scholarly journals Predictive Factors of Legionella pneumophila Contamination in Cooling Tower Water

2014 ◽  
Vol 7 ◽  
pp. ASWR.S12972
Author(s):  
Pipat Luksamijarulkul ◽  
Sumawadee Kornkrerkkiat ◽  
Chayaporn Saranpuetti ◽  
Dusit Sujirarat
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Water Temperature ◽  
Predictive Factors ◽  
Legionella Pneumophila ◽  
Water Samples ◽  
Cooling Tower ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Tower Model

A cross-sectional study of 160 water samples collected from 72 cooling towers in 4 hospitals, 7 department stores, and 3 hotels in Bangkok was carried out to investigate Legionella pneumophila contamination and its predictive factors. All water samples were cultured for Legionella spp. and tested for L. pneumophila by real-time polymerase chain reaction (PCR). Some cooling tower parameters were measured and recorded. Data were analyzed using χ 2 -test, odds ratio and stepwise logistic regression analysis at the significant level of α = 0.05. Results revealed that the Legionella spp. contamination was 20.0% (32/160) and for L. pneumophila was 61.3% (98/160). The sensitivity of real-time PCR was higher than that of the culture. Factors significantly associated with L. pneumophila contamination by χ 2 -test were: the cooling tower model, size, use duration, pH, water temperature, use of ozone, and residual free chlorine (95% CI of OR > 1.0, P < 0.05). After stepwise logistic regression analysis, four predictive factors remained. These included the cooling tower model being a cross-flow type (adjusted OR = 3.1, 95% CI = 1.2-7.8, P = 0.017), use duration >5 years (adjusted OR = 3.6, 95% CI = 1.3-10.1, P = 0.016), water temperature <29.4°C (adjusted OR = 7.9, 95% CI = 2.1-29.6, P = 0.002), and residual free chlorine <0.2 ppm (adjusted OR = 8.5, 95% CI = 2.1-34.9, P = 0.003). Additionally, the risk probability for L. pneumophila contamination was estimated to be 13.9-97.1%, depending on the combination of predictive factors.

scholarly journals Allogeneic Red Blood Cell Transfusion Rate and Risk Factors in Elderly Patients With Femoral Neck Fracture After Hemiarthroplasty

2020 ◽  
Author(s):  
Rui Yue ◽  
Minghui Yang ◽  
Xiaohui Deng ◽  
Ping Zhang
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Femoral Neck ◽  
Elderly Patients ◽  
Blood Cell ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Neck Fracture ◽  
Multivariate Stepwise Logistic Regression

Abstract Objective: To determine the rate and risk factors of allogeneic red blood cell transfusions (ABT) in elderly patients with femoral neck fracture (FNF) after hemiarthroplasty (HA). Methods: We conducted a study on elderly patients (≥65 years old) who were admitted to the geriatric trauma orthopedics ward of Beijing Jishuitan Hospital for HA treatment from March 2018 to February 2019 due to FNF. The perioperative data were collected retrospectively, and univariate and multivariate stepwise logistic regression analysis were performed to determine the postoperative ABT rate and its risk factors. Results: 343 patients were included in the study, of which 151 (44.0%) received ABT after surgery. Multivariate stepwise logistic regression analysis showed that preoperative low hemoglobin (Hb), high intraoperative blood loss (IBL), advanced age, and low body mass index (BMI) are independent risk factors of ABT after HA in elderly FNF patients. Conclusion: ABT after HA is a common phenomenon in elderly patients with FNF. Their postoperative ABT needs are related to preoperative low Hb, high IBL, advanced age, and low BMI. Therefore, ABT can be reduced from the above aspects.

scholarly journals A Stepwise Logistic Regression Analysis: An application toward Poultry Farm Data in Johor

2018 ◽  
Vol 7 (3.28) ◽  
pp. 68
Author(s):  
Siti Afiqah Muhamad Jamil ◽  
Mohd Asrul Affendi Abdullah ◽  
Kek Sie Long ◽  
Nur Fazilla Mohd Jupri ◽  
Mustafa Mamat
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Information Criterion ◽  
Stepwise Logistic Regression ◽  
Ratio Test ◽  
Poultry Farm ◽  
Omnibus Test ◽  
Stepwise Logistic Regression Analysis ◽  
Spss Software

The aims of this study are to fit a logistic regression model towards the fly problem in a farm and to identify the variables that are associated with the fly problem in a poultry farm. By using SPSS software, this study used ‘FORWARD STEPWISE’ and ‘BACKWARD STEPWISE’ methods to perform the analysis. Compared to linear regression analysis, logistic regression does not require rigorous assumptions to be met. This study used Likelihood Ratio test, Omnibus test and Hosmer and Lemeshow test to validate and to test the fit of poultry farm data. Akaike Information Criterion (AIC) is calculated to observe the difference between the methods of stepwise used by SPSS software in this study. As a result, logistic regression is fit towards poultry farm data by a stepwise procedure. BACKWARD STEPWISE seems to be more suitable for conducting the stepwise method of analysis. Besides, variables that influence the problem of fly in a poultry are the wasps, distance and number of flies. 

scholarly journals Allogeneic Red Blood Cell Transfusion Rate and Risk Factors in Elderly Patients with Femoral Neck Fracture after Hemiarthroplasty

2020 ◽  
Author(s):  
Rui Yue ◽  
Minghui Yang ◽  
Xiaohui Deng ◽  
Ping Zhang
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Femoral Neck ◽  
Elderly Patients ◽  
Blood Cell ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Neck Fracture ◽  
Multivariate Stepwise Logistic Regression

Abstract Objective: To determine the rate and risk factors of allogeneic red blood cell transfusions (ABT) in elderly patients with femoral neck fracture (FNF) after hemiarthroplasty (HA). Methods: We conducted a study on elderly patients (≥65 years old) who were admitted to the geriatric trauma orthopedics ward of Beijing Jishuitan Hospital for HA treatment from March 2018 to February 2019 due to FNF. The perioperative data were collected retrospectively, and univariate and multivariate stepwise logistic regression analysis were performed to determine the postoperative ABT rate and its risk factors. Results: 445 patients were included in the study, of which 177 (39.8%) received ABT after surgery. Multivariate stepwise logistic regression analysis showed that preoperative low hemoglobin (Hb), high intraoperative blood loss (IBL), advanced age, and low body mass index (BMI) are independent risk factors of ABT after HA in elderly FNF patients. Conclusion: ABT after HA is a common phenomenon in elderly patients with FNF. Their postoperative ABT needs are related to preoperative low Hb, high IBL, advanced age, and low BMI. Therefore, ABT can be reduced from the above aspects.

Risk factors for global longitudinal strain impairment in renal transplant recipients with preserved left ventricular ejection fraction

2019 ◽  
Author(s):  
Marta Obremska ◽  
Anna Goździk ◽  
Maciej Szymczak ◽  
Dorota Zysko ◽  
Maria Boratyńska ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ejection Fraction ◽  
Cardiovascular Mortality ◽  
Longitudinal Strain ◽  
Global Longitudinal Strain ◽  
Stepwise Logistic Regression ◽  
Transplant Recipients ◽  
Kidney Transplant Recipients ◽  
Stepwise Logistic Regression Analysis

Abstract Background . Patients with chronic kidney disease, including these after kidney transplantation (KTx), have higher cardiovascular mortality. Global longitudinal strain (GLS) detects subtle changes in the left ventricle (LV) and is more sensitive predictor of cardiovascular mortality than the LV ejection fraction (LVEF).The aim of this study was to assess the prevalence of impaired GLS among kidney transplant recipients with preserved LVEF. We also aimed to identify possible clinical factors responsible for GLS impairment. Methods . A total of 79 patients following KTx with preserved LVEF and no history of cardiac disease were evaluated. We assessed echocardiogram parameters with the calculation of GLS, laboratory parameters, presence of diabetes, hypertension, duration of haemodialysis (HD) and time after KTx. An impaired GLS value was set on ≥-18%. The Classification and Regression Trees and stepwise logistic regression analysis were used to identify the factors related to impaired GLS. Results. Among 79 (42 females, mean age 60.3) kidney transplant recipients with preserved LVEF, 39% had impaired GLS. In stepwise logistic regression analysis, two variables related with GLS≥-18%: duration of HD before KTx longer than 27.5 months (OR 4.48, 95% CI 1.46-14.38, P=0.01), and eGFR greater than 60mL/min/1.73m2 (OR 0.155, 95% CI 0.024-0.967, P= 0.049). Conclusions. In our study group, a total of 39% of KTx patients with preserved LVEF had impaired GLS. The main risk factor of GLS impairment was the prolonged time of HD (>27.5 months) prior to KTx, whereas a good renal graft function (eGFR>60mL/min/1.73m2) only marginally proved to be a protective factor.

Pediatric Risk of Mortality and Hospital Infection

2004 ◽  
Vol 25 (9) ◽  
pp. 783-785 ◽  
Author(s):  
Aglai Arantes ◽  
Eduardo da Silva Carvalho ◽  
Eduardo A. S. Medeiros ◽  
Calil Kairalla Farhat ◽  
Orlando Cesar Mantese
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Intensive Care ◽  
Nosocomial Infections ◽  
Pediatric Intensive Care ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Risk Of Mortality ◽  
Utilization Ratio

AbstractWe studied the association of Pediatric Risk of Mortality scores with nosocomial infections among 341 critically ill patients admitted to a pediatric intensive care unit between June 1998 and December 2000. Through stepwise logistic regression analysis, the best predictors for nosocomial infections were device utilization ratio, antimicrobial therapy, and length of stay.

Cytopathology of malignant mesothelioma: A stepwise logistic regression analysis

1992 ◽  
Vol 8 (4) ◽  
pp. 333-341 ◽  
Author(s):  
Mark W. Stevens ◽  
Anthony S-Y. Leong ◽  
Nicola L. Fazzalari ◽  
Kaylene D. Dowling ◽  
Douglas W. Henderson
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Malignant Mesothelioma ◽  
Logistic Regression Analysis ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis
Sign in / Sign up

Export Citation Format

Share Document