Quantitative assessment of the effects of outside temperature on farrowing rate in gilts and sows by using a multivariate logistic regression model

2018 ◽  
Vol 89 (8) ◽  
pp. 1187-1193 ◽  
Author(s):  
Yosuke Sasaki ◽  
Madoka Fujie ◽  
Shingo Nakatake ◽  
Tadahiro Kawabata
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Quantitative Assessment ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Farrowing Rate

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.

scholarly journals Seroprevalence of Herpes Simplex Virus Type 2 and Human Herpesvirus Type 8 among Prostate Cancer Patients Compared to Cancer-Free Men

2021 ◽  
Vol 14 (4) ◽  
Author(s):  
Solmaz Ohadian Moghadam ◽  
Kamyar Mansori ◽  
Mohammad Reza Nowroozi ◽  
Davoud Afshar ◽  
Ali Nowroozi
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Human Herpesvirus ◽  
Multivariate Logistic Regression Model ◽  
Statistical Association ◽  
Multivariate Logistic Regression ◽  
Simplex Virus ◽  
Significant Statistical Association

Background: As one of the most prevalent cancers in men, prostate cancer is a condition with multiple causes. Viral infections have been identified as one of the major sources of elevated incidence of prostate cancer. Objectives: The purpose of this research was to assess the association of the risk of prostate cancer and its aggressiveness with seropositivity of herpes simplex virus 2 (HSV-2) and/or human herpesvirus 8 (HHV-8). Methods: Totally, 103 men with prostate cancer as cases and 81 healthy individuals as controls were included in this case-control analysis and provided a serum sample. The specific IgG antibodies against HSV-2 and HHV-8 were screened by enzyme-linked immunosorbent assay (ELISA). To determine the association between HSV-2, HHV-8, prostate-specific antigen (PSA) level, and demographic variables with incidence of prostate cancer, univariate and multivariate logistic regression models were applied. Results: The results of the univariate logistic regression model showed a statistically significant association between HSV-2 and HHV-8 seropositivity, PSA level, age, and smoking with prostate cancer incidence (P ≤ 0.20). The multivariate logistic regression model results after adjusting for the potential confounding variables showed a significant statistical association between the mean of PSA level [adjusted odds ratio (OR): 3.44; 95% CI: 2.15 - 5.51; P < 0.001) and incidence of prostate cancer. Moreover, the results of univariate and multivariate logistic regression model showed a significant statistical association between age [adjusted OR: 0.88; 95% confidence interval (CI): 0.81 - 0.95; P = 0.001] and HSV-2 and also significant statistical association was found between PSA (adjusted OR: 1.02; 95% CI: 1.005 - 1.03; P = 0.006) and HHV-8. Conclusions: Although the seroprevalence of HSV-2 and HHV-8 was higher in patients with prostate cancer than in the control group, it cannot be concluded that there is a significant association between the seropositivity of these viruses and prostate cancer incidence. However, the findings showed a significant statistical association between age and seropositivity of HSV-2 and also a significant statistical association between PSA levels and seropositivity of HHV-8.

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