scholarly journals Bacterial Pneumonia Co-Infection in COVID-19 Patients

2020 ◽  
Vol 4 (2) ◽  
pp. 47-52
Author(s):  
Abdul Karim Othman ◽  
Mohd Nazri Ali ◽  
Nurul Ilani Bahar ◽  
Nurul Aimi Mustaffa ◽  
Wan Nasrudin Wan Ismail ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Severe Acute Respiratory Syndrome ◽  
Elderly Patients ◽  
Rapid Detection ◽  
Bacterial Pneumonia ◽  
Organ Failures ◽  
Potential Risk Factors ◽  
Bacterial Characterization

The objective of this study was to highlight the emergence of COVID-19 bacteria pneumonia co-infections in patients infected with SARS-Cov-2 and risk factors related to its incidence and outcomes. We reported two cases of elderly patients with multiple comorbidities infected with SARS-Cov-2 and developed COVID-19 bacterial pneumonia requiring admission to intensive care unit (ICU) with one mortality preceded by septicemic shock and multi-organ failures. Observing the potential risk factors for being infected with SARS-Cov-2 and developing COVID-19 bacterial pneumonia we strongly advocate for rapid detection of COVID-19 bacterial pneumonia in SARS-Cov-2 infected patients and rapidly characterized the bacterial involved for a better outcome and importantly for efficient antimicrobial stewardship. COVID-19 bacterial pneumonia is an emerging disease requiring rapid detection and bacterial characterization with the ongoing management for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2  Keywords: COVID-19, bacterial pneumonia, acute respiratory syndrome

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.

Skin Integrity in Critically Ill and Injured Children

2007 ◽  
Vol 16 (6) ◽  
pp. 568-574 ◽  
Author(s):  
Christine A. Schindler ◽  
Theresa A. Mikhailov ◽  
Kay Fischer ◽  
Gloria Lukasiewicz ◽  
Evelyn M. Kuhn ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Ventilatory Support ◽  
Pediatric Intensive Care ◽  
Cost Of Care ◽  
Respiratory Illnesses ◽  
Skin Breakdown ◽  
Potential Risk Factors

Background Skin breakdown increases the cost of care, may lead to increased morbidity, and has negative psychosocial implications because of secondary scarring or alopecia. The scope of this problem has not been widely studied in critically ill and injured children. Objectives To determine the incidence of skin breakdown in critically ill and injured children and to compare the characteristics of patients who experience skin breakdown with those of patients who do not. Methods Admission and follow-up data for a 15-week period were collected retrospectively on children admitted to a large pediatric intensive care unit. The incidence of skin breakdown was calculated. The risk for skin breakdown associated with potential risk factors (relative risk) and 95% confidence intervals were determined. Results The sample consisted of 401 distinct stays in the intensive care unit for 373 patients. During the 401 stays, skin breakdown occurred in 34 (8.5%), redness in 25 (6.2%), and breakdown and redness in 13 (3.2%); the overall incidence was 18%. Patients who had skin breakdown or redness were younger, had longer stays, and were more likely to have respiratory illnesses and require mechanical ventilatory support than those who did not. Patients who had skin breakdown or redness had a higher risk of mortality than those who did not. Conclusions Risk factors for skin breakdown were similar to those previously reported. Compared with children of other ages, children 2 years or younger are at higher risk for skin breakdown.

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 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.

Factors and Outcomes of Intensive Care Unit Readmission in Elderly Patients

Gerontology ◽  
2021 ◽  
pp. 1-9
Author(s):  
Song-I Lee ◽  
Younsuck Koh ◽  
Jin Won Huh ◽  
Sang-Bum Hong ◽  
Chae-Man Lim
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Elderly Patients ◽  
Hospital Mortality ◽  
Tertiary Care ◽  
Multivariable Analysis ◽  
Care Hospital ◽  
Medical Reason ◽  
Chronic Heart Disease

<b><i>Introduction:</i></b> An increase in age has been observed among patients admitted to the intensive care unit (ICU). Age is a well-known risk factor for ICU readmission and mortality. However, clinical characteristics and risk factors of ICU readmission of elderly patients (≥65 years) have not been studied. <b><i>Methods:</i></b> This retrospective single-center cohort study was conducted in a total of 122-bed ICU of a tertiary care hospital in Seoul, Korea. A total of 85,413 patients were enrolled in this hospital between January 1, 2007, and December 31, 2017. The odds ratio of readmission and in-hospital mortality was calculated by logistic regression analysis. <b><i>Results:</i></b> Totally, 29,503 patients were included in the study group, of which 2,711 (9.2%) had ICU readmissions. Of the 2,711 readmitted patients, 472 patients were readmitted more than once (readmitted 2 or more times to the ICU, 17.4%). In the readmitted patient group, there were more males, higher sequential organ failure assessment (SOFA) scores, and hospitalized for medical reasons. Length of stay (LOS) in ICU and in-hospital were longer, and 28-day and in-hospital mortality was higher in readmitted patients than in nonreadmitted patients. Risk factors of ICU readmission included the ICU admission due to medical reason, SOFA score, presence of chronic heart disease, diabetes mellitus, chronic kidney disease, transplantation, use of mechanical ventilation, and initial ICU LOS. ICU readmission and age (over 85 years) were independent predictors of in-hospital mortality on multivariable analysis. The delayed ICU readmission group (&#x3e;72 h) had higher in-hospital mortality than the early readmission group (≤72 h) (20.6 vs. 16.2%, <i>p</i> = 0.005). <b><i>Conclusions:</i></b> ICU readmissions occurred in 9.2% of elderly patients and were associated with poor prognosis and higher mortality.

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.

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