Systematically comparing COVID-19 with the 2009 influenza pandemic for hospitalized patients

Impact of SARS-CoV-2 on Viral Respiratory Infections in Patients with Hematological Malignancies

Patients with hematological malignancies (HMs) are at high risk of respiratory viral infections due to the intrinsic deterioration of the immune system and chemotherapy treatments. In the recent past, SARS-CoV-2 respiratory viral infection has been responsible for most infectious complications in HMs. We analyzed 2950 samples from 505 patients admitted to the Hematology department from 2019 to 2023. The aim of this study was to determine the epidemiological trend of respiratory viruses in the SARS-CoV-2 era, the characteristics of the patients involved and their outcomes. In our analysis, we found a reduction in non-SARS-CoV-2 respiratory viral (NSRV) positivity during the pandemic period, although these data did not show statistical significance. Most of the HMs involved were Multiple Myeloma and Acute Myeloid Leukemia. Overall mortality rate was very low and characterized by the progression of the HMs as well as the worsening of respiratory failure. In conclusion, a reduction in non-COVID viral infections was highlighted, probably also thanks to the increase in prevention measures and environmental modifications of the viral background.

Acute and post-acute respiratory complications of SARS-CoV-2 infection: population-based cohort study in South Korea and Japan

Considering the significant burden of post-acute COVID-19 conditions among patients infected with SARS-CoV-2, we aimed to identify the risk of acute respiratory complications or post-acute respiratory sequelae. A binational population-based cohort study was conducted to analyze the risk of acute respiratory complications or post-acute respiratory sequelae after SARS-CoV-2 infection. We used a Korean nationwide claim-based cohort (K-COV-N; n = 2,312,748; main cohort) and a Japanese claim-based cohort (JMDC; n = 3,115,606; replication cohort) after multi-to-one propensity score matching. Among 2,312,748 Korean participants (mean age, 47.2 years [SD, 15.6]; 1,109,708 [48.0%] female), 17.1% (394,598/2,312,748) were infected with SARS-CoV-2. The risk of acute respiratory complications or post-acute respiratory sequelae is significantly increased in people with SARS-CoV-2 infection compared to the general population (acute respiratory complications: HR, 8.06 [95% CI, 6.92-9.38]; post-acute respiratory sequelae: 1.68 [1.62-1.75]), and the risk increased with increasing COVID-19 severity. We identified COVID-19 vaccination as an attenuating factor, showing a protective association against acute or post-acute respiratory conditions. Furthermore, while the excess post-acute risk diminished with time following SARS-CoV-2 infection, it persisted beyond 6 months post-infection. The replication cohort showed a similar pattern in the association. Our study comprehensively evaluates respiratory complications in post-COVID-19 conditions, considering attenuating factors such as vaccination status, post-infection duration, COVID-19 severity, and specific respiratory conditions.

Rapid response teams in the Eastern Mediterranean Region: Results from the baseline survey of country-level capacities, operations and outbreak response capabilities

The aim of this study is to assess WHO/Eastern Mediterranean region (WHO/EMR) countries capacities, operations and outbreak response capabilities. Cross-sectional study was conducted targeting 22 WHO/EMR countries from May to June 2021. The survey covers 8 domains related to 15 milstones and key performance indicators (KPIs) for RRT. Responses were received from 14 countries. RRTs are adequately organised in 9 countries (64.3%). The mean retention rate of RRT members was 85.5% ± 22.6. Eight countries (57.1%) reported having standard operating procedures, but only three countries (21.4%) reported an established mechanism of operational fund allocation. In the last 6 months, 10,462 (81.9%) alerts were verified during the first 24 h. Outbreak response was completed by the submission of final RRT response reports in 75% of analysed outbreaks. Risk Communication and Community Engagement (RCCE) activities were part of the interventional response in 59.5% of recent outbreaks. Four countries (28.6%) reported an adequate system to assess RRTs operations. The baseline data highlights four areas to focus on: developing and maintaining the multidisciplinary nature of RRTs through training, adequate financing and timely release of funds, capacity and system building for implementing interventions, for instance, RCCE, and establishing national monitoring and evaluation systems for outbreak response.

Distinguishing COVID-19 from seasonal influenza in patients under age 65 years-a retrospective observational cohort study comparing the 2009 influenza A (H1N1) and 2022 SARS-CoV-2 pandemics

Introduction

This study explored the differences in clinical characteristics between the 2009 pandemic influenza A (H1N1) and SARS-CoV-2 BA.2 variant (Omicron) infections in patients younger than age 65 years, to improve identification of these diseases and better respond to the current epidemic.

Methods

Data from 127 patients with the 2009 pandemic influenza A (H1N1) diagnosed between May and July of 2009 and 3,265 patients with Omicron diagnosed between March and May of 2022 were collected. Using a 1:2 match based on age (difference <2 years), sex, and underlying diseases, data from 115 patients with the 2009 pandemic influenza A (H1N1) infection (H1N1 group) and 230 patients with SARS-CoV-2 Omicron BA.2 infection (Omicron group) were analyzed. The clinical manifestations were compared between the groups, logistic regression was performed to identify possible independent risk factors for each group, and multiple linear regression was used to analyze the factors predicting time for nucleic acid negativization (NAN).

Results

The median [interquartile range] age of the two groups was 21 [11, 26] years. Compared with the H1N1 group, the Omicron group had: lower white blood cell counts and C-reactive protein levels; less fever, nasal congestion, sore throat, cough, sputum, and headache; and more olfactory loss, muscle soreness, and lactate dehydrogenase (LDH) abnormalities. Patients in the Omicron group used fewer antibiotics and antiviral drugs, and the time for NAN was longer (17 [14,20] VS 4 [3,5] days, P<0.001). Logistic regression showed that fever, cough, headache, and increased white blood cell count were more strongly correlated with the H1N1 group, while muscle soreness and LDH abnormalities were more strongly correlated with the Omicron group. Fever (B 1.529, 95% confidence interval [0.149,2.909], P=0.030) significantly predicted a longer time for NAN in patients with Omicron.

Discussion

There are significant differences in clinical characteristics between SARS-CoV-2 Omicron infection and the 2009 pandemic influenza A (H1N1) infection. Recognition of these differences has important implications for clinical practice.

History Repeats Itself: The Relevance of Historical Pandemics to the Medical School Curriculum

Introduction

The dramatic global impact of the coronavirus pandemic has increased consideration on epidemiological progressions of pandemics. Measures implemented to reduce viral transmission have been largely historical, comparable in nature with the 1918 and 2009 influenza pandemics, demonstrating the importance of clinicians' awareness on historical pandemics. Despite this, literature suggests medical students' knowledge on previous pandemics is poor.

Objectives

This study aims to gather stakeholder information from UK medical students on the importance of including the history of pandemics in the medical school curriculum.

Methods

A cross-sectional cohort study conducted via a mixed question type online survey was distributed to all UK medical schools to explore stakeholder views. Grounded theory emergent coding was used to generate themes to free-text answers and SPSS and Excel were used to analyse quantitative data using pivot tables and Fishers exact tests.

Results

Two hundred and forty-one students consented to take part from eight medical schools in the UK with 98% of these students completing the questionnaire. 34% of students reported having teaching on pandemics with 78% of students stating it would be beneficial. Knowledge was poor with 5.7% of students achieving 100% on knowledge-based questions. 72% of students believed that learning about the history of medicine would be beneficial with 87% of these students referring to 'benefiting (the) future' in their answers. Additionally, 79% of students thought it would be beneficial to learn about historical pandemics with reference to the current COVID-19 pandemic.

Conclusion

To date, this is the only UK based study assessing stakeholders' views on including the history of pandemics in the medical school curriculum. Our findings demonstrate that medical students wish to have more historical content included in their degree to better prepare tomorrow's doctors for situations that may occur when history repeats itself.

Not a New Story: Place- and Race-Based Disparities in COVID-19 and Influenza Hospitalizations among Medicaid-Insured Adults in New York City

While SARS-CoV-2 is a novel virus, contagious respiratory illnesses are not a new problem. Limited research has examined the extent to which place- and race-based disparities in severe illness are similar across waves of the COVID-19 pandemic and historic influenza seasons. In this study, we focused on these disparities within a low-income population, those enrolled in Medicaid in New York City. We used 2015-2020 New York State Medicaid claims to compare the characteristics of patients hospitalized with COVID-19 during three separate waves of 2020 (first wave: January 1-April 30, 2020; second wave: May 1-August 31, 2020; third wave: September 1-December 31, 2020) and with influenza during the 2016 (July 1, 2016-June 30, 2017) and 2017 influenza seasons (July 1, 2017-June 30, 2018). We found that patterns of hospitalization by race/ethnicity and ZIP code across the two influenza seasons and the first wave of COVID-19 were similar (increased risk among non-Hispanic Black (aOR = 1.17, 95% CI: 1.10-1.25) compared with non-Hispanic white Medicaid recipients). Black/white disparities in hospitalization dissipated in the second COVID wave and reversed in the third wave. The commonality of disparities across influenza seasons and the first wave of COVID-19 suggests there are community factors that increase hospitalization risk across novel respiratory illness incidents that emerge in the period before aggressive public health intervention. By contrast, convergence in hospitalization patterns in later pandemic waves may reflect, in part, the distinctive public health response to COVID-19.

Symptom Clustering Patterns and Population Characteristics of COVID-19 Based on Text Clustering Method

Background

Descriptions of single clinical symptoms of coronavirus disease 2019 (COVID-19) have been widely reported. However, evidence of symptoms associations was still limited. We sought to explore the potential symptom clustering patterns and high-frequency symptom combinations of COVID-19 to enhance the understanding of people of this disease.

Methods

In this retrospective cohort study, a total of 1,067 COVID-19 cases were enrolled. Symptom clustering patterns were first explored by a text clustering method. Then, a multinomial logistic regression was applied to reveal the population characteristics of different symptom groups. In addition, time intervals between symptoms onset and the first visit were analyzed to consider the effect of time interval extension on the progression of symptoms.

Results

Based on text clustering, the symptoms were summarized into four groups. Group 1: no-obvious symptoms; Group 2: mainly fever and/or dry cough; Group 3: mainly upper respiratory tract infection symptoms; Group 4: mainly cardiopulmonary, systemic, and/or gastrointestinal symptoms. Apart from Group 1 with no obvious symptoms, the most frequent symptom combinations were fever only (64 cases, 47.8%), followed by dry cough only (42 cases, 31.3%) in Group 2; expectoration only (21 cases, 19.8%), followed by expectoration complicated with fever (10 cases, 9.4%) in Group 3; fatigue complicated with fever (12 cases, 4.2%), followed by headache complicated with fever was also high (11 cases, 3.8%) in Group 4. People aged 45–64 years were more likely to have symptoms of Group 4 than those aged 65 years or older (odds ratio [OR] = 2.66, 95% CI: 1.21–5.85) and at the same time had longer time intervals.

Conclusions

Symptoms of COVID-19 could be divided into four clustering groups with different symptom combinations. The Group 4 symptoms (i.e., mainly cardiopulmonary, systemic, and/or gastrointestinal symptoms) happened more frequently in COVID-19 than in influenza. This distinction could help deepen the understanding of this disease. The middle-aged people have a longer time interval for medical visit and was a group that deserve more attention, from the perspective of medical delays.

Impact of Asthma on Severity and Outcomes in COVID-19

BACKGROUND

We conducted this systematic review to evaluate whether asthma increases the risk of severe disease and adverse outcomes among subjects with COVID-19.

METHODS

We queried the PubMed and Embase databases for studies indexed through December 2020. We included studies providing data on severe disease, hospitalization, ICU care, need for mechanical ventilation, or mortality among subjects with COVID-19 with and without asthma. We calculated the relative risk for each reported outcome of interest and used random effects modeling to summarize the data.

RESULTS

We retrieved 1,832 citations, and included 90 studies, in our review. Most publications reported data retrieved from electronic records of retrospective subject cohorts. Only 25 studies were judged to be of high quality. Subjects with asthma and COVID-19 had a marginally higher risk of hospitalization (summary relative risk 1.13, 95% CI 1.03-1.24) but not for severe disease (summary relative risk 1.17, 95% CI 0.62-2.20), ICU admission (summary relative risk 1.13, 95% CI 0.96-1.32), mechanical ventilation (summary relative risk 1.05, 95% CI 0.85-1.29), or mortality (summary relative risk 0.92, 95% CI 0.82-1.04) as compared to subjects with COVID-19 without asthma.

CONCLUSIONS

Comorbid asthma increases risk of COVID-19-related hospitalization but not severe disease or other adverse outcomes in subjects with COVID-19.

Assessing SARS-CoV-2 Vertical Transmission and Neonatal Complications

We designed and implemented a prospective study to analyze the maternal and neonatal outcomes associated with COVID-19 and determine the likelihood of viral transmission to the fetus and newborn by collecting samples from amniotic fluid, placenta, umbilical cord blood, and breast milk. The study followed a prospective observational design, starting in July 2020 and lasting for one year. A total of 889 pregnant women were routinely tested for SARS-CoV-2 infection in an outpatient setting at our clinic, using nasal swabs for PCR testing. A total of 76 women were diagnosed with COVID-19. The positive patients who accepted study enrollment were systematically analyzed by collecting weekly nasal, urine, fecal, and serum samples, including amniotic fluid, placenta, umbilical cord, and breast milk at hospital admission and postpartum. Mothers with COVID-19 were at a significantly higher risk of developing gestational hypertension and giving birth prematurely by c-section than the general pregnant population. Moreover, their mortality rates were substantially higher. Their newborns did not have negative outcomes, except for prematurity, and an insignificant number of newborns were infected with SARS-CoV-2 (5.4%). No amniotic fluid samples were positive for SARS-CoV-2, and only 1.01% of PCR tests from breast milk were confirmed positive. Based on these results, we support the idea that SARS-CoV-2 positive pregnant women do not expose their infants to an additional risk of infection via breastfeeding, close contact, or in-utero. Consequently, we do not support maternal–newborn separation at delivery since they do not seem to be at an increased risk of SARS-CoV-2 infection.

A lab-on-a-chip platform for integrated extraction and detection of SARS-CoV-2 RNA in resource-limited settings

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the unprecedented global pandemic of coronavirus disease-2019 (COVID-19). Efforts are needed to develop rapid and accurate diagnostic tools for extensive testing, allowing for effective containment of the infection via timely identification and isolation of SARS-CoV-2 carriers. Current gold standard nucleic acid tests require many separate steps that need trained personnel to operate specialist instrumentation in laboratory environments, hampering turnaround time and test accessibility, especially in low-resource settings. We devised an integrated on-chip platform coupling RNA extraction based on immiscible filtration assisted by surface tension (IFAST), with RNA amplification and detection via colorimetric reverse-transcription loop mediated isothermal amplification (RT-LAMP), using two sets of primers targeting open reading frame 1a (ORF1a) and nucleoprotein (N) genes of SARS-CoV-2. Results were identified visually, with a colour change from pink to yellow indicating positive amplification, and further confirmed by DNA gel electrophoresis. The specificity of the assay was tested against HCoV-OC43 and H1N1 RNAs. The assay based on use of gene N primers was 100% specific to SARS-CoV-2 with no cross-reactivity to HCoV-OC43 nor H1N1. Proof-of-concept studies on water and artificial sputum containing genomic SARS-CoV-2 RNA showed our IFAST RT-LAMP device to be capable of extracting and detecting 470 SARS-CoV-2 copies mL^-1 within 1 h (from sample-in to answer-out). IFAST RT-LAMP is a simple-to-use, integrated, rapid and accurate COVID-19 diagnostic platform, which could provide an attractive means for extensive screening of SARS-CoV-2 infections at point-of-care, especially in resource-constrained settings.

Expression of Surfactant Protein D Distinguishes Severe Pandemic Influenza A(H1N1) from Coronavirus Disease 2019

The differentiation between influenza and coronavirus disease 2019 (COVID-19) could constitute a diagnostic challenge during the ongoing winter owing to their clinical similitude. Thus, novel biomarkers are required to enable making this distinction. Here, we evaluated whether the surfactant protein D (SP-D), a collectin produced at the alveolar epithelium with known immune properties, was useful to differentiate pandemic influenza A(H1N1) from COVID-19 in critically ill patients. Our results revealed high serum SP-D levels in patients with severe pandemic influenza but not those with COVID-19. This finding was validated in a separate cohort of mechanically ventilated patients with COVID-19 who also showed low plasma SP-D levels. However, plasma SP-D levels did not distinguish seasonal influenza from COVID-19 in mild-to-moderate disease. Finally, we found that high serum SP-D levels were associated with death and renal failure among severe pandemic influenza cases. Thus, our studies have identified SP-D as a unique biomarker expressed during severe pandemic influenza but not COVID-19.

Inhaled corticosteroids do not adversely impact outcomes in COVID-19 positive patients with COPD: An analysis of Cleveland Clinic's COVID-19 registry

Inhaled Corticosteroids (ICS) are commonly prescribed to patients with severe COPD and recurrent exacerbations. It is not known what impact ICS cause in terms of COVID-19 positivity or disease severity in COPD. This study examined 27,810 patients with COPD from the Cleveland Clinic COVID-19 registry between March 8th and September 16th, 2020. Electronic health records were used to determine diagnosis of COPD, ICS use, and clinical outcomes. Multivariate logistic regression was used to adjust for demographics, month of COVID-19 testing, and comorbidities known to be associated with increased risk for severe COVID-19 disease. Amongst the COPD patients who were tested for COVID-19, 44.1% of those taking an ICS-containing inhaler tested positive for COVID-19 versus 47.2% who tested negative for COVID-19 (p = 0.033). Of those who tested positive for COVID-19 (n = 1288), 371 (28.8%) required hospitalization. In-hospital outcomes were not significantly different when comparing ICS versus no ICS in terms of ICU admission (36.8% [74/201] vs 31.2% [53/170], p = 0.30), endotracheal intubation (21.9% [44/201] vs 16.5% [28/170], p = 0.24), or mortality (18.4% [37/201] vs 20.0% [34/170], p = 0.80). Multivariate logistic regression demonstrated no significant differences in hospitalization (adj OR 1.12, CI: 0.90-1.38), ICU admission (adj OR: 1.31, CI: 0.82-2.10), need for mechanical ventilation (adj OR 1.65, CI: 0.69-4.02), or mortality (OR: 0.80, CI: 0.43-1.49). In conclusion, ICS therapy did not increase COVID-19 related healthcare utilization or mortality outcome in patients with COPD followed at the Cleveland Clinic health system. These findings should encourage clinicians to continue ICS therapy for COPD patients during the COVID-19 pandemic.

A Comparative Systematic Review of COVID-19 and Influenza

Background: Both SARS-CoV-2 and influenza virus share similarities such as clinical features and outcome, laboratory, and radiological findings. Methods: Literature search was done using PubMed to find MEDLINE indexed articles relevant to this study. As of 25 November 2020, the search has been conducted by combining the MeSH words “COVID-19” and “Influenza”. Results: Eighteen articles were finally selected in adult patients. Comorbidities such as cardiovascular diseases, diabetes, and obesity were significantly higher in COVID-19 patients, while pulmonary diseases and immunocompromised conditions were significantly more common in influenza patients. The incidence rates of fever, vomiting, ocular and otorhinolaryngological symptoms were found to be significantly higher in influenza patients when compared with COVID-19 patients. However, neurologic symptoms and diarrhea were statistically more frequent in COVID-19 patients. The level of white cell count and procalcitonin was significantly higher in influenza patients, whereas thrombopenia and elevated transaminases were significantly more common in COVID-19 patients. Ground-grass opacities, interlobular septal thickening, and a peripheral distribution were more common in COVID-19 patients than in influenza patients where consolidations and linear opacities were described instead. COVID-19 patients were significantly more often transferred to intensive care unit with a higher rate of mortality. Conclusions: This study estimated differences of COVID-19 and influenza patients which can help clinicians during the co-circulation of the two viruses.

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients

Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning-based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg's method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.