Risk Factors For Severe COVID-19 Research Articles

Addressing data quality issues to assess clinical and epidemiological risk factors for COVID-19 among documented cases in Liberia: a single-centre, retrospective, observational study

IntroductionIdentification of risk factors for severe COVID-19 can help to inform case management in resource-constrained settings. We aimed to rigorously but retroactively address data issues to identify risk factors for...

Clinical features of and severity risk factors for COVID-19 in adults during the predominance of SARS-CoV-2 XBB variants in Okinawa, Japan.

Since 2023, COVID-19 induced by SARS-CoV-2 XBB variants have been a global epidemic. The XBB variant-induced epidemic was largest in the Okinawa Prefecture among areas in Japan, and healthcare institutions have been burdened by increased COVID-19 hospitalizations. This study aimed to evaluate the clinical features of XBB variant-induced COVID-19 and risk factors for severe COVID-19. This retrospective study included adult patients hospitalized for COVID-19 between May and July 2023 at four tertiary medical institutions in Okinawa, Japan. Patients with bacterial infection-related complications were excluded. According to oxygen supplementation and intensive care unit admission, patients were divided into two groups, mild and severe. Patient backgrounds, symptoms, and outcomes were compared between both groups, and the risk factors for severe COVID-19 were analyzed using a multivariate logistic regression model. In total of 367 patients included, the median age was 75 years, with 18.5% classified into the severe group. The all-cause mortality rate was 4.9%. Patients in the severe group were more older, had more underlying diseases, and had a higher mortality rate (13.2%) than those in the mild group (3.0%). Multivariate logistic regression analysis showed that diabetes mellitus was an independent risk factor for severe COVID-19 (95% confidence interval [CI], 1.002-3.772), whereas bivalent omicron booster vaccination was an independent factor for less severe COVID-19 (95% CI, 0.203-0.862). This study implies that assessing risk factors in older adults is particularly important in the era of omicron variants.

Risk factors for a severe course of COVID-19 in a hotspot clinic during the first and second wave of the SARS-CoV-2 pandemic in Germany

During the first wave of the SARS-CoV-2 pandemic, Weiden Hospital was a hotspot and was thus in an exceptional medical situation. This study deals with the question of whether the recognized risk factors for a severe course of COVID-19 also apply to the patients treated in Weiden Hospital during this time or whether other factors could have influenced patient outcomes. In a retrospective analysis, data on 669 patients of Weiden Hospital with proven SARS-CoV-2 infections in the first year of the pandemic were evaluated. Risk factors for a severe case of COVID-19 were determined from medical and demographic information in a univariate analysis and subjected to logistic regression. The logistic regression analysis was performed for the overall collective as well as separately for patients from Wave 1 (3-6/2020) and 2 (7-12/2020). Looking at all of 2020, significant risk factors for severe COVID-19 included being male, being a smoker, being 71 years or older, and a history of depression. All other commonly recognized risk factors were not applicable for the Weiden collective. When looking at both waves separately, in Wave 1 age was not a significant risk factor, whereas in Wave 2 an age of 61 years or older was associated with an increased risk of severe progression. For patients who were admitted to hospital in Wave 2, the risk of severe progression was reduced almost by half. It can be assumed that patient outcomes in Weiden's hotspot collective were predominantly determined by non-individual factors, like the difficult care situation in a hotspot clinic at the beginning of the pandemic. In preparation for future pandemics, provision of sufficient resources might significantly contribute to better patient outcomes.

Association of COVID-19 risk factors with systemic fungal infections in hospitalized patients.

A new category of systemic co-infections that emerged with the COVID-19 pandemic is known as COVID-19-associated (CA) fungal infections, which include pulmonary aspergillosis (CAPA), candidiasis (CAC), and mucormycosis (CAM). We aimed to study the association between patient characteristics of hospitalized COVID-19 patients, COVID-19 comorbidities, and COVID-19 therapies with secondary non-superficial fungal infections. We performed descriptive and regression analyses of data from 4,999 hospitalized COVID-19 patients from the University of Kentucky Healthcare (UKHC) system. The patients with secondary systemic fungal infections had a 6-fold higher risk of death than those without such infections. Generally, the risk factors for severe COVID-19 (age, obesity, cardiovascular disease, diabetes, and lack of COVID-19 vaccination) were strong predictors of a secondary fungal infection. However, several characteristics had much higher risks, suggesting that a causative link may be at play: ICU admission, mechanical ventilation, length of hospital stay, and steroid use. In sum, this study found that the known risk factors for severe COVID-19 disease, age, diabetes, cardiovascular disease, obesity, ventilation, and high steroid doses were all predictors of a secondary fungal infection. Steroid therapy may need to be modified to account for a risk or a presence of a fungal infection in vulnerable patients.

Impact of COVID-19 infection on genitourinary cancer management. SOGUG-COVID-19: A spanish, multicenter, observational study.

The COVID-19 pandemic is a great burden worldwide, but its impact on patients with genitourinary cancer (GUC) is poorly characterized. This study aimed to characterize the clinical features and evolution of GUC patients affected by COVID-19 in Spain. SOGUG-COVID-19 was an observational ambispective non-interventional study that recruited patients with SARS-CoV-2 infection who had been treated for GUC in 32 Spanish hospitals. Data were collected from patients' medical records in a short period of time, coinciding with the first waves of COVID-19, when the mortality was also higher in the general population. From November 2020 to April 2021, 408 patients were enrolled in the study. The median age was 70years, and 357 patients (87.5%) were male. Most frequent Cancer Origin was: prostate (40.7%), urothelial (31.4%) and kidney (22.1%). Most patients (71.3%) were diagnosed at the metastatic stage, and 33.3% had poorly differentiated histology. Anticancer treatment during the infection was reported in 58.3% of patients, and 21.3% had received immunotherapy prior to or concurrent with the infection. The most frequent COVID-19 symptoms were pyrexia (49.0%), cough (38.2%) and dyspnea (31.9%). Median age was higher for patients with pneumonia (p < 0.001), patchy infiltrates (p = 0.005), ICU admission (p < 0.001) and death (p < 0.001). Tumor stage was associated with complications (p = 0.006). The fatality rate was 19.9% and the 6-month COVID-19-specific survival rate was 79.7%. Patients with genitourinary cancers seem exceptionally vulnerable to COVID-19 regardless of tumor type or anticancer therapy. Age and tumor stage were the only identified risk factors for severe COVID-19.

Predictors of COVID-19 severity in autoimmune disease patients: A retrospective study during full epidemic decontrol in China

BackgroundEarly identification of risk factors for adverse COVID-19 progression in patients with autoimmune diseases is crucial for patient management, but data on the Chinese population are scarce. ObjectivesThe purpose of this study was to identify predictors of severe COVID-19 in patients using blood cell ratios, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and other inflammatory markers. MethodsA retrospective study of 855 patients (746 females; median age 49 years) with autoimmune diseases and concurrent COVID-19 was conducted from December 2022 to February 2023 at the Rheumatology and Immunology Department of the First Affiliated Hospital of Nanchang University. Disease severity was assessed according to the 8th edition of the National Health Commission of the People's Republic of China's COVID-19 Diagnosis and Treatment Guidelines. The clinical classification criteria group mild and moderate cases as nonsevere cases and severe and critical cases as severe cases. A multivariate logistic regression model was established to evaluate the relationships between COVID-19 severity and demographic characteristics, comorbidities, medication use, and laboratory findings. ResultsThe PLR, NLR, and SII were significantly greater in the severe COVID-19 group than in the nonsevere group (all P < 0.05). In addition to classical independent clinical risk factors, increases in the PLR (OR: 1.004, 95 % CI: 1.001∼1.007, p = 0.001), NLR (OR: 1.180, 95 % CI: 1.041∼1.337, p = 0.010), and SII (OR: 0.999, 95 % CI: 0.998∼1.000, p = 0.005) were identified as risk factors for severe COVID-19 in patients with autoimmune diseases. After adjusting for clinical risk factors, the PLR (AUC: 0.592 vs. 0.865; P < 0.05), NLR (AUC: 0.670 vs. 0.866; P < 0.05), and SII (AUC: 0.616 vs. 0.864; P < 0.05) demonstrated higher predictive values. ConclusionEarly prediction of severe COVID-19 in patients with autoimmune diseases can be achieved using the NLR, PLR, and SII.

Higher intraindividual variability of body mass index is associated with elevated risk of COVID-19 related hospitalization and post-COVID conditions.

Cardiometabolic diseases are risk factors for COVID-19 severity. The extent that cardiometabolic health represents a modifiable factor to mitigate the short- and long-term consequences from SARS-CoV-2 remains unclear. Our objective was to evaluate the associations between intraindividual variability of cardiometabolic health indicators and COVID-19 related hospitalizations and post-COVID conditions (PCC) among a relatively healthy population. This retrospective, multi-site cohort study was a post-hoc analysis among individuals with cardiometabolic health data collected during routine blood donation visits in 24 US states (2009-2018) and who responded to COVID-19 questionnaires (2021-2023). Intraindividual variability of blood pressure (systolic, diastolic), total circulating cholesterol, and body mass index (BMI) were defined as the coefficient of variation (CV) across all available donation timepoints (ranging from 3 to 74); participants were categorized into CV quartiles. Associations were evaluated by multivariable binomial regressions. Overall, 3344 participants provided 42,090 donations (median 9 [IQR 5, 17]). The median age was 48 years (38, 56) at the first study donation. 1.2% (N = 40) were hospitalized due to COVID-19 and 15.5% (N = 519) had PCC. Higher BMI variability was associated with greater risk of COVID-19 hospitalization (4th quartile aRR 4.15 [95% CI 1.31, 13.11], p = 0.02; 3rd quartile aRR 3.41 [95% CI 1.09, 10.69], p = 0.04). Participants with higher variability of BMI had greater risk of PCC (4th quartile aRR 1.29 [95% CI 1.02, 1.64]; p = 0.04). Intraindividual variability of blood pressure (systolic, diastolic) and total circulating cholesterol were not associated with COVID-19 hospitalization or PCC risk (all p > 0.05). From causal mediation analysis, the association between the highest quartiles of BMI variability and PCC was not mediated by hospitalization (p > 0.05). Higher intraindividual variability of BMI was associated with COVID-19 hospitalization and PCC risk. Our findings underscore the need for further elucidating mechanisms that explain these associations and importance for consistent maintenance of body weight.

The Interplay of Chronic Hepatitis C and COVID-19: Implications for Prognosis and Treatment.

Introduction Chronic hepatitis C (CHC) remains a significant public health concern due to both hepatic and extrahepatic manifestations associated with substantial morbidity and mortality. The emergence of SARS-CoV-2 has raised concerns about the outcomes of COVID-19 in CHC patients. Method We conducted a retrospective analysis of patients with CHC and SARS-CoV-2 infection admitted to a tertiary care hospital between 2020 and 2023. We performed a global analysis of the entire batch of patients and, later, we evaluated the patients according to the severity of the SARS-CoV-2 infection Results The cohort included 89 patients (63 females, 26 males) with a median age of 65 years. Most patients were hospitalized in 2021. Common clinical manifestations included fever, cough, digestive symptoms, and headache. The most frequent comorbidities were renal disease, thyroid disorders, and cancer. Univariate logistic regression analysis identified older age, hospitalization in 2021, and respiratory failure as risk factors for severe COVID-19. Elevated lactate dehydrogenaselevels were also associated with an increased risk of severe COVID-19. Regarding CHC, detectablehepatitis C virus viremia was associated with more severe liver disease (p<0.01). Conclusion Patients with CHC and SARS-CoV-2 infection have a substantial risk of severe outcomes. Early identification and management of these patients are crucial to improve their prognosis.

Retrospective Study on the Features and Outcomes of a Tuscany COVID-19 Hospitalized Patients Cohort: Preliminary Results.

Background: A few months after the COVID-19 pandemic onset, knowledge of SARS-CoV-2 infection and outcomes and treatments blew up. This paper aimed to evaluate the features of a Tuscany COVID-19 hospitalized cohort and to identify risk factors for COVID-19 severity. Methods: This retrospective observational COVID-19 cohort study (1 March 2020-1 March 2021) was conducted on patients ≥ 18 years old, admitted to Tuscany Hospital, and subjected to follow-up within 12 months after discharge. Patients were enrolled at Pisana, Senese and Careggi University Hospitals, and South East, North West, and Center Local Hospitals. Results: 2888 patients (M = 58.5%, mean age = 66.2 years) were enrolled, of whom 14.3% (N = 413) were admitted to an intensive care unit. Smokers were 25%, and overweight and obese 65%. The most used drugs were corticosteroids, antacids, antibiotics, and antithrombotics, all antiviral drugs, with slight differences between 2020 and 2021. A strong association was found between outcomes of evolution towards critical COVID-19 (non-invasive mechanical ventilation (NIV) and/or admission to intensive care) and smoking (RR = 4.91), ex-smoking (RR = 3.48), overweight (RR = 1.30), obese subjects (RR = 1.62), comorbidities (aRR = 1.38). The alteration of liver enzymes (aspartate aminotransferase, alanine aminotransferase, or gamma-glutamyl transpeptidase) was associated with NIV (aOR = 2.28). Conclusions: Our cohort, characterized by patients with a mean age of 66.2 years, showed 65% of patients were overweight and obese. Smoking/ex-smoking, overweight/obesity, and other comorbidities were associated with COVID-19 adverse outcomes. The findings also demonstrated that alterations in liver enzymes were associated with worse outcomes.

A predictive model to explore risk factors for severe COVID-19

With the rapid spread of the novel coronavirus (COVID-19), a sustained global pandemic has emerged. Globally, the cumulative death toll is in the millions. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. We conducted a retrospective analysis of the characteristics of COVID-19 patients. This analysis includes clinical features upon initial hospital admission, relevant laboratory test results, and imaging findings. We aimed to identify risk factors for severe illness and to construct a predictive model for assessing the risk of severe COVID-19. We collected and analyzed electronic medical records of confirmed COVID-19 patients admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023. According to the WHO diagnostic criteria for the novel coronavirus, we divided the patients into two groups: severe and non-severe, and compared their clinical, laboratory, and imaging data. Logistic regression analysis, the least absolute shrinkage and selection operator (LASSO) regression, and receiver operating characteristic (ROC) curve analysis were used to identify the relevant risk factors for severe COVID-19 patients. Patients were divided into a training cohort and a validation cohort. A nomogram model was constructed using the “rms” package in R software. Among the 346 patients, the severe group exhibited significantly higher respiratory rates, breathlessness, altered consciousness, neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LDH) levels compared to the non-severe group. Imaging findings indicated that the severe group had a higher proportion of bilateral pulmonary inflammation and ground-glass opacities compared to the non-severe group. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic performance was maximized when NLR, respiratory rate (RR), and LDH were combined. Based on the statistical analysis results, we developed a COVID-19 severity risk prediction model. The total score is calculated by adding up the scores for each of the twelve independent variables. By mapping the total score to the lowest scale, we can estimate the risk of COVID-19 severity. In addition, the calibration plots and DCA analysis showed that the nomogram had better discrimination power for predicting the severity of COVID-19. Our results showed that the development and validation of the predictive nomogram had good predictive value for severe COVID-19.

GWAS-Identified Loci are Associated with Obesity and Type 2 Diabetes Mellitus in Patients with Severe COVID-19.

Comorbidities such as obesity and type 2 diabetes mellitus (T2DM) have emerged as critical risk factors exacerbating the severity and mortality of COVID-19. Meanwhile, numerous genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased susceptibility to severe COVID-19. This study investigated whether SNPs previously identified by GWAS as risk factors for severe COVID-19 also correlate with common comorbidities-obesity and T2DM-in hospitalized patients with severe COVID-19. DNA samples from 199 hospitalized COVID-19 patients were genotyped using probe-based PCR for 10 GWAS SNPs previously implicated in severe COVID-19 outcomes (rs143334143 CCHCR1, rs111837807 CCHCR1, rs17078346 SLC6A20-LZTFL1, rs17713054 SLC6A20-LZTFL1, rs7949972 ELF5, rs61882275 ELF5, rs12585036 ATP11A, rs67579710 THBS3, THBS3-AS1, rs12610495 DPP9, rs9636867 IFNAR2). The analysis revealed significant associations between certain SNPs and the increased risk of obesity and T2DM in severe COVID-19 patients. Specifically, rs17713054 SLC6A20-LZTFL1 (risk allele A; odds ratio (OR) = 2.34, 95% confidence interval (CI) = 1.24-4.4, p = 0.007) and rs7949972 ELF5 SNP (risk allele T; OR = 1.79, 95% CI = 1.11-2.91, p = 0.015) were associated with increased risk of obesity. SNP rs9636867 IFNAR2 was associated with a higher risk of T2DM (risk allele G, OR = 8.28, 95% CI = 1.69-40.64, p = 0.027). Using the model-based multifactor dimensionality reduction (MB-MDR) approach, the six most significant gene-gene interaction patterns associated with obesity in severe COVID-19 patients were identified and included five polymorphic loci: rs7949972, rs17713054, rs61882275, rs12585036, and rs143334143, participating in two or more of the most significant G-G interactions (pperm < 0.05). In total, the best models of G-G interactions associated with T2DM in patients with severe COVID-19 included eight polymorphic loci, six of which, rs7949972, rs61882275, rs12585036, rs143334143, rs67579710, and rs12610495, were involved in two or more of the most significant G-G interactions. Our study provides novel insights into the genetic associations between GWAS-identified SNPs and the risk of obesity and T2DM in patients with severe COVID-19.

Prediction of Clinical Severity of COVID-19 Using a Combination of Heparin-Binding Protein, Interleukin-6, and C-Reactive Protein: A Retrospective Study.

Systemic inflammation stands as a pivotal factor tightly interwoven with the progression of COVID-19. This study endeavors to elucidate the significance of three key inflammatory molecules, that is, heparin-binding protein (HBP), interleukin-6 (IL-6), and C-reactive protein (CRP), in assessing the severity and prognostic implications of COVID-19. The demographic, clinical, and laboratory data were retrospectively collected from a cohort of 214 adult patients diagnosed with COVID-19. Patients were divided into two groups: nonsevere (n = 93; 43.5%) and severe (n = 121; 56.5%). Additionally, based on their organ function, patients were categorized into nonorgan failure (n = 137) and organ failure (n = 77) groups. The levels of inflammation-related cytokines were then compared among these defined groups. The severe group was characterized by a higher proportion of males, older age, and longer hospital stays compared to nonsevere cases. Additionally, severe cases exhibited a higher prevalence of underlying diseases and organ failure. Statistical analysis revealed significantly elevated levels of HBP, IL-6, and CRP in the severe group. HBP, IL-6, and CRP were identified as independent risk factors for severe COVID-19. Furthermore, a combined assessment of these biomarkers demonstrated superior diagnostic sensitivity (85.10%) and specificity (95.70%) for predicting COVID-19 severity. A positive relationship between elevated HBP, IL-6, and CRP levels and impaired organ function was also observed. The predictive efficiency significantly increased (hazard ratio = 3.631, log-rank p = 0.003) when two or more of them were combinedly used. Notably, elevated levels of HBP, IL-6, and CRP were associated with an increased risk of mortality. In conclusion, the combined assessment of HBP, IL-6, and CRP offers enhanced accuracy and specificity in predicting the severity, organ failure, and mortality risk associated with COVID-19.

Predictive factors for COVID-19 severity and mortality in hospitalized children

BackgroundUnderstanding the factors influencing disease progression and severity in pediatric COVID-19 cases is essential for effective management and intervention strategies. This study aimed to evaluate the discriminative ability of clinical and laboratory parameters to identify predictors of COVID-19 severity and mortality in hospitalized children.MethodsIn this multicenter retrospective cohort study, we included 468 pediatric patients with COVID-19. We developed a predictive model using their demographic, clinical, and laboratory data. The performance of the model was assessed using various metrics including sensitivity, specificity, positive predictive value rates, and receiver operating characteristics (ROC).ResultsOur findings demonstrated strong discriminatory power, with an area under the curve (AUC) of 0.818 for severity and 0.873 for mortality prediction. Key risk factors for severe COVID-19 in children include low albumin levels, elevated C-reactive protein (CRP), lactate dehydrogenase (LDH), and underlying medical conditions. Furthermore, ROC curve analysis highlights the predictive value of CRP, LDH, and albumin, with AUC values of 0.789, 0.752, and 0.758, respectively.ConclusionOur study indicates that laboratory values are valuable in predicting COVID-19 severity in children. Various factors, including CRP, LDH, and albumin levels, demonstrated statistically significant differences between patient groups, suggesting their potential as predictive markers for disease severity. Implementing predictive analyses based on these markers could aid clinicians in making informed decisions regarding patient management.

Disease-modifying therapies, outcomes, risk factors and severity of COVID-19 in multiple sclerosis: A MENACTRIMS registry based study

BackgroundThere is a lack information regarding risk factors associated with worse COVID-19 outcomes in patients with multiple sclerosis (MS) in the MENA region. MethodsThis is a multicenter, retrospective cohort study that included all MS patients with a suspected or confirmed COVID-19 infection using the MENACTRIMS registry. The association of demographics, disease characteristics, and use of disease-modifying therapies (DMTs) with outcomes and severity of COVID-19 were evaluated by multivariate logistic model. ResultsA total of 600 MS patients with confirmed (n = 542) or highly suspected (n = 58) COVID-19 were analyzed. Seventy-three patients (12.2 %) had a COVID-19 severity score of ≥3 on a 7-point ordinal scale (ranging from 1 [not hospitalized with no limitations on activities] to 7 [death] with a cutoff at 3 [hospitalized and not requiring supplemental oxygen]), and 15 patients (2.5 %) died. Out of 73 patients with a severity score ≥3, 90.4 % were on DMTs; 50.6 % of them were on anti-CD20, including ocrelizumab and rituximab. Multivariate logistic regression showed that older age (odds ratio per 10 years, 1.4 [95 %CI, 1.0–1.8]), disability (OR for EDSS 3.0–5.5, 2.9 [95 %CI. 1.5–5.7], OR for EDSS ≥6.0, 2.3 [95 %CI. 1.0–5.1]), obesity (OR, 3.0 [95 %CI, 1.5–6.0]), and treatment with rituximab (OR, 9.0 [95 %CI, 3.1–25.3]) or off-label immunosuppressive medications (OR, 5.6 [95 %CI. 1.1–27.8]) were risk factors for moderate or severe COVID-19. ConclusionIn this registry-based study of MS patients, age, sex, EDSS, obesity, progressive MS were risk factors for severe COVID-19. Moreover, there was an association found between exposure to anti-CD20 DMTs and COVID-19 severity.

Early inflammatory profiles predict maximal disease severity in COVID-19: An unsupervised cluster analysis

BackgroundThe inflammatory changes that underlie the heterogeneous presentations of COVID-19 remain incompletely understood. In this study we aimed to identify inflammatory profiles that precede the development of severe COVID-19, that could serve as targets for optimised delivery of immunomodulatory therapies and provide insights for the development of new therapies. MethodsWe included individuals sampled <10 days from COVID-19 symptom onset, recruited from both inpatient and outpatient settings. We measured 61 biomarkers in plasma, including markers of innate immune and T cell activation, coagulation, tissue repair and lung injury. We used principal component analysis and hierarchical clustering to derive biomarker clusters, and ordinal logistic regression to explore associations between cluster membership and maximal disease severity, adjusting for known risk factors for severe COVID-19. ResultsIn 312 individuals, median (IQR) 7 (4–9) days from symptom onset, we found four clusters. Cluster 1 was characterised by low overall inflammation, cluster 2 was characterised by higher levels of growth factors and markers of endothelial activation (EGF, VEGF, PDGF, TGFα, PAI-1 and p-selectin). Cluster 3 and 4 both had higher overall inflammation. Cluster 4 had the highest levels of most markers including markers of innate immune activation (IL6, procalcitonin, CRP, TNFα), and coagulation (D-dimer, TPO), in contrast cluster 3 had the highest levels of alveolar epithelial injury markers (RAGE, ST2), but relative downregulation of growth factors and endothelial activation markers, suggesting a dysfunctional inflammatory pattern. In unadjusted and adjusted analysis, compared to cluster 1, cluster 3 had the highest odds of progressing to more severe disease (unadjusted OR (95%CI) 9.02 (4.53–17.96), adjusted OR (95%CI) 6.02 (2.70–13.39)). ConclusionEarly inflammatory profiles predicted subsequent maximal disease severity independent of risk factors for severe COVID-19. A cluster with downregulation of growth factors and endothelial activation markers, and early evidence of alveolar epithelial injury, had the highest risk of severe COVID-19.

Impact of COVID-19 on work loss in the United States- A retrospective database analysis

Objectives This study investigates the utilization of work absence benefits among United States (US) employees diagnosed with COVID-19, examining frequency, duration, cost, and types of work loss benefits used. Methods This retrospective analysis of the Workpartners Research Reference Database (RRDb) included employees eligible for short- and long-term disability (STD and LTD employer-sponsored benefits, respectively), and other paid work absence benefits from 2018 to 2022. Workpartners RRDb includes over 3.5 million employees from over 500 self-insured employers across the US. Employees were identified by codes from adjudicated medical and disability claims for COVID-19 (2020–2022) and influenza, as well as prescription claims for COVID-19 treatments. Associated payments were quantified for each absence reason. Results Approximately 1 million employees were eligible for employer-sponsored paid leave benefits between January 2018 and December 2022. The mean age was 37 years (22% >50 years), and 49.4% were females. COVID-19 was the 2nd most common reason for an STD claim (6.9% of all STD claims) and 13th for an LTD claim (1.7% of all LTD claims) from 2020–2022. The mean duration for COVID-19 STD claims was 24 days (N = 3,731, mean claim=$3,477) versus 10 days for influenza (N = 283, mean claim=$1,721). The mean duration for an LTD claim for COVID-19 was 153 days (N = 11, mean claim=$19,254). Only 21.5% of employees with STD claims in the COVID-19 cohort had prior COVID-19-associated medical or pharmacy claims; over half (range 53%–61%) had documented high risk factors for severe COVID-19. Conclusion COVID-19 and influenza have the potential to cause work loss in otherwise healthy employees. In this analysis, COVID-19 was the second most frequent reason for an STD claim at the start of the pandemic and remained high (ranked 5th) in 2022. These results highlight the impact of COVID-19 on work loss beyond the acute phase. Comprehensively evaluating work loss implications may help employers prioritize strategies, such as vaccinations and timely treatments, to mitigate the impact of COVID-19 on employees and their companies.

Oral antivirals for COVID-19 among patients with cancer.

Immunocompromised individuals, such as those diagnosed with cancer, are at a significantly higher risk for severe illness and mortality when infected with SARS-CoV-2 (COVID-19) than the general population. Two oral antiviral treatments are approved for COVID-19: Paxlovid® (nirmatrelvir/ritonavir) and Lagevrio® (molnupiravir). There is a paucity of data regarding the benefit from these antivirals among immunocompromised patients with cancer, and recent studies have questioned their efficacy among vaccinated patients, even those with risk factors for severe COVID-19. We evaluated the efficacy and safety of nirmatrelvir/ritonavir and molnupiravir in preventing severe illness and death using our database of 457 patients with cancer and COVID-19 from Brown University-affiliated hospitals. Sixty-seven patients received nirmatrelvir/ritonavir or molnupiravir and were compared to 45 concurrent controls who received no antiviral treatment despite being eligible to receive it. Administration of nirmatrelvir/ritonavir or molnupiravir was associated with improved survival and lower 90-day all-cause and COVID-19-attributed mortality (p < 0.05) and with lower peak O2 requirements (ordinal odds ratio [OR] 1.52, 95% confidence interval [CI] 0.92-2.56). Acknowledging the small size of our sample as a limitation, we concluded that early antiviral treatment might be beneficial to immunocompromised individuals, particularly those with cancer, when infected with SARS-CoV-2. Larger-scale, well-stratified studies are needed in this patient population.

Association between asthma and COVID-19 severity during Omicron epidemic: a retrospective cohort study using real-world data

BackgroundThe available evidence presented inconsistencies and inconclusive findings regarding the associations between co-existing asthma and mortality among COVID-19 patients. The objective of the current study is to investigate the relationship between asthma and severe outcomes after SARS-CoV-2 Omicron infection in an infection-naïve population.MethodsA retrospective cohort study using propensity score matching was conducted. The COVID-19 patients requiring hospitalisation in Hong Kong from January 1, 2022, to November 13, 2022, an Omicron-predominated period, were identified. Severe clinical outcomes were defined as ICU admission and inpatient death after the first positive PCR results as well as a composite outcome of both.ResultsOf the 74,396 hospitalised COVID-19 patients admitted, 1,290 asthma patients and 18,641 non-asthma patients were included in the matched cohort. The rates of death and the composite outcome were 15·3% and 17·2%, respectively, among the non-asthma patients,12·2% and 13·6%, respectively, among the asthma patients, with adjusted hazard ratios equal to 0·775 (95% CI: 0·660–0·909) and 0·770 (95% CI: 0·662–0·895), respectively. The negative association was more apparent in the elderly and female groups. Asthma remained a factor that lowered the risk of disease severity even though the patients were not fully vaccinated with at least two doses.ConclusionsWe used real-world data to demonstrate that asthma was not a risk factor for COVID-19 severity of the infections of Omicron variant, even though the patients were not fully vaccinated.

Do rheumatic diseases, long-term glucocorticoids, and immunosuppressant treatment, and vaccination impact the COVID-19 severity? Insight from a retrospective cohort study.

The impact of rheumatic diseases, long-term medication, and vaccination on COVID-19 severity remain insufficiently understood, hindering effective patient management. This study aims to investigate factors influencing COVID-19 severity in Chinese rheumatic patients and to provide real-world evidence for patient care. We conducted a retrospective observational study consisting of two cohorts, followed by a nested case-control analysis. The outpatient cohort included non-severe COVID-19 patients, while the inpatient cohort included consecutive severe COVID-19 inpatients. Additionally, rheumatic patients from both cohorts were included for the nested case-control study. Clinical information was obtained from electronic medical records and surveys. A total of 749 outpatients and 167 inpatients were enrolled. In the outpatient cohort, rheumatic diseases were identified as a risk factor for the severity of dyspnea (No rheumatic disease: OR = 0.577, 95% CI = 0.396-0.841, p = .004), but not for mortality, length of hospitalization, or hospitalization costs in the inpatient cohort. Long-term glucocorticoids use was identified as an independent risk factor for severity of dyspnea in rheumatic patients (OR = 1.814, 95% CI = 1.235-2.663, p = .002), while vaccination and immunosuppressant treatment showed no association. Vaccination was identified as a protective factor against hospitalization due to COVID-19 in patients with rheumatic diseases (OR = 0.031, 95% CI = 0.007-0.136, p < .001), whereas long-term glucocorticoids and immunosuppressant treatment showed no association. Rheumatic diseases and long-term glucocorticoids use are significant risk factors for COVID-19 severity in the Chinese population, whereas emphasizing the protective effects of vaccines against COVID-19 severity is crucial. Additionally, the investigation provides preliminary support for the concept that long-term immunosuppressant therapy does not necessarily require additional prescription adjustments.

Bariatric surgery and COVID-19 outcomes: results from the PaTH to Health: Diabetes study

BackgroundObesity and type 2 diabetes mellitus (T2DM) are risk factors for severe COVID-19 infection. Bariatric surgery (BSG) is an effective treatment of obesity through weight loss and may reduce COVID-19 severity. ObjectivesWe examined the effect of BSG on COVID-19 outcomes in patients with or at risk of T2DM. SettingElectronic health record data from the PaTH Clinical Data Research Network, a partnership of 5 health systems reviewed from March 1, 2020, to December 31, 2020. MethodsAmbulatory and in-hospital patient encounters with COVID-19 diagnosis and obesity were identified. We constructed 2 patient groups: BSG and non-BSG (NBSG). The BSG group included patients with at least 1 encounter for the BSG procedure code and/or 1 BSG diagnosis code; the NBSG group included patients with no procedure or diagnosis code for BSG with body mass index (BMI) ≥40 or BMI ≥35 and at least 2 obesity-related co-morbidities. We matched 1 patient in the BSG group to 2 patients in the NBSG group based on age, gender (sex defined at birth), race and ethnicity, group (T2DM and at risk of T2DM), and site. The primary outcome was 30-day outcomes of COVID-19 severity. ResultsAfter matching, we found that patients with BSG had lower odds of respiratory failure (41%) and ventilation/intensive care unit (ICU) admission/death (52%). Patients in the BSG group had lower odds of hospitalization, pneumonia, respiratory failure, and the most severe COVID-19 outcomes combined (ventilation/ICU admission/death). T2DM was identified as a risk factor for COVID-19 severity in the BSG group. ConclusionsThis retrospective, matched-cohort analysis found BSG to have a protective effect against severe COVID-19 outcomes.