Two fitness inference schemes compared using allele frequencies from 1,068,391 sequences sampled in the UK during the COVID-19 pandemic (preprint)

Throughout the course of the SARS-CoV-2 pandemic, genetic variation has contributed to the spread and persistence of the virus. For example, various mutations have allowed SARS-CoV-2 to escape antibody neutralization or to bind more strongly to the receptors that it uses to enter human cells. Here, we compared two methods that estimate the fitness effects of viral mutations using the abundant sequence data gathered over the course of the pandemic. Both approaches are grounded in population genetics theory but with different assumptions. One approach, tQLE, features an epistatic fitness landscape and assumes that alleles are nearly in linkage equilibrium. Another approach, MPL, assumes a simple, additive fitness landscape, but allows for any level of correlation between alleles. We characterized differences in the distributions of fitness values inferred by each approach and in the ranks of fitness values that they assign to sequences across time. We find that in a large fraction of weeks the two methods are in good agreement as to their top-ranked sequences, i.e., as to which sequences observed that week are most fit. We also find that agreement between ranking of sequences varies with genetic unimodality in the population in a given week.

Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury (preprint)

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.

Establishment of a real-time recombinase-aided isothermal amplification (RT-RAA) assay for the rapid detection of bovine respiratory syncytial virus (preprint)

Background Bovine respiratory syncytial virus (BRSV) is a major cause of bovine respiratory disease, resulting in significant losses to the cattle industry. For rapid detection of BRSV, reverse transcription recombinase polymerase amplification assays targeting the F gene were developed by integrating the fluorescence detection platform (RT-RAA).Results The developed RT-RAA assays allowed the exponential amplification of the target fragment in 20 min at a constant temperature of 39°C. The RT-RAA assays also showed good specificity for BRSV, with no cross-reactions with Infectious Bovine Rhinotracheitis Virus (IBRV), Bovine Parainfluenza Virus Type 3 (BPIV3), Bovine Viral Diarrhea Virus (BVDV) and Bovine Coronavirus (BCoV). With the standard RNA of BRSV serving as a template, the limit of detection for RT-RAA was 5 × 102 copies per reaction. Forty clinical samples collected from cattle with respiratory disease were tested, and the positive rate was 7.5% (3/40), consistent with results using the conventional PCR method reported previously.Conclusion An RT-RAA assay for BRSV detection was established in this study. The method is specific and sensitive and can be completed within 20 min at 39℃. These results ascertain that the developed RT-RAA assays are effective diagnostic tools for rapidly detecting BRSV in resource-limited settings, which may be applied for clinical detection of BRSV.

Psychological capital and alienation among patients with COVID-19 infection: the mediating role of social support.

BACKGROUND: COVID-19 infection continues all over the world, causing serious physical and psychological impacts to patients. Patients with COVID-19 infection suffer from various negative emotional experiences such as anxiety, depression, mania, and alienation, which seriously affect their normal life and is detrimental to the prognosis. Our study is aimed to investigate the effect of psychological capital on alienation among patients with COVID-19 and the mediating role of social support in this relationship. METHODS: The data were collected in China by the convenient sampling. A sample of 259 COVID-19 patients completed the psychological capital, social support and social alienation scale and the structural equation model was adopted to verify the research hypotheses. RESULTS: Psychological capital was significantly and negatively related to the COVID-19 patients' social alienation (p < .01). And social support partially mediated the correlation between psychological capital and patients' social alienation (p < .01). CONCLUSION: Psychological capital is critical to predicting COVID-19 patients' social alienation. Social support plays an intermediary role and explains how psychological capital alleviates the sense of social alienation among patients with COVID-19 infection.

Early autoimmunity and outcome in virus encephalitis: a retrospective study based on tissue-based assay.

To explore the autoimmune response and outcome in the central nervous system (CNS) at the onset of viral infection and correlation between autoantibodies and viruses. METHODS: A retrospective observational study was conducted in 121 patients (2016-2021) with a CNS viral infection confirmed via cerebrospinal fluid (CSF) next-generation sequencing (cohort A). Their clinical information was analysed and CSF samples were screened for autoantibodies against monkey cerebellum by tissue-based assay. In situ hybridisation was used to detect Epstein-Barr virus (EBV) in brain tissue of 8 patients with glial fibrillar acidic protein (GFAP)-IgG and nasopharyngeal carcinoma tissue of 2 patients with GFAP-IgG as control (cohort B). RESULTS: Among cohort A (male:female=79:42; median age: 42 (14-78) years old), 61 (50.4%) participants had detectable autoantibodies in CSF. Compared with other viruses, EBV increased the odds of having GFAP-IgG (OR 18.22, 95% CI 6.54 to 50.77, p<0.001). In cohort B, EBV was found in the brain tissue from two of eight (25.0%) patients with GFAP-IgG. Autoantibody-positive patients had a higher CSF protein level (median: 1126.00 (281.00-5352.00) vs 700.00 (76.70-2899.00), p<0.001), lower CSF chloride level (mean: 119.80±6.24 vs 122.84±5.26, p=0.005), lower ratios of CSF-glucose/serum-glucose (median: 0.50[0.13-0.94] vs 0.60[0.26-1.23], p=0.003), more meningitis (26/61 (42.6%) vs 12/60 (20.0%), p=0.007) and higher follow-up modified Rankin Scale scores (1 (0-6) vs 0 (0-3), p=0.037) compared with antibody-negative patients. A Kaplan-Meier analysis revealed that autoantibody-positive patients experienced significantly worse outcomes (p=0.031). CONCLUSIONS: Autoimmune responses are found at the onset of viral encephalitis. EBV in the CNS increases the risk for autoimmunity to GFAP.

Stability analysis and optimal control on a multi-strain coinfection model with amplification and vaccination

In this paper, a multi-strain coinfection model with amplification (or mutation) is established to characterize the interaction between common strain and amplified strain, as well as vaccination. The basic reproduction number ℛ0 is derived, from which the criteria on the existence and local (or global) stability of equilibria (including disease-free, dominant-strain and coexistence-strain) are established. By analyzing the effectiveness of vaccination, we find that a critical inoculation level could make the disease eliminate when ℛ0<1, while inefficient vaccines could cause backward bifurcation when ℛ0<1. Based on sensitivity analysis and realistic control policy, the optimal strategy of disease control is obtained. The theoretical results are illustrated by numerical simulation and clinical data of COVID-19 in Morocco.

The Global, Regional, and National Uterine Cancer Burden Attributable to High BMI from 1990 to 2019: A Systematic Analysis of the Global Burden of Disease Study 2019.

Uterine cancer (UC) is the most common gynecologic malignancy, and high body mass index (BMI) is a poor prognostic factor for UC. However, the associated burden has not been fully assessed, which is crucial for women's health management and the prevention and control of UC. Therefore, we utilized the Global Burden of Disease Study (GBD) 2019 to describe the global, regional, and national UC burden due to high BMI from 1990 to 2019. The data show that globally, women's high BMI exposure is increasing annually, with most regions having higher rates of high BMI exposure than the global average. In 2019, 36,486 [95% uncertainty interval (UI): 25,131 to 49,165] UC deaths were attributed to high BMI globally, accounting for 39.81% (95% UI: 27.64 to 52.67) of all UC deaths. The age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years (DALY) rate (ASDR) for high BMI-associated UC remained stable globally from 1990 to 2019, with significant differences across regions. Higher ASDR and ASMR were found in higher socio-demographic index (SDI) regions, and lower SDI regions had the fastest estimated annual percentage changes (EAPCs) for both rates. Among all age groups, the fatal outcome of UC with high BMI occurs most frequently in women over 80 years old.

Advantages and prospects of traditional Chinese medicine in treating COVID-19

Towards the end of 2019, a novel coronavirus pneumonia (coronavirus disease 19, COVID-19) caused by SARS-CoV-2 infection emerged in Wuhan. The SARS-CoV-2 virus quickly spread across the globe, seriously affecting public health and economic development of countries worldwide. Currently, antiviral drugs developed for COVID-19 lack strong clinical trial support and the high mutation rate of the virus causes difficulties in vaccine development, thus a complex and delayed large scale role out of an efficacious vaccine. Traditional Chinese medicine (TCM) has been used for treating various conditions for thousands of years and has a unique systems theory. It can be individualized into specific therapeutic regimens according to the patients' physical condition, clinical symptoms, and other distinguishing factors. In addition, TCM often has different effects at different disease stages, thus contributing to disease prevention, treatment, and rehabilitation. Existing evidence shows that TCM has efficacy in the treatment of COVID-19. The active ingredients of TCM have various pharmacological properties, including antiviral, anti-inflammatory, and immunomodulatory activity, with clinical trials showing that these prescriptions reduce symptoms of COVID-19, promote viral clearance, and ultimately improve survival in infected patients. This article discusses the advantages and mechanisms of TCM in the treatment of COVID-19, hoping to provide a reference platform in the fight against the disease. [ FROM AUTHOR] Copyright of Traditional Medicine Research is the property of TMR Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 or seasonal influenza? How to distinguish in people younger than 65 years old: A retrospective observational cohort study comparing the 2009 pandemic influenza A H1N1 with 2022 SARS-CoV-2 Omicron BA.2 outbreaks in China. (preprint)

Objective: This study attempted to explore the difference of clinical characteristics in H1N1 influenza infection and SARS-CoV-2 Omicron infection in people younger than 65 years old, in order to better identify the two diseases. Methods: A total of 127 H1N1 influenza patients diagnosed from May 2009 to July 2009 and 3265 patients diagnosed and identified as SARS-CoV-2 Omicron BA.2 variant from March 2022 to May 2022 were admitted in this study. Through the 1 : 2 match based on age (The difference is less than 2 years), gender and underlying diseases115 patients with H1N1 infection and 230 patients with SARS-CoV-2 Omicron BA.2 infection (referred to as H1N1 group and Omicron group) were included in the statistics. The clinical manifestations of H1N1 group were compared with those of Omicron group. Logistic regression was performed to analyze the possible independent risk factors of H1N1 group and Omicron group. And multiple linear regression was used to analyze the factors for time for nucleic acid negativization (NAN) . Results: The median age of the two groups was 21 [11,26] years. Compared with the H1N1 group, the Omicron group had lower white blood cell count and CRP levels, less fever, nasal congestion, sore throat, cough, sputum and headache, while more olfactory loss, muscle soreness and LDH abnormalities. The Omicron group used less antibiotics and antiviral drugs, and the NAN time was longer (17 [ 14,20] VS 4 [ 3,5], P < 0.001). After logistic regression, it was found that fever, cough, headache, and increased white blood cell count were more correlated with the H1N1 group, while muscle soreness and LDH abnormalities were more correlated with the Omicron group. After analyzing the factors of NAN time, it was found that fever (B 1.529, 95 % CI [0.149,2.909], P = 0.030) significantly predicted longer NAN time in Omicron patients. Conclusion: This study comprehensively evaluated the similarities and differences in clinical characteristics between SARS-CoV-2 Omicron infection and 2009 H1N1 influenza infection, which is of great significance for a better understanding for these diseases.

Prevalence of bovine coronavirus in cattle in China: A systematic review and meta-analysis.

Bovine coronavirus (BCoV) is one of the important pathogens that cause calf diarrhea (CD), winter dysentery (WD), and the bovine respiratory disease complex (BRDC), and spreads worldwide. An infection of BCoV in cattle can lead to death of young animals, stunted growth, reduced milk production, and milk quality, thus bringing serious economic losses to the bovine industry. Therefore, it is necessary to prevent and control the spread of BCoV. Here, a systematic review and meta-analysis was conducted to assess the prevalence of BCoV in cattle in China before 2022. A total of 57 articles regarding the prevalence of BCoV in cattle in China were collected from five databases (PubMed, ScienceDirect, CNKI, VIP, and Wan Fang). Based on the inclusion criteria, a total of 15,838 samples were included, and 6,136 were positive cases. The overall prevalence of BCoV was 30.8%, with the highest prevalence rate (60.5%) identified in South China and the lowest prevalence (15.6%) identified in Central China. We also analyzed other subgroup information, included sampling years, sample sources, detection methods, breeding methods, age, type of cattle, presence of diarrhea, and geographic and climatic factors. The results indicated that BCoV was widely prevalent in China. Among all subgroups, the sample sources, detection methods, breeding methods, and presence or absence of diarrheal might be potential risk factors responsible for BCoV prevalence. It is recommended to strengthen the detection of BCoV in cattle, in order to effectively control the spread of BCoV.

Psychological Capital and Alienation among patients with COVID-19 infection: The mediating role of Social Support (preprint)

Background COVID-19 infection continues all over the world, causing serious physical and psychological impacts to patients. Patients with COVID-19 infection suffer from various negative emotional experiences such as anxiety, depression, mania, and alienation, which seriously affect their normal life and is detrimental to the prognosis. Our study is aimed to investigate the effect of psychological capital on alienation among patients with COVID-19 and the mediating role of social support in this relationship.Methods The data were collected in China by the convenient sampling method. A sample of 259 COVID-19 patients completed the psychological capital, social support and social alienation scale and the structural equation model was adopted to verify the research hypotheses.Results Psychological capital was significantly and negatively related to the COVID-19 patients’ social alienation (p < .01). And social support partially mediated the association between psychological capital and patients’ social alienation (p < .01).Conclusion Psychological capital is critical to predicting COVID-19 patients’ social alienation. Social support plays an intermediary role and explains how psychological capital alleviates the sense of social alienation among patients with COVID-19 infection.

Artificial intelligence-based internet hospital pharmacy services in China: Perspective based on a case study

Background: Recently, internet hospitals have been emerging in China, saving patients time and money during the COVID-19 pandemic. In addition, pharmacy services that link doctors and patients are becoming essential in improving patient satisfaction. However, the existing internet hospital pharmacy service mode relies primarily on manual operations, making it cumbersome, inefficient, and high-risk. Objective: To establish an internet hospital pharmacy service mode based on artificial intelligence (AI) and provide new insights into pharmacy services in internet hospitals during the COVID-19 pandemic. Methods: An AI-based internet hospital pharmacy service mode was established. Initially, prescription rules were formulated and embedded into the internet hospital system to review the prescriptions using AI. Then, the “medicine pick-up code,” which is a Quick Response (QR) code that represents a specific offline self-pick-up order, was created. Patients or volunteers could pick up medications at an offline hospital or drugstore by scanning the QR code through the window and wait for the dispensing machine or pharmacist to dispense the drugs. Moreover, the medication consultation function was also operational. Results: The established internet pharmacy service mode had four major functional segments: online drug catalog search, prescription preview by AI, drug dispensing and distribution, and AI-based medication consultation response. The qualified rate of AI preview was 83.65%. Among the 16.35% inappropriate prescriptions, 49% were accepted and modified by physicians proactively and 51.00% were passed after pharmacists intervened. The “offline self-pick-up” mode was preferred by 86% of the patients for collecting their medication in the internet hospital, which made the QR code to be fully applied. A total of 426 medication consultants were served, and 48.83% of them consulted outside working hours. The most frequently asked questions during consultations were about the internet hospital dispensing process, followed by disease diagnosis, and patient education. Therefore, an AI-based medication consultation was proposed to respond immediately when pharmacists were unavailable. Conclusion: The established AI-based internet hospital pharmacy service mode could provide references for pharmacy departments during the COVID-19 pandemic. The significance of this study lies in ensuring safe/rational use of medicines and raising pharmacists’ working efficiency.

Temporal epistasis inference from more than 3 500 000 SARS-CoV-2 genomic sequences.

We use direct coupling analysis (DCA) to determine epistatic interactions between loci of variability of the SARS-CoV-2 virus, segmenting genomes by month of sampling. We use full-length, high-quality genomes from the GISAID repository up to October 2021 for a total of over 3 500 000 genomes. We find that DCA terms are more stable over time than correlations but nevertheless change over time as mutations disappear from the global population or reach fixation. Correlations are enriched for phylogenetic effects, and in particularly statistical dependencies at short genomic distances, while DCA brings out links at longer genomic distance. We discuss the validity of a DCA analysis under these conditions in terms of a transient auasilinkage equilibrium state. We identify putative epistatic interaction mutations involving loci in spike.

Development of Clinical Risk Scores for Detection of COVID-19 in Suspected Patients During a Local Outbreak in China: A Retrospective Cohort Study

Objectives: To develop and internally validate two clinical risk scores to detect coronavirus disease 2019 (COVID-19) during local outbreaks. Methods: Medical records were extracted for a retrospective cohort of 336 suspected patients admitted to Baodi hospital between 27 January to 20 February 2020. Multivariate logistic regression was applied to develop the risk-scoring models, which were internally validated using a 5-fold cross-validation method and Hosmer-Lemeshow (H-L) tests. Results: Fifty-six cases were diagnosed from the cohort. The first model was developed based on seven significant predictors, including age, close contact with confirmed/suspected cases, same location of exposure, temperature, leukocyte counts, radiological findings of pneumonia and bilateral involvement (the mean area under the receiver operating characteristic curve [AUC]:0.88, 95% CI: 0.84–0.93). The second model had the same predictors except leukocyte and radiological findings (AUC: 0.84, 95% CI: 0.78–0.89, Z = 2.56, p = 0.01). Both were internally validated using H-L tests and showed good calibration (both p > 0.10). Conclusion: Two clinical risk scores to detect COVID-19 in local outbreaks were developed with excellent predictive performances, using commonly measured clinical variables. Further external validations in new outbreaks are warranted.

De novo podocytopathy following moderna COVID-19 vaccine: A case report and racial disproportionality in adverse effect reports.

In this study, we report a case of de novo minimal change disease shortly after the second dose of the Moderna COVID-19 vaccine. A previously healthy 51-year-old Asian man presented with lower-limb edema and foamy urine 3 days after receiving the second dose of the vaccine. Laboratory data revealed the following: serum creatinine, 0.65 mg/dl; serum albumin, 1.3 g/dl; urine protein-to-creatinine ratio, 15.3 g. A renal biopsy was performed, and minimal change in the disease was confirmed. The patient achieved complete remission in the tenth week after starting treatment with prednisolone (1 mg/kg/day). Ethnic differences may influence the adverse effects of drugs and vaccines. However, there is very limited data to address the influence of ethnic diversity on disease prevalence, clinical presentation, and treatment outcomes in COVID-19 vaccine-associated glomerulonephritis.

Analysis of the effects of predictive nursing in hospitalization of patients with COVID-19

This study aimed to explore the clinical application effects of predictive nursing in nursing management of patients with new type of coronavirus pneumonia during hospitalization. A retrospective study was used to collect the demographic and clinical characteristics of the patients. The medical team of our hospital supporting the First Hospital of Wuhan City implemented predictive mursing interventions on 83 confirmed patients with new type of coronavirus pneumonia, 2 of whom were mild, 73 were severe, and 8 were critical ill, admitted from 14 February to 17 March, 2020. As of 17 March, 74 patients have been discharged, 12 cases converted from severe to mild, 2 cases were transferred to other department, 3 cases were transferred to other hospital, 3 cases died. One patient died of stroke at admission. The medical staffs in the clinical treatment data of 83 patients(using logistic ordered regression analysis) found that: age may be the factor that promotes the development of the new type of coronavirus pneumonia (B=-0.09, P<0.05), for a total of 8 critically ill patients with an average age of>73 vears;patients with a history of coronary heart disease may be at a greater risk during treatment(B=-2.39, P<0.05): smoking history may adverse the recovery progression (B=1.52,P<0.05);the abnormal albumin (ALB) and hemoglobin (HGB) indicators may speed up the disease process (B=-0.40, -0.05;P<0.00,0.00). Following the guidelines for clinical nursing of new type of coronavirus pneumonia, foreseeably formulate timely treatment and nursing intervention protocols, propose and strictly implement medical care measures for the new type of coronavirus pneumonia have obvious effects in the clinical treatment of severe and critically ill patients.

Epidemiological characteristics and incubation period of coronavirus disease 2019 in Anhui Province. (Special Issue: Spatio-temporal dynamics analysis, risk assessment and emergency management for COVID-19 epidemic.) [Chinese]

Objective: To analyze the epidemiological characteristics and incubation of coronavirus disease 2019(COVID-19)from Jan. 22 to Mar. 8, 2020 in Anhui Province, in order to provide the basis for further understanding of the transmission pattern of COVID-19 and formulating regional control measures.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

Hypertension Exacerbates Severity and Outcomes of COVID-19 in Elderly Patients: A Retrospective Observational Study.

OBJECTIVE: To evaluate the impact of hypertension on the clinical outcome of COVID-19 patients aged 60 years old and older. METHODS: This single-center retrospective cohort study enrolled consecutive COVID-19 patients aged 60 years old and older, who were admitted to Liyuan Hospital from January 1, 2020 to April 25, 2020. All included patients were divided into two groups: hypertension and nonhypertension group. The baseline demographic characteristics, laboratory test results, chest computed tomography (CT) images and clinical outcomes were collected and analyzed. The prognostic value of hypertension was determined using binary logistic regression. RESULTS: Among the 232 patients included in the analysis, 105 (45.3%) patients had comorbid hypertension. Compared to the nonhypertension group, patients in the hypertension group had higher neutrophil-to-lymphocyte ratios, red cell distribution widths, lactate dehydrogenase, high-sensitivity C-reactive protein, D-dimer and severity of lung lesion, and lower lymphocyte counts (all P<0.05). Furthermore, the hypertension group had a higher proportion of intensive care unit admissions [24 (22.9%) vs. 14 (11.0%), P=0.02) and deaths [16 (15.2%) vs. 3 (2.4%), P<0.001] and a significantly lower probability of survival (P<0.001) than the nonhypertension group. Hypertension (OR: 4.540, 95% CI: 1.203-17.129, P=0.026) was independently correlated with all-cause in-hospital death in elderly patients with COVID-19. CONCLUSION: The elderly COVID-19 patients with hypertension tend to have worse conditions at baseline than those without hypertension. Hypertension may be an independent prognostic factor of poor clinical outcome in elderly COVID-19 patients.