scholarly journals The significance of C-reactive protein for the prediction of net-adverse clinical outcome in patients with acute pulmonary embolism

2020 ◽  
Vol 77 (1) ◽  
pp. 35-40
Author(s):  
Rade Milic ◽  
Boris Dzudovic ◽  
Bojana Subotic ◽  
Slobodan Obradovic ◽  
Ivan Soldatovic ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Logistic Regression ◽  
Clinical Outcome ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Acute Pulmonary Embolism ◽  
Clinical Manifestations ◽  
Multivariate Logistic Regression Model ◽  
Significant Independent Predictor ◽  
End Point

Background/Aim. Acute pulmonary embolism (APE) may have different clinical manifestations. Also, its outcome can range from complete recovery to early death. Major bleeding (MB) as a due of the therapy also contributes to the overall adverse outcome. So far, it is unknown what the best predictors are for short-term mortality and MB among the several commonly used biomarkers. The aim of this study was to evaluate the significance of Creactive protein (CRP) and other biomarkers for the prediction of adverse clinical outcomes. Methods. This clinical, observational, retrospective-prospective study included 219 consecutive adult patients treated for APE. Results. Among 219 patients, 22 (10%) died within the first month after diagnosis. Twenty seven patients (12.3%) had at least one episode of MB. Composite end-point [netadverse clinical outcome (NACO)] was estimated in 47 (21.5%) of patients. The average values of all biomarkers were higher in the group of patient who died, and differences were statistically significant. Similar results were obtained for composite end-point. In terms of MB, none of biomarkers did not have significance, but CRP had a slight tendency toward significance. Results from univariate logistic regression model showed that troponin was statistically significant predictor of 30-day mortality. However, after adjusting for other variables, in multivariate logistic regression model troponin failed to be significant independent predictor of 30-day mortality. Unlike troponin, CRP and brain natriuretic peptide (BNP) were significant in all models ? uni and multivariate (they were independent predictors of 30-day mortality). Conclusion. CRP has a good predictive value for 30-day mortality and NACO, and potential for MB in patients treated for APE.

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 Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors

2020 ◽  
Vol 17 (13) ◽  
pp. 4852
Author(s):  
Pouya Gholizadeh ◽  
Behzad Esmaeili
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Odds Ratio ◽  
Logistic Regression Model ◽  
The Body ◽  
Occupational Accidents ◽  
Multivariate Logistic Regression Model ◽  
Ratio Analysis ◽  
Accident Scenarios ◽  
Significant Factors

The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most significant factors that can explain the fatality rates in different scenarios. Thereafter, this study performed an odds ratio analysis among significant factors to determine which factors increase the likelihood of fatality. For example, it was found that having a fatal accident is 4.4 times more likely when the source is a “vehicle” than when it is a “tool, instrument, or equipment”. After validating the consistency of the model, 105 accident scenarios were developed and assessed using the model. The findings revealed which severe accident scenarios happen commonly to people in this trade, with nine scenarios having fatality rates of 50% or more. The highest fatality rates occurred in “fencing, installing lights, signs, etc.” tasks in “alteration and rehabilitation” projects where the source of injury was “parts and materials”. The proposed analysis/modeling approach can be applied among all specialty contracting companies to identify and prioritize more hazardous situations within specific trades. The proposed model-development process also contributes to the body of knowledge around accident analysis by providing a framework for analyzing accident reports through a multivariate logistic regression model.

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.

Pretreatment metabolomic markers associated with therapeutic responses to FOLFOX with bevacizumab in chemotherapy-naive patients with colorectal cancer.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 540-540
Author(s):  
Yusuke Tanigawara ◽  
Shinji Sugimoto ◽  
Kei Muro
Keyword(s):  
Colorectal Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Treatment Response ◽  
Logistic Regression Model ◽  
Hazard Analysis ◽  
Serum Levels ◽  
Multivariate Logistic Regression Model ◽  
Pretreatment Serum ◽  
Therapeutic Responses

540 Background: FOLFOX with bevacizumab (BV) is a standard treatment for metastatic colorectal cancer (mCRC); however, its clinical response is around 50% and there is no way to predict responders prior to therapy. In this study, we attempted to identify new biomarkers associated with positive therapeutic responses by means of a comprehensive metabolomic analysis of patient serum. Methods: Serum collected from 68 mCRC patients, who were registered in a phase II study of first-line FOLFOX with BV treatment (Nishina, JJCO 2013) was used to conduct a comprehensive metabolomic quantification analysis using a capillary electrophoresis time-of-flight mass spectrometry. Responders (Rs: n = 37) and non-responders (NRs: n = 31) were defined as those achieving a status of CR/PR and SD/PD, respectively, which were assessed by an extramural review board using RECIST. Statistical analyses were performed using a logistic regression model for treatment response and Cox proportional hazard analysis for overall survival (OS). Results: Among 470 annotated endogenous metabolites, cysteine-glutathione disulphide (CSG), gamma-glutamyl cysteine (gGC) and hypoxanthine (HPX) were identified as significant (p < 0.05) metabolites associated with positive therapeutic responses by both response and survival analyses. Patients were divided into two groups according to cutoff values of pretreatment serum levels of these metabolites. The actual response rate in the high CSG, gGC and HPX group were 86, 69 and 8% whereas those in the low group were 40, 26 and 64%, respectively. The hazard ratio (HR) for OS in CSG, gGC and HPX were 0.28 (p < 0.01), 0.37 (p < 0.01) and 2.39 (p < 0.05), respectively. Using CSG, gGC and HPX, we have developed a multivariate logistic regression model to predict Rs/NRs and survival benefit based on the three metabolite levels in pretreatment serum. Sensitivity, specificity, ROC AUC and HR of OS between Rs and NRs were 89%, 65%, 0.83 and 0.36 (p = 0.002), respectively. Conclusions: We identified three novel pretreatment serum metabolomic markers that are associated with treatment response to FOLFOX with BV in chemotherapy-naive mCRC patients. Clinical trial information: UMIN000001490.

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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