Identification of Endotypes of Hospitalized COVID-19 Patients

Evolution of serious and life-threatening COVID-19 pneumonia as the SARS-CoV-2 pandemic progressed: an observational study of mortality to 60 days after admission to a 15-hospital US health system

OBJECTIVE

In order to predict at hospital admission the prognosis of patients with serious and life-threatening COVID-19 pneumonia, we sought to understand the clinical characteristics of hospitalised patients at admission as the SARS-CoV-2 pandemic progressed, document their changing response to the virus and its variants over time, and identify factors most importantly associated with mortality after hospital admission.

DESIGN

Observational study using a prospective hospital systemwide COVID-19 database.

SETTING

15-hospital US health system.

PARTICIPANTS

26 872 patients admitted with COVID-19 to our Northeast Ohio and Florida hospitals from 1 March 2020 to 1 June 2022.

MAIN OUTCOME MEASURES

60-day mortality (highest risk period) after hospital admission analysed by random survival forests machine learning using demographics, medical history, and COVID-19 vaccination status, and viral variant, symptoms, and routine laboratory test results obtained at hospital admission.

RESULTS

Hospital mortality fell from 11% in March 2020 to 3.7% in March 2022, a 66% decrease (p<0.0001); 60-day mortality fell from 17% in May 2020 to 4.7% in May 2022, a 72% decrease (p<0.0001). Advanced age was the strongest predictor of 60-day mortality, followed by admission laboratory test results. Risk-adjusted 60-day mortality had all patients been admitted in March 2020 was 15% (CI 3.0% to 28%), and had they all been admitted in May 2022, 12% (CI 2.2% to 23%), a 20% decrease (p<0.0001). Dissociation between observed and predicted decrease in mortality was related to temporal change in admission patient profile, particularly in laboratory test results, but not vaccination status or viral variant.

CONCLUSIONS

Hospital mortality from COVID-19 decreased substantially as the pandemic evolved but persisted after hospital discharge, eclipsing hospital mortality by 50% or more. However, after accounting for the many, even subtle, changes across the pandemic in patients' demographics, medical history and particularly admission laboratory results, a patient admitted early in the pandemic and predicted to be at high risk would remain at high risk of mortality if admitted tomorrow.

Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality

BACKGROUND

There exists consistent empirical evidence in the literature pointing out ample heterogeneity in terms of the clinical evolution of patients with COVID-19. The identification of specific phenotypes underlying in the population might contribute towards a better understanding and characterization of the different courses of the disease. The aim of this study was to identify distinct clinical phenotypes among hospitalized patients with SARS-CoV-2 pneumonia using machine learning clustering, and to study their association with subsequent clinical outcomes as severity and mortality.

METHODS

Multicentric observational, prospective, longitudinal, cohort study conducted in four hospitals in Spain. We included adult patients admitted for in-hospital stay due to SARS-CoV-2 pneumonia. We collected a broad spectrum of variables to describe exhaustively each case: patient demographics, comorbidities, symptoms, physiological status, baseline examinations (blood analytics, arterial gas test), etc. For the development and internal validation of the clustering/phenotype models, the dataset was split into training and test sets (50% each). We proposed a sequence of machine learning stages: feature scaling, missing data imputation, reduction of data dimensionality via Kernel Principal Component Analysis (KPCA), and clustering with the k-means algorithm. The optimal cluster model parameters -including k, the number of phenotypes- were chosen automatically, by maximizing the average Silhouette score across the training set.

RESULTS

We enrolled 1548 patients, each of them characterized by 92 clinical attributes (d=109 features after variable encoding). Our clustering algorithm identified k=3 distinct phenotypes and 18 strongly informative variables: Phenotype A (788 cases [50.9% prevalence] - age ∼ 57, Charlson comorbidity ∼ 1, pneumonia CURB-65 score ∼ 0 to 1, respiratory rate at admission ∼ 18 min-1, FiO2 ∼ 21%, C-reactive protein CRP ∼ 49.5 mg/dL [median within cluster]); phenotype B (620 cases [40.0%] - age ∼ 75, Charlson ∼ 5, CURB-65 ∼ 1 to 2, respiration ∼ 20 min-1, FiO2 ∼ 21%, CRP ∼ 101.5 mg/dL); and phenotype C (140 cases [9.0%] - age ∼ 71, Charlson ∼ 4, CURB-65 ∼ 0 to 2, respiration ∼ 30 min-1, FiO2 ∼ 38%, CRP ∼ 152.3 mg/dL). Hypothesis testing provided solid statistical evidence supporting an interaction between phenotype and each clinical outcome: severity and mortality. By computing their corresponding odds ratios, a clear trend was found for higher frequencies of unfavourable evolution in phenotype C with respect to B, as well as more unfavourable in phenotype B than in A.

CONCLUSION

A compound unsupervised clustering technique (including a fully-automated optimization of its internal parameters) revealed the existence of three distinct groups of patients - phenotypes. In turn, these showed strong associations with the clinical severity in the progression of pneumonia, and with mortality.

The Impact of the Novel Coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Pandemic on Case Fatality Rates and Cost of Surgical Care in Brazil

Background The coronavirus disease 2019 (COVID-19) pandemic provoked disruptions in healthcare delivery. Following the recommendations of major surgical societies and surgical departments globally, most surgeries were widely canceled or postponed, causing significant disruptions to healthcare delivery worldwide, including in Brazil. Brazil's public healthcare system - Sistema Único de Saúde (SUS) was particularly affected, with a substantial decline in elective procedures, especially during the pandemic's early stages. The impact of the pandemic on surgical services in Brazil has not been adequately studied since most studies only cover the early phases of the pandemic. Thus, this study aims to analyze the case fatality rates and costs, associated with the different surgical procedure subgroups performed during the pre-pandemic, pandemic, recovery, and post-pandemic periods in all five regions of Brazil. Methods A retrospective cross-sectional design was used to examine surgical cases from 2019 to 2022. Data was divided into four time periods, named as the pre-pandemic (March-December 2019), pandemic (March-December 2020), recovery (March-December 2021), and post-pandemic (March-December 2022), and was analyzed for the cost of surgical procedures in the aforementioned four periods. In addition, the case fatality rates and rate ratios in the four periods stratified according to region were calculated. Results The cost of surgical procedures decreased during the pandemic and recovery period compared with pre-pandemic for all procedures except thoracic surgery where it was higher in the recovery period than pre-pandemic. No statistically significant change in cost was observed in surgeries of the central and peripheral nervous system, circulatory system, obstetric, and oncology. Case fatality rates increased among all five regions of Brazil in pandemic and recovery periods compared to pre-pandemic and post-pandemic periods. Case fatality rates increased during the pandemic and/or recovery as compared to pre-pandemic in all procedures except visual apparatus and obstetric surgeries were not affected by the pandemic in terms of case fatality rates. Conclusion The COVID-19 pandemic had a significant impact on surgical care costs and case fatality rates for surgery in Brazil. There was a decreasing trend in the costs of procedures during the pandemic, followed by a gradual recovery to baseline values, except for thoracic surgery. Case fatality rates rose initially and then declined, ultimately reaching baseline levels. The pandemic posed significant challenges to the healthcare system, affecting medical services, including surgical care.

COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study

BACKGROUND

We have an incomplete understanding of COVID-19 characteristics at hospital presentation and whether underlying subphenotypes are associated with clinical outcomes and therapeutic responses.

METHODS

For this cross-sectional study, we extracted electronic health data from adults hospitalized between 1 March and 30 August 2020 with a PCR-confirmed diagnosis of COVID-19 at five New York City Hospitals. We obtained clinical and laboratory data from the first 24 h of the patient's hospitalization. Treatment with tocilizumab and convalescent plasma was assessed over hospitalization. The primary outcome was mortality; secondary outcomes included intubation, intensive care unit (ICU) admission and length of stay (LOS). First, we employed latent class analysis (LCA) to identify COVID-19 subphenotypes on admission without consideration of outcomes and assigned each patient to a subphenotype. We then performed robust Poisson regression to examine associations between COVID-19 subphenotype assignment and outcome. We explored whether the COVID-19 subphenotypes had a differential response to tocilizumab and convalescent plasma therapies.

RESULTS

A total of 4620 patients were included. LCA identified six subphenotypes, which were distinct by level of inflammation, clinical and laboratory derangements and ranged from a hypoinflammatory subphenotype with the fewest derangements to a hyperinflammatory with multiorgan dysfunction subphenotypes. Multivariable regression analyses found differences in risk for mortality, intubation, ICU admission and LOS, as compared to the hypoinflammatory subphenotype. For example, in multivariable analyses the moderate inflammation with fever subphenotype had 3.29 times the risk of mortality (95% CI 2.05, 5.28), while the hyperinflammatory with multiorgan failure subphenotype had 17.87 times the risk of mortality (95% CI 11.56, 27.63), as compared to the hypoinflammatory subphenotype. Exploratory analyses suggested that subphenotypes may differential respond to convalescent plasma or tocilizumab therapy.

CONCLUSION

COVID-19 subphenotype at hospital admission may predict risk for mortality, ICU admission and intubation and differential response to treatment.KEY MESSAGEThis cross-sectional study of COVID patients admitted to the Mount Sinai Health System, identified six distinct COVID subphenotypes on admission. Subphenotypes correlated with ICU admission, intubation, mortality and differential response to treatment.

Early Prediction and HRCT Evaluation of Post Covid-19 Related Lung Fibrosis

Background

Early detection of post-COVID-19-related lung fibrosis is very important for the early introduction of treatment and to minimize morbidity and mortality. The aim of this study is the early detection and evaluation of post-COVID-19 fibrosis by high-resolution computed tomography (HRCT).

Methods

This prospective study included 115 patients irrespective of age and sex, who tested positive for the SARS-CoV-2 by nasopharyngeal swab (RT PCR), admitted to the Dhaka North City Corporation (DNCC) dedicated COVID-19 hospital, Dhaka, and discharged after recovery. Patients went through a chest HRCT scan at least once during their hospital stay and another scan during follow-up after hospital discharge and 8 to 12 weeks of negative RT-PCR report.

Result

Among 100 patients 23 patients had >50% of total lung involvement by visual assessment. Thirty-three patients had 25% to 50% of total lung volume involvement. Twenty-seven patients had less than 25% of total lung involvement, whereas 17 patients had no visual fibrotic change on the follow-up HRCT scan. A statistical association was found between age, gender, smoking, and severe form of lung fibrosis (P < .05). Patients with mild CT severity score (⩽8) had a very good prognosis. Patients who were admitted to the hospital for more than 15 days were more prone to developing moderate and severe forms of fibrosis. Patients who received at least 2 doses of the COVID-19 vaccine had less severe forms of fibrosis as well as more cases of complete radiological recovery. On the HRCT scan, most of the patients had bilateral, peripheral (68%), and predominant mid & lower lobar parenchymal involvement.

Conclusion

Early detection and HRCT evaluation of post-COVID-19 related lung fibrosis is very crucial for early management and introduction of anti-fibrotic drugs.

Glucocorticoid Therapy in COVID-19

Coronavirus disease 2019 (COVID-19) pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in significant mortality in pandemic proportions. Inflammation in response to the infection contributes to the pathogenesis of pneumonia. This review will discuss prior studies on the use of glucocorticoids to treat respiratory infections, the rationale for the use glucocorticoids in COVID-19, and review of existing data. We will also highlight outstanding research questions for future studies.

Recent developments in the immunopathology of COVID-19

There has been an important change in the clinical characteristics and immune profile of Coronavirus disease 2019 (COVID-19) patients during the pandemic thanks to the extensive vaccination programs. Here, we highlight recent studies on COVID-19, from the clinical and immunological characteristics to the protective and risk factors for severity and mortality of COVID-19. The efficacy of the COVID-19 vaccines and potential allergic reactions after administration are also discussed. The occurrence of new variants of concerns such as Omicron BA.2, BA.4, and BA.5 and the global administration of COVID-19 vaccines have changed the clinical scenario of COVID-19. Multisystem inflammatory syndrome in children (MIS-C) may cause severe and heterogeneous disease but with a lower mortality rate. Perturbations in immunity of T cells, B cells, and mast cells, as well as autoantibodies and metabolic reprogramming may contribute to the long-term symptoms of COVID-19. There is conflicting evidence about whether atopic diseases, such as allergic asthma and rhinitis, are associated with a lower susceptibility and better outcomes of COVID-19. At the beginning of pandemic, the European Academy of Allergy and Clinical Immunology (EAACI) developed guidelines that provided timely information for the management of allergic diseases and preventive measures to reduce transmission in the allergic clinics. The global distribution of COVID-19 vaccines and emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with reduced pathogenic potential dramatically decreased the morbidity, severity, and mortality of COVID-19. Nevertheless, breakthrough infection remains a challenge for disease control. Hypersensitivity reactions (HSR) to COVID-19 vaccines are low compared to other vaccines, and these were addressed in EAACI statements that provided indications for the management of allergic reactions, including anaphylaxis to COVID-19 vaccines. We have gained a depth knowledge and experience in the over 2 years since the start of the pandemic, and yet a full eradication of SARS-CoV-2 is not on the horizon. Novel strategies are warranted to prevent severe disease in high-risk groups, the development of MIS-C and long COVID-19.

The Influence of SARS-CoV-2 Variants on National Case-Fatality Rates: Correlation and Validation Study

Background

In 2021, new variants of the SARS-CoV-2 virus appeared with increased transmissibility and virulence as compared with the original wild variant. The first variants of concern (VoCs), Alpha (B1.1.7) and Gamma (P.1), first appeared in the United Kingdom and Brazil, respectively. The Delta (B.1.617.2) variant, seen in India in October 2020, dominated COVID-19 infections across all regions through the second half of 2021.

Objective

This research explores the degree to which SARS-CoV-2 VoCs generate waves of fluctuations in case-fatality rates (CFRs) across countries in several regions, increase the risk of mortality to persons with certain comorbidities, and decrease the risk of mortality as the percentage of fully vaccinated populations increases.

Methods

This analysis introduces a measure of the temporal dynamics of COVID-19 infections in the form of a proxy CFR (pCFR), which can be compared among countries. It uses economic and demographic data reported by the World Bank and International Monetary Fund, plus publicly available epidemiological and medical statistics reported to the relevant national and international public health authorities. From these ecological data, pandemic average and daily COVID-19 CFRs and their correlations with potential cofactors were computed for 2021, a year dominated by the spread of World Health Organization-designated VoCs. The study does not investigate disease pathology; rather, it compares the daily case rates and pCFRs to reveal underlying contributing factors that vary from country to country and region to region.

Results

The in-depth global regression analysis of cofactors found that the strongest single correlation with COVID-19 fatality was 0.36 (SD 0.02) with P<.001 for chronic kidney disease. No other single physiological cofactors display positive correlations exceeding 0.26 (SD 0.26), with P=.008 (asthma) and P=.01 (coronary disease). The study confirms that the pCFR is a valuable metric for tracking waves of infection due to different VoCs within countries.

Conclusions

The influence of social, economic, and medical cofactors on the CFR due to VoCs remains qualitatively similar, albeit strengthened, to the levels found for the wild strain. The strong regional variations of the influence of all cofactors observed for the wild strain persists in infections for all VoCs with very strong correlation coefficients seen in the Middle East for asthma (0.76), coronary heart disease (0.60), lung disease (0.70), and chronic kidney disease (0.52). Strong regional variations emphasize the influence on COVID-19 mortality due to regional differences in national economics, patterns of health care policies, and variations in cultural practices and environment. The pCFR-based analysis reveals clear patterns of the spread of VoCs across regions, but there is little evidence for the spread of the Lambda and Mu (B.1.621) variants of interest outside of South America.

Characteristics and Prognostic Factors of Pulmonary Fibrosis After COVID-19 Pneumonia

Background

Few is known about the long-term pulmonary sequelae after COVID-19 infection. Hence, the aim of this study is to characterize patients with persisting pulmonary sequelae at follow-up after hospitalization. We also aimed to explore clinical and radiological predictors of pulmonary fibrosis following COVID-19.

Methods

Two hundred and 20 consecutive patients were evaluated at 3–6 months after discharge with high-resolution computed tomography (HRCT) and categorized as recovered (REC) or not recovered (NOT-REC). Both HRCTs at hospitalization (HRCT₀), when available, and HRCT₁ during follow-up were analyzed semiquantitatively as follows: ground-glass opacities (alveolar score, AS), consolidations (CONS), and reticulations (interstitial score, IS).

Results

A total of 175/220 (80%) patients showed disease resolution at their initial radiological evaluation following discharge. NOT-REC patients (45/220; 20%) were mostly older men [66 (35–85) years vs. 56 (19–87); p = 0.03] with a longer in-hospital stay [16 (0–75) vs. 8 (1–52) days; p < 0.0001], and lower P/F at admission [233 (40–424) vs. 318 (33–543); p = 0.04]. Moreover, NOT-REC patients presented, at hospital admission, higher ALV [14 (0.0–62.0) vs. 4.4 (0.0–44.0); p = 0.0005], CONS [1.9 (0.0–26.0) vs. 0.4 (0.0–18.0); p = 0.0064], and IS [11.5 (0.0– 29.0) vs. 0.0 (0.0–22.0); p < 0.0001] compared to REC patients. On multivariate analysis, the presence of CONS and IS at HRCT₀ was independent predictors of radiological sequelae at follow-up [OR 14.87 (95% CI: 1.25–175.8; p = 0.03) and 28.9 (95% CI: 2.17–386.6; p = 0.01, respectively)].

Conclusions

In our population, only twenty percent of patients showed persistent lung abnormalities at 6 months after hospitalization for COVID-19 pneumonia. These patients are predominantly older men with longer hospital stay. The presence of reticulations and consolidation on HRCT at hospital admission predicts the persistence of radiological abnormalities during follow-up.

Optimized Protocols for the Propagation and Quantification of Infectious Murine Hepatitis Virus (MHV-A59) Using NCTC Clone 1469 and 929 Cells

Murine hepatitis virus (MHV) is a non-human pathogen betacoronavirus that is evolutionarily and structurally related to the human pathogenic viruses SARS-CoV, MERS-CoV, and SARS-CoV-2. However, unlike the human SARS and MERS viruses, MHV requires a biosafety level 2 laboratory for propagating and safe handling, making it a potentially suitable surrogate virus. Despite this utility, few papers discussed the propagation and quantification of MHV using cell lines readily available in biorepositories making their implementations not easily reproducible. This article provides protocols for propagating and quantifying MHV-A59 using the recommended NCTC clone 1469 and clone 929 cell lines from American Type Culture Collection (ATCC). More specifically, the methods detail reviving cells, routine cell passaging, preparing freeze stocks, infection of NCTC clone 1469 with MHV and subsequent harvesting, and plaque assay quantification of MHV using NCTC clone 929 cells. Using these protocols, a BSL-2 laboratory equipped for cell culture work would generate at least 6.0 log plaque-forming units (PFU) per mL of MHV lysate and provide an optimized overlay assay using either methylcellulose or agarose as overlays for the titration of infectious virus particles. The protocols described here are intended to be utilized for persistence and inactivation studies of coronaviruses.