scholarly journals Predictors of Healthcare Associated Infections and Their Role on Mortality in an Intensive Care Unit

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
G Migliara ◽  
V Baccolini ◽  
L M Salvatori ◽  
A Angelozzi ◽  
C Isonne ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Intensive Care ◽  
Surveillance System ◽  
Regression Models ◽  
Propensity Scores ◽  
Logistic Models ◽  
Mortality Data ◽  
Healthcare Associated Infections ◽  
Logistic Regression Models ◽  
Healthcare Associated

Abstract Background Healthcare associated Infections (HAIs) represent a significant burden in terms of mortality, morbidity, length of stay and costs for patients in intensive care units (ICUs). In this study, we analyzed the predictors of HAIs development and assessed the HAIs association with mortality. Data were retrieved from a general ICU active surveillance system of a large teaching hospital in Rome. Methods Logistic regression models were built to quantify the association between demographic and clinical factors and the development of HAIs, device-related HAIs and Multi Drug Resistant (MDR)-associated HAIs. The HAIs independent predictors were used to create propensity scores (PS) specific for each model, that was subsequently used to adjust the association between these conditions and mortality in logistic regression models. Results From May 2016 to September 2019, 864 patients were included in the surveillance system, 236 (27.3%) of which had at least one HAI during their hospitalization. Specifically, 162 (18.8%) patients had at least a device-related HAI and the overall mortality rate was 34.3%. Factors associated with the HAIs and the device-related HAIs were mechanical ventilation and admission for trauma. The PS-adjusted logistic models showed an association between HAI and device-related HAI and mortality (OR 1.82, 95%CI 1.30-2.54; OR 2.03, 95%CI 1.40-2.95, respectively). MDR-associated HAIs had a significant association with diabetes mellitus; however, these infections weren't associated with mortality (OR 1.42, 95%CI 0.98-2.08), even in the subgroup of infected patients (OR 0.99, 95%CI 0.56-1.73). Conclusions The study confirms the association between HAIs and device-related HAIs with mortality in ICUs. Apparently, MDR-associated infection subset appears not having a specific association with mortality. However, given the extra effort that these infections require to be managed, they should be adequately surveilled and contrasted. Key messages Healthcare associated infections are strongly associated with mortality in ICU. MDR-associated infections do not seem to give a specific drawback in our setting.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

2009 ◽  
Vol 48 (03) ◽  
pp. 306-310 ◽  
Author(s):  
C. E. Minder ◽  
G. Gillmann
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Logistic Models ◽  
Binary Logistic Regression ◽  
Logistic Regression Models ◽  
Logistic Analysis ◽  
Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

scholarly journals Defensible inferences from a nested sequence of logistic regressions: a guide for the perplexed

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Gulsah Gurkan ◽  
Yoav Benjamini ◽  
Henry Braun
Keyword(s):  
Logistic Regression ◽  
Cognitive Skills ◽  
Regression Models ◽  
Large Scale ◽  
Family Background ◽  
National Income ◽  
Logistic Models ◽  
Logistic Regression Models ◽  
Logistic Regressions ◽  
The Impact

AbstractEmploying nested sequences of models is a common practice when exploring the extent to which one set of variables mediates the impact of another set. Such an analysis in the context of logistic regression models confronts two challenges: (i) direct comparisons of coefficients across models are generally biased due to the changes in scale that accompany the changes in the set of explanatory variables, (ii) conducting a large number of tests induces a problem of multiplicity that can lead to spurious findings of significance if not heeded. This article aims to illustrate a practical strategy for conducting analyses in the face of these challenges. The challenges—and how to address them—are illustrated using a subset of the findings reported by Braun (Large-scale Assess Educ 6(4):1–52, 2018. 10.1186/s40536-018-0058-x), drawn from the Programme for the International Assessment of Adult Competencies (PIAAC), an international, large-scale assessment of adults. For each country in the dataset, a nested pair of logistic regression models was fit in order to investigate the role of Educational Attainment and Cognitive Skills in mediating the impact of family background and demographic characteristics on the location of an individual’s annual income in the national income distribution. A modified version of the Karlson–Holm–Breen (KHB) method was employed to obtain an unbiased estimate of the true differences in the coefficients between nested logistic models. In order to address the issue of multiplicity, a recent generalization of the Benjamini–Hochberg (BH) False Discovery Rate (FDR)-controlling procedure to hierarchically structured hypotheses was employed and compared to two conventional methods. The differences between the changes in coefficients calculated conventionally and with the KHB adjustment varied from negligible to very substantial. When combined with the actual magnitudes of the coefficients, we concluded that the more proximal factors indeed act as strong mediators for the background factors, but less so for Age, and hardly at all for Gender. With respect to multiplicity, applying the FDR-controlling procedure yielded results very similar to those obtained by applying a standard per-comparison procedure, but quite a few more discoveries in comparison to the Bonferroni procedure. The KHB methodology illustrated here can be applied wherever there is interest in comparing nested logistic regressions. Modifications to account for probability sampling are practicable. The categorization of variables and the order of entry should be determined by substantive considerations. On the other hand, the BH procedure is perfectly general and can be implemented to address multiplicity issues in a broad range of settings.

scholarly journals Challenges with Surveillance of Healthcare-Associated Infections in Intensive Care Units in South Africa

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Saajida Mahomed ◽  
Ozayr Mahomed ◽  
A. Willem Sturm ◽  
Stephen Knight ◽  
Prashini Moodley
Keyword(s):  
South Africa ◽  
Organizational Culture ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Surveillance System ◽  
Health Sector ◽  
Central Line ◽  
Healthcare Associated Infections ◽  
Extensive Consultation ◽  
Healthcare Associated

Background. The incidence of healthcare-associated infections (HAIs) in the public health sector in South Africa is not known due to the lack of a surveillance system. We report on the challenges experienced in the implementation of a surveillance system for HAIs in intensive care units (ICUs). Methods. A passive, paper-based surveillance system was piloted in eight ICUs to measure the incidence of ventilator-associated pneumonia, catheter-associated urinary tract infection, and central line-associated bloodstream infection. Extensive consultation with the ICU clinical and nursing managers informed the development of the surveillance system. The Plan-Do-Study-Act method was utilized to guide the implementation of the surveillance. Results. The intended outputs of the surveillance system were not fully realized due to incomplete data. The organizational culture did not promote the collection of surveillance data. Nurses felt that the surveillance form added to their workload, and the infection control practitioners were unable to adequately supervise the process due to competing work demands. Conclusions. A manual system that adds to the administrative workload of nurses is not an effective method of measuring the burden of HAIs. Change management is required to promote an organizational culture that supports accurate data collection for HAIs.

scholarly journals Epidemiology of Carbapenem-resistant Enterobacteriaceae in Egyptian intensive care units using National Healthcare–associated Infections Surveillance Data, 2011–2017

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Sara Kotb ◽  
Meghan Lyman ◽  
Ghada Ismail ◽  
Mohammad Abd El Fattah ◽  
Samia A. Girgis ◽  
...  
Keyword(s):  
Intensive Care ◽  
Surveillance System ◽  
Carbapenem Resistance ◽  
Surveillance Data ◽  
Infection Prevention And Control ◽  
Healthcare Associated Infections ◽  
Specimen Type ◽  
Carbapenem Resistant ◽  
Healthcare Associated ◽  
Carbapenem Resistant Enterobacteriaceae

Abstract Objective To describe the epidemiology of carbapenem-resistant Enterobacteriaceae (CRE) healthcare-associated infections (HAI) in Egyptian hospitals reporting to the national HAI surveillance system. Methods Design: Descriptive analysis of CRE HAIs and retrospective observational cohort study using national HAI surveillance data. Setting: Egyptian hospitals participating in the HAI surveillance system. The patient population included patients admitted to the intensive care unit (ICU) in participating hospitals. Enterobacteriaceae HAI cases were Klebsiella, Escherichia coli, and Enterobacter isolates from blood, urine, wound or respiratory specimen collected on or after day 3 of ICU admission. CRE HAI cases were those resistant to at least one carbapenem. For CRE HAI cases reported during 2011–2017, a hospital-level and patient-level analysis were conducted using only the first CRE isolate by pathogen and specimen type for each patient. For facility, microbiology, and clinical characteristics, frequencies and means were calculated among CRE HAI cases and compared with carbapenem-susceptible Enterobacteriaceae HAI cases through univariate and multivariate logistic regression using STATA 13. Results There were 1598 Enterobacteriaceae HAI cases, of which 871 (54.1%) were carbapenem resistant. The multivariate regression analysis demonstrated that carbapenem resistance was associated with specimen type, pathogen, location prior to admission, and length of ICU stay. Between 2011 and 2017, there was an increase in the proportion of Enterobacteriaceae HAI cases due to CRE (p-value = 0.003) and the incidence of CRE HAIs (p-value = 0.09). Conclusions This analysis demonstrated a high and increasing burden of CRE in Egyptian hospitals, highlighting the importance of enhancing infection prevention and control (IPC) programs and antimicrobial stewardship activities and guiding the implementation of targeted IPC measures to contain CRE in Egyptian ICU’s .

scholarly journals Quantification of rhBMP2 in bioactive bone materials

2019 ◽  
Vol 7 (1) ◽  
pp. 71-75
Author(s):  
Huan Lian ◽  
Han Wang ◽  
Qianqian Han ◽  
Chunren Wang
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Logistic Model ◽  
Transforming Growth Factor ◽  
Logistic Models ◽  
Transforming Growth Factor Β ◽  
C2c12 Cells ◽  
Logistic Regression Models ◽  
Alp Activity

Abstract Bone morphogenetic protein (BMP), belongs to transforming growth factor-β (TGF-β) superfamily except BMP-1. Implanting BMP into muscular tissues induces ectopic bone formation at the site of implantation, which provides opportunity for the treatment of bone defects. Recombinant human BMP-2 (rhBMP-2) has been used clinically, but the lack of standard methods for quantifying rhBMP-2 biological activity greatly hindered the progress of commercialization. In this article, we describe an in vitro rhBMP-2 quantification method, as well as the data analyzation pipeline through logistic regression in RStudio. Previous studies indicated that alkaline phosphatase (ALP) activity of C2C12 cells was significantly increased when exposed to rhBMP-2, and showed dose-dependent effects in a certain concentration range of rhBMP-2. Thus, we chose to quantify ALP activity as an indicator of rhBMP-2 bioactivity in vitro. A sigmoid relationship between the ALP activity and concentration of rhBMP-2 was discovered. However, there are tons of regression models for such a non-linear relationship. It has always been a major concern for researchers to choose a proper model that not only fit data accurately, but also have parameters representing practical meanings. Therefore, to fit our rhBMP-2 quantification data, we applied two logistic regression models, three-parameter log-logistic model and four-parameter log-logistic model. The four-parameter log-logistic model (adj-R2 > 0.98) fits better than three-parameter log-logistic model (adj-R2 > 0.75) for the sigmoid curves. Overall, our results indicate rhBMP-2 quantification in vitro can be accomplished by detecting ALP activity and fitting four-parameter log-logistic model. Furthermore, we also provide a highly adaptable R script for any additional logistic models.

scholarly journals The impact of the COVID-19 pandemic on healthcare-associated infections in intensive care unit patients: a retrospective cohort study

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
V. Baccolini ◽  
G. Migliara ◽  
C. Isonne ◽  
B. Dorelli ◽  
L. C. Barone ◽  
...  
Keyword(s):  
Intensive Care ◽  
Regression Models ◽  
Multivariable Analysis ◽  
Control Measures ◽  
Healthcare Associated Infections ◽  
Risk Of Death ◽  
Infection Control Measures ◽  
Tract Infections ◽  
Healthcare Associated ◽  
The Impact

Abstract Background During the intensive care units’ (ICUs) reorganization that was forced by the COVID-19 emergency, attention to traditional infection control measures may have been reduced. Nevertheless, evidence on the effect of the COVID-19 pandemic on healthcare-associated infections (HAIs) is still limited and mixed. In this study, we estimated the pandemic impact on HAI incidence and investigated the HAI type occurring in COVID-19 patients. Methods Patients admitted to the main ICU of the Umberto I teaching hospital of Rome from March 1st and April 4th 2020 were compared with patients hospitalized in 2019. We assessed the association of risk factors and time-to-first event through multivariable Fine and Grey’s regression models, that consider the competitive risk of death on the development of HAI (Model 1) or device related-HAI (dr-HAI, Model 2) and provide estimates of the sub-distribution hazard ratio (SHR) and its associated confidence interval (CI). A subgroup analysis was performed on the 2020 cohort. Results Data from 104 patients were retrieved. Overall, 59 HAIs were recorded, 32 of which occurred in the COVID-19 group. Patients admitted in 2020 were found to be positively associated with both HAI and dr-HAI onset (SHR: 2.66, 95% CI 1.31–5.38, and SHR: 10.0, 95% CI 1.84–54.41, respectively). Despite being not confirmed at the multivariable analysis, a greater proportion of dr-HAIs seemed to occur in COVID-19 patients, especially ventilator-associated pneumonia, and catheter-related urinary tract infections. Conclusions We observed an increase in the incidence of patients with HAIs, especially dr-HAIs, mainly sustained by COVID-19 patients. A greater susceptibility of these patients to device-related infections was hypothesized, but further studies are needed.

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