scholarly journals Pressure Ulcers Prevalence and Potential Risk Factors Among Intensive Care Unit Patients in Governmental Hospitals in Palestine: A Cross-sectional Study

2019 ◽  
Vol 12 (1) ◽  
pp. 121-126 ◽  
Author(s):  
Jamal A.S. Qaddumi ◽  
Omar Almahmoud
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Potential Risk ◽  
Pressure Ulcers ◽  
Advisory Panel ◽  
Cross Sectional ◽  
Data Set ◽  
Potential Risk Factors ◽  
The Government

Aim: To determine the prevalence rate and the potential risk factors of pressure ulcers (PUs) among patients in the intensive care unit (ICU) departments of the government hospitals in Palestine. Methods: A quantitative, cross-sectional, descriptive analytical study was carried out in five government hospital intensive care units in four different Palestinian cities between September 27, 2017, and October 27, 2017. The data of 109 out of 115 (94.78%) inpatients were analyzed. The Minimum Data Set (MDS) recommended by the European Pressure Ulcer Advisory Panel (EPUAP) was used to collect inpatients’ information. Results: The result of the analysis showed that the prevalence of pressure ulcers in the ICU departments was 33%, and the prevalence of PUs when excluding stage one was 7.3%. The common stage for pressure ulcers was stage one. It was also determined that the most common risk factors for the development of pressure ulcers were the number of days in the hospital, moisture, and friction. Conclusion: According to the recent studies in the Asian States, the prevalence of pressure ulcers in Palestine is considerably higher than in China and Jordan. However, it is still lower than the prevalence reported in comparable published studies in Western Europe. Increasing the staff’s knowledge about PUs screening and preventive measures is highly recommended in order to decrease the burden of PUs.

scholarly journals Incidence of Delirium in Critically Ill Cancer Patients

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Luis A. Sánchez-Hurtado ◽  
Nancy Hernández-Sánchez ◽  
Mario Del Moral-Armengol ◽  
Humberto Guevara-García ◽  
Francisco J. García-Guillén ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Cancer Patients ◽  
Critically Ill ◽  
Potential Risk ◽  
Assessment Method ◽  
Icu Stay ◽  
Potential Risk Factors

Objective. The aim of this study was to estimate the incidence of delirium and its risk factors among critically ill cancer patients in an intensive care unit (ICU). Materials and Methods. This is a prospective cohort study. The Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) was measured daily at morning to diagnose delirium by a physician. Delirium was diagnosed when the daily was positive during a patient’s ICU stay. All patients were followed until they were discharged from the ICU. Using logistic regression, we estimated potential risk factors for developing delirium. The primary outcome was the development of ICU delirium. Results. There were 109 patients included in the study. Patients had a mean age of 48.6 ± 18.07 years, and the main reason for admission to the ICU was septic shock (40.4%). The incidence of delirium was 22.9%. The mortality among all subjects was 15.6%; the mortality rate in patients who developed delirium was 12%. The only variable that had an association with the development of delirium in the ICU was the days of use of mechanical ventilation (OR: 1.06; CI 95%: 0.99–1.13;p=0.07). Conclusion. Delirium is a frequent condition in critically ill cancer patients admitted to the ICU. The duration in days of mechanical ventilation is potential risk factors for developing delirium during an ICU stay. Delirium was not associated with a higher rate of mortality in this group of patients.

scholarly journals Risk Factors for Development of Intestinal Colonization with Imipenem-Resistant Pseudomonas aeruginosa in the Intensive Care Unit Setting

2011 ◽  
Vol 32 (7) ◽  
pp. 719-722 ◽  
Author(s):  
Anthony D. Harris ◽  
J. Kristie Johnson ◽  
Kerri A. Thom ◽  
Daniel J. Morgan ◽  
Jessina C. McGregor ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Pseudomonas Aeruginosa ◽  
Intensive Care ◽  
Potential Risk ◽  
Selective Pressure ◽  
Intensive Care Unit Setting ◽  
Intestinal Colonization ◽  
Potential Risk Factors

Risk factors for development of intestinal colonization by imipenem-resistant Pseudomonas aeruginosa (IRPA) may differ between those who acquire the organism via patient-to-patient transmission versus by antibiotic selective pressure. The aim of this study was to quantify potential risk factors for the development of IRPA not due to patient-to-patient transmission.

scholarly journals Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

10.2196/23128 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23128
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

BACKGROUND Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. OBJECTIVE The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. METHODS In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. RESULTS Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. CONCLUSIONS The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

Potential Risk Factors for Autism in Children Requiring Neonatal Intensive Care Unit

2021 ◽  
Author(s):  
Marília Barbosa de Matos ◽  
Tiago S. Bara ◽  
Érico P. G. Felden ◽  
Mara L. Cordeiro
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Potential Risk ◽  
Neonatal Intensive Care ◽  
Feeding Tube ◽  
Autism Spectrum ◽  
Perinatal Complications ◽  
Potential Risk Factors

Abstract Background The etiology of autism spectrum disorder (ASD) is complex and involves the interplay of genetic and environmental factors. Aim We sought to identify potential prenatal, perinatal, and neonatal risk factors for ASD in a unique population of children who had perinatal complications and required care in a neonatal intensive care unit (NICU). Methods This prospective cohort study included 73 patients discharged from a NICU who received long-term follow-up at the largest children's hospital in Brazil. Potential risk factors were compared between 44 children with a diagnosis of ASD and 29 children without using the Mann–Whitney U test. Proportions were analyzed using the chi-square test. Simple and multiple logistic regression tests were performed. Results Of 38 factors analyzed, the following 7 were associated with ASD: family history of neuropsychiatric disorders (p = 0.049); maternal psychological distress during pregnancy (p = 0.007); ≥ 26 days in the NICU (p = 0.001); feeding tube for ≥ 15 days (p = 0.014); retinopathy of prematurity (p = 0.022); use of three or more antibiotics (p = 0.008); and co-sleeping until up to 2 years of age (p = 0.004). Conclusion This study found associations between specific risk factors during critical neurodevelopmental periods and a subsequent diagnosis of ASD. Knowledge of the etiologic factors that may influence the development for ASD is paramount for the development of intervention strategies and improvement of prognoses.

scholarly journals Sero-epidemiological survey of bluetongue disease in one-humped camel (Camelus dromedarius) in Kassala State, Eastern Sudan

2021 ◽  
Vol 74 (1) ◽  
Author(s):  
Molhima M. Elmahi ◽  
Mohammed O. Hussien ◽  
Abdel Rahim E. Karrar ◽  
Amira M. Elhassan ◽  
Abdel Rahim M. El Hussein
Keyword(s):  
Risk Factors ◽  
Potential Risk ◽  
Animal Health ◽  
Water Source ◽  
Economic Losses ◽  
Enzyme Linked Immunosorbent Assay ◽  
Serum Samples ◽  
Cross Sectional ◽  
Potential Risk Factors ◽  
Eastern Sudan

Abstract Background Bluetongue (BT) is a vector-borne viral disease of ruminant and camelid species which is transmitted by Culicoides spp. The causative agent of BT is bluetongue virus (BTV) that belongs to genus Orbivirus of the family Reoviridae. The clinical disease is seen mainly in sheep but mostly sub-clinical infections of BT are seen in cattle, goats and camelids. The clinical reaction of camels to infection is usually not apparent. The disease is notifiable to the World Organization for Animal Health (OIE), causing great economic losses due to decreased trade and high mortality and morbidity rates associated with bluetongue outbreaks. The objective of this study was to investigate the seroprevalence of BTV in camels in Kassala State, Eastern Sudan and to identify the potential risk factors associated with the infection. A cross sectional study using a structured questionnaire survey was conducted during 2015–2016. A total of 210 serum samples were collected randomly from camels from 8 localities of Kassala State. The serum samples were screened for the presence of BTV specific immunoglobulin (IgG) antibodies using a competitive enzyme-linked immunosorbent assay (cELISA). Results Seropositivity to BTV IgG was detected in 165 of 210 camels’ sera accounting for a prevalence of 78.6%. Potential risk factors to BTV infection were associated with sex (OR = 0.061, p-value = 0.001) and seasonal river as water source for drinking (OR = 32.257, p-value = 0.0108). Conclusions Sex and seasonal river as water source for drinking were considered as potential risk factors for seropositivity to BTV in camels. The high prevalence of BTV in camels in Kassala State, Eastern Sudan, necessitates further epidemiological studies of BTV infection in camels and other ruminant species to better be able to control BT disease in this region.

Pressure ulcer risk factors in an ICU population

1995 ◽  
Vol 4 (5) ◽  
pp. 361-367 ◽  
Author(s):  
MK Jiricka ◽  
P Ryan ◽  
MA Carvalho ◽  
J Bukvich
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Pressure Ulcer ◽  
Pressure Ulcers ◽  
Motor Vehicle ◽  
Convenience Sample ◽  
Medical Diagnoses ◽  
Trauma Population ◽  
Braden Scale

BACKGROUND: Although it is well known that pressure ulcers are associated with negative patient outcomes and increased hospital cost, there is little research related to pressure ulcers in an intensive care unit population. OBJECTIVE: To determine the relative contribution of risk factors in the development of pressure ulcers in intensive care unit patients. METHOD: In an exploratory descriptive design, a convenience sample of 85 adults was used. Patients were enrolled in the study within 24 hours of admission to the intensive care unit; data were collected every other day until discharge from the intensive care unit. Instruments included a demographic data form, Braden Scale for Predicting Pressure Sore Risk, Skin Assessment Tool, and Decubitus Ulcer Potential Analyzer. RESULTS: The most common reasons for admission to the intensive care unit included multiple trauma from motor vehicle accidents, gunshot and stab wounds, and gastrointestinal bleeding. A pressure ulcer developed in 48 subjects. There were no significant differences in age, gender, history of diabetes or smoking, or medical diagnoses between patients in whom a pressure ulcer developed and those in whom it did not. Data analysis indicated that a Braden Scale score of 11, rather than the recommended score of 16, was statistically significant for predicting pressure ulcer risk. CONCLUSIONS: The results suggest that a cut-off score on the Braden Scale could be specific to an intensive care unit trauma population.

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