Not all COVID-19 pandemic waves are alike

COVID-19 pandemic - Cocktail of variants, a study from Northern India

Context

The aim of the study was to identify and monitor the circulating strains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the samples received at our center and update the existing national and international genomic surveillance data.

Introduction

SARS-CoV-2 is no exception to the basic nature of the viruses ability to change and evolve. Since its first report in December 2019 from Wuhan, China, multiple variants of the virus have emerged and been reported. Five variants of concern have been recognized and reported by the Centers for Disease Control and Prevention, which are associated with variable degrees of transmissibility and mortality.

Materials and Methods

Nasopharyngeal and oropharyngeal swabs received in viral transport medium at the Viral Research Diagnostic Laboratory were processed for reverse transcription-polymerase chain reaction for SARS-CoV-2. Whole genome sequencing (WGS) was performed for selective positive samples using Oxford Nanopore sequencing technology, using MinKNOW software for data acquisition.

Statistical Analysis

The clades were assigned using Nextclade v2.4.1 software. The statistical analysis was calculated using OpenEpi version 3, an open-source calculator, and two by two.

Results

Variants reported over the study period included Alpha, Kappa, Delta, and Omicron. Delta dominated in the year 2021, while Omicron was the dominant variant in 2022. In both the dominant variants, asymptomatics contributed to around 30-40% of positives. Intensive care unit admissions and mortality were higher in the Delta variant, while vaccination history and travel history were higher in the patients with Omicron variant.

Conclusion

The trend tracking of these variants has been important in view of public health, enabling early interventions to control the spread of the disease and foresight in preparation for the situation.

Temporal trends of severity and outcomes of critically ill patients with COVID-19 after the emergence of variants of concern: A comparison of two waves

BACKGROUND

The emergence of SARS-CoV-2 variants led to subsequent waves of COVID-19 worldwide. In many countries, the second wave of COVID-19 was marked by record deaths, raising the concern that variants associated with that wave might be more deadly. Our aim was to compare outcomes of critically-ill patients of the first two waves of COVID-19.

METHODS

This retrospective cohort included critically-ill patients admitted between March-June 2020 and April-July 2021 in the largest academic hospital in Brazil, which has free-access universal health care system. We compared admission characteristics and hospital outcomes. The main outcome was 60-day survival and we built multivariable Cox model based on a conceptual causal diagram in the format of directed acyclic graph (DAG).

RESULTS

We included 1583 patients (1315 in the first and 268 in the second wave). Patients in the second wave were younger, had lower severity scores, used prone and non-invasive ventilatory support more often, and fewer patients required mechanical ventilation (70% vs 80%, p<0.001), vasopressors (60 vs 74%, p<0.001), and dialysis (22% vs 37%, p<0.001). Survival was higher in the second wave (HR 0.61, 95%CI 0.50-0.76). In the multivariable model, admission during the second wave, adjusted for age, SAPS3 and vaccination, was not associated with survival (aHR 0.85, 95%CI 0.65-1.12).

CONCLUSIONS

In this cohort study, patients with COVID-19 admitted to the ICU in the second wave were younger and had better prognostic scores. Adjusted survival was similar in the two waves, contrasting with record number of hospitalizations, daily deaths and health system collapse seen across the country in the second wave. Our findings suggest that the combination of the burden of severe cases and factors such as resource allocation and health disparities may have had an impact in the excess mortality found in many countries in the second wave.

Monitoring and tracking the spread of SARS-CoV-2 in Asturias, Spain

Mutational analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can quantify the relative importance of variants over time, enable dominant mutations to be identified, and facilitate near real-time detection, comparison and tracking of evolving variants. SARS-CoV-2 in Asturias, an autonomous community of Spain with a large ageing population, and high levels of migration and tourism, was monitored and tracked from the beginning of the pandemic in February 2020 until its decline and stabilization in August 2021, and samples were characterized using whole genomic sequencing and single nucleotide polymorphisms. Data held in the GISAID database were analysed to establish patterns in the appearance and persistence of SARS-CoV-2 strains. Only 138 non-synonymous mutations occurring in more than 1 % of the population with SARS-CoV-2 were found, identifying ten major variants worldwide (seven arose before January 2021), 19 regional and one local. In Asturias only 17 different variants were found. After vaccination, no further regional major variants were found. Only half of the defined variants circulated and no new variants were generated, indicating that infection control measures such as rapid diagnosis, isolation and vaccination were efficient.

Outcomes and characteristics of patients hospitalized for COVID-19 in British Columbia, Ontario and Quebec during the Omicron wave

BACKGROUND

Omicron is the current predominant variant of concern of SARS-CoV-2. We hypothesized that vaccination alters outcomes of patients hospitalized with COVID-19 during the Omicron wave and that these patients have different characteristics and outcomes than in previous waves.

METHODS

This is a substudy of the Host Response Mediators in Coronavirus (COVID-19) Infection (ARBs CORONA I) trial, which included adults admitted to hospital with acute COVID-19 up to July 2022 from 9 hospitals in British Columbia, Ontario and Quebec. We excluded emergency department visits without hospital admission, readmissions and admissions for another reason. Using adjusted regression analysis, we compared mortality and organ dysfunction between vaccinated (≥ 2 doses) and unvaccinated patients during the Omicron wave, as well as between all patients in the Omicron and first 3 waves of the COVID-19 pandemic.

RESULTS

During the Omicron wave, 28-day mortality was significantly lower in vaccinated (n = 19/237) than unvaccinated hospitalized patients (n = 12/127) (adjusted odds ratio [OR] 0.36, 95% confidence interval [CI] 0.15-0.89); vaccinated patients had lower risk of admission to the intensive care unit, invasive ventilation and acute respiratory distress syndrome and shorter hospital length of stay. Patients hospitalized during the Omicron wave had more comorbidities than in previous waves, and lower 28-day mortality than in waves 1 and 2 (adjusted OR 0.38, 95% CI 0.24-0.59; and 0.42, 95% CI 0.26-0.65) but not wave 3 (adjusted OR 0.81, 95% CI 0.43-1.51) and had less organ dysfunction than in the first 2 waves.

INTERPRETATION

Patients who were at least double vaccinated had lower mortality than unvaccinated patients hospitalized during the Omicron wave. Patients hospitalized during the Omicron wave had more chronic disease and lower mortality than in the first 2 waves, but not wave 3. Changes in vaccination, treatments and predominant SARS-CoV-2 variant may have decreased mortality in patients hospitalized during the Omicron wave.

Low thoracic skeletal muscle index is associated with negative outcomes in 244 patients with respiratory COVID-19

BACKGROUND

Sarcopenia is associated with negative outcomes in intensive care unit (ICU) patients and during chronic diseases. We aimed to evaluate if low skeletal muscle index (SMI) measured by computed tomography (CT) at the thoracic level is associated with poor outcomes in hospitalized patients with respiratory COVID-19.

METHODS

Patients admitted to the hospital between March 1st and June 9, 2020 with a confirmed diagnosis of respiratory COVID-19 in the Emergency Department were included in this retrospective cohort study. SMI was assessed from a transverse CT image at the T12 level. We analysed the association between thoracic SMI and mortality, ICU admissions, infections, length of stay and gravity scores.

RESULTS

We included 244 patients, whose median age was 62 (20-95) years, mean body mass index was 28,6 kg/m2, and 34% were obese patients. 102 patients (41,8%) had low thoracic SMI. On multivariable analysis, low thoracic SMI was associated with more infections (OR = 1,88 [1,06-2,98]) and increased length of stay (OR = 1,87 [1,14-3,49]) but not with mortality (OR = 1.37 [0.54-3.52]), whereas it was inversely associated with ICU admission (OR = 5,56 [1,96-16,67].

CONCLUSION

Low SMI measured by CT at the thoracic level T12 is associated with negative outcomes in patients with respiratory COVID-19.

A Retrospective Analysis of Ventilatory Strategy Comparing Non-invasive Ventilation (NIV) With Invasive Ventilation in Patients Admitted With Severe COVID-19 Pneumonia

Background The second wave of the COVID-19 pandemic in India saw a sudden upsurge of critically ill patients getting admitted to the ICU. The guidance for respiratory support was unclear in the early phase. But later reports showed lower mortality with non-invasive ventilation (NIV) than with intubation. The aim of this study was to assess the end result of initial methods of ventilation in COVID-19 patients. Methodology Patients admitted to ICU with COVID-19 were categorized as group 1 (IPPV-intubated within 24 hrs of admission), group 2 (NIV -NIV only), group 3 (NIV+ IPPV-intubated after 24 hrs), and group 4 (NRBM - Non-Rebreathing Mask only). All causes in the hospital or 30-day mortality, length of stay in ICU, and incidence of pneumothorax were compared between groups. Logistic regression analysis was done to determine the odds of mortality. Results The overall mortality rate among patients admitted to tertiary care centers was 15% and the rate among patients in ICU was 54.07%. Patients in group 1 and group 3 had significantly high mortality rates of 90.47% and 93.75%, respectively, as compared to 51.28% in group 2 patients. The odds of mortality were high in group 3 (OR 29.57, 95% CI 4.51 and 193.52) and group 1 (OR 8.01, 95% CI 1.35 and 47.48). Conclusion In a resource-limited setting, the use of NIV is associated with higher survival in COVID-19 patients. The prognosis of patients who are intubated early or after a trial of NIV is the same with increased odds of mortality.

Temporal Trend of COVID-19 Clinical Severity and the Ethnic/Racial Disparity: A Report from the Maryland Study Group

OBJECTIVES

This study aimed to evaluate the temporal trend of novel coronavirus disease 2019 (COVID-19) symptoms and severity of clinical outcomes among pregnant women over a calendar year in the State of Maryland and compare clinical outcomes between different ethnic and racial groups.

STUDY DESIGN

We conducted a retrospective, multicenter observational study of the temporal trend of COVID-19 clinical presentation during pregnancy in the State of Maryland. We reviewed consecutive charts of adult pregnant females, aged 18 to 55 years, with laboratory-confirmed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection between March 1, 2020, and February 28, 2021, and managed within the University of Maryland Medical System and Johns Hopkins Medicine. We excluded cases with insufficient data for assessing the COVID-19 diagnosis, pregnancy status, or clinical outcomes. We evaluated the evolution of COVID-19 symptoms at the time of presentation. Also, we compared COVID-19 infection rate, hospitalization rate, oxygen use, and intensive care unit (ICU) admission rates between different ethnic and racial groups.

RESULTS

We included 595 pregnant women with laboratory-confirmed COVID-19 over the study period. The prevalence of respiratory and systemic symptoms decreased over time with incidence rate ratios (IRRs) of 0.91 per month (95% confidence interval [CI]: 0.88-0.95) and 0.87 per month (95% CI: 0.83-0.95), respectively. The prevalence of hospitalization, O₂ requirement, and ICU admission decreased over time with IRRs of 0.86 per month (95% CI: 0.82-0.91), 0.91 per month (95% CI: 0.84-0.98), and 0.70 per month (95% CI: 0.57-0.85), respectively. The Hispanic and Black populations had a higher COVID-19 infection rate and hospitalization rate than the non-Hispanic White population (p = 0.004, < 0.001, and < 0.001, respectively).

CONCLUSION

Understanding the concepts of viral evolution could potentially help the fight against pandemics like COVID-19. Moreover, this might improve the knowledge of how pandemics affect disadvantaged populations and help close the gap in health care inequities.

KEY POINTS

· A trade-off between virulence and transmissibility is determined by the natural selection of viruses.. · Understanding the concepts of viral evolution can help the fight against pandemics like COVID-19.. · Evolution of SARS-CoV-2 over time resulted in decreased virulence and increased infectivity..

The Impact of Demographic, Clinical Characteristics and the Various COVID-19 Variant Types on All-Cause Mortality: A Case-Series Retrospective Study

(1) Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly evolved into a pandemic affecting virtually every country in the world. We evaluated the demographic, clinical, laboratory, and all-cause mortality of moderate and severe COVID-19 patients admitted to a tertiary care hospital in Oman during the different COVID-19 waves and variant types. (2) Methods: A case-series retrospective study was carried out between 12 March 2020 and 30 June 2022. All adults over the age of 18 with laboratory-confirmed COVID-19 were enrolled. Analyses were performed using univariate and multivariate statistics. (3) Results: A total of 1462 confirmed cases enrolled with the mean age of the cohort was 55 ± 17 years with significant differences among the groups (p = 0.006). A total of 63% and 80% of the patients were males and citizens of Oman, respectively. Patients infected with the Alpha COVID-19 variant type were more likely to have acute respiratory distress syndrome (ARDS) (p < 0.001), stay longer in the hospital (p < 0.001), and get admitted to the intensive care unit (ICU) (p < 0.001). At the same time, those who had the Omicron COVID-19 type were more likely to have renal impairment (p < 0.001) and less likely to be associated with non-invasive ventilation (NIV) (p = 0.001) compared with other COVID-19 variant types. The Delta (adjusted odds ratio (aOR), 1.8; 95% confidence interval (CI): 1.22−2.66; p = 0.003) and Omicron (aOR, 1.88; 95% CI: 1.09−3.22; p = 0.022) COVID-19 variant types were associated with higher all-cause mortality when compared to the initial COVID-19 variant. Old age (aOR, 1.05; 95% CI: 1.04−1.06; p < 0.001), the presence of respiratory disease (aOR, 1.58; 95% CI: 1.02−2.44; p = 0.04), ICU admission (aOR, 3.41; 95% CI: 2.16−5.39; p < 0.001), lower eGFR (aOR, 1.61; 95% CI: 1.17−2.23; p = 0.004), and ARDS (aOR, 5.75; 95% CI: 3.69−8.98; p < 0.001) were also associated with higher mortality while NIV requirements were associated with lower odds of dying (aOR, 0.65; 95% CI: 0.46−0.91; p = 0.012). (4) Conclusions: Alpha and Delta variants were associated with a longer hospital stay, need for intensive care, mechanical ventilation, and increased mortality. Old age, cardiac renal dysfunction were commonly associated with Omicron variants. Large-scale national studies to further assess the risk factors for mortality related to COVID-19 waves are warranted.

Characterizing pandemic waves: A latent class analysis of COVID-19 spread across US counties

The spread of the COVID-19 pandemic is observed to follow the shape of "waves" (i.e., the rise and fall of population-adjusted daily new infection cases with time). Different geographic regions of the world have experienced different position and span of these waves over time. The presence and strength of these waves broadly characterize the dynamics of the pandemic spread in a given area, so their characterization is important to draw meaningful intervention and mitigation plans tailored for that area. In this paper, we propose a novel technique to represent the trend of COVID-19 spread as a sequence of a fixed-length text string defined on three symbols: R (rise), S (Steady), and F (fall). These strings, termed as trend strings, enabled us searching for specific patterns in them (such as for waves). After analyzing county-level infection data, we observe that, US counties-despite their wide variation in trend strings-can be grouped into a number of heterogeneous classes each of which might have a representative COVID spread pattern over time (in terms of presence and propensity of waves). To this end, we conduct a latent class analysis to cluster 3142 US counties into four distinct classes based on their wave characteristics for one year pandemic data (January 2020 to January 2021). We observe that counties in each class have distinct socio-demographics, location, and human mobility characteristics. In short summary, counties have differing number of waves (class 1 counties have only one wave and class 3 counties have three) and their positions also vary (class 1 had the wave later in the year whereas class 3 had waves throughout the year). We believe that this way of characterizing pandemic waves would provide better insights in understanding the complex dynamics of COVID-19 spread and its future evolution, and would, therefore, help in taking class-specific policy interventions.

Microbiological and Clinical Findings of SARS-CoV-2 Infection after 2 Years of Pandemic: From Lung to Gut Microbiota

Early recognition and prompt management are crucial for improving survival in COVID-19 patients, and after 2 years of the pandemic, many efforts have been made to obtain an early diagnosis. A key factor is the use of fast microbiological techniques, considering also that COVID-19 patients may show no peculiar signs and symptoms that may differentiate COVID-19 from other infective or non-infective diseases. These techniques were developed to promptly identify SARS-CoV-2 infection and to prevent viral spread and transmission. However, recent data about clinical, radiological and laboratory features of COVID-19 at time of hospitalization could help physicians in early suspicion of SARS-CoV-2 infection and distinguishing it from other etiologies. The knowledge of clinical features and microbiological techniques will be crucial in the next years when the endemic circulation of SARS-CoV-2 will be probably associated with clusters of infection. In this review we provide a state of the art about new advances in microbiological and clinical findings of SARS-CoV-2 infection in hospitalized patients with a focus on pulmonary and extrapulmonary characteristics, including the role of gut microbiota.

Covid-19, an unfinished story

The Covid-19 pandemic appeared in China in December 2019 as a cluster of transmissible pneumonia caused by a new betacoronavirus. On March 11, 2020, the World Health Organization (WHO) declared it a pandemic. Covid-19 is a mild infection in 80% of cases, serious in 15% and critical in 5%. Symptomatic forms include a first phase of flu-like viral invasion, and at times a second phase, dysimmune and inflammatory, with acute respiratory distress syndrome, multiorgan failure and thromboembolic complications. Degree of severity is related to age and comorbidities. SARS-CoV-2 is the third highly pathogenic Betacoronavirus to cross the species barrier. Its genome, an RNA of 29,903 nucleotides, shows strong homogeneity with bat coronaviruses from southern China, but the conditions for its passage in humans have yet to be elucidated. Mutations can give rise to variants of concern (VOC) that are more transmissible and able to evade the host's immune response. Several VOCs have succeeded and replaced one another: Alpha in October 2020, Beta and Gamma in December 2020, Delta in spring 2021 and Omicron in November 2021. The Covid-19 pandemic has evolved in five waves of unequal amplitude and severity, with geographical disparities. Worldwide, it has caused 395,000,000 confirmed cases including 5,700,000 deaths. Epidemiological surveillance applies several indicators (incidence rate, test positivity rate, effective R and occupancy rate of intensive care beds) supplemented by genomic monitoring to detect variants by sequencing. Non-pharmacological measures, particularly face mask wearing, have been effective in preventing the transmission of SARS-CoV-2. Few currently available drugs have proven useful, with the exception of dexamethazone for patients requiring oxygen therapy. Development of SARS-CoV-2 vaccines began early on many platforms. Innovation was brought about by the Pfizer-BioNTech and Moderna messenger RNA vaccines, which claim protective efficacy of 95% and 94.1% respectively, far higher than the 70% minimum set by the WHO. Governments have hesitated between two strategies, mitigation and suppression. The second has been favored in critical periods such as April 2020, when 2.5 billion people throughout the world were confined. Vaccination campaigns got underway at the end of December 2020 and progressed without reaching sufficient herd immunity, leading some nations to consider compulsory vaccination or to require a vaccine or health pass, in order for persons to access different activities. Will the pandemic stop with Omicron and become endemic? This part of the Covid-19 story remains to be told.

A strategy to optimize the peptide-based inhibitors against different mutants of the spike protein of SARS-CoV-2

The SARS-CoV-2 virus has caused high-priority health concerns at a global level. Vaccines have stalled the proliferation of viruses to some extent. Yet, the emergence of newer, potentially more infectious, and dangerous mutants such as Delta and Omicron are among the major challenges in finding a more permanent solution for this pandemic. The effectiveness of antivirals Molnupiravir and Paxlovid, authorized for emergency use by the FDA, are yet to be assessed on a larger population. Patients with a high risk of disease progression have received treatment with antibody-cocktail. Most of the mutations leading to the new lineage of SARS-CoV-2 are found in the spike protein of this virus that plays a key role in facilitating host entry. The current study has investigated how to modify a promising peptide-based inhibitor of spike protein, LCB3, against common mutations, N501Y and K417N in the target protein so that it retains its efficacy against the spike protein. LCB3 being a prototype for protein-based inhibitors is an ideal testing system to learn about protein-based inhibitors. This study proposes the substitutions of amino acid residues of LCB3 inhibitor using a structure-based approach that considers free energy decomposition of residues, the distance between atoms, and the interaction among amino acids. The binding free energy calculations suggest a possible improvement in the binding affinity of existing inhibitor LCB3 to the mutant forms of the S-protein using simple substitutions at specific positions of the inhibitor. This approach, being general, can be used in different inhibitors and other mutations and help in fighting against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

A Comprehensive Comparison of Clinical Presentation and Outcomes of Kidney Transplant Recipients with COVID-19 during Wave 1 versus Wave 2 at a Tertiary Care Center, India

Data comparing the clinical spectrum of COVID-19 in kidney transplant recipients (KTRs) during the first and second waves of the pandemic in India is limited. Our single-center retrospective study compared the clinical profile, mortality, and associated risk factors in KTRs with COVID-19 during the 1st wave (1^st February 2020 to 31^st January 2021) and the second wave (1^st March-31^st August 2021). 156 KTRs with PCR confirmed SARS-CoV-2 infection treated at a tertiary care hospital in New Delhi during the 1st and the second waves were analyzed. The demographics and baseline transplant characteristics of the patients diagnosed during both waves were comparable. Patients in the second wave reported less frequent hospitalization, though the intensive care unit (ICU) and ventilator requirements were similar. Strategies to modify immunosuppressants such as discontinuation of antinucleoside drugs with or without change in calcineurin inhibitors and the use of steroids were similar during both waves. Overall patient mortality was 27.5%. The demographics and baseline characteristics of survivors and nonsurvivors were comparable. A higher percentage of nonsurvivors presented with breathing difficulty, low SpO₂, and altered sensorium. Both wave risk factors for mortality included older age, severe disease, ICU/ventilator requirements, acute kidney injury (AKI) needing dialysis, Chest Computerized Tomographic (CT) scan abnormalities, and higher levels of inflammatory markers particularly D-dimer and interleukin-6 levels. Conclusions. KTRs in both COVID-19 waves had similar demographics and baseline characteristics, while fewer patients during the second wave required hospitalization. The D-dimer and IL-6 levels are directly correlated with mortality.

Analysis of COVID-19 inpatients in France during first lockdown of 2020 using explainability methods

The COVID-19 pandemic rapidly puts a heavy pressure on hospital centers, especially on intensive care units. There was an urgent need for tools to understand typology of COVID-19 patients and identify those most at risk of aggravation during their hospital stay. Data included more than 400 patients hospitalized due to COVID-19 during the first wave in France (spring of 2020) with clinical and biological features. Machine learning and explainability methods were used to construct an aggravation risk score and analyzed feature effects. The model had a robust AUC ROC Score of 81%. Most important features were age, chest CT Severity and biological variables such as CRP, O2 Saturation and Eosinophils. Several features showed strong non-linear effects, especially for CT Severity. Interaction effects were also detected between age and gender as well as age and Eosinophils. Clustering techniques stratified inpatients in three main subgroups (low aggravation risk with no risk factor, medium risk due to their high age, and high risk mainly due to high CT Severity and abnormal biological values). This in-depth analysis determined significantly distinct typologies of inpatients, which facilitated definition of medical protocols to deliver the most appropriate cares for each profile.

Graphical Abstract

Critically ill COVID-19 patients in northeast Brazil: mortality predictors during the first and second waves including SAPS 3

BACKGROUND

The Simplified Acute Physiology Score (SAPS) 3 is a reliable score to predict mortality. This study aims to investigate the predictive values of SAPS 3 and other clinical parameters for death in critically ill coronavirus disease 2019 (COVID-19) patients.

METHODS

This is a prospective study in a tertiary hospital for patients who required intensive care due to COVID-19 infection in northeast Brazil. Two distinct groups were constructed according to the epidemiological data: first wave and second wave. The severity of patients admitted was estimated using the SAPS 3 score.

RESULTS

A total of 767 patients were included: 290 were enrolled in the first wave and 477 in the second wave. Patients in the first wave had more comorbidities, were put on mechanical ventilation and required dialysis and vasopressors more frequently (p<0.05). During the second wave, non-invasive ventilation was more often required (p<0.05). In both periods, older patients and higher SAPS 3 scores on admission were associated with death (p<0.05). Non-invasive ventilation use showed a negative association with death only in the second wave period. In the first wave, the SAPS 3 score was more useful (area under the curve [AUC] 0.897) in predicting death in critically ill COVID-19 patients than in the second wave (AUC 0.810).

CONCLUSION

The SAPS 3 showed very reliable predictive values for death during the waves of the COVID-19 pandemic, mostly together with kidney and pulmonary dysfunction.

Impact of Type 2 Diabetes Mellitus on the Incidence and Outcomes of COVID-19 Needing Hospital Admission According to Sex: Retrospective Cohort Study Using Hospital Discharge Data in Spain, Year 2020

(1) Background: To analyze incidence and in-hospital mortality (IHM) of COVID-19 needing hospital admission in Spain (2020) in patients with T2DM. (2) Methods: We conducted a retrospective cohort study. Using the Spanish Register of Specialized Care-Basic Minimum Database we estimated age-adjusted incidence rates (IR). (3) Results: We included 203,488 patients (56.77% men), of whom 45,620 (22.41%) had T2DM. Age-adjusted IRs/1000 for men with and without T2DM was 12.90 and 5.87, respectively (IRR 2.20; 95% CI 2.18−2.22; p < 0.001), and for women with and without T2DM was 9.23 and 4.27, respectively (IRR 2.16; 95% CI 2.13−2.19; p < 0.001). Crude IHM was 23.86% in people with T2DM, and 15.94% in non-T2DM people (p < 0.001). After matching, intensive-care admission (7.37% vs. 6.15%; p < 0.001) and IHM (23.37% vs. 20.41%; p < 0.001) remained higher in women with T2DM. After matching, IHM among T2DM men was 1.5% higher than among non-T2DM men (24.27% vs. 22.72%; p < 0.001). Men with T2DM had a 34% higher IHM than women with T2DM. Prevalent T2DM increased IHM among women (1.09; 95% 1.03−1.16) and men (1.05; 95% 1.01−1.10). (4) Conclusions: Incidence rates of COVID-19 needing hospital admission were higher in men vs. women, and for people with T2DM vs. non-T2DM. Men had higher IHM beside T2DM status. Prevalent T2DM was associated with higher IHM for both sexes.

Organ dysfunction and death in patients admitted to hospital with COVID-19 in pandemic waves 1 to 3 in British Columbia, Ontario and Quebec, Canada: a cohort study

BACKGROUND

There have been multiple waves in the COVID-19 pandemic in many countries. We sought to compare mortality and respiratory, cardiovascular and renal dysfunction between waves in 3 Canadian provinces.

METHODS

We conducted a substudy of the ARBs CORONA I study, a multicentre Canadian pragmatic observational cohort study that examined the association of pre-existing use of angiotensin receptor blockers with outcomes in adults admitted to hospital with acute COVID-19 up to April 2021 from 9 community and teaching hospitals in 3 Canadian provinces (British Columbia, Ontario and Quebec). We excluded emergency department admissions without hospital admission, readmissions and admissions for another reason. We used logistic and 0-1-inflated β regression models to compare 28-day and in-hospital mortality, and the use of invasive mechanical ventilation, vasopressors and renal replacement therapy (RRT) between the first 3 waves of the COVID-19 pandemic in these provinces.

RESULTS

A total of 520, 572 and 245 patients in waves 1, 2 and 3, respectively, were included. Patients in wave 3 were on average younger and had fewer comorbidities than those in waves 1 and 2. The unadjusted 28-day mortality rate was significantly lower in wave 3 (7.8%) than in wave 1 (18.3%) (odds ratio [OR] 0.43, 95% confidence interval [CI] 0.24-0.78) and wave 2 (16.3%) (OR 0.46, 95% CI 0.27-0.79). After adjustment for differences in baseline characteristics, the difference in 28-day mortality remained significant (adjusted OR wave 3 v. wave 1: 0.46, 95% CI 0.26-0.81; wave 3 v. wave 2: 0.52, 95% CI 0.29-0.91). In-hospital mortality findings were similar. Use of invasive mechanical ventilation or vasopressors was less common in waves 2 and 3 than in wave 1, and use of RRT was less common in wave 3 than in wave 1.

INTERPRETATION

Severity of illness decreased (lower mortality and less use of organ support) across waves among patients admitted to hospital with acute COVID-19, possibly owing to changes in patient demographic characteristics and management, such as increased use of dexamethasone. Continued application of proven therapies may further improve outcomes.

STUDY REGISTRATION

ClinicalTrials.gov, no. NCT04510623.

Clinical Presentation and Outcome of Hospitalized Patients With COVID-19 in the First and Second Waves in Saudi Arabia

Introduction

The world had witnessed the occurrence of multiple waves of the SARS-CoV-2. Data comparing the clinical characteristics and outcomes of hospitalized patients in Saudi Arabia during the first and second waves are lacking. This study compares the characteristics and the outcomes of patients in these 2 waves.

Methods

This is a retrospective case series of hospitalized patients with confirmed SARS-CoV-2. We compared epidemiological, demographic, laboratory, and clinical data.

Results

The study included hospitalized patients admitted up to February 28, 2021 as the first wave and those admitted from March 1, 2021 as the second wave. There were 378 patients in the first wave and 241 patients in the second wave. Patients in the first wave were significantly younger (mean age and SD of 47.5 ± 20 vs 55.3 ± 18.2 years; p <0.001). In relation to symptoms, shortness of breath, wheezes, myalgia, tachypnea, and respiratory distress were significantly more common in the second wave than the first wave. On the other hand, sore throat was more common in the first wave than the second wave. Patients in the second wave had higher mean values of lymphocytes count, platelet counts, and ALT than those in the first wave. Patients in the first wave were more likely to receive antibiotics and antiviral therapy and had higher death rate (16.2% vs 8.4%; p = 0.001).

Conclusion

The study showed that patients in the second wave were younger and had a lower rate of death than the first wave.

Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis

Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evolution through the following waves. Data was collected retrospectively from a prospectively maintained database from a University Hospital in Paris area, over a year corresponding to the first three waves of COVID-19 in France. Evolution of patient characteristics between non-severe and severe cases through the waves was analyzed with a classical multivariate logistic regression along with a complementary Machine-Learning-based analysis using explainability methods. On 1076 hospitalized patients, severe forms concerned 29% (123/429), 31% (66/214) and 18% (79/433) of each wave. Risk factors of the first wave included old age (≥ 70 years), male gender, diabetes and obesity while cardiovascular issues appeared to be a protective factor. Influence of age, gender and comorbidities on the occurrence of severe COVID-19 was less marked in the 3rd wave compared to the first 2, and the interactions between age and comorbidities less important. Typology of hospitalized patients with severe forms evolved rapidly through the waves. This evolution may be due to the changes of hospital practices and the early vaccination campaign targeting the people at high risk such as elderly and patients with comorbidities.

Analysis of SARS-CoV-2 Variants From 24,181 Patients Exemplifies the Role of Globalization and Zoonosis in Pandemics

After the end of the first epidemic episode of SARS-CoV-2 infections, as cases began to rise again during the summer of 2020, we at IHU Méditerranée Infection in Marseille, France, intensified the genomic surveillance of SARS-CoV-2, and described the first viral variants. In this study, we compared the incidence curves of SARS-CoV-2-associated deaths in different countries and reported the classification of SARS-CoV-2 variants detected in our institute, as well as the kinetics and sources of the infections. We used mortality collected from a COVID-19 data repository for 221 countries. Viral variants were defined based on ≥5 hallmark mutations along the whole genome shared by ≥30 genomes. SARS-CoV-2 genotype was determined for 24,181 patients using next-generation genome and gene sequencing (in 47 and 11% of cases, respectively) or variant-specific qPCR (in 42% of cases). Sixteen variants were identified by analyzing viral genomes from 9,788 SARS-CoV-2-diagnosed patients. Our data show that since the first SARS-CoV-2 epidemic episode in Marseille, importation through travel from abroad was documented for seven of the new variants. In addition, for the B.1.160 variant of Pangolin classification (a.k.a. Marseille-4), we suspect transmission from farm minks. In conclusion, we observed that the successive epidemic peaks of SARS-CoV-2 infections are not linked to rebounds of viral genotypes that are already present but to newly introduced variants. We thus suggest that border control is the best mean of combating this type of introduction, and that intensive control of mink farms is also necessary to prevent the emergence of new variants generated in this animal reservoir.

Computed tomography-defined body composition as prognostic markers for unfavourable outcomes and in-hospital mortality in coronavirus disease 2019

BACKGROUND

Low skeletal muscle mass (LSMM) and visceral fat areas can be assessed by cross-sectional images. These parameters are associated with several clinically relevant factors in various disorders with predictive and prognostic implications. Our aim was to establish the effect of computed tomography (CT)-defined LSMM and fat areas on unfavourable outcomes and in-hospital mortality in coronavirus disease 2019 (COVID-19) patients based on a large patient sample.

METHODS

MEDLINE library, Cochrane, and Scopus databases were screened for the associations between CT-defined LSMM as well as fat areas and in-hospital mortality in COVID-19 patients up to September 2021. In total, six studies were suitable for the analysis and included into the present analysis.

RESULTS

The included studies comprised 1059 patients, 591 men (55.8%) and 468 women (44.2%), with a mean age of 60.1 years ranging from 48 to 66 years. The pooled prevalence of LSMM was 33.6%. The pooled odds ratio for the effect of LSMM on in-hospital mortality in univariate analysis was 5.84 [95% confidence interval (CI) 1.07-31.83]. It was 2.73 (95% CI 0.54-13.70) in multivariate analysis. The pooled odds ratio of high visceral fat area on unfavourable outcome in univariate analysis was 2.65 (95% CI 1.57-4.47).

CONCLUSIONS

Computed tomography-defined LSMM and high visceral fat area have a relevant association with in-hospital mortality in COVID-19 patients and should be included as relevant prognostic biomarkers into clinical routine.

The Second- vs First-wave COVID-19: More of the Same or a Lot Worse? A Comparison of Mortality between the Two Waves in Patients Admitted to Intensive Care Units in Nine Hospitals in Western Maharashtra

Background

India, along with the rest of the world, faced the challenging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. The second wave in India lagged behind that in the Western world, due to different timing of seasons. There is scarce data about the differences between the two waves, for intensive care unit (ICU) patients. We present the data of 3,498 patients from 9 ICUs of western Maharashtra.

Materials and methods

We collected prospective data of hospitalized, RT-PCR confirmed, coronavirus-2019 (COVID-19) patients, from nine tertiary centers, after institutional ethics committee (IEC) approval. Then, we segregated and analyzed the data of patients admitted to the ICU, for comorbidities, high-resolution computed tomography (HRCT) score, ventilatory support, etc. The primary outcomes were ICU and hospital mortality. We also performed multivariable analysis for predictors of ICU mortality.

Results

Overall, there were 3,498 ICU patients. In the first wave, 1,921 patients needed ICU admission, while in the second wave, 1,577 patients. Patients in the second wave had significantly higher ICU (26.1 vs 13.4%, p <0.001) and hospital mortality (29.9 vs 18.2%, p <0.001) and need for ventilatory support of any type. More patients received steroids during the second wave. On multivariable regression, male gender, ICU admission during the second wave, increasing HRCT score, and need for intubation and mechanical ventilation were significant predictors of ICU mortality.

Conclusion

ICU patients admitted during the two waves were of the similar age, but there were more females, and more patients had comorbidities during the second wave. The ICU and hospital mortality were significantly higher during the second wave.

How to cite this article

Zirpe KG, Dixit S, Kulkarni AP, Pandit RA, Ranganathan P, Prasad S, et al. The Second- vs First-wave COVID-19: More of the Same or a Lot Worse? A Comparison of Mortality between the Two Waves in Patients Admitted to Intensive Care Units in Nine Hospitals in Western Maharashtra. Indian J Crit Care Med 2021; 25(12):1343-1348.

Trends in all-cause mortality of hospitalized patients due to SARS-CoV-2 infection from a monocentric cohort in Milan (Lombardy, Italy)

Background

Robust data on case fatality rate (CFR) among inpatients with COVID-19 are still lacking, and the role of patient characteristics in in-hospital deaths remains under-investigated. This study quantified the overall CFR and described its trend in a cohort of hospitalized patients with SARS-CoV-2 in Italy. Admission to ICU, death, or discharge were the secondary outcomes.

Methods

This retrospective study is based on administrative health data and electronic case records of inpatients consecutively admitted to Niguarda Hospital between 21 February and 8 November 2020.

Results

An overall CFR of 18% was observed. CFR was significantly reduced during the second wave of contagion (1 June to 30 September, 16%) compared with the first wave (21 February to 31 May, 21% p = 0.015). Such reduction was mainly observed among male inpatients between 40 and 80 years with limited comorbidities. Admission to ICU was associated with a high risk of mortality in both waves. The incidence of severe disease and the need for ICU admission were lower in the second wave.

Conclusion

CFR in SARS-CoV-2 inpatients was demonstrated to decrease over time. This reduction may partly reflect the changes in hospital strategy and clinical practice. The reasons for this improvement should be further investigated to plan an exit strategy in case of future outbreaks.

Key messages

What is already known on this topic

Before the advent of anti-COVID-19 vaccines, a multi-wave pattern of contagion was observed, and this trend conditioned the inpatient case fatality rate (CFR), which varied over time accordingly to the waves of contagion.

Only preliminary results on the in-hospital mortality trend are available, along with a partial analysis of its determinants. Consequently, robust data on CFR among inpatients with SARS-CoV-2 infection are still lacking, and the role of patient characteristics in in-hospital deaths remains under-investigated.

What this study adds

This study shows that the in-hospital mortality in patients with SARS-CoV-2 infection decreases over time.

Such reduction was mainly observed among male inpatients between 40 and 80 years with limited comorbidities. Admission to ICU was invariably associated with a high risk of mortality during the whole study period (21 February to 8 November 2020), but the incidence of severe disease and the need for ICU admission were lower in the second wave of contagions (1 October to 8 November 2020). This reduction may partly reflect the impact of changes in hospital strategy and clinical practice. The reasons for this improvement should be further investigated to inform the response to future outbreaks and to plan exit strategy by prioritizing high-risk populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-021-01675-y.

COVID-19 pneumonia: pathophysiology and management

Coronavirus disease 2019 (COVID-19) pneumonia is an evolving disease. We will focus on the development of its pathophysiologic characteristics over time, and how these time-related changes determine modifications in treatment. In the emergency department: the peculiar characteristic is the coexistence, in a significant fraction of patients, of severe hypoxaemia, near-normal lung computed tomography imaging, lung gas volume and respiratory mechanics. Despite high respiratory drive, dyspnoea and respiratory rate are often normal. The underlying mechanism is primarily altered lung perfusion. The anatomical prerequisites for PEEP (positive end-expiratory pressure) to work (lung oedema, atelectasis, and therefore recruitability) are lacking. In the high-dependency unit: the disease starts to worsen either because of its natural evolution or additional patient self-inflicted lung injury (P-SILI). Oedema and atelectasis may develop, increasing recruitability. Noninvasive supports are indicated if they result in a reversal of hypoxaemia and a decreased inspiratory effort. Otherwise, mechanical ventilation should be considered to avert P-SILI. In the intensive care unit: the primary characteristic of the advance of unresolved COVID-19 disease is a progressive shift from oedema or atelectasis to less reversible structural lung alterations to lung fibrosis. These later characteristics are associated with notable impairment of respiratory mechanics, increased arterial carbon dioxide tension (P_aCO2), decreased recruitability and lack of response to PEEP and prone positioning.

COVID-19 pneumonia cannot be correctly described, analysed and treated if the time-factor is not taken into accounthttps://bit.ly/3AOKxc4

High anti-SARS-CoV-2 antibody seroconversion rates before the second wave in Manaus, Brazil, and the protective effect of social behaviour measures: results from the prospective DETECTCoV-19 cohort

Background

The city of Manaus, Brazil, has seen two collapses of the health system due to the COVID-19 pandemic. We report anti-SARS-CoV-2 nucleocapsid IgG antibody seroconversion rates and associated risk factors in Manaus residents before the second wave of the epidemic in Brazil.

Methods

A convenience sample of adult (aged ≥18 years) residents of Manaus was recruited through online and university website advertising into the DETECTCoV-19 study cohort. The current analysis of seroconversion included a subgroup of DETECTCoV-19 participants who had at least two serum sample collections separated by at least 4 weeks between Aug 19 and Oct 2, 2020 (visit 1), and Oct 19 and Nov 27, 2020 (visit 2). Those who reported (or had no data on) having a COVID-19 diagnosis before visit 1, and who were positive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at visit 1 were excluded. Using an in-house ELISA, the reactivity index (RI; calculated as the optical density ratio of the sample to the negative control) for serum anti-SARS-CoV-2 nucleocapsid IgG antibodies was measured at both visits. We calculated the incidence of seroconversion (defined as RI values ≤1·5 at visit 1 and ≥1·5 at visit 2, and a ratio >2 between the visit 2 and visit 1 RI values) during the study period, as well as incidence rate ratios (IRRs) through cluster-corrected and adjusted Poisson regression models to analyse associations between seroconversion and variables related to sociodemographic characteristics, health access, comorbidities, COVID-19 exposure, protective behaviours, and symptoms.

Findings

2496 DETECTCoV-19 cohort participants returned for a follow-up visit between Oct 19 and Nov 27, 2020, of whom 204 reported having COVID-19 before the first visit and 24 had no data regarding previous disease status. 559 participants were seropositive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at baseline. Of the remaining 1709 participants who were seronegative at baseline, 71 did not meet the criteria for seroconversion and were excluded from the analyses. Among the remaining 1638 participants who were seronegative at baseline, 214 showed seroconversion at visit 2. The seroconversion incidence was 13·06% (95% CI 11·52–14·79) overall and 6·78% (5·61–8·10) for symptomatic seroconversion, over a median follow-up period of 57 days (IQR 54–61). 48·1% of seroconversion events were estimated to be asymptomatic. The sample had higher proportions of affluent and higher-educated people than those reported for the Manaus city population. In the fully adjusted and corrected model, risk factors for seroconversion before visit 2 were having a COVID-19 case in the household (IRR 1·49 [95% CI 1·21–1·83]), not wearing a mask during contact with a person with COVID-19 (1·25 [1·09–1·45]), relaxation of physical distancing (1·31 [1·05–1·64]), and having flu-like symptoms (1·79 [1·23–2·59]) or a COVID-19 diagnosis (3·57 [2·27–5·63]) between the first and second visits, whereas working remotely was associated with lower incidence (0·74 [0·56–0·97]).

Interpretation

An intense infection transmission period preceded the second wave of COVID-19 in Manaus. Several modifiable behaviours increased the risk of seroconversion, including non-compliance with non-pharmaceutical interventions measures such as not wearing a mask during contact, relaxation of protective measures, and non-remote working. Increased testing in high-transmission areas is needed to provide timely information about ongoing transmission and aid appropriate implementation of transmission mitigation measures.

Funding

Ministry of Education, Brazil; Fundação de Amparo à Pesquisa do Estado do Amazonas; Pan American Health Organization (PAHO)/WHO.

Experimental and Compassionate Drug Use During the First Wave of the COVID-19 Pandemic: A Retrospective Single-Center Study

INTRODUCTION

Concomitant experimental/compassionate drug administration has been all-pervasive in the treatment of COVID-19 patients. The objective of this study was to study the relationship between patient severity, the number of experimental/compassionate medications received (main outcome measure), and patient outcomes [survival to hospital discharge and length of hospital stay (LOS)].

METHODS

Retrospective analysis of data collected in real time during the first pandemic wave in a tertiary care hospital. Data included patient demographics, comorbidities, admission vital signs, laboratory values, most extreme respiratory intervention during hospitalization, and data regarding treatment with compassionate/experimental drugs during their stay.

RESULTS

Overall, 292 PCR-confirmed patients with symptoms of COVID-19 were studied (March/April, 2020). Increasing respiratory support correlated with both LOS and mortality. Patients were more likely to receive more than 1 experimental/compassionate drugs as respiratory support escalated, ranging from 3% (n = 4/136) among patients on room air to 77.3% (n = 17/22) of mechanically ventilated/ECMO patients (P < 0.001, linear by linear association). The mean number of experimental/compassionate drugs received also increased with escalating respiratory support (P < 0.001, one-way ANOVA). After adjustment for severity of patient condition, administration of more experimental/compassionate drugs was unrelated to survival (P = 0.24), but was related to increased LOS (P < 0.001).

CONCLUSION

Patients that were hospitalized in worse condition were more likely to receive more experimental/compassionate drugs. Treatment was unrelated to survival but may have been related to LOS. This finding raises questions regarding the results of studies on medication effects that adjusted for multiple drug administration.

Ruling Out Coronavirus Disease 2019 in Patients with Pneumonia: The Role of Blood Cell Count and Lung Ultrasound

Coronavirus disease 2019 (COVID-19) is characterized by a distinctive blood leucocyte pattern and B-lines on lung ultrasound (LUS) as marker of alveolar-interstitial syndrome. We aimed to evaluate the accuracy of blood leucocyte count alone or in combination with LUS for COVID-19 diagnosis. We retrospectively enrolled consecutive patients diagnosed with community acquired pneumonia (CAP) at hospital admission to derive and validate cutoff values for blood cell count that could be predictive of COVID-19 before confirmation by the nucleic acid amplification test (NAAT). Cutoff values, generated and confirmed in inception (41/115, positive/negative patients) and validation (100/180, positive/negative patients) cohorts, were ≤17 and ≤10 cells/mm³ for basophils and eosinophils, respectively. Basophils and/or eosinophils below cutoff were associated with sensitivity of 98% (95%CI, 94–100) and negative likelihood ratio of 0.04 (95%CI, 0.01–0.11). In a subgroup of 265 subjects, the sensitivity of B-line on LUS was 15% lower (p < 0.001) than that of basophils and/or eosinophils below cutoff. The combination of B-lines with basophils and eosinophils below cutoff was associated with a moderate increase of the positive likelihood ratio: 5.0 (95%CI, 3.2–7.7). In conclusion, basophil and eosinophil counts above the generated cutoff virtually rule out COVID-19 in patients with CAP. Our findings can help optimize patient triage pending the NAAT results.

Clinical Characteristics and Outcome of Patients with Suspected COVID-19 in Emergency Department (RESILIENCY Study II)

Objectives: COVID-19 may show no peculiar signs and symptoms that may differentiate it from other infective or non-infective etiologies; thus, early recognition and prompt management are crucial to improve survival. The aim of this study was to describe clinical, laboratory, and radiological characteristics and outcomes of hospitalized COVID-19 patients compared to those with other infective or non-infective etiologies. Methods: We performed a prospective study from March 2020 to February 2021. All patients hospitalized for suspected or confirmed COVID-19 were prospectively recruited. All patients were evaluated according to a predefined protocol for diagnosis of suspected SARS-CoV-2 infection. The primary endpoint was evaluation of clinical, laboratory, and radiological characteristics associated or not with COVID-19 etiology at time of hospitalization in an emergency department. Results: A total of 1036 patients were included in the study: 717 (69%) patients with confirmed COVID-19 and 319 (31%) without COVID-19, hospitalized for other causes. The main causes of hospitalization among non-COVID-19 patients were acute heart failure (44%) and bacterial pneumonia (45.8%). Overall, 30-day mortality was 9% among the COVID-19 group and 35% in the non-COVID-19 group. Multivariate analysis showed variables (fever > 3 days, dry cough, acute dyspnea, lymphocytes < 1000 × 10³/µL, and ferritin > 250 ng/mL) independently associated with COVID-19 etiology. A decision tree was elaborated to early detect COVID-19 patients in the emergency department. Finally, Kaplan–Meier curves on 30-day survival in COVID-19 patients during the first wave (March–May 2020, n = 289 patients) and the second wave (October–February 2021, n = 428 patients) showed differences between the two study periods (p = 0.021). Conclusions: Patients with confirmed diagnosis of COVID-19 may show peculiar characteristics at time of hospitalization that could help physicians to distinguish from other infective or non-infective etiologies. Finally, a different 30-day mortality rate was observed during different periods of the pandemic.