scholarly journals Effect of COVID-19 Vaccination on Seizures in Patients With Epilepsy: a Multicenter, Retrospective Study

2021 ◽  
Author(s):  
Xiqin Fang ◽  
Shan Qiao ◽  
Ranran Zhang ◽  
Tingting Yang ◽  
Zhihao Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Statistical Significance ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
P Value ◽  
Binary Logistic Regression Analysis ◽  
Patients With Epilepsy ◽  
Vaccination Period

Abstract Background: Previous study have shown that seizures may occur as a result of vaccination. This study aimed to evaluate the risk and correlative factors of seizures in patients with epilepsy (PWE) after being vaccinated with COVID-19 and to provide reference opinions for PWE to receive COVID-19 vaccine.Methods: We retrospectively enrolled PWE patients who were vaccinated against COVID-19 in the epilepsy centers of nine hospitals in China. The binary logistic regression analysis included variables with a P-value less than 0.1 in the univariate analysis.Results: The study included 290 patients, of which 40 (13.8%) developed seizures within 14 days after vaccination, whereas 250 (86.2%) remained seizure-free. The binary logistic regression analysis revealed statistical significance in seizures within three months before vaccination (P<0.001, OR=10.121, 95% CI: 4.301-23.816) and withdrawal or reduction of anti-seizures medications (ASM) during the peri-vaccination period (P=0.027, OR=4.452, 95% CI: 1.182-16.768). In addition, 32 of 33 patients (97.0%) who were seizure-free within three months before vaccination and had normal EEG results before vaccination did not have any seizures within 14 days following vaccination.Conclusions: SARS-CoV-2 may induce epilepsy through an inflammatory cascade. It is recommended to provide the COVID-19 vaccine to seizure-free patients for at least three months before vaccination, and the vaccination is safer if EEG result is normal. During peri-vaccination period, all PWE should be prohibited from reducing ASM dosage. PWE with well-controlled seizures who have discontinued ASM might consider resuming ASM during the peri-vaccination period if their EEG results are aberrant.

scholarly journals The Value of Inflammatory Cell Count and Its Ratio in the Diagnosis of Prostate Cancer

2020 ◽  
Author(s):  
Wanli Duan ◽  
Boyu Xu ◽  
Qian Deng ◽  
Peng Tie ◽  
Yongyi Cheng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cell Count ◽  
Influencing Factors ◽  
Logistic Regression Analysis ◽  
Inflammatory Cell ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis

Abstract Background: Inflammatory cells play an important role in the occurrence and development of cancer. In recent years, the value of inflammatory cell count and its ratio in the diagnosis of prostate cancer has been controversial.Methods: A retrospective analysis of 475 patients with transrectal prostate puncture with TPSA>4 ng/ml in the Third Affiliated Hospital of Xi’an Jiaotong University. Univariate analysis, multivariate analysis and Receiver Operating Characteristics curve analysis were performed to analysis the factor of age, TPSA, FPSA, PV, NC, LC, PC, NLR, PLR in the diagnosis value of prostate cancer, and further analysis the value of inflammatory cell count and its ratio in different TPSA groups of prostate cancer (4 ng/ml <TPSA ≤ 10 ng/ml, 10 ng/ml ＜TPSA ≤ 20 ng/ml, TPSA ＞ 20 ng/ml). Results: The results of Univariate analysis in the overall data showed that Age, TPSA, PV, NC, PLC were influencing factors in the diagnosis of prostate cancer. The Area Under the Curve (AUC) of NC was 56.2% with a sensitivity of 72.2% and specificity of 41.5% in the cut -off point of 4.52 (p = 0.021). The PLC cut-off point of 205.5 gives 55.6% AUC with 67.2% sensitivity and 4.2% specificity. Multivariate binary logistic regression analysis results showed that Age, TPSA, PV, NC were independent influencing factors for the diagnosis of prostate cancer; In different TPSA group studies, it was found that NC, NLR, and PLR were valuable for the diagnosis of prostate cancer when TPSA>20 ng/ml，at the NC cut-off point of 4.52 with 67.3% AUC, 71.2% sensitivity and 62.5% specificity(p＜0.001), at the NLR cut-off point of 3.14 with 65.4% AUC, 67.2% sensitivity and 61.1% specificity (p＜0.001), and at the PLR cut-off point of 135.8 with 62.8% AUC , 57.6% sensitivity and 66.7% specificity (p＜0.001). Multivariate binary logistic regression analysis results showed that when TPSA>20 ng/ml, the higher Age, TPSA , Low PV and NC are independent risk factors affecting the diagnosis of prostate cancer. Conclusion: NC has promising value in predicting prostate cancer, especially when TPSA>20 ng/ml. A further prospective study in validating its diagnostic value was needed.

Contribution of the Heparin Neutralization Step to the Anti-PF4 ELISA.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3981-3981
Author(s):  
Jeffrey S. Dlott ◽  
Nadia N. Amen ◽  
Ruth A. Frock ◽  
Peter P. Chou ◽  
Monica V. Gallivan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Drug Exposure ◽  
Statistical Significance ◽  
Binary Logistic Regression ◽  
Serotonin Release ◽  
Thrombin Inhibitor ◽  
Binary Logistic Regression Analysis ◽  
Heparin Neutralization

Abstract The combination of anti-PF4 ELISA and serotonin release assay (SRA) has a reported sensitivity approximating 100% for diagnosing heparin-induced thrombocytopenia (HIT) - 7th ACCP Chest Supplement. Current therapeutic guidelines are to initiate therapy with a direct thrombin inhibitor (DTI) based on clinical parameters and to use HIT assays for diagnostic confirmation. Consequently, timely lab data may potentially minimize unnecessary drug exposure, reduce cost, and decrease morbidity. Due to test complexity, the ELISA results consistently precede the SRAs - the “gold standard” for HIT testing. This study examines the predictive value of the optical density (OD) reading and heparin neutralization procedure (100 IU/ml) for determining positive SRA. 200 patients with orders for the anti-PF4 ELISA (GTI) were retested with and without 100 IU of unfractionated heparin. Confirmation by heparin neutralization was reported as % inhibition (&gt;50% inhibition = positive) as calculated by manufacturer’s instructions. Negative SRA Positive SRA Negative, OD/0.4 34 2 Positive, OD/0.4 120 44 The sensitivity and specificity of the ELISA was 95.6% and 21.6% respectively. The heparin neutralization step improved the specificity to 38.6% and did not alter the sensitivity. Figure 1 shows a single variable binary logistic regression analysis demonstrating the relationship between OD and probability for a positive SRA. An additional binary logistic regression analysis showed no statistical significance for the % neutralization with heparin. A receiver operators curve (ROC) (Figure 2)demonstrating an optimal sensitivity/specificity was generated for both the neat and the heparin neutralized samples. The curves were virtually identical with the same OD cut-off of 1.008. The specificity was nominally improved with the neutralization procedure. Figure 1 Figure 1. Figure 2 Figure 2. The heparin neutralization step only marginally improves the specificity of the anti-PF4 ELISA and does not correlate with a positive SRA result, and therefore, does not justify the added expense. Higher ELISA OD measurements correlate significantly with the probability of a positive SRA.

scholarly journals Willingness for COVID-19 Vaccination and Influencing factors in Nurses from Tangshan City, China: A Cross-sectional Study

2021 ◽  
Author(s):  
Yunlong Wang ◽  
Lihuan Dong ◽  
Yajing Liang ◽  
Zonghai Guo ◽  
Jie Cheng ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Influencing Factors ◽  
Logistic Regression Analysis ◽  
Univariate Analysis ◽  
Herd Immunity ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Neutral Position ◽  
Cross Sectional

Abstract Background: To understand the willingness of nurses from Tangshan City to undergo Coronavirus disease 2019 (COVID-19) vaccination and identify its influencing factors.Methods: In January 2021, 255 nurses from five medical institutions across Tangshan City were investigated through convenience sampling on a network platform of Sojump. Furthermore, SPSS Statistics v24.0 software was used for general descriptive analysis, rank-sum test and binary logistic regression analysis to analyse willingness rates for COVID-19 vaccination and corresponding influencing factors.Results: A total of 255 valid questionnaires were collected (recovery rate, 85.71%). Among the 255 nurses surveyed, the willingness rate was 67.8%. The major reasons for willingness to undergo COVID-19 vaccination included a high-risk work environment necessitating COVID-19 vaccination (87.9%), expected benefits to themselves and others (72.8%) and worries regarding COVID-19 infection (66.5%). Meanwhile, reasons for unwillingness to undergo COVID-19 vaccination in 82 nurses included concerns regarding safety (89.0%), effectiveness (62.2%) and the possibility of fake vaccines (32.9%). Furthermore, among the 255 nurses, 43.9% were willing to recommend COVID-19 vaccination (n = 112), with the main reasons being prevention of COVID-19 infection (100.0%) and herd immunity via universal vaccination (90.2%). Moreover, the main reasons for unwillingness to recommend COVID-19 vaccination included poor popularity and wait-and-see attitude (85.3%) and hesitancy to recommend given the absence of COVID-19 vaccines (67.1%). With regard to attitude towards COVID-19 vaccine marketing, 149 (58.5%) nurses expressed favour, 101 (39.6%) nurses took a neutral position and 5 (1.9%) expressed displeasure. Univariate analysis revealed that male nurses, as well as those who actively inquired and sought information on COVID-19 vaccines, had significantly higher willingness for vaccination compared to female nurses and those not actively seeking information (P < 0.05). Meanwhile, binary logistic regression analysis indicated that gender, degree of education, working years and active inquiry or search for vaccine information were associated with willingness for COVID-19 vaccination (P < 0.05).Conclusion: This study highlights the need for further improving the willingness of nurses from Tangshan City to undergo and recommend COVID-19 vaccination. Relevant departments should therefore publicise information regarding COVID-19 vaccines for medical staff to ensure the safe and effective promotion thereof.

scholarly journals Impact of Estradiol Monitoring on the Prediction of Intrauterine Insemination Outcome

2013 ◽  
Vol 4 (3) ◽  
pp. 88-92
Author(s):  
Remzi Atilgan ◽  
Mustafa Ekinci ◽  
Ekrem Sapmaz ◽  
Zehra Sema Ozkan
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Ovarian Stimulation ◽  
Intrauterine Insemination ◽  
Statistical Significance ◽  
Binary Logistic Regression ◽  
Estradiol Level ◽  
Binary Logistic Regression Analysis ◽  
Estradiol Levels

ABSTRACT Purpose To evaluate the predictive value of estradiol levels on the day of human chorionic gonadotropin (hCG) administration on intrauterine insemination success rate. Materials and methods The present study included 200 intrauterine insemination (IUI) cycles performed between June 2011 and October 2012. All IUI cycles were preceded by ovarian stimulation with gonadotropins starting on cycle day 3. A single IUI was performed 24 to 36 hours after hCG administration. Binary logistic regression analysis was performed to define the covariates of IUI success. The main outcome measure, the clinical pregnancy rate per cycle, was assessed according to the estradiol level. Results With LR ± 2 and AUC=0.73, ROC analysis revealed out the estradiol level as 465 pg/ml to predict the pregnancy with 60% sensitivity and 66% specificity. Binary logistic regression analysis identified the presence of estradiol levels higher than 465 pg/ml (p < 0.01, 95% CI = 0.147 – 0.687) and stimulation duration (p < 0.01, 95% CI = 0.201 – 0.705) as the covariates approached statistical significance for IUI success. Conclusion Estradiol level >465 pg/ml on the day of hCG administration might point out advanced outcome on mild ovarian stimulation combined with insemination. How to cite this article Atilgan R, Ekinci M, Sapmaz E, Ozkan ZS. Impact of Estradiol Monitoring on the Prediction of Intrauterine Insemination Outcome. Int J Infertility Fetal Med 2013;4(3):88-92.

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.

scholarly journals Predicting organizational performance from motivation in Oromia Seed Enterprise Bale branch

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Askalech Feyisa Jobira ◽  
Abdulnasir Abdulmelike Mohammed
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Intrinsic Motivation ◽  
Organizational Performance ◽  
Logistic Regression Analysis ◽  
Extrinsic Motivation ◽  
Binary Logistic Regression ◽  
Moderate Level ◽  
Binary Logistic Regression Analysis ◽  
Study Results

AbstractMotivation is one of the most researched yet crucial topics in academia from various perspectives. Despite this, researches show mixed results about the effect of extrinsic motivation on intrinsic motivation and organizational performance. Studies in Ethiopia also lack causal analysis and theoretical underpinning that made contributions from academia very little. Hence, this research is important to assess the effect of extrinsic motivation on intrinsic motivation and organizational performance from a cognitive evaluation theory perspective. The researchers adopted an explanatory research design with a quantitative approach. The entire 119 employees of the Oromia Seed Enterprise, Bale branch were included in the study to collect primary data through a close-ended questionnaire. The collected data was processed by SPSS software version 20. The relationship analysis was addressed by correlation and binary logistic regression analysis. Seen from extrinsic and intrinsic motivation aspects, the findings of the study showed that Oromia Seed Enterprise had a moderate level of organizational performance and a moderate level of employees’ motivation. The correlation analysis result indicated that employees’ extrinsic and intrinsic motivation had a positive relationship with organizational performance. The binary logistic regression analysis also indicated that extrinsic and intrinsic motivation had a positive and significant influence on organizational performance. However, the interaction effect of intrinsic and extrinsic motivation on organizational performance was not significant, implying the absence of influence when both intrinsic and extrinsic motivations happen at the same time. Finally, the study results have a theoretical contribution for compensating the lack of actual experience in the Ethiopian organization’s context. Equally, the understanding of the moderated relationship among the study variables may encourage Oromia Seed Enterprise and its managers to develop a practical motivation system, which entertains the complex interaction of motivation variables to improve organizational performance. In addition, studies of this nature can inform policymakers to strengthen an incentive system as well as other motivation veins in the Ethiopian public organizations.

scholarly journals Binary Logistic Regression Analysis of Variables That Influence Poverty in Central Java

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Sendi Nugraha Nurdiansah ◽  
Laelatul Khikmah
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Binary Logistic Regression ◽  
Equation Model ◽  
Binary Logistic Regression Analysis ◽  
Poor People ◽  
Regional Government ◽  
Central Java ◽  
The Government

The phenomenon of poverty is a serious problem faced by almost every country in the world. This is because poverty can affect various aspects of people's lives. One of the causes of poverty is due to lack of income and assets to meet basic needs such as food, clothing, housing, health level and acceptable education. In addition, poverty occurs because of the powerlessness of society to get out of the problems it faces. The Central Java regional government incorporated poverty issues into the Regional Medium-Term Development Plan (RPJMD) because Central Java has a high number of poor people. This was done as an effort by the Central Java government to reduce poverty. Therefore, research is needed to find out the variables that most influence poverty in order to assist the government in developing the RPJMD. To find out what factors influence poverty in Central Java with the dichotomous categorical response variable, binary logistic regression analysis was used. The results showed that based on the analysis conducted did not obtain a logistic regression equation model because there were no significant parameters because there were no variables that had a sig value <0.05. Existing variables are Number of Population, Female Head of Household, Number of Children not in School, Number of Disabled Individuals, Number of Chronic Disease Individuals, Unemployment, Non-Electricity Lighting Sources, Unprotected Drinking Water Sources, Kerosene and Wood Cooking Fuels, Location Facilities Defecation (BAB) Not Available, so there are no variables that affect the level of poverty in Central Java Province.

scholarly journals Factors affecting internet use among university students in Sarawak, Malaysia: an empirical study

2020 ◽  
Vol 7 (3) ◽  
pp. 848
Author(s):  
M. Mizanur Rahman ◽  
M. Taha Arif ◽  
Fready Luke ◽  
Santha Letchumi ◽  
Fatin Nabila ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
University Students ◽  
Logistic Regression Analysis ◽  
Internet Use ◽  
Binary Logistic Regression ◽  
Problematic Internet Use ◽  
The Internet ◽  
Binary Logistic Regression Analysis ◽  
Factors Affecting

Background: The internet has become an indispensable tool for communication, academic research, information and entertainment. However, heavy users of the internet lead to less confidence in social skills and the tendency to be isolated. The study aimed to assess the pattern of internet use and factors affecting problematic internet use among university students.Methods: This cross-sectional study conducted among the students of a university in Sarawak, Malaysia. A multistage cluster sampling technique was adapted to select the participants. Data were collected from 463 students by self-administered questionnaire. Hierarchical binary logistic regression analysis was done to determine the potential factors for problematic internet use.Results: The mean age of the students was 22 years, with a standard deviation of 1.6 years. Two-fifths (61.8%) of the students had no problematic internet use. However, 35.4% had moderate and 2.8% had severe problematic internet use. Hierarchical binary logistic regression analysis found that age of the students, year of study, duration of daily internet use and use of social networking like Skype appeared to be potential predictors of problematic internet use (p<0.05).Conclusions: This study was conducted in only one university, thus did not depict the overall scenarios of the country. The implications of the findings are still worth noting in the process of designing internet addiction studies among university students. Overall, this study has unearthed some useful insights which can serve as a guide to more elaborate studies.

Clinical Factors Associated with the Diagnosis of Granulomatosis with Polyangiitis

2017 ◽  
Vol 156 (3) ◽  
pp. 484-488 ◽  
Author(s):  
Erdem Eren ◽  
Toygar Kalkan ◽  
Seçil Arslanoğlu ◽  
Mustafa Özmen ◽  
Kazım Önal ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Nasal Mucosa ◽  
Odds Ratio ◽  
Granulomatosis With Polyangiitis ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Significant Difference ◽  
History Of

Objective To determine the predictive value of nasal endoscopic findings and symptoms in the diagnosis of granulomatosis with polyangiitis (GPA). Study Design A cross-sectional study. Setting A tertiary university hospital. Subjects and Methods A total of 116 adults were enrolled in the study: 19 patients with GPA, 29 patients with other rheumatic diseases, and 68 healthy volunteers. All patients were examined with a flexible endoscope, and nasal endoscopic images were recorded and evaluated blindly. The medical history of each patient was taken by a physician blinded to the patient’s diagnosis. Results Univariate analysis indicated a statistically significant difference in rhinorrhea ( P = .002), postnasal drip ( P = .015), epistaxis ( P < .001), and saddle nose ( P = .017). However, binary logistic regression analysis demonstrated that only history of epistaxis ( P = .012; odds ratio, 5.6) was statistically significant in predicting GPA. Univariate analysis showed a statistically significant difference in nasal secretion ( P = .028), nasal septal perforation ( P < .017), nasal crusting ( P < .001), nasal adhesion ( P < .001), nasal granuloma ( P = .017), and hemorrhagic fragile nasal mucosa ( P < .001). A binary logistic regression analysis demonstrated that only hemorrhagic fragile nasal mucosa ( P < .001; odds ratio, 52.9) was a statistically significant predictor of GPA. Conclusions Given the results of this study, we believe that hemorrhagic fragile nasal mucosa and history of recurrent epistaxis may put patients at risk for GPA and should be investigated accordingly.

scholarly journals Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies

2018 ◽  
Vol 46 (9) ◽  
pp. 3656-3664 ◽  
Author(s):  
Wenbo Xu ◽  
Yang Zhao ◽  
Shiyan Nian ◽  
Lei Feng ◽  
Xuejing Bai ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Density Lipoprotein ◽  
Binary Logistic Regression ◽  
Lipoprotein Cholesterol ◽  
Binary Logistic Regression Analysis ◽  
Confounding Factors ◽  
Analysis Strategies ◽  
Glutamyl Transferase

Objective To investigate the importance of controlling confounding factors during binary logistic regression analysis. Methods Male coronary heart disease (CHD) patients (n = 664) and healthy control subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric acid, cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100, lipoprotein a, homocysteine, total bilirubin, direct bilirubin, indirect bilirubin, and γ-glutamyl transferase. Associations between these indexes and CHD were assessed by logistic regression, and results were compared by using different analysis strategies. Results 1) Without controlling for confounding factors, 14 indexes were directly inputted in the analysis process, and 11 indexes were finally retained. A model was obtained with conflicting results. 2) According to the application conditions for logistic regression analysis, all 14 indexes were weighed according to their variances and the results of correlation analysis. Seven indexes were finally included in the model. The model was verified by receiver operating characteristic curve, with an area under the curve of 0.927. Conclusions When binary logistic regression analysis is used to evaluate the complex relationships between risk factors and CHD, strict control of confounding factors can improve the reliability and validity of the analysis.

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