the cardiovascular manifestations in these patients. The aim of this study was to analyse the daily troponin I and D-dimer levels and their impact on the need for intensive care and on mortality rates of COVID-19-infected patients. Two-hundred and five patients who were hospitalised between 20 March and 5 May 2020, with a diagnosis of moderate-to-severe COVID-19 pneumonia, were analysed retrospectively. Serum troponin I and D-dimer levels were recorded for at least 10 days after admission. The average age was higher in the group of patients who died compared to the group who were discharged (67.79 ± 14.9 vs 56.87 ± 18.15 years, respectively, p < 0.001). The presence of hypertension, diabetes mellitus, previous coronary bypass surgery, heart failure, chronic renal failure and chronic obstructive pulmonary disease statistically significantly affected mortality rates (p = 0.003, 0.004, 0.045, 0.02, 0.003, 0.007, respectively). The first 10 days of measurements of troponin I and D-dimer were associated with intensive care requirements and mortality (p < 0.001). Both troponin I and D-dimer were higher in the group who died compared to the patients requiring intensive care. Troponin I values of ≥ 16.05 pg/ml on the seventh day were related to the need for intensive care [area under the curve (AUC) 0.896, sensitivity 78.6%, specificity 78.3%, p < 0.001). Troponin I values ≥ 30.25 pg/ml on the ninth day were related to mortality (AUC 0.920, sensitivity 89.5%, specificity 89.3%, p < 0.001). D-dimer values ≥ 878 hg/ml on the second day were associated with intensive care need (AUC 0.896, sensitivity 78.6%, specificity 78.3%, p < 0.001). D-dimer values ≥ 1 106 hg/ml on the 10th day were associated with mortality (AUC 0.817, sensitivity 68.4%, specificity 65.2%, p < 0.001). It was observed that hospitalisation periods ≥ 9.5 days were associated with mortality (AUC 0.738, sensitivity 68.4%, specificity 65.9%, p < 0.001). We showed that hospitalisations ≥ 9.5 days in duration were related to increased mortality rates. Troponin I and D-dimer follow-up values in the serum were more effective than other inflammatory markers in predicting mortality and the need for intensive care. A high troponin I value should alert the clinician in terms of clinical deterioration.

COVID-19 Pneumonia Followed by Anti-signal Recognition Particle Antibody-positive Fibrosing Interstitial Lung Disease: A Case Report.

Coronavirus disease 2019 (COVID-19) pneumonia is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is frequently accompanied by various sequelae. Interstitial lung diseases (ILDs) are observed in COVID-19 pneumonia patients after recovery, probably due to persistent inflammation in the lungs. We herein report a case of ILD with anti-signal recognition particle antibodies following severe COVID-19 pneumonia. The patient was diagnosed with ILD three months after COVID-19 pneumonia. Although the exact mechanism is unknown, the autoantibody-induced immune response might have been the pulmonary fibrosis trigger in this patient.

A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module.

During the past three years, the coronavirus disease 2019 (COVID-19) has swept the world. The rapid and accurate recognition of covid-19 pneumonia are ,therefore, of great importance. To handle this problem, we propose a new pipeline of deep learning framework for diagnosing COVID-19 pneumonia via chest X-ray images from normal, COVID-19, and other pneumonia patients. In detail, the self-trained YOLO-v4 network was first used to locate and segment the thoracic region, and the output images were scaled to the same size. Subsequently, the pre-trained convolutional neural network was adopted to extract the features of X-ray images from 13 convolutional layers, which were fused with the original image to form a 14-dimensional image matrix. It was then put into three parallel pyramid multi-layer perceptron (MLP)-Mixer modules for comprehensive feature extraction through spatial fusion and channel fusion based on different scales so as to grasp more extensive feature correlation. Finally, by combining all image features from the 14-channel output, the classification task was achieved using two fully connected layers as well as Softmax classifier for classification. Extensive simulations based on a total of 4099 chest X-ray images were conducted to verify the effectiveness of the proposed method. Experimental results indicated that our proposed method can achieve the best performance in almost all cases, which is good for auxiliary diagnosis of COVID-19 and has great clinical application potential.

Opportunity for severe and critical COVID-19 pneumonia treatment with corticosteroids: a retrospective cohort study.

The coronavirus disease 2019 (COVID-19) pandemic has been the most significant infectious disease outbreak worldwide in the past 3 years, with the potential to progress to severe pneumonia and trigger systemic inflammatory response, posing a threat to human health and life. This study aims to explore the use of corticosteroids for COVID-19 and provide recommendations on the timing and dosage of the treatment. We conducted a retrospective cohort study, enrolling 100 with COVID-19 pneumonia between December 2022 and January 2023. The diagnosis of severe and critical COVID-19 pneumonia patients was according to China's Ninth Edition of the Diagnosis and Treatment Plan for COVID-19 Pneumonia. T test and univariate proportional hazard analysis were employed to investigate the opportunity of corticosteroids therapy in relation to patients' prognosis. Compared to COVID-19 pneumonia patients treated with corticosteroids in the early phase, those who received late-phase corticosteroid therapy had a higher proportion of intensive care unit (ICU) admission (P=0.01), longer hospital stay (P=0.006), lower in-hospital survival rate (P=0.03), and slower recovery (P<0.001). A significant difference was also observed in logistic univariate proportional hazard analysis. The early administration of corticosteroid therapy has been shown to significantly improve the prognosis of COVID-19 pneumonia patients, promoting recovery with significant clinical significance. Our recommendation for the administration of corticosteroid therapy is to be applied on the 6th-9th day of persisting unrelieved symptoms of COVID-19 pneumonia.

Population Risk Factors for Severe Disease and Mortality in COVID-19 in the United States during the Pre-Vaccine Era: A Retrospective Cohort Study of National Inpatient Sample

Background-Previous studies on coronavirus disease 2019 (COVID-19) were limited to specific geographical locations and small sample sizes. Therefore, we used the National Inpatient Sample (NIS) 2020 database to determine the risk factors for severe outcomes and mortality in COVID-19. Methods-We included adult patients with COVID-19. Univariate and multivariate logistic regression was performed to determine the predictors of severe outcomes and mortality in COVID-19. Results-1,608,980 (95% CI 1,570,803-1,647,156) hospitalizations with COVID-19 were included. Severe complications occurred in 78.3% of COVID-19 acute respiratory distress syndrome (ARDS) and 25% of COVID-19 pneumonia patients. The mortality rate for COVID-19 ARDS was 54% and for COVID-19 pneumonia was 16.6%. On multivariate analysis, age > 65 years, male sex, government insurance or no insurance, residence in low-income areas, non-white races, stroke, chronic kidney disease, heart failure, malnutrition, primary immunodeficiency, long-term steroid/immunomodulatory use, complicated diabetes mellitus, and liver disease were associated with COVID-19 related complications and mortality. Cardiac arrest, septic shock, and intubation had the highest odds of mortality. Conclusions-Socioeconomic disparities and medical comorbidities were significant determinants of mortality in the US in the pre-vaccine era. Therefore, aggressive vaccination of high-risk patients and healthcare policies to address socioeconomic disparities are necessary to reduce death rates in future pandemics.

Characteristics of cardiac injury complicating with acute kidney injury and mortality risk in coronavirus disease 2019 (COVID-19) patients

Objectives: This study aimed to identify the clinical features of cardiac injury complicating with acute kidney injury (AKI) and its risk for a fatal outcome in patients infected with coronavirus disease 2019 (COVID-19) pneumonia. Methods: Initial signs and symptoms and clinical laboratory, radiological, and treatment information were obtained from seven hospitals in China from January 23, 2020, to March 15, 2020. Results: Of 438 patients, 36 (8.22%) displayed isolated cardiac injury, 17 (3.88%) had isolated AKI, and 17 (3.88%) displayed cardiac injury complicating with AKI. Compared with patients without cardiac injury or AKI, patients with isolated cardiac injury, isolated AKI, and cardiac injury complicating with AKI were older (55, 65, 74 vs. 48 years, P &lt; 0.0001) and critically severe. More patients displayed fatigue, dyspnea, and comorbidities in the group with cardiac injury complicating with AKI. Moreover, the indexes reflecting myocardial, renal, liver, and coagulation dysfunctions and infection-related factors were significantly different among the four groups. After adjustment for covariates, patients with cardiac injury complicating with AKI had a higher hazard ratio for mortality (6.64; 95% confidence interval, 1.51–29.30). Conclusion: Cardiac injury complicating with kidney injury significantly increased the risk for in-hospital mortality in COVID-19 pneumonia patients. Therefore, early detection at admission and careful monitoring of myocardial and renal injury through biomarkers during hospitalization is recommended to reduce the harm to patients.

A 30-day Survival and Safety of Percutaneous Tracheostomy in Moderate-to-severe COVID-19 Pneumonia Patients: A Single-center Experience.

In coronavirus disease-2019 (COVID-19) pneumonia, guidelines on timing and method of tracheostomy are evolving. The aim of the study was to analyze the outcomes of moderate-to-severe COVID-19 pneumonia patients who required tracheostomy and the safety with regard to the risk of transmission to the healthcare workers. We retrospectively analyzed 30-day survival outcome of a total of 70 moderate-to-severe COVID-19 pneumonia patients on a ventilator, wherein tracheostomy was performed only in 28 (tracheostomy group), and the remaining were with endotracheal intubation beyond 7 days (non-tracheostomy group). Besides demographics, comorbidities and clinical data including 30-day survival and complications of tracheostomy were analyzed in both groups with respect to the timing of tracheostomy from the day of intubation. Healthcare workers were monitored for COVID-19 symptoms by carrying out periodical COVID tests. The 30-day survival of the tracheostomy group was 75% as compared to 26.2% of the non-tracheostomy group. The majority of the patients (71.4%) had severe disease with PaO2/FiO2 (P/F ratio) <100. The first wave showed an 80% (4/5) while the second wave 100% (8/8) thirty days survival in the tracheostomy group performed before 13 days. All patients during the second wave underwent tracheostomy before 13 days with a median of 12th day from the day of intubation. These tracheostomies were performed percutaneously at the bedside, without any major complications and no transmission of disease to healthcare workers. Early percutaneous tracheostomy within 13 days of intubation demonstrated a good 30-day survival rate in severe COVID-19 pneumonia patients. Shah M, Bhatuka N, Shalia K, Patel M. A 30-day Survival and Safety of Percutaneous Tracheostomy in Moderate-to-severe COVID-19 Pneumonia Patients: A Single-center Experience. Indian J Crit Care Med 2022;26(10):1120-1125.

Lung Abscess as a Delayed Complication in a COVID-19 Pneumonia Patient: A Case Report

Introduction: In March 2020, the World Health Organization (WHO) proclaimed coronavirus disease 2019 (COVID-19) a global pandemic. Indonesia is one of the nations that is still dealing with the COVID-19 outbreak. COVID-19 has several complications, including lung abscess in extremely rare cases. We presented the first reported COVID-19 patient in Indonesia with a delayed lung abscess. Case: A 30-year-old man presented to the hospital with breathlessness and tested positive for COVID-19. Chest X-ray revealed typical COVID-19 pneumonia. He was discharged after 16 days of hospitalization and was educated on using oxygen at home lest the breathlessness recurred. We planned to evaluate the patient’s chest X-ray after 2 weeks of being discharged. The follow-up chest X-ray revealed an air-fluid level in the upper lobe of the right lung, indicating a lung abscess. The patient was treated with antibiotics for 2–3 weeks. Clinical follow-up 4 weeks after the treatment revealed no symptoms, and chest X-ray showed significant improvement. Conclusion: Lung abscess is one of the rare complications of COVID-19. It is a pulmonary infection that creates an air-fluid level by forming a cavity in the lung parenchyma. Notably, this complication manifested 2 weeks after the patient was discharged. COVID-19 can have several unexpected complications, including lung abscess. It is crucial to monitor patients after being discharged for such complications, especially if they are symptomatic.

Radiological Findings in SARS-CoV-2 Viral Pneumonia Compared to Other Viral Pneumonias: A Single-Centre Study.

Background Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic. Aims We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. Study Design. The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus. Methods Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups—non-COVID-19 and COVID-19 pneumonia—and compared statistically with chi-squared tests and multiple regression analysis of independent variables. Results RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign (β = 0.097, p < 0.05) and pleural effusion (β = 10.631, p < 0.05) on COVID-19 pneumonia patients. Conclusion The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.

A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19.

BackgroundThe coronavirus disease 2019 (COVID-19) outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in detecting COVID-19-related pneumonia is that it is often manifested as a “silent pneumonia”, i.e. pulmonary auscultation that sounds “normal” using a standard stethoscope. Chest computed tomography is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclude its utilisation as a screening tool for COVID-19 pneumonia. In this study we hypothesised that COVID-19 pneumonia, “silent” to the human ear using a standard stethoscope, is detectable using a full-spectrum auscultation device that contains a machine-learning analysis.MethodsLung sound signals were acquired, using a novel full-spectrum (3–2000 Hz) stethoscope, from 164 COVID-19 pneumonia patients, 61 non-COVID-19 pneumonia patients and 141 healthy subjects. A machine-learning classifier was constructed and the data were classified into three groups: 1) normal lung sounds, 2) COVID-19 pneumonia and 3) non-COVID-19 pneumonia.ResultsStandard auscultation found that 72% of the non-COVID-19 pneumonia patients had abnormal lung sounds compared with only 25% of the COVID-19 pneumonia patients. The classifier's sensitivity and specificity for the detection of COVID-19 pneumonia were 97% and 93%, respectively, when analysing the sound and infrasound data, and they were reduced to 93% and 80%, respectively, without the infrasound data (p<0.01 difference in receiver operating characteristic curves with and without infrasound).ConclusionsThis study reveals that useful clinical information exists in the infrasound spectrum of COVID-19-related pneumonia and machine-learning analysis applied to the full spectrum of lung sounds is useful in its detection.