Both microbiological surveillance and audit of procedures improve reprocessing of flexible bronchoscopes and patient safety

2021 ◽  
pp. 1-7
Author(s):  
Philippe Saliou ◽  
Lila Calmettes ◽  
Hervé Le Bars ◽  
Christopher Payan ◽  
Valérie Narbonne ◽  
...  
Keyword(s):  
Risk Factors ◽  
Quality Control ◽  
Regression Model ◽  
Poisson Regression ◽  
Control Measure ◽  
Poisson Regression Model ◽  
Microbiological Contamination ◽  
Quality Control Measure ◽  
The Impact ◽  
Microbiological Surveillance

Abstract Background: Microbiological surveillance of bronchoscopes and automatic endoscope reprocessors (AERs)/washer disinfectors as a quality control measure is controversial. Experts also are divided on the infection risks associated with bronchoscopic procedures. Objective: We evaluated the impact of routine microbiological surveillance and audits of cleaning/disinfection practices on contamination rates of reprocessed bronchoscopes. Design: Audits were conducted of reprocessing procedures and microbiological surveillance on all flexible bronchoscopes used from January 2007 to June 2020 at a teaching hospital in France. Contamination rates per year were calculated and analyzed using a Poisson regression model. The risk factors for microbiological contamination were analyzed using a multivariable logistical regression model. Results: In total, 478 microbiological tests were conducted on 91 different bronchoscopes and 57 on AERs. The rate of bronchoscope contamination significantly decreased between 2007 and 2020, varying from 30.2 to 0% (P < .0001). Multivariate analysis confirmed that retesting after a previous contaminated test was significantly associated with higher risk of bronchoscope contamination (OR, 2.58; P = .015). This finding was explained by the persistence of microorganisms in bronchoscopes despite repeated disinfections. However, the risk of persistent contamination was not associated with the age of the bronchoscope. Conclusions: Our results confirm that bronchoscopes can remain contaminated despite repeated reprocessing. Routine microbial testing of bronchoscopes for quality assurance and audit of decontamination and disinfection procedures can improve the reprocessing of bronchoscopes and minimize the rate of persistent contamination.

The Analysis of Influencing Factors on the Number of Storms

2012 ◽  
Vol 166-169 ◽  
pp. 2649-2653
Author(s):  
Bin Hui Wang ◽  
Zhi Jian Wang ◽  
Si Ling Chen
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Poisson Regression ◽  
West African ◽  
The Other ◽  
Poisson Regression Model ◽  
Probability Of Occurrence ◽  
Wet Weather ◽  
The Impact ◽  
Non Parametric

By using both parametric and non-parametric tests, noticed that both EI Nino and West African Wetness have significant impact on the number of storms. A Poisson Regression Model is then be used to further explores the impact of different variables to the number of storms. In particular, warm phase of EI Nino and dry weather has suppress impact on the number of storms while cold phase of Nino and wet weather encourage storms. Under the combination impact of both EI Nino and West African Wetness, the probability of occurrence of extreme storms is higher than under the other conditions.

Abstract 008: Machine Learning For Sudden Cardiac Death Prediction: The Artherosclerosis Risk In Communities Study

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Zhi Yu ◽  
Shannon Wongvibulsin ◽  
Linda Zhou ◽  
Kunihiro Matsushita ◽  
Pradeep Natarajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Regression Model ◽  
Poisson Regression ◽  
Clinical Characteristics ◽  
Prediction Models ◽  
Learning Approach ◽  
Time Varying ◽  
Poisson Regression Model ◽  
Machine Learning Approach

Introduction: Sudden cardiac death (SCD) is the leading cause of death in the US and has significant public health impact. However, effective risk stratification for SCD remains lacking as current prediction models do not address the dynamic impact of time-varying risk factors including interim clinical events on SCD risk. Hypothesis: A recently developed machine learning approach that uses time-dependent variables and incorporate complex interactions between risk factors, Random Forest for Survival, Longitudinal, and Multivariate Data (RF-SLAM), will be able to improve SCD risk prediction. Methods: ARIC study participants were followed for adjudicated SCD. RF-SLAM partitions the information for each individual into multiple units (analogous to risk sets) and uses Poisson regression log-likelihood as the split statistic thus allowing for modeling time-varying variables. It was compared to a Poisson regression model with stepwise selection to predict SCD. Time-varying variables collected at four visits were used as candidate predictors for both prediction models, including demographics and clinical characteristics, anthropometric variables, lifestyle factors, cardiac risk factors, medication, laboratory values and biomarkers, electrophysiologic variables, and other cardiac functional indices. Predictive accuracy was assessed by area under the receiver operating characteristic curve (AUC) through out-of-bag prediction for RF-SLAM model and 10-fold cross validation for Poisson regression model. Results: Over 25 years follow-up, 590 SCD events occurred among 15792 ARIC participants mean age 54 years (55% women). Compared to Poisson regression (cross-validated mean AUC 0.75), RF-SLAM model improved prediction (mean AUC 0.83). RF-SLAM model identified prior coronary heart disease (CHD) as the top predictor for SCD. Other predictors selected by RF-SLAM included clinical characteristics (diabetes, prior myocardial infarction, prior stroke, and prior heart failure), electrophysiologic variables (T wave abnormality in any of leads I, aVL, and V6, and ST junction & segment depression in any of leads I, aVL, or V6), medication (anti-hypertensive medications and anti-diabetic medications), biomarkers (N-terminal pro-B-type natriuretic peptide, troponin T, troponin I, and creatinine), subclinical atherosclerotic indices (carotid intima-media thickness), as well as race, sex and visit. Using the 17 predictors selected by RF-SLAM model to fit a Poisson regression model, a generalized linear model, resulted in a mean AUC of 0.73, suggesting that the interactions captured by random forest improve prediction performance. Conclusions: Applying a novel machine-learning approach with time-varying predictors improves the prediction of SCD. Clinical characteristics, especially prior CHD, are important for predicting SCD in the general population.

Risk Associated with Transportation and Lairage on Hide Contamination with Salmonella enterica in Finished Beef Cattle at Slaughter

2008 ◽  
Vol 71 (11) ◽  
pp. 2228-2232 ◽  
Author(s):  
G. A. DEWELL ◽  
C. A. SIMPSON ◽  
R. D. DEWELL ◽  
D. R. HYATT ◽  
K. E. BELK ◽  
...  
Keyword(s):  
Risk Factors ◽  
Relative Risk ◽  
Beef Cattle ◽  
Regression Model ◽  
Potential Risk ◽  
Poisson Regression ◽  
Salmonella Enterica ◽  
Poisson Regression Model ◽  
Potential Risk Factors ◽  
Culture Negative

Transportation of cattle to the slaughter plant could influence hide contamination with Salmonella enterica. Fecal and hide samples were obtained from 40 lots of cattle at the feedlot and again at the slaughter plant. Potential risk factors for hide contamination were evaluated. A multilevel Poisson regression model was used to determine whether transportation and lairage were associated with hide contamination by Salmonella. Cattle with hide samples positive for Salmonella at the feedlot had twice the risk of having positive slaughter hide samples compared with cattle without positive feedlot hide samples (relative risk [RR], 1.9). Cattle transported in trailers from which samples positive for Salmonella were collected had twice the risk of having positive slaughter hide samples compared with cattle transported in culture-negative trailers (RR, 2.3). Cattle transported for long distances had twice the risk of having positive hide samples at slaughter compared with cattle transported shorter distances (RR, 2.3). Cattle held in lairage pens contaminated with feces had twice the risk of having positive slaughter hide samples compared with cattle held in clean pens (RR, 1.8). Cattle held off feed longer than 18 h before loading had twice the risk of having positive slaughter hide samples compared with cattle held off feed for shorter times (RR, 1.7). Cattle that were agitated during loading had twice the risk of having positive slaughter hide samples compared with cattle that were calm (RR, 2.2). These findings suggest that variables associated with transportation and lairage can impact the presence of Salmonella on the hides of cattle at slaughter.

scholarly journals The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huihui Zhang ◽  
Yini Liu ◽  
Fangyao Chen ◽  
Baibing Mi ◽  
Lingxia Zeng ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Linear Models ◽  
Intervention Strategy ◽  
Health Resources ◽  
Sociodemographic Factors ◽  
Information Criteria ◽  
Poisson Regression Model ◽  
Geographically Weighted Regression Model ◽  
Auto Correlation

Abstract Background Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective. Methods Official surveillance data about the COVID-19 and sociodemographic information in China’s 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis. Results Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran’s I = − 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased. Conclusions There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.

A multivariate Poisson regression model for count data

2021 ◽  
pp. 1-17
Author(s):  
J. M. Muñoz-Pichardo ◽  
R. Pino-Mejías ◽  
J. García-Heras ◽  
F. Ruiz-Muñoz ◽  
M. Luz González-Regalado
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Poisson Regression Model

Monitoring social networks based on Zero-inflated Poisson regression model

2021 ◽  
pp. 1-17
Author(s):  
Narges Motalebi ◽  
Mohammad Saleh Owlia ◽  
Amirhossein Amiri ◽  
Mohammad Saber Fallahnezhad
Keyword(s):  
Social Networks ◽  
Regression Model ◽  
Poisson Regression ◽  
Poisson Regression Model

scholarly journals Age and Sex Specific Trends in Incidence of Juvenile Idiopathic Arthritis in Danish Birth Cohorts from 1992 to 2002: A Nationwide Register Linkage Study

2021 ◽  
Vol 18 (16) ◽  
pp. 8331
Author(s):  
Isabel Cardoso ◽  
Peder Frederiksen ◽  
Ina Olmer Specht ◽  
Mina Nicole Händel ◽  
Fanney Thorsteinsdottir ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Regression Model ◽  
Incidence Rate ◽  
Poisson Regression ◽  
Linkage Study ◽  
Incidence Rates ◽  
Age Group ◽  
Poisson Regression Model ◽  
Birth Cohorts ◽  
Age And Sex

This study reports age- and sex-specific incidence rates of juvenile idiopathic arthritis (JIA) in complete Danish birth cohorts from 1992 through 2002. Data were obtained from the Danish registries. All persons born in Denmark, from 1992–2002, were followed from birth and until either the date of first diagnosis recording, death, emigration, 16th birthday or administrative censoring (17 May 2017), whichever came first. The number of incident JIA cases and its incidence rate (per 100,000 person-years) were calculated within sex and age group for each of the birth cohorts. A multiplicative Poisson regression model was used to analyze the variation in the incidence rates by age and year of birth for boys and girls separately. The overall incidence of JIA was 24.1 (23.6–24.5) per 100,000 person-years. The rate per 100,000 person-years was higher among girls (29.9 (29.2–30.7)) than among boys (18.5 (18.0–19.1)). There were no evident peaks for any age group at diagnosis for boys but for girls two small peaks appeared at ages 0–5 years and 12–15 years. This study showed that the incidence rates of JIA in Denmark were higher for girls than for boys and remained stable over the observed period for both sexes.

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