scholarly journals Virtual determination of sex: Estimating cut off value of digital metric traits of foramen magnum on three-dimensional computed tomography with receiver operating characteristic and logistic regression analysis

2021 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
HarishKumar S Agarwal ◽  
PardamanSingh Setia ◽  
Suryamani Pandey
Keyword(s):  
Computed Tomography ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Three Dimensional ◽  
Foramen Magnum ◽  
Metric Traits

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

Plasma YAP1 predicts esophageal varices and the risk of variceal bleeding in liver cirrhosis

2021 ◽  
Author(s):  
Wen Zhao ◽  
Ningning Xue ◽  
Po Cui ◽  
Lingdi Liu ◽  
Yiqi Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Liver Cirrhosis ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Esophageal Varices ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Receiver Operating

Aim: To explore the predictive value of plasma YAP1 for esophageal varices (EV) and high-risk EV (HRV) in patients with liver cirrhosis. Materials & methods: A total of 208 patients with liver cirrhosis were enrolled and categorized into four groups. Correlation analysis, logistic regression analysis and receiver operating characteristic curve analysis were performed to evaluate the diagnostic performance of plasma YAP1 for EV and HRV. Results: Plasma YAP1 levels were significantly elevated with the occurrence and progression of EV in cirrhotic patients. The multivariate logistic regression analysis revealed that plasma YAP1 is an independent predictor for EV and HRV. For predicting EV and HRV, the YAP1 cut-off values of 5.43 and 6.98 ng/ml yielded the area under the receiver operating characteristic curves of 0.944 and 0.955, respectively. Conclusion: Plasma YAP1 is a potential novel noninvasive biomarker for predicting EV and HRV in patients with liver cirrhosis.

Power contrast injections through a totally implantable venous power port: A retrospective multicenter study

2019 ◽  
Vol 35 (4) ◽  
pp. 268-272 ◽  
Author(s):  
Ryong seong Son ◽  
Yun Gyu Song ◽  
Jeonghyun Jo ◽  
Byeong-Ho Park ◽  
Gyoo-sik Jung ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Contrast Media ◽  
Logistic Regression Analysis ◽  
Computed Tomography Scan ◽  
Power Injection ◽  
Oncologic Patients ◽  
Computed Tomography Scans ◽  
Tomography Scan

Objectives To evaluate the feasibility and safety of power injection of contrast media through totally implantable venous power ports during computed tomography scans in oncologic patients. Methods The study population consisted of 417 patients who underwent computed tomography scan through a totally implantable venous power port. Clinical data were examined. Logistic regression analysis was used to assess the associations between clinical covariables and computed tomography scan failure. Results Successful computed tomography scans were achieved in 534 of 540 examinations (98.9%). Logistic regression analysis showed that contrast media above a 350 concentration was significantly associated with computed tomography scan failure (95% confidence interval: 1.01–1.13, p = 0.012). No major complications were noted. Conclusions Power injection of contrast media through a totally implantable venous power port for computed tomography examination is feasible and safe. This procedure provides an acceptable alternative in oncologic patients with inadequate peripheral intravenous access when computed tomography examination with contrast enhancement is needed.

scholarly journals Predictors for Development of Oxaliplatin-induced Peripheral Neuropathy in Cancer Patients as Determined by Ordered Logistic Regression Analysis

2021 ◽  
Author(s):  
Yuko Kanbayashi ◽  
Takeshi Ishikawa ◽  
Yoshiaki Kuriu ◽  
Yusuke Tabuchi ◽  
Eigo Otsuji ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Peripheral Neuropathy ◽  
Cancer Patients ◽  
Logistic Regression Analysis ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Ordered Logistic Regression ◽  
Number Of Cycles ◽  
Significant Factors

Abstract Purpose This retrospective study aimed to identify predictors for the development of oxaliplatin-induced peripheral neuropathy (OXAIPN). Methods Between January 2017 and March 2021, a total 322 cancer patients at our hospital who were receiving oxaliplatin were enrolled. For the regression analysis of factors associated with oxaliplatin-induced peripheral neuropathy, variables were extracted manually from medical charts. The level of OXAIPN was evaluated using the National Cancer Institute’s Common Terminology Criteria for Adverse Events (version 5). Multivariate ordered logistic regression analysis was performed to identify predictors for the development of OXAIPN. Optimal cut-off thresholds were determined using receiver operating characteristic (ROC) analysis. Values of P <0.05 (2-tailed) were considered significant. Results Significant factors identified included body mass index (BMI) (odds ratio [OR] = 1.06, 95% confidence interval [CI] = 1.00–1.12; P = 0.046), number of cycles (OR = 1.09, 95%CI = 1.05–1.14; P <0.0001), S-1 plus oxaliplatin (SOX) regimen (OR = 0.54, 95%CI = 0.32–0.92; P = 0.023), concomitant use of proton pump inhibitors (PPIs) (OR = 1.64, 95%CI = 1.05–2.58; P = 0.031) and concomitant use of analgesic adjuvant (OR = 3.30, 95%CI = 1.09–9.97; P = 0.035). Conclusion BMI, number of cycles, SOX regimen, concomitant use of PPIs and concomitant use of analgesic drugs were identified as significant predictors for the development of OXAIPN.

Determination of the factors affecting pre-service teachers’ technological self-efficacy beliefs by logistic regression analysis

2021 ◽  
Vol 9 (2) ◽  
pp. 481-488
Author(s):  
Nurcan Tekin ◽  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Personal Computer ◽  
Self Efficacy ◽  
Material Design ◽  
Efficacy Beliefs ◽  
Instructional Technologies ◽  
Factors Affecting

Technology transfer to classes has become very important for teachers, regardless of their field. The purpose of this study is to determine how pre-service teachers with high and low level of technological self-efficacy beliefs are predicted by the independent variables. These variables are attitude towards technology, attitude towards instructional technologies and material design course, having a personal computer. The study sample of this relational study consisted of 193 pre-service teachers. As data collection tools, Technological Self-Efficacy Belief Scale, Attitude towards Technology Scale, Attitude towards Instructional Technologies Scale, and a questionnaire developed by researchers were administered. Logistic regression analysis, which is used when the dependent variable is categorical, was employed in data analysis. According to analysis results, the variables of having a personal computer (Wald = 4.23, df = 1, p < 0.05), attitude towards technology (Wald = 13.66, df = 1, p < 0.01) and attitude towards instructional technologies and material design course (Wald = 6.17, df = 1, p < 0.01) had a significant effect on pre-service teachers’ technological self-efficacy beliefs. Particularly, the variable of having a personal computer significantly increased pre-service teachers’ technological self-efficacy beliefs by 20%. In this context, various recommendations were offered to the researchers, institutions and instructors.

Differential Diagnosis of Malignant Thyroid Calcification Nodule Based on Computed Tomography Image Texture

2021 ◽  
Vol 11 (3) ◽  
pp. 767-772
Author(s):  
Wenxian Peng ◽  
Yijia Qian ◽  
Yingying Shi ◽  
Shuyun Chen ◽  
Kexin Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Ensemble Learning ◽  
Logistic Regression Analysis ◽  
Texture Features ◽  
Support Vector ◽  
Classification Algorithms ◽  
Feature Optimization

Purpose: Calcification nodules in thyroid can be found in thyroid disease. Current clinical computed tomography systems can be used to detect calcification nodules. Our aim is to identify the nature of thyroid calcification nodule based on plain CT images. Method: Sixty-three patients (36 benign and 27 malignant nodules) found thyroid calcification nodules were retrospectively analyzed, together with computed tomography images and pathology finding. The regions of interest (ROI) of 6464 pixels containing calcification nodules were manually delineated by radiologists in CT plain images. We extracted thirty-one texture features from each ROI. And nineteen texture features were picked up after feature optimization by logistic regression analysis. All the texture features were normalized to [0, 1]. Four classification algorithms, including ensemble learning, support vector machine, K-nearest neighbor, decision tree, were used as classification algorithms to identity the benign and malignant nodule. Accuracy, PPV, NPV, SEN, and AUC were calculated to evaluate the performance of different classifiers. Results: Nineteen texture features were selected after feature optimization by logistic regression analysis (P <0.05). Both Ensemble Learning and Support Vector Machine achieved the highest accuracy of 97.1%. The PPV, NPV, SEN, and SPC are 96.9%, 97.4%, 98.4%, and 95.0%, respectively. The AUC was 1. Conclusion: Texture features extracted from calcification nodules could be used as biomarkers to identify benign or malignant thyroid calcification.

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