Clinical course and risk factors for mortality of COVID-19 patients with pre-existing cirrhosis: a multicentre cohort study.
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ABSTRACT:
SUBMITTER: Qi X
PROVIDER: S-EPMC7815629 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
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Clinical course and risk factors for mortality of COVID-19 patients with pre-existing cirrhosis: a multicentre cohort study.
Qi Xiaolong X Liu Yanna Y Wang Jitao J Fallowfield Jonathan A JA Wang Jianwen J Li Xinyu X Shi Jindong J Pan Hongqiu H Zou Shengqiang S Zhang Hongguang H Chen Zhenhuai Z Li Fujian F Luo Yan Y Mei Mei M Liu Huiling H Wang Zhengyan Z Li Jinlin J Yang Hua H Xiang Huihua H Li Xiaodan X Liu Tao T Zheng Ming-Hua MH Liu Chuan C Huang Yifei Y Xu Dan D Li Xiaoguo X Kang Ning N He Qing Q Gu Ye Y Zhang Guo G Shao Chuxiao C Liu Dengxiang D Zhang Lin L Li Xun X Kawada Norifumi N Jiang Zicheng Z Wang Fengmei F Xiong Bin B Takehara Tetsuo T Rockey Don C DC
Gut 20200520 2
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Project description:BackgroundCoronavirus disease 2019 (COVID-19) pandemic continues to escalate intensively worldwide. Massive studies on general populations with SARS-CoV-2 infection have revealed that pre-existing comorbidities were a major risk factor for the poor prognosis of COVID-19. Notably, 49-75% of COVID-19 patients had no comorbidities, but this cohort would also progress to severe COVID-19 or even death. However, risk factors contributing to disease progression and death in patients without chronic comorbidities are largely unknown; thus, specific clinical interventions for those patients are challenging.MethodsA multicenter, retrospective study based on 4806 COVID-19 patients without chronic comorbidities was performed to identify potential risk factors contributing to COVID-19 progression and death using LASSO and a stepwise logistic regression model.ResultsAmong 4806 patients without pre-existing comorbidities, the proportions with severe progression and mortality were 34.29% and 2.10%, respectively. The median age was 47.00 years [interquartile range, 36.00-56.00], and 2162 (44.99%) were men. Among 51 clinical parameters on admission, age ≥ 47, oxygen saturation < 95%, increased lactate dehydrogenase, neutrophil count, direct bilirubin, creatine phosphokinase, blood urea nitrogen levels, dyspnea, increased blood glucose and prothrombin time levels were associated with COVID-19 mortality in the entire cohort. Of the 3647 patients diagnosed with non-severe COVID-19 on admission, 489(13.41%) progressed to severe disease. The risk factors associated with COVID-19 progression from non-severe to severe illness were increased procalcitonin levels, SpO2 < 95%, age ≥ 47, increased LDH, activated partial thromboplastin time levels, decreased high-density lipoprotein cholesterol levels, dyspnea and increased D-dimer levels.ConclusionsCOVID-19 patients without pre-existing chronic comorbidities have specific traits and disease patterns. COVID-19 accompanied by severe bacterial infections, as indicated by increased procalcitonin levels, was highly associated with disease progression from non-severe to severe. Aging, impaired respiratory function, coagulation dysfunction, tissue injury, and lipid metabolism dysregulation were also associated with disease progression. Once factors for multi-organ damage were elevated and glucose increased at admission, these findings indicated a higher risk for mortality. This study provides information that helps to predict COVID-19 prognosis specifically in patients without chronic comorbidities.
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2021-10-13 | E-MTAB-11084 | biostudies-arrayexpress
Project description:Recent exposure to seasonal coronaviruses (sCoVs) may stimulate cross-reactive antibody responses against SARS-CoV-2. Previous studies have shown divergent results regarding protective or damaging immunity induced by prior exposure to sCoVs. It is still unknown whether pre-existing humoral immunity may play a role in the vaccine-induced neutralization and antibody responses. In this study, we collected 36 paired sera in healthy volunteers before and after immunization with inactivated SARS-CoV-2 vaccines, and analyzed the distribution and intensity of pre-existing antibody responses at the epitope level before vaccine immunization, as well as the relationship between pre-existing sCoVs immunity and vaccine-induced neutralization.
2021-10-29 | E-MTAB-11117 | biostudies-arrayexpress
Project description:COVID-19 can be life-threatening to individuals with chronic diseases. To prevent severe outcomes, it is critical that we comprehend pre-existing molecular abnormalities found in common health conditions that predispose patients to poor prognoses. In this study, we focused on 14 pre-existing health conditions for which increased hazard ratios of COVID-19 mortality have been documented. We hypothesized that dysregulated gene expression in these pre-existing health conditions were risk factors of COVID-19 related death, and the magnitude of dysregulation (measured by fold change) were correlated with the severity of COVID-19 outcome (measured by hazard ratio). To test this hypothesis, we analyzed transcriptomics data sets archived before the pandemic in which no sample had COVID-19. For a given pre-existing health condition, we identified differentially expressed genes by comparing individuals affected by this health condition with those unaffected. Among genes differentially expressed in multiple health conditions, the fold changes of 70 upregulated genes and 181 downregulated genes were correlated with hazard ratios of COVID-19 mortality. These pre-existing dysregulations were molecular risk factors of severe COVID-19 outcomes. These genes were enriched with endoplasmic reticulum and mitochondria function, proinflammatory reaction, interferon production, and programmed cell death that participate in viral replication and innate immune responses to viral infections. Our results suggest that impaired innate immunity in pre-existing health conditions is associated with increased hazard of COVID-19 mortality. The discovered molecular risk factors are potential prognostic biomarkers and targets for therapeutic intervention.
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Project description:ObjectiveDevelop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital.DesignRetrospective cohort study.SettingA multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020.ParticipantsSARS-CoV-2 positive patients (age ≥18) admitted to the hospital.Main outcome measures21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis.Results2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81).ConclusionBoth models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.
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Project description:BackgroundPoorer performance on standard tests of cognitive function is related to an elevated risk of death from lower respiratory tract infections. Whether pre-pandemic measures of cognition are related to COVID-19 mortality is untested.MethodsUK Biobank, a prospective cohort study, comprises around half a million people who were aged 40 to 69 years at study induction between 2006 and 2010 when a reaction time test was administered to the full sample, and verbal-numeric reasoning assessed in a subgroup. Death from COVID-19 was ascertained from participant linkage to a UK-wide national registry.ResultsBetween April 1st and September 23rd 2020, there were 388 deaths (138 women) ascribed to COVID-19 in the 494,932 individuals (269,602 women) with a reaction time test result, and 125 such deaths (38 women) in the 180,198 (97,794 women) for whom there were data on verbal-numeric reasoning. In analyses adjusted for age, sex, and ethnicity, a one standard deviation (118.2 msec) slower reaction time was related to a higher rate of death from COVID-19 (hazard ratio; 95% confidence interval: 1.18; 1.09, 1.28). A one standard deviation disadvantage (2.16 point) on the verbal-numeric reasoning test was also associated with an elevated risk of death (1.32; 1.09, 1.59). Attenuation after adjustment for additional covariates followed a similar pattern for both measures of cognition. For verbal-numeric reasoning, for instance, the hazard ratios were 1.22 (0.98, 1.51) after control for socioeconomic status, 1.16 (0.96, 1.41) after lifestyle factors, 1.25 (1.04, 1.52) after co-morbidity, and 1.29 (1.01, 1.64) after physiological indices.ConclusionsIn the present study, poorer performance on two pre-pandemic indicators of cognitive function, including reaction time, a knowledge-reduced measure, was related to death ascribed to COVID-19.
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