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ObjectiveAlthough the impact of the variants of COVID-19 on the general population is diminishing, there is still a certain mortality rate for severe and critically ill patients, especially for the elderly with comorbidities. The present study investigated whether the D-dimer to albumin ratio (DAR) can predict the severity of illness and mortality in COVID-19 patients.MethodsA total of 1,993 patients with COVID-19 were retrospectively reviewed and the association of DAR with severe or critical illness or death during hospitalization was analyzed. The area under the ROC curve was used to screen the best indicators, Chi-square test, rank sum test, and univariate and multivariate binary logistic regression analysis were used to calculate the mean value of difference and adjusted odds ratio (aORs) with their 95% CI, and finally, survival was analyzed using Kaplan–Meier (KM) curves.ResultsAmong 1,993 patients with COVID-19, 13.4% were severely ill, and the mortality rate was 2.3%. The area under the curve (AUC) using DAR to predict severe and critically ill patients was higher than that using other parameters. The best cut-off value of DAR was 21 in the ROC with a sensitivity of 83.1% and a specificity of 68.7%. After adjusting age, gender, comorbidities, and treatment, the binary logistic regression analysis showed that elevated DAR was an independent risk factor for severely ill and mortality of COVID-19 patients. The KM curve suggested that patients with a higher DAR was associated with worse survival. The negative predictive value of DAR (21) for adverse prognosis and death was 95.98 and 99.84%, respectively, with a sensitivity of 80.9 and 95.65%, respectively.ConclusionThe DAR may be an important predictor for severe illness and mortality in COVID-19 patients.

Objective:This study aimed to explore the signal detection method for allergic reactions induced by inpatient iodixanol injection.Methods:A database of 3,719,217 hospitalized patients from 20 large Chinese general hospitals was processed and analyzed using the prescription sequence symmetry analysis (PSSA) method.Results:126,680 inpatients who used iodixanol and were concurrently treated with anti-allergic drugs were analyzed. In the medical records of these patients, only 32 had documented iodixanol allergies. Statistical analysis identified 22 drugs in 4 categories—calcium preparations, adrenergic/dopaminergic agents, glucocorticoids, and antihistamines—as marker drugs. With time intervals of 3, 7, and 28 days, the adjusted sequence ratios (aSRs) for all anti-allergics and the 4 categories were greater than 1. The 7-day aSRs were 2.12 (95% CI: 2.08–2.15), 1.70 (95% CI: 1.68–1.73), 3.85 (95% confidence interval [CI]: 3.75–2.30), 2.30 (95% CI: 2.26–2.35), and 1.95 (95% CI: 1.89–2.02), respectively. The proportions of adverse drug events indicated by each signal were as follows: all anti-allergics (2.92%–3%), calcium gluconate (0.19%–0.52%), adrenergic/dopaminergic agents (2.20%–3.37%), glucocorticoids (3.13%–3.76%), and antihistamines (1.05%–1.32%).Conclusion:This first multi-center Chinese inpatient database study detected iodixanol-induced allergy signals, revealing that reactions may be much higher than those in collected spontaneous reports. Iodixanol risk exposure was closer to actual pharmaceutical care findings. PSSA application with ≤7-day intervals appears better suited for monitoring late allergic reaction signals with these drugs.

RationaleCOVID-19 pandemic has imposed tremendous stress and burden on the economy and society worldwide. There is an urgent demand to find a new model to estimate the deterioration of patients inflicted by Omicron variants.ObjectiveThis study aims to develop a model to predict the deterioration of elderly patients inflicted by Omicron Sub-variant BA.2.MethodsCOVID-19 patients were randomly divided into the training and the validation cohorts. Both Lasso and Logistic regression analyses were performed to identify prediction factors, which were then selected to build a deterioration model in the training cohort. This model was validated in the validation cohort.Measurements and main resultsThe deterioration model of COVID-19 was constructed with five indices, including C-reactive protein, neutrophil count/lymphocyte count (NLR), albumin/globulin ratio (A/G), international normalized ratio (INR), and blood urea nitrogen (BUN). The area under the ROC curve (AUC) showed that this model displayed a high accuracy in predicting deterioration, which was 0.85 in the training cohort and 0.85 in the validation cohort. The nomogram provided an easy way to calculate the possibility of deterioration, and the decision curve analysis (DCA) and clinical impact curve analysis (CICA)showed good clinical net profit using this model.ConclusionThe model we constructed can identify and predict the risk of deterioration (requirement for ventilatory support or death) in elderly patients and it is clinically practical, which will facilitate medical decision making and allocating medical resources to those with critical conditions.

ObjectiveThe aim of the present study is to assess the utility of C-reactive protein to Lymphocyte Ratio (CLR) in predicting short-term clinical outcomes of patients infected by SARS-CoV-2 BA.2.2.MethodsThis retrospective study was performed on 1,219 patients with laboratory-confirmed SARS-CoV-2 BA.2.2 to determine the association of CLR with short-term clinical outcomes. Independent Chi square test, Rank sum test, and binary logistic regression analysis were performed to calculate mean differences and adjusted odds ratios (aORs) with their 95% CI, respectively.ResultsOver 8% of patients admitted due to SARS-CoV-2 BA.2.2. were critically ill. The best cut-off value of CLR was 21.25 in the ROC with a sensitivity of 72.3% and a specificity of 86%. After adjusting age, gender, and comorbidities, binary logistic regression analysis showed that elevated CLR was an independent risk factor for poor short-term clinical outcomes of COVID-19 patients.ConclusionC-reactive protein to Lymphocyte Ratio is a significant predictive factor for poor short-term clinical outcomes of SARS-CoV-2 BA.2.2 inflicted patients.

BackgroundPeritoneal dissemination (PD) is the most common mode of metastasis for advanced gastric cancer (GC) with poor prognosis. It is of great significance to accurately predict preoperative PD and develop optimal treatment strategies for GC patients. Our study assessed the diagnostic potential of serum tumor markers and clinicopathologic features, to improve the accuracy of predicting the presence of PD in GC patients.MethodsIn our study, 1264 patients with GC at Fudan University Shanghai Cancer Center and Wenzhou people’s hospital from 2018 to 2020 were retrospectively analyzed, including 316 cases of PD and 948 cases without PD. All patients underwent enhanced CT scan or magnetic resonance imaging (MRI) before surgery and treatment. Clinicopathological features, including tumor diameter and tumor stage (depth of tumor invasion, nearby lymph node metastasis and distant metastasis), were obtained by imaging examination. The independent risk factors for PD were screened through univariate and multivariate logistic regression analyses, and the results were expressed with 95% confidence intervals (CIs). A model of PD diagnosis and prediction was established by using Cox proportional hazards regression model of training set. Furthermore, the accuracy of the prediction model was verified by ROC curve and calibration plots.ResultsUnivariate analysis showed that PD in GC was significantly related to tumor diameter (odds ratio (OR)=12.06, p

BackgroundThe complement system acts as an integral part of the innate immune response, which acts primarily to remove pathogens and injured cells. Emerging evidence has shown the activation of the immune regulatory function of complements in the tumor microenvironment (TME). We revealed the expression levels of various complements in human cancers and their role in tumor prognosis and immune infiltration.MethodsThe differential expression of complements was explored via the Tumor Immune Estimation Resource (TIMER) site and the Oncomine database. To investigate whether these differentially expressed complements have correlation with the prognosis of gastric cancer (GC) and colon cancer, their impact on survival was assessed using the PrognoScan database and Kaplan-Meier plotter. The correlations between complements and tumor immune-infiltrating levels and immune gene markers were statistically explored in TIMER based on Spearman’s correlation coefficients and p-values.ResultsIn two colon cancer cohorts, an increased expression level of DAF (CD55) has statistically significant correlation with poor disease-free survival (DFS). High C3, CR4, and C5aR1 expression levels were significantly related with poor prognosis in GC patients. In addition, C3, CR4, and C5aR1 expression was positively related to the tumor purity and infiltration levels of multiple immune cells in stomach adenocarcinoma (STAD). Moreover, the expression levels of C3, CR4, and C5aR1 were also strongly correlated with various immune marker sets, such as those of tumor-associated macrophages (TAMs), M1 and M2 macrophages, T cell exhaustion, Tregs, and DCs, in STAD. Additionally, CD55 has positive correlation with few immune cell infiltration levels in colon adenocarcinoma (COAD), but its correlation with immune marker sets was not statistically significant.ConclusionThese findings confirm the relationship between various complements and tumor prognosis and immune infiltration in colon cancer and GC. CD55 may serve as an indicator on the survival prognosis of patients with colon cancer. Furthermore, as biomarkers for poor prognosis in GC, complements C3, CR4, and C5aR1 may provide potential biological targets for GC immunotherapy.

Objective: This study aimed to explore the signal detection method for allergic reactions induced by inpatient iodixanol injection. Methods: A database of 3,719,217 hospitalized patients from 20 large Chinese general hospitals was processed and analyzed using the prescription sequence symmetry analysis (PSSA) method. Results: 126,680 inpatients who used iodixanol and were concurrently treated with anti-allergic drugs were analyzed. In the medical records of these patients, only 32 had documented iodixanol allergies. Statistical analysis identified 22 drugs in 4 categories--calcium preparations, adrenergic/dopaminergic agents, glucocorticoids, and antihistamines--as marker drugs. With time intervals of 3, 7, and 28 days, the adjusted sequence ratios (aSRs) for all anti-allergics and the 4 categories were greater than 1. The 7-day aSRs were 2.12 (95% CI: 2.08-2.15), 1.70 (95% CI: 1.68-1.73), 3.85 (95% confidence interval [CI]: 3.75-2.30), 2.30 (95% CI: 2.26-2.35), and 1.95 (95% CI: 1.89-2.02), respectively. The proportions of adverse drug events indicated by each signal were as follows: all anti-allergics (2.92%-3%), calcium gluconate (0.19%-0.52%), adrenergic/dopaminergic agents (2.20%-3.37%), glucocorticoids (3.13%-3.76%), and antihistamines (1.05%-1.32%). Conclusion: This first multi-center Chinese inpatient database study detected iodixanol-induced allergy signals, revealing that reactions may be much higher than those in collected spontaneous reports. Iodixanol risk exposure was closer to actual pharmaceutical care findings. PSSA application with =7-day intervals appears better suited for monitoring late allergic reaction signals with these drugs. [ABSTRACT FROM AUTHOR]