scholarly journals Clinical Outcomes of Adult Patients Hospitalized with COVID-19 after Vaccination

2021 ◽  
Vol 6 (4) ◽  
pp. 175
Author(s):  
Markos Kalligeros ◽  
Fadi Shehadeh ◽  
Evangelia K. Mylona ◽  
Matthew Kaczynski ◽  
Saisanjana Kalagara ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Logistic Regression Model ◽  
Primary Outcome ◽  
Adult Patients ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Absolute Priority ◽  
Risk Of Death

Vaccination remains the most effective way to prevent COVID-19. The aim of the present study was to assess the incidence of COVID-19 hospitalizations after vaccination, as well as the effect of prior vaccination on hospitalization outcomes among patients with COVID-19. We analyzed and compared all consecutive patients, with or without prior vaccination, who were admitted to our hospital network due to COVID-19 from January to April 2021. Our primary outcome was to identify and describe cases of COVID-19 hospitalized after vaccination. We also utilized a multivariate logistic regression model to investigate the association of previous vaccination with hospitalization outcomes. We identified 915 consecutive patients hospitalized due to COVID-19 with 91/915 (10%) previously vaccinated with at least one dose of a COVID-19 vaccine. Utilizing our multivariate logistic regression model, we found that prior vaccination, regardless of the number of doses or days since vaccination, was associated with decreased mortality (aOR 0.44, 95% CI: 0.20–0.98) when compared to unvaccinated individuals. Our study showed that COVID-19 related hospitalization after vaccination may occur to a small percentage of patients, mainly those who are partially vaccinated. However, our findings underline that prior vaccination, even when partial, is associated with a decreased risk of death. Ongoing vaccination efforts should remain an absolute priority.

scholarly journals The impact of early neuraminidase inhibitor therapy on clinical outcomes in patients hospitalised with influenza A-related pneumonia: a multicenter, retrospective study

2020 ◽  
Author(s):  
Liang Chen ◽  
Xiudi Han ◽  
YanLi Li ◽  
Chunxiao Zhang ◽  
Xiqian Xing
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Influenza A ◽  
Logistic Regression Model ◽  
Neuraminidase Inhibitor ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Severe Influenza ◽  
The Impact

Abstract Background Guidelines emphasize prompt antiviral treatment in severe influenza patients. Although nearly a 50% of severe influenza present with pneumonia, the effect of early (≤ 2 days after illness onset) neuraminidase inhibitor (NAI) use on the clinical outcomes of influenza A-related pneumonia (FluA-p) has rarely been assessed. Furthermore, data about the administration of NAIs in the real-world management of Flu-p in China are limited.Methods: Data of patients hospitalised with FluA-p from five teaching hospitals in China from 1 January 2013 to 31 December 2018 were reviewed retrospectively. The impact of early NAI therapy on the outcomes in FluA-p patients, and the indications of early NAI administration by clinicians were evaluated by logistic regression analysis.Results: In total, 693 FluA-p patients were included. Of these patients, 33.5% (232/693) were treated early. After adjusting for weighted propensity scores for treatment, systemic corticosteroid and antibiotic use, a multivariate logistic regression model showed that early NAI therapy was associated with decreased risk for invasive ventilation [odds ratio (OR) 0.511, 95% confidence interval (CI) 0.312–0.835, p = 0.007) and 30-day mortality (OR 0.533, 95% CI 0.210–0.807, p < 0.001) in FluA-p patients. A multivariate logistic regression model confirmed early NAI use (OR 0.415, 95% CI 0.195–0.858, p = 0.001) was a predictor for 30-day mortality in FluA-p patients and a positive rapid influenza diagnostic test was the only indication (OR 3.586, 95% CI 1.259–10.219, p < 0.001) related to the prescription of early NAI by clinicians.Conclusions: Early NAI therapy is associated with better outcomes in FluA-p patients. Improved education and training of clinicians on the guidelines of influenza are needed.

scholarly journals The impact of early neuraminidase inhibitor therapy on clinical outcomes in patients hospitalised with influenza A-related pneumonia: a multicenter, retrospective study

2020 ◽  
Author(s):  
Liang Chen ◽  
Xiudi Han ◽  
YanLi Li ◽  
Chunxiao Zhang ◽  
Xiqian Xing
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Influenza A ◽  
Logistic Regression Model ◽  
Neuraminidase Inhibitor ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Severe Influenza ◽  
The Impact

Abstract Background Guidelines emphasize prompt antiviral treatment in severe influenza patients. Although nearly a 50% of severe influenza present with pneumonia, the effect of early (≤ 2 days after illness onset) neuraminidase inhibitor (NAI) use on the clinical outcomes of influenza A-related pneumonia (FluA-p) has rarely been assessed. And there is limited data about the administration of NAIs in the real-world management of Flu-p in China.Methods Data of patients hospitalised with FluA-p from five teaching hospitals in China from 1 January 2013 to 31 December 2018 were reviewed retrospectively. The impact of early NAI therapy on the outcomes in FluA-p patients, and the indications of early NAI administration by clinicians were evaluated by logistic regression analysis.Results Totally, 693 FluA-p patients were included. Of these patients, 33.5% (232/693) were treated early. After adjusting for weighted propensity scores for treatment, systemic corticosteroid and antibiotic use, a multivariate logistic regression model showed that early NAI therapy was associated with decreased risk for invasive ventilation [ odds ratio ( OR ) 0.511, 95% confidence interval (CI) 0.312–0.835, p = 0.007) and 30-day mortality ( OR 0.533, 95% CI 0.210–0.807, p < 0.001) in FluA-p patients. A multivariate logistic regression model confirmed early NAI use ( OR 0.415, 95% CI 0.195–0.858, p = 0.001) was a predictor for 30-day mortality in FluA-p patients and a positive rapid influenza diagnostic test was the only indication ( OR 3.586, 95% CI 1.259–10.219, p < 0.001) related to the prescription of early NAI by clinicians.Conclusions Early NAI therapy is associated with better outcomes in FluA-p patients. Improved education and training of clinicians on the guidelines of influenza are needed.

scholarly journals The impact of early neuraminidase inhibitor therapy on clinical outcomes in patients hospitalised with influenza A-related pneumonia: a multicenter, retrospective study

2020 ◽  
Author(s):  
Liang Chen ◽  
Xiudi Han ◽  
YanLi Li ◽  
Chunxiao Zhang ◽  
Xiqian Xing
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Influenza A ◽  
Logistic Regression Model ◽  
Neuraminidase Inhibitor ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Severe Influenza ◽  
The Impact

Abstract Background Guidelines emphasize prompt antiviral treatment in severe influenza patients. Although nearly a 50% of severe influenza present with pneumonia, the effect of early (≤ 2 days after illness onset) neuraminidase inhibitor (NAI) use on the clinical outcomes of influenza A-related pneumonia (FluA-p) has rarely been assessed. And there is limited data about the administration of NAIs in the real-world management of Flu-p in China. Methods Data of patients hospitalised with FluA-p from five teaching hospitals in China from 1 January 2013 to 31 December 2018 were reviewed retrospectively. The impact of early NAI therapy on the outcomes in FluA-p patients, and the indications of early NAI administration by clinicians were evaluated by logistic regression analysis. Results Totally, 693 FluA-p patients were included. Of these patients, 33.5% (232/693) were treated early. After adjusting for weighted propensity scores for treatment, systemic corticosteroid and antibiotic use, a multivariate logistic regression model showed that early NAI therapy was associated with decreased risk for invasive ventilation [ odds ratio ( OR ) 0.511, 95% confidence interval (CI) 0.312–0.835, p = 0.007) and 30-day mortality ( OR 0.533, 95% CI 0.210–0.807, p < 0.001) in FluA-p patients. A multivariate logistic regression model confirmed early NAI use ( OR 0.415, 95% CI 0.195–0.858, p = 0.001) was a predictor for 30-day mortality in FluA-p patients and a positive rapid influenza diagnostic test was the only indication ( OR 3.586, 95% CI 1.259–10.219, p < 0.001) related to the prescription of early NAI by clinicians. Conclusions Early NAI therapy is associated with better outcomes in FluA-p patients. Improved education and training of clinicians on the guidelines of influenza are needed.

Identification of characteristics associated with long-term survival in patients with metastatic urothelial carcinoma (mUC) who received durvalumab (D) with or without tremelimumab (T) in clinical studies.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 441-441
Author(s):  
Marie Alt ◽  
Carlos Stecca ◽  
Shaum Kabadi ◽  
Benga Kazeem ◽  
Srikala S. Sridhar
Keyword(s):  
Logistic Regression ◽  
Phase I ◽  
Regression Model ◽  
Clinical Studies ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Term Survival ◽  
Long Term Survival

441 Background: Immune checkpoint inhibitors (ICI) have changed the landscape of mUC, yet outcomes are variable as some patients (pts) do not respond to treatment while others have a durable response. To optimally select pts who may derive benefit from ICIs, predictive factors are required. This retrospective, post-hoc analysis evaluated pt characteristics to determine differences between short and long-term survivors among pts with mUC who received D (anti–PD-L1) with or without T (anti–CTLA-4) in two clinical studies. Methods: Pts with platinum-refractory mUC who received D monotherapy in the phase I/II study 1108 (10 mg/kg Q2W, up to 12 mo) or D+T in the phase I study 10 (D at 20 mg/kg + T at 1 mg/kg Q4W for 4 mo, then D at 10 mg/kg Q2W for 12 mo) were included. Pt characteristics, tumor characteristics, radiological assessments, and biological assessments were collected. The primary outcome measure was long-term overall survival (OS). Pts were categorized as OS ≥2 yrs (from 1st dose of study drug) or OS <2 yrs. A univariate analysis was conducted on each baseline characteristic to assess independent associations with long-term OS; a multivariate logistic regression model was employed including each variable with a p-value ≤0.1 as factors or covariates. Results: A total of 367 pts with mUC were included in the analysis: 88 (24.0%) had OS ≥2 yrs (range: 2.09–4.99) and 279 (76.0%) had OS <2 yrs (range: 0.03–1.98). Pts with OS ≥2 yrs had a significantly higher objective response rates than those with OS <2 yrs (71.6% vs 5.7%; p<0.0001) and a significantly longer duration of response (median 2.3 yrs vs 0.39 yrs; p<0.0001). The characteristics included in the multivariate logistic regression model are listed in the Table. Long-term OS was significantly associated with ECOG PS, PD-L1 status, baseline hemoglobin level, and baseline absolute neutrophils count. Conclusions: Our analyses show that several characteristics, including tumor response to treatment, are associated with long-term OS for pts with mUC treated with D or D+T. Further investigation into these and other characteristics may provide additional insights into long-term survival outcomes with ICIs. [Table: see text]

scholarly journals A nomogram for predicting mortality in patients with COVID-19 and solid tumors: a multicenter retrospective cohort study

2020 ◽  
Vol 8 (2) ◽  
pp. e001314
Author(s):  
Chao Liu ◽  
Li Li ◽  
Kehan Song ◽  
Zhi-Ying Zhan ◽  
Yi Yao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Solid Tumors ◽  
Neutrophil Count ◽  
Mortality Risk ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Lemeshow Test ◽  
Wbc Count

BackgroundIndividualized prediction of mortality risk can inform the treatment strategy for patients with COVID-19 and solid tumors and potentially improve patient outcomes. We aimed to develop a nomogram for predicting in-hospital mortality of patients with COVID-19 with solid tumors.MethodsWe enrolled patients with COVID-19 with solid tumors admitted to 32 hospitals in China between December 17, 2020, and March 18, 2020. A multivariate logistic regression model was constructed via stepwise regression analysis, and a nomogram was subsequently developed based on the fitted multivariate logistic regression model. Discrimination and calibration of the nomogram were evaluated by estimating the area under the receiver operator characteristic curve (AUC) for the model and by bootstrap resampling, a Hosmer-Lemeshow test, and visual inspection of the calibration curve.ResultsThere were 216 patients with COVID-19 with solid tumors included in the present study, of whom 37 (17%) died and the other 179 all recovered from COVID-19 and were discharged. The median age of the enrolled patients was 63.0 years and 113 (52.3%) were men. Multivariate logistic regression revealed that increasing age (OR=1.08, 95% CI 1.00 to 1.16), receipt of antitumor treatment within 3 months before COVID-19 (OR=28.65, 95% CI 3.54 to 231.97), peripheral white blood cell (WBC) count ≥6.93 ×109/L (OR=14.52, 95% CI 2.45 to 86.14), derived neutrophil-to-lymphocyte ratio (dNLR; neutrophil count/(WBC count minus neutrophil count)) ≥4.19 (OR=18.99, 95% CI 3.58 to 100.65), and dyspnea on admission (OR=20.38, 95% CI 3.55 to 117.02) were associated with elevated mortality risk. The performance of the established nomogram was satisfactory, with an AUC of 0.953 (95% CI 0.908 to 0.997) for the model, non-significant findings on the Hosmer-Lemeshow test, and rough agreement between predicted and observed probabilities as suggested in calibration curves. The sensitivity and specificity of the model were 86.4% and 92.5%.ConclusionIncreasing age, receipt of antitumor treatment within 3 months before COVID-19 diagnosis, elevated WBC count and dNLR, and having dyspnea on admission were independent risk factors for mortality among patients with COVID-19 and solid tumors. The nomogram based on these factors accurately predicted mortality risk for individual patients.

scholarly journals Early Prediction of the Carbapenem Resistance Gram-negative Bacteria Carriage in Intensive Care Unit using Machine-Learning

2020 ◽  
Author(s):  
Qiqiang Liang ◽  
Qinyu Zhao ◽  
Xin Xu ◽  
Yu Zhou ◽  
Man Huang
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Carbapenem Resistance ◽  
Multivariate Logistic Regression Model ◽  
Gram Negative Bacteria ◽  
Multivariate Logistic Regression ◽  
Gram Negative ◽  
Better Than

Abstract Background The prevention and control of carbapenem-resistance gram-negative bacteria (CR-GNB) is the difficulty and focus for clinicians in the intensive care unit (ICU). This study construct a CR-GNB carriage prediction model in order to predict the CR-GNB incidence in one week. Methods The database is comprised of nearly 10,000 patients. the model is constructed by the multivariate logistic regression model and three machine learning algorithms. Then we choose the optimal model and verify the accuracy by daily predicted and recorded the occurrence of CR-GNB of all patients admitted for 4 months. Results There are 1385 patients with positive CR-GNB cultures and 1535 negative patients in this study. Forty-five variables have statistical significant differences. We include the 17 variables in the multivariate logistic regression model and build three machine learning models for all variables. In terms of accuracy and the area under the receiver operating characteristic (AUROC) curve, the random forest is better than XGBoost and multivariate logistic regression model, and better than decision tree model (accuracy: 84% >82%>81%>72%), (AUROC: 0.9089 > 0.8947 ≈ 0.8987 > 0.7845). In the 4-month prospective study, 81 cases were predicted to be positive in CR-GNB culture within 7 days, 146 cases were predicted to be negative, 86 cases were positive, and 120 cases were negative, with an overall accuracy of 84% and AUROC of 91.98%. Conclusions Prediction models by machine learning can predict the occurrence of CR-GNB colonization or infection within a week period, and can real-time predict and guide medical staff to identify high-risk groups more accurately.

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