scholarly journals Factors contributing to delay intensive care unit admission of critically ill patients from the adult emergency Department in Tikur Anbessa Specialized Hospital

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Helen Teklie ◽  
Hywet Engida ◽  
Birhanu Melaku ◽  
Abdata Workina
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Intensive Care Unit Admission ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Transfer Time ◽  
Multivariate Logistic Regression ◽  
Icu Admission ◽  
Specialized Hospital

Abstract Background The transfer time for critically ill patients from the emergency department (ED) to the Intensive care unit (ICU) must be minimal; however, some factors prolong the transfer time, which may delay intensive care treatment and adversely affect the patient’s outcome. Purpose To identify factors affecting intensive care unit admission of critically ill patients from the emergency department. Patients and methods A cross-sectional study design was conducted from January 13 to April 12, 2020, at the emergency department of Tikur Anbesa Specialized Hospital. All critically ill patients who need intensive care unit admission during the study period were included in the study. A pretested structured questionnaire was adapted from similar studies. The data were collected by chart review and observation. Then checked data were entered into Epi-data version 4.1 and cleaned data was exported to SPSS Version 25 for analysis. Descriptive statistics, bivariate and multivariate logistic regression were used to analyze the data. Result From the total of 102 critically ill patients who need ICU admission 84.3% of them had prolonged lengths of ED stay. The median length of ED stay was 13.5 h with an IQR of 7–25.5 h. The most common reasons for delayed ICU admission were shortage of ICU beds 56 (65.1%) and delays in radiological examination results 13(15.1%). On multivariate logistic regression p < 0.05 male gender (AOR = 0.175, 95% CI: (0.044, 0.693)) and shortage of ICU bed (AOR = 0.022, 95% CI: (0.002, 0.201)) were found to have a significant association with delayed intensive care unit admission. Conclusion there was a delay in ICU admission of critically ill patients from the ED. Shortage of ICU bed and delay in radiological investigation results were the reasons for the prolonged ED stay.

scholarly journals Intensive care unit admission protocol controlled by intensivists can reduce transfer delays from the emergency department in critically ill patients

2018 ◽  
Vol 26 (2) ◽  
pp. 84-90 ◽  
Author(s):  
Ji Eun Kim ◽  
Seul Lee ◽  
Jinwoo Jeong ◽  
Dong Hyun Lee ◽  
Jin-Heon Jeong
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Length Of Stay ◽  
Intensive Care Unit Admission ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Unit Length ◽  
Intensive Care Unit Length ◽  
Before And After

Background: Delayed transfer of patients from the emergency department to the intensive care unit is associated with adverse clinical outcomes. Critically ill patients with delayed admission to the intensive care unit had higher in-hospital mortality and increased hospital length of stay. Objectives: We investigated the effects of an intensive care unit admission protocol controlled by intensivists on the emergency department length of stay among critically ill patients. Methods: We designed the intensive care unit admission protocol to reduce the emergency department length of stay in critically ill patients. Full-time intensivists determined intensive care unit admission priorities based on the severity of illness. Data were gathered from patients who were admitted from the emergency department to the intensive care unit between 1 April 2016 and 30 November 2016. We retrospectively analyzed the clinical data and compared the emergency department length of stay between patients admitted from the emergency department to the intensive care unit before and after intervention. Results: We included 292 patients, 120 and 172 were admitted before and after application of the intensive care unit admission protocol, respectively. The demographic characteristics did not differ significantly between the groups. After intervention, the overall emergency department length of stay decreased significantly from 1045.5 (425.3–1665.3) min to 392.0 (279.3–686.8) min (p < 0.001). Intensive care unit length of stay also significantly decreased from 6.0 (4.0–11.8) days to 5.0 (3.0–10.0) days (p = 0.015). Conclusion: Our findings suggest that introduction of the intensive care unit admission protocol controlled by intensivists successfully decreased the emergency department length of stay and intensive care unit length of stay among critically ill patients at our institution.

scholarly journals Using machine learning tools to predict outcomes for emergency department intensive care unit patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qiangrong Zhai ◽  
Zi Lin ◽  
Hongxia Ge ◽  
Yang Liang ◽  
Nan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Learning Tools ◽  
Critical Patients

AbstractThe number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study’s objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.

scholarly journals Clinical characteristics and outcomes of patients with heart failure admitted to the intensive care unit with coronavirus disease 2019 (COVID19): a multicenter cohort study

2021 ◽  
Vol 20 (Supplement_1) ◽  
Author(s):  
H Badreldin ◽  
DR Hafidh ◽  
DR Bin Saleh ◽  
DR Al Sulaiman ◽  
DR Al Juhani ◽  
...  
Keyword(s):  
Heart Failure ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Clinical Characteristics ◽  
P Value ◽  
Heart Failure Patients ◽  
Icu Admission ◽  
Patients With Heart Failure

Abstract Funding Acknowledgements Type of funding sources: None. Background Patients with heart failure in the setting of COVID-19 requiring admission to the intensive care unit may present a set of unique challenges. There is limited data to describe the clinical characteristics and outcomes in this subset of the patient population. Purpose The study"s purpose was to extensively describe the characteristics and outcomes of heart failure patients admitted to the intensive care unit with COVID-19 compared to non-heart failure patients . Methods We conducted a multicenter, prospective analysis for all adult critically ill patients with heart failure admitted to intensive care units (ICUs) between March 1 to August 31, 2020, with an objectively confirmed diagnosis of COVID-19. Results A total of 723 critically ill patients with COVID-19 had been admitted in ICUs, 59 patients with heart failure, and 664 patients with no heart failure before ICU admission. Heart failure patients had significantly more comorbid conditions such as diabetes mellitus, hypertension, dyslipidemia, atrial fibrillation, and acute coronary syndrome. Higher baseline severity scores (APACHE II & SOFA score) and nutritional risk (NUTRIC Score) were observed in heart failure patients. Also, heart failure patients had more acute kidney injury during ICU admission and required more mechanical ventilation within 24 hours of ICU admission. Patients with heart failure had a similar incidence of thrombosis compared to patients with no heart failure. Critically ill patients with COVID-19 and heart failure had similar ICU length of stay (LOS), mechanical ventilation duration, and hospital LOS compared to patients with no heart failure. During ICU stay, patients with heart failure had more in-hospital and ICU deaths in comparison to the non-heart failure group (64.3% vs. 44.6%, P-value &lt;0.01) and (54.5% vs. 39%, P-value = 0.02) respectively. Conclusion In this observational study evaluating the clinical characteristics and outcomes of critically ill COVID-19 patients with heart failure, patients with COVID-19 and heart failure had similar ICU LOS, duration of MV and hospital LOS, thrombosis rate compared to patients with no heart failure. However, during ICU stay, patients with heart failure had more in-hospital and ICU deaths than the non-heart failure group.

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