Temporal and farm-management-associated variation in faecal-pat prevalence ofCampylobacter fetusin sheep and cattle

2013 ◽  
Vol 142 (6) ◽  
pp. 1196-1204 ◽  
Author(s):  
J. S. DUNCAN ◽  
A. J. H. LEATHERBARROW ◽  
N. P. FRENCH ◽  
D. H. GROVE-WHITE
Keyword(s):  
Logistic Regression ◽  
Farm Management ◽  
Multiple Logistic Regression ◽  
Seasonal Periodicity ◽  
Culture Techniques ◽  
Regression Modelling ◽  
Increased Risk ◽  
Geographical Regions ◽  
Logistic Regression Modelling ◽  
Cattle And Sheep

SUMMARYThe faecal-pat prevalence (as estimated by culture) ofCampylobacter fetusfrom cattle and sheep on 19 farms in rural Lancashire was investigated using standardCampylobacterculture techniques and PCR during a 2-year longitudinal study.C. fetuswas isolated from 9·48% [95% confidence interval (CI) 8·48–10·48] of cattle faecal pats and 7·29% (95% CI 6·21–9·62) of sheep faecal pats. There was evidence of significant differences in shedding prevalence between geographical regions; cows in geographical zone 3 had an increased risk of sheddingC. fetuscompared to cows in geographical zones 1 and 2 (OR 6·64, 95% CI 1·67–26·5,P = 0·007), as did cows at pasture (OR 1·66, 95% CI 1·01–2·73,P = 0·046) compared to when housed. Multiple logistic regression modelling demonstrated underlying seasonal periodicity in both species.

scholarly journals An improved medical admissions risk system using multivariable fractional polynomial logistic regression modelling

QJM ◽  
2009 ◽  
Vol 103 (1) ◽  
pp. 23-32 ◽  
Author(s):  
B. Silke ◽  
J. Kellett ◽  
T. Rooney ◽  
K. Bennett ◽  
D. O’Riordan
Keyword(s):  
Logistic Regression ◽  
Regression Modelling ◽  
Fractional Polynomial ◽  
Logistic Regression Modelling

Risk factors and outcomes of postoperative emergency response team activation: a matched case-control study

2020 ◽  
Vol 22 (1) ◽  
pp. 6-14
Author(s):  
Matthew I Hardman ◽  
◽  
S Chandralekha Kruthiventi ◽  
Michelle R Schmugge ◽  
Alexandre N Cavalcante ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Postoperative Complications ◽  
Emergency Response ◽  
Case Control Study ◽  
Case Control ◽  
Multiple Logistic Regression ◽  
Increased Risk ◽  
Response Team ◽  
Control Study

OBJECTIVE: To determine patient and perioperative characteristics associated with unexpected postoperative clinical deterioration as determined for the need of a postoperative emergency response team (ERT) activation. DESIGN: Retrospective case–control study. SETTING: Tertiary academic hospital. PARTICIPANTS: Patients who underwent general anaesthesia discharged to regular wards between 1 January 2013 and 31 December 2015 and required ERT activation within 48 postoperative hours. Controls were matched based on age, sex and procedure. MAIN OUTCOME MEASURES: Baseline patient and perioperative characteristics were abstracted to develop a multiple logistic regression model to assess for potential associations for increased risk for postoperative ERT. RESULTS: Among 105 345 patients, 797 had ERT calls, with a rate of 7.6 (95% CI, 7.1–8.1) calls per 1000 anaesthetics (0.76%). Multiple logistic regression analysis showed the following risk factors for postoperative ERT: cardiovascular disease (odds ratio [OR], 1.61; 95% CI, 1.18–2.18), neurological disease (OR, 1.57; 95% CI, 1.11–2.22), preoperative gabapentin (OR, 1.60; 95% CI, 1.17–2.20), longer surgical duration (OR, 1.06; 95% CI, 1.02–1.11, per 30 min), emergency procedure (OR, 1.54; 95% CI, 1.09–2.18), and intraoperative use of colloids (OR, 1.50; 95% CI, 1.17–1.92). Compared with control participants, ERT patients had a longer hospital stay, a higher rate of admissions to critical care (55.5%), increased postoperative complications, and a higher 30-day mortality rate (OR, 3.36; 95% CI, 1.73–6.54). CONCLUSION: We identified several patient and procedural characteristics associated with increased likelihood of postoperative ERT activation. ERT intervention is a marker for increased rates of postoperative complications and death.

scholarly journals Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

2021 ◽  
Vol 8 ◽  
Author(s):  
Robert A. Reed ◽  
Andrei S. Morgan ◽  
Jennifer Zeitlin ◽  
Pierre-Henri Jarreau ◽  
Héloïse Torchin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Expert Opinion ◽  
Logistic Regression Model ◽  
Population Based ◽  
Regression Modelling ◽  
Preterm Babies ◽  
Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

Mid-long term oil spill forecast based on logistic regression modelling of met-ocean forcings

2019 ◽  
Vol 146 ◽  
pp. 962-976 ◽  
Author(s):  
Helios Chiri ◽  
Ana Julia Abascal ◽  
Sonia Castanedo ◽  
Raul Medina
Keyword(s):  
Logistic Regression ◽  
Oil Spill ◽  
Regression Modelling ◽  
Logistic Regression Modelling
Sign in / Sign up

Export Citation Format

Share Document