SARS-CoV-2 vaccine-induced immune thrombotic thrombocytopenia.

SARS-CoV-2 vaccines have been carefully developed and significantly alleviate the global pandemic. However, a rare but severe complication after vaccination of adenoviral vector vaccines has attracted worldwide attention. It is characterized by thrombosis at unusual sites (often cerebral or abdominal), thrombocytopenia, and the presence of antibodies against platelet factor 4 (PF4), termed vaccine-induced immune thrombotic thrombocytopenia (VITT). Its pathogenesis is similar to that of heparin-induced thrombocytopenia (HIT). VITT progresses rapidly and has a high mortality rate. Clinicians and the public should raise their vigilance to this disease so that accurate and timely treatment is provided.

Prognostic Value of Leucocyte to High-Density Lipoprotein-Cholesterol Ratios in COVID-19 Patients and the Diabetes Subgroup.

Background: Blood parameters, such as neutrophil-to-lymphocyte ratio, have been identified as reliable inflammatory markers with diagnostic and predictive value for the coronavirus disease 2019 (COVID-19). However, novel hematological parameters derived from high-density lipoprotein-cholesterol (HDL-C) have rarely been studied as indicators for the risk of poor outcomes in patients with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection. Here, we aimed to assess the prognostic value of these novel biomarkers in COVID-19 patients and the diabetes subgroup. Methods: We conducted a multicenter retrospective cohort study involving all hospitalized patients with COVID-19 from January to March 2020 in five hospitals in Wuhan, China. Demographics, clinical and laboratory findings, and outcomes were recorded. Neutrophil to HDL-C ratio (NHR), monocyte to HDL-C ratio (MHR), lymphocyte to HDL-C ratio (LHR), and platelet to HDL-C ratio (PHR) were investigated and compared in both the overall population and the subgroup with diabetes. The associations between blood parameters at admission with primary composite end-point events (including mechanical ventilation, admission to the intensive care unit, or death) were analyzed using Cox proportional hazards regression models. Receiver operating characteristic curves were used to compare the utility of different blood parameters. Results: Of 440 patients with COVID-19, 67 (15.2%) were critically ill. On admission, HDL-C concentration was decreased while NHR was high in patients with critical compared with non-critical COVID-19, and were independently associated with poor outcome as continuous variables in the overall population (HR: 0.213, 95% CI 0.090-0.507; HR: 1.066, 95% CI 1.030-1.103, respectively) after adjusting for confounding factors. Additionally, when HDL-C and NHR were examined as categorical variables, the HRs and 95% CIs for tertile 3 vs. tertile 1 were 0.280 (0.128-0.612) and 4.458 (1.817-10.938), respectively. Similar results were observed in the diabetes subgroup. ROC curves showed that the NHR had good performance in predicting worse outcomes. The cutoff point of the NHR was 5.50. However, the data in our present study could not confirm the possible predictive effect of LHR, MHR, and PHR on COVID-19 severity. Conclusion: Lower HDL-C concentrations and higher NHR at admission were observed in patients with critical COVID-19 than in those with noncritical COVID-19, and were significantly associated with a poor prognosis in COVID-19 patients as well as in the diabetes subgroup.

Severity of COVID-19 in Cancer patients versus patients without Cancer: A Propensity Score Matching Analysis.

Purpose: Data are extremely limited with regards to the impact of COVID-19 on cancer patients. Our study explored the distinct clinical features of COVID-19 patients with cancer. Experimental Design: 189 COVID-19 patients, including 16 cancer patients and 173 patients without cancer, were recruited. Propensity score 1:4 matching (PSM) was performed between cancer patients and patients without cancer based on age, gender and comorbidities. Survival was calculated by the Kaplan-Meier method and the difference was compared by the log-rank test. Results: PSM analysis yielded 16 cancer patients and 64 propensity score-matched patients without cancer. Compared to patients without cancer, cancer patients tended to have leukopenia and elevated high-sensitivity C-reactive protein (hs-CRP) and procalcitonin. For those with critical COVID-19, cancer patients had an inferior survival than those without cancer. Also, cancer patients with severe/critical COVID-19 tended to be male and present with low SPO2 and albumin, and high hs-CRP, lactate dehydrogenase and blood urea nitrogen on admission compared to those with mild COVID-19. In terms of risk factors, recent cancer diagnosis (within 1 year of onset of COVID-19) and anti-tumor treatment within 3 months of COVID-19 diagnosis were associated with inferior survival. Conclusions: We found COVID-19 patients with cancer have distinct clinical features as compared to patients without cancer. Importantly, cancer patients with critical COVID-19 were found to have poorer outcomes compared to those without cancer. In the cancer cohort, patients with severe/critical COVID-19 presented with a distinct clinical profile from those with mild COVID-19; short cancer history and recent anti-cancer treatment were associated with inferior survival.

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study.

Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients. Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis. Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = -0.535, p < 0.001). Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

Persistent Bacterial Coinfection of a COVID-19 Patient Caused by a Genetically Adapted Pseudomonas aeruginosa Chronic Colonizer.

Pseudomonas aeruginosa is a biofilm-forming opportunistic pathogen which causes chronic infections in immunocompromised patients and leads to high mortality rate. It is identified as a common coinfecting pathogen in COVID-19 patients causing exacerbation of illness. In our hospital, P. aeruginosa is one of the top coinfecting bacteria identified among COVID-19 patients. We collected a strong biofilm-forming P. aeruginosa strain displaying small colony variant morphology from a severe COVID-19 patient. Genomic and transcriptomic sequencing analyses were performed with phenotypic validation to investigate its adaptation in SARS-CoV-2 infected environment. Genomic characterization predicted specific genomic islands highly associated with virulence, transcriptional regulation, and DNA restriction-modification systems. Epigenetic analysis revealed a specific N6-methyl adenine (m6A) methylating pattern including methylation of alginate, flagellar and quorum sensing associated genes. Differential gene expression analysis indicated that this isolate formed excessive biofilm by reducing flagellar formation (7.4 to 1,624.1 folds) and overproducing extracellular matrix components including CdrA (4.4 folds), alginate (5.2 to 29.1 folds) and Pel (4.8-5.5 folds). In summary, we demonstrated that P. aeuginosa clinical isolates with novel epigenetic markers could form excessive biofilm, which might enhance its antibiotic resistance and in vivo colonization in COVID-19 patients.

Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.

BACKGROUND: To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness. METHODS: A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC). RESULTS: The mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913-1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757-1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906-1.000). CONCLUSIONS: A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.

High expression of ACE2 and TMPRSS2 and clinical characteristics of COVID-19 in colorectal cancer patients.

Little is known of the patterns of expression of ACE2 and TMPRSS2 or the clinical characteristics of COVID-19 in patients with COVID-19 and colorectal cancer. We found in both bulk and single-cell RNA-seq profiles that ACE2 and TMPRSS2 were expressed at high levels on tumor and normal colorectal epithelial tissues. Clinically, patients with colorectal cancer and COVID-19 were more likely to have lymphopenia, higher respiratory rate, and high hypersensitive C-reactive protein levels than matched patients with COVID-19 but without cancer. These results suggest that patients with colorectal cancer may be particularly susceptible to SARS-CoV-2 infection. Further mechanistic studies are needed to support our findings.

Impaired Fasting Glucose and Diabetes Are Related to Higher Risks of Complications and Mortality Among Patients With Coronavirus Disease 2019.

Background: Diabetes correlates with poor prognosis in patients with COVID-19, but very few studies have evaluated whether impaired fasting glucose (IFG) is also a risk factor for the poor outcomes of patients with COVID-19. Here we aimed to examine the associations between IFG and diabetes at admission with risks of complications and mortality among patients with COVID-19. Methods: In this multicenter retrospective cohort study, we enrolled 312 hospitalized patients with COVID-19 from 5 hospitals in Wuhan from Jan 1 to Mar 17, 2020. Clinical information, laboratory findings, complications, treatment regimens, and mortality status were collected. The associations between hyperglycemia and diabetes status at admission with primary composite end-point events (including mechanical ventilation, admission to intensive care unit, or death) were analyzed by Cox proportional hazards regression models. Results: The median age of the patients was 57 years (interquartile range 38-66), and 172 (55%) were women. At the time of hospital admission, 84 (27%) had diabetes (and 36 were new-diagnosed), 62 (20%) had IFG, and 166 (53%) had normal fasting glucose (NFG) levels. Compared to patients with NFG, patients with IFG and diabetes developed more primary composite end-point events (9 [5%], 11 [18%], 26 [31%]), including receiving mechanical ventilation (5 [3%], 6 [10%], 21 [25%]), and death (4 [2%], 9 [15%], 20 [24%]). Multivariable Cox regression analyses showed diabetes was associated increased risks of primary composite end-point events (hazard ratio 3.53; 95% confidence interval 1.48-8.40) and mortality (6.25; 1.91-20.45), and IFG was associated with an increased risk of mortality (4.11; 1.15-14.74), after adjusting for age, sex, hospitals and comorbidities. Conclusion: IFG and diabetes at admission were associated with higher risks of adverse outcomes among patients with COVID-19.