ABSTRACT
To analyze the clinical characteristics and outcomes of unvaccinated adult patients on maintenance hemodialysis infected with SARS-CoV-2 Omicron subvariant BA.5.2.The clinical data of 427 maintenance hemodialysis patients infected with SARS-CoV-2 Omicron subvariant BA.5.2 in our hospital were retrospectively collected. The patients were grouped according to the severity of the disease and compared. The clinical outcome and two-month follow-up were analyzed.These results suggest that CRP level, procalcitonin level, and bicarbonate concentration are related to the severity of disease caused by SARS-CoV-2 omicron BA.5.2 infection in unimmunized MHD patients. In addition, the co-bacterial infection may be an important cause of severe illness. Therefore, strengthen the treatment of critically ill patients, and actively and effectively control infection and secondary infection; Effective vaccination is the key to improving clinical outcomes to prevent the conversion of ordinary patients to severe and critical cases. Fever, age, ORF1ab gene value, and arterial oxygen partial pressure may be independent risk factors for disease severity in COVID-19 patients.
Subject(s)
Critical Illness , Bacterial Infections , Fever , COVID-19ABSTRACT
Background: Elderliness is known risk factor for severe progression of COVID-19 due to compromised immunity, however aberrant hyperactive immune response including autoimmunity might be responsible for younger patients. Methods: 162 patients tested with autoimmunological detections were enrolled, and study of “Severe” cases and “Non-severe” controls was retrospectively analyzed. Results: Multivariable analysis involving antinuclear autoimmunity manifests correlation of disease severity with middle age and attenuates the risk of age older than 65. Middle age (45≤age≤65) and female turn out to be the risk factors after hierarchical cluster analysis, before which however sex was not correlated. We find antinuclear autoimmunity to be strongly correlated with severity for the middle-aged (OR= 21.000, 95% CI 4.893- 90.126, p< 0.001) and female (OR= 16.044, 95% CI 4.717- 54.568, p< 0.001), especially for the middle-aged female (Pearson R= 0.770, p< 0.001). Incidence of symptoms fever and chest distress, and complication myocardial injury are statistically more frequent in patients with positive antinuclear antibody, compared with those negative. Severe patients with positive antinuclear antibody possess significantly shorter onset of symptoms to severity time (p= 0.021), indicating quicker progression, and interestingly, present more incidence (21%) of post-remission aggravation, compared with those negative (6%). Conclusions: The presence of antinuclear autoimmunity potentially makes COVID-19 prone to severe progression, especially for the middle-aged and female, probably even quicker.
Subject(s)
COVID-19 , Fever , Brain ConcussionABSTRACT
Background: Considering the current situation of the novel coronavirus disease (COVID-19) epidemic control, it is highly likely that COVID-19 and influenza may coincide during the approaching winter season. However, there is no available tool that can rapidly and precisely distinguish between these two diseases in the absence of laboratory evidence of specific pathogens. Methods: Laboratory confirmed COVID-19 and influenza patients from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) between December 1, 2019 and February 29, 2020, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms. The specificity, sensitivity, positive and negative predictive values (PPV/NPV), accuracy and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the model performance. Findings: The data from 357 COVID-19 and 1893 influenza patients from ZHWU were divided into a training and a testing set in the ratio 7:3. The external test used the data of 308 COVID-19 and 312 influenza patients from WNH. In the training and testing sets, the model achieved good performance in identifying COVID-19 from influenza with an accuracy of 0.968 (AUC, 0.943 (95%CI 0.925, 0.962)) and 0.960 (AUC, 0.928 (95%CI 0.897, 0.959)), respectively. Our decision tree suggested that older age (>16 years), higher hsCRP (>14.2 mg/L) and lower monocyte (≤0.68×109/L) drive the prediction towards COVID-19. In addition, the external test determined a COVID-19 prediction accuracy of 0.839 (AUC, 0.839 (95%CI: 0.811, 0.868). Interpretation: Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions having massive COVID-19 and influenza cases while limited resources for laboratory test of specific pathogens. Funding: National Natural Science Foundation of China (81900097) and the Emergency Response Project of Hubei Science and Technology Department (2020FCA002, 2020FCA023).Declaration of Interests: None reported.Ethics Approval Statement: This study was approved by the Medical Ethics Committee, Zhongnan Hospital of Wuhan University (Clinical Ethical Approval No. 2020020).
Subject(s)
COVID-19ABSTRACT
Background. Risk scores are urgently needed to assist clinicians in predicting the risk of death in severe patients with SARS-CoV-2 infection in the context of millions of people infected, rapid disease progression, and shortage of medical resources.Method. A total of 139 severe patients with SARS-CoV-2 from China and Iran were included. Using data from China (training dataset, n = 96), prediction models were developed based on logistic regression models, nomogram and risk scoring system for simplification. Leave-one-out cross validation was used for internal validation and data from Iran (test dataset, n = 43) for external validation. Results. The NSL model (Area under the curve (AUC) 0.932) and NL model (AUC 0.903) were developed based on neutrophil percentage (NE), lactate dehydrogenase (LDH) with or without oxygen saturation (SaO2) using the training dataset. Compared with the training dataset, the predictability of NSL model (AUC 0.910) and NL model (AUC 0.871) were similar in the test dataset. The risk scoring systems corresponding to these two models were established for clinical application. The AUCs of the NSL and NL scores were 0.928 and 0.901 in the training dataset, respectively. At the optimal cut-off value of NSL score, the sensitivity was 94% and specificity was 82%. In addition, for NL score, the sensitivity and specificity were 94% and 75%, respectively.Conclusion. NSL and NL score are straightforward means for clinicians to predict the risk of death in severe patients. NL score could be used in selected regions where patients’ SaO2 cannot be tested.
Subject(s)
COVID-19ABSTRACT
Background: Coronavirus disease 2019 (COVID-19) has been widely spread and caused tens of thousands of deaths, mainly in patients with severe COVID-19.Methods: Patients with COVID-19 were retrospectively analyzed. Clinical characteristics were compared, and LASSO regression as well as multivariate analysis were used to screen variables and establish prediction model. Findings: A total of 2529 patients with COVID-19 was retrospectively analyzed, and 452 eligible severe COVID-19 were used for finally analysis. In training cohort, the median age was 66·0 years while it was 73·0 years in non-survivors. Patients aged 60-75 years accounted for the largest proportion of infected populations and mortality toll. Anti-SARS-CoV-2 antibodies were monitored up to 54 days, and IgG levels reached the highest during 20-30 days. About 60.2% of severe patients had complications. Acute myocardial injury was the earliest injured organ, whereas the time from acute kidney injury to death was the shortest. Age, diabetes, coronary heart disease (CHD), percentage of lymphocytes (LYM%), procalcitonin (PCT), serum urea, C reactive protein and D-dimer (DD), were identified associated with mortality by LASSO binary logistic regression. Then multivariate analysis was performed to conclude that old age, CHD, LYM%, PCT and DD remained independent risk factors for mortality. Based on the above variables, a scoring system of COVID-19 (CSS) was established and divided into low-risk and high-risk groups. This model displayed good discrimination (AUC=0·919) and calibration (P =0·264). The complications in low-risk and high-risk groups were significantly different. We also found that the use of corticosteroids in low-risk groups increased hospital stays by 4·5 days (P =0·036) and durations of disease by 7·5 days (P =0 · 012) compared with no corticosteroids.Interpretation: Old age, CHD, LYM%, PCT and DD were independently related to mortality. CSS was useful for predicting in-hospital mortality and complications, and it could help clinicians to identify high-risk patients with poor prognosis.Funding Statement: This work was supported by the Key Project for Anti-2019 novel Coronavirus Pneumonia from the Ministry of Science and Technology, China (grant number 2020YFC0845500). Declaration of Interests: All authors declare no competing interests.Ethics Approval Statement: This study was conducted according to the principles of Helsinki and approved by the Ethics Committee of Zhongnan Hospital of Wuhan University (No.2020063).
Subject(s)
Coronavirus Infections , Diabetes Mellitus , Coronary Disease , Acute Kidney Injury , COVID-19ABSTRACT
Background Since the outbreak of COVID-19, the application of appropriate treatment strategy for COVID-19 patients, notably for the severe patients, was a huge challenge in case management. Therefore, we aimed to evaluate the effectiveness of antiviral treatment in severe COVID-19 patients.Methods A retrospective cohort study was conducted from January 8, 2020 to March 9, 2020 in four designated hospitals of COVID-19 in Wuhan, China. 138 severe COVID-19patients with above 18 years were included in this study. 109 patients receiving the antiviral treatment were selected in antiviral group. The remaining 29 patients were in control group. The primary outcome of the study was in-hospital death and length of hospitalization. Secondary outcomes included ICU admission, length of stays in ICU, use of mechanical ventilation, length of mechanical ventilation, and the development of complications. Univariate analysis and Kaplan-Meier curves were used to examined the association between antiviral treatment and the clinical outcomes of the COVID-19 patients.Results 48 (44.0%) and 15 (51.7%) deaths were occurred in antiviral and control groups, respectively. Antiviral treatment was not associated with the rate of fatal outcome in COVID-19 patients (P > 0.05). Among the survival patients, the median length of hospitalization was 11.0 days (IQR: 6.5–18.0) and 16.0 days (IQR: 8.5–26.0) in antiviral and control groups, respectively. No significant association was identified between the antiviral treatment and the length of hospitalization in survival patients (P > 0.05). Moreover, the antiviral treatment was not statistically associated with ICU admission, mechanical ventilation and length of mechanical ventilation (P > 0.05, respectively). However, the length of ICU stays in deaths was different both groups (P < 0.05). The median length of ICU stays in deaths was 7.0 days (IQR: 3.0-14.3) and 15.5 days (IQR: 8.3–21.8) in antiviral and control groups, respectively. The occurrence of majority of complications were similar both groups. Sepsis was the single complication in which the occurrence rates were statistical different between the antiviral group and control group (40.4% vs 13.8%, P < 0.01).Conclusion No benefit of antiviral treatment in severe COVID-19 patients was observed in our study. Clinical physicians should cautiously prescribe the antiviral drugs in severe COVID-19 patients.
Subject(s)
COVID-19 , Sepsis , DeathABSTRACT
Understanding the epidemiological and clinical characteristics of fatal cases infected with SARS-CoV-2 is import to develop appropriate preventable intervention programs in hospitals. Demographic data, clinical symptoms, clinical course, co-morbidities, laboratory findings, CT scans, treatments and complications of 162 fatal cases were retrieved from electric medical records in 5 hospitals of Wuhan, China. The median age was 69.5 years old (IQR: 63.0-77.25; range: 29-96). 112 (69.1%) cases were men. Hypertension (45.1%) was the most common co-morbidity, but 59 (36.4%) cases had no co-morbidity. At admission, 131 (81.9%) cases were assessed as severe or critical. However, 39 (18.1%) were assessed as moderate. Moderate cases had a higher prevalence of hypertension and chronic lung disease comparing with severe or critical cases (P<0.05, respectively). 126 (77.8%) and 132 (81.5%) cases received antiviral treatment and glucocorticoids, respectively. 116 (71.6%) cases were admitted to ICU and 137 (85.1%) cases received mechanical ventilation. Respiratory failure or acute respiratory distress syndrome (93.2%) was the most common complication. The young cases of COVID-19, without co-morbidity and in a moderate condition at admission could develop fatal outcome. We need to be more cautious in case management of COVID-19 for preventing the fatal outcomes.
Subject(s)
Lung Diseases , Respiratory Distress Syndrome , Hypertension , COVID-19 , Respiratory InsufficiencyABSTRACT
Background: Coronavirus disease-19 (COVID-19) has spread rapidly and has become a world health threaten. Its risk factors with death were still unknow. White blood cells (WBC) as a reflection of inflammation had play a vital role in COVID-19, however its level with death were still not know. Methods: In this retrospective, single-center study, all confirmed patients with COVID-19 on admission at West Branch of Union Hospital from Jan 29 to Feb 28, were collected and analyzed. Demographic and clinical data including laboratory examinations were analyzed and compared between recovery and death patients.Results: A total of 163 patients including 33 death cases were included in this study. Significant associations were found between WBC level and death (HR = 1.14, 95%CI: 1.09-1.20, p<0.001). The regression analysis results showed there was a significant association between WBC level and death (HR = 5.72, 95%CI: 2.21-14.82, p < 0.001) when use the second quartile as a cutoff value (> 6.16×10^9/L). The difference was still existing after we adjusting for confounding factors (HR = 6.26, 95%CI: 1.72-22.77, p = 0.005). In addition, Kaplan-meier survival analysis showed that there was a significant decline of the cumulative survival rate (p < 0.001) in those with WBC level ≥ 6.16×10^9/L.Conclusion: WBC at admission is significantly corelated with death in COVID-19 patients. Higher level of WBC should be given more attention in the treatment of COVID-19.
Subject(s)
COVID-19 , Inflammation , DeathABSTRACT
Background Coronavirus disease-19 (COVID-19) has spread rapidly, with a growing number of cases confirmed around the world. This study explores the relationship of fasting blood glucose (FBG) at admission with mortality. Methods In this retrospective, single-center study, we analyzed the clinical characteristics of confirmed cases of COVID-19 in Wu Han from 29 January 2020 to 23 February 2020. Cox proportional hazard regression analysis was performed to evaluate the relationship between FBG and mortality. Results A total of 107 patients were enrolled in our study. The average age was 59.49 ± 13.33 and the FBG at admission was 7.35 ± 3.13 mmol/L. There were 16 people died of COVID-19 with an average age 68.1 ± 9.5 and the FBG was 8.94 ± 4.76 mmol/L. Regression analysis showed that there were significant association between FBG and death (HR = 1.13, 95%CI: 1.02-1.24). After adjusting for covariables, the significance still exists. In addition, our result showed that FBG > 7.0 mmol/L or diabetic mellitus can significantly increase mortality after adjusting for the age and gender. Conclusions This study suggests that FBG at admission is an effective and reliable indicator for disease prognosis in COVID-19 patients.