scholarly journals Factors Related to Delirium of Intensive Care Unit Patients in Korea: A Systematic Review

2021 ◽  
Author(s):  
Nam Young Kim ◽  
Se Ang Ryu ◽  
Yun Hee Kim
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Data Extraction ◽  
Extraction Methods ◽  
Apache Ii Score ◽  
Unplanned Extubation ◽  
Icu Patients ◽  
Artificial Airway ◽  
Associated Risk Factors ◽  
High Level

Background: The delirium in Intensive Care Units (ICU) patients is a major cause of unplanned extubation, increased length of hospital day. This study aimed to review systematically risk factors associated with the occurrence of delirium among ICU patients in South Korea. Methods: Data collection was done with domestic literature search databases including KMbase, KoreaMed, KISS, and KisTi and also with hand searching, from February 17 to May 19, 2019. Two researchers independently selected research literatures, and three researchers summarized and identified related variables based on data extraction methods. Results: Overall, 140 articles were identified, 18 articles met the inclusion criteria for review. According to the results of the methodological quality, one article was found to have a high level of quality, while the remaining 17 articles belonged to the medium level. Those factors were highly associated with delirium by more than 9 out of the 18 selected studies were listed as follows; age, the application of ventilator, APACHE II score, comorbidity, the application of restraint, and educational level. In addition, catheter insertion and the application of artificial airway had significant relevance with the occurrence of delirium. Conclusion: For management of delirium among ICU patients, it is necessary to eliminate and prevent delirium-associated risk factors, and also to detect and treat the delirium early through regular monitoring using an appropriate screening tool for delirium.

Risk factors for and influence of bloodstream infections on mortality: a 1-year prospective study in a Greek intensive-care unit

2008 ◽  
Vol 137 (5) ◽  
pp. 727-735 ◽  
Author(s):  
M. PRATIKAKI ◽  
E. PLATSOUKA ◽  
C. SOTIROPOULOU ◽  
T. VASSILAKOPOULOS ◽  
O. PANIARA ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Distress Syndrome ◽  
Severity Of Illness ◽  
Bloodstream Infections ◽  
Apache Ii Score ◽  
Incidence Risk ◽  
History Of ◽  
Gram Negative Organisms ◽  
High Level

SUMMARYTo determine the incidence, risk factors for, and the influence of bloodstream infections (BSIs) on mortality of patients in intensive-care units (ICUs), prospectively collected data from all patients with a stay in an ICU >48 h, during a 1-year period, were analysed. Of 572 patients, 148 developed a total of 232 BSI episodes (incidence 16·3 episodes/1000 patient-days). Gram-negative organisms with high level of resistance to antibiotics were the most frequently isolated pathogens (157 strains, 67·8%). The severity of illness on admission, as estimated by APACHE II score (OR 1·07, 95% CI 1·04–1·1, P<0·001), the presence of acute respiratory distress syndrome (OR 3·57, 95% CI 1·92–6·64, P<0·001), and a history of diabetes mellitus (OR 2·37, 95% CI 1·36–4·11, P=0·002) were risk factors for the occurrence of BSI whereas the development of an ICU-acquired BSI was an independent risk factor for death (OR 1·76, 95% CI 1·11–2·78, P=0·015). Finally, the severity of organ dysfunction on the day of the first BSI episode, as estimated by SOFA score, and the level of serum albumin, independently affected the outcome (OR 1·44, 95% CI 1·22–1·7, P<0·001 and OR 0·47, 95% CI 0·23–0·97, P=0·04 respectively).

scholarly journals Risk Factors and Outcomes for Intestinal Carriage of AmpC-Hyperproducing Enterobacteriaceae in Intensive Care Unit Patients

2015 ◽  
Vol 60 (3) ◽  
pp. 1883-1887 ◽  
Author(s):  
Simon Poignant ◽  
Jérôme Guinard ◽  
Aurélie Guigon ◽  
Laurent Bret ◽  
Didier-Marc Poisson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Multivariate Analysis ◽  
Intensive Care ◽  
Intestinal Colonization ◽  
Icu Patients ◽  
Content Type ◽  
Lengths Of Stay ◽  
High Level

In a cohort of 1,209 intensive care unit (ICU) patients, the prevalence of intestinal colonization with high-level expressed AmpC cephalosporinase-producingEnterobacteriaceae(HLAC-PE) rose steadily from 2% at admission to 30% in patients with lengths of stay (LOS) exceeding 4 weeks. In multivariate analysis, LOS was the main predictor of carriage acquisition after adjustment on antimicrobial exposure. HLAC-PE infection occurred in 15% of carriers. Carriage and infection were associated with a marked increase in carbapenem consumption.

scholarly journals Beta-Lactams Toxicity in the Intensive Care Unit: An Underestimated Collateral Damage?

2021 ◽  
Vol 9 (7) ◽  
pp. 1505
Author(s):  
Claire Roger ◽  
Benjamin Louart
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Clinical Manifestations ◽  
Acute Interstitial Nephritis ◽  
Convulsive Status Epilepticus ◽  
Beta Lactam ◽  
Beta Lactams ◽  
Drug Induced ◽  
Icu Patients

Beta-lactams are the most commonly prescribed antimicrobials in intensive care unit (ICU) settings and remain one of the safest antimicrobials prescribed. However, the misdiagnosis of beta-lactam-related adverse events may alter ICU patient management and impact clinical outcomes. To describe the clinical manifestations, risk factors and beta-lactam-induced neurological and renal adverse effects in the ICU setting, we performed a comprehensive literature review via an electronic search on PubMed up to April 2021 to provide updated clinical data. Beta-lactam neurotoxicity occurs in 10–15% of ICU patients and may be responsible for a large panel of clinical manifestations, ranging from confusion, encephalopathy and hallucinations to myoclonus, convulsions and non-convulsive status epilepticus. Renal impairment, underlying brain abnormalities and advanced age have been recognized as the main risk factors for neurotoxicity. In ICU patients, trough concentrations above 22 mg/L for cefepime, 64 mg/L for meropenem, 125 mg/L for flucloxacillin and 360 mg/L for piperacillin (used without tazobactam) are associated with neurotoxicity in 50% of patients. Even though renal complications (especially severe complications, such as acute interstitial nephritis, renal damage associated with drug induced hemolytic anemia and renal obstruction by crystallization) remain rare, there is compelling evidence of increased nephrotoxicity using well-known nephrotoxic drugs such as vancomycin combined with beta-lactams. Treatment mainly relies on the discontinuation of the offending drug but in the near future, antimicrobial optimal dosing regimens should be defined, not only based on pharmacokinetics/pharmacodynamic (PK/PD) targets associated with clinical and microbiological efficacy, but also on PK/toxicodynamic targets. The use of dosing software may help to achieve these goals.

scholarly journals Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Comparative Machine Learning Study (Preprint)

2020 ◽  
Author(s):  
Sujeong Hur ◽  
Ji Young Min ◽  
Junsang Yoo ◽  
Kyunga Kim ◽  
Chi Ryang Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Patient Safety ◽  
Intensive Care ◽  
Predictive Value ◽  
Prediction Models ◽  
Support Vector ◽  
Unplanned Extubation ◽  
Electronic Health Record Data ◽  
Icu Patients

BACKGROUND Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered as the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. OBJECTIVE This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. METHODS This study was conducted an academic tertiary hospital in Seoul. The hospital had approximately 2,000 inpatient beds and 120 intensive care unit (ICU) beds. The number of patients, on daily basis, was approximately 9,000 for the out-patient. The number of annual ICU admission was approximately 10,000. We conducted a retrospective study between January 1, 2010 and December 31, 2018. A total of 6,914 extubation cases were included. We developed an unplanned extubation prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used area under the receiver operator characteristic curve (AUROC). Sensitivity, specificity, positive predictive value negative predictive value, and F1-score were also determined for each model. For performance evaluation, we also used calibration curve, the Brier score, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS Among the 6,914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was more likely to occur during the night shift compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality was higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.762, and for SVM was 0.740. CONCLUSIONS We successfully developed and validated machine learning-based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787, which was obtained using RF. CLINICALTRIAL N/A

scholarly journals Molecular detection and antibiotic resistance pattern of extended-spectrum beta-lactamase producing Escherichia coli in a Tertiary Hospital in Enugu, Nigeria

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Ifeyinwa N. Nwafia ◽  
Martin E. Ohanu ◽  
Samuel O. Ebede ◽  
Uchenna C. Ozumba
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Intensive Care Unit Admission ◽  
Beta Lactamase ◽  
Extended Spectrum Beta Lactamase ◽  
E Coli ◽  
Extended Spectrum ◽  
Associated Risk Factors

Abstract Background The use of antibiotic agents in the treatment of infectious diseases has greatly contributed to the decrease in morbidity and mortality, but these great advances in treatment are being undermined by the rapidly increasing antimicrobial resistant organisms. Extended-spectrum beta-lactamases are enzymes hydrolyzing the beta lactam antibiotics, including third generation cephalosporins and monobactams but not cephamycins and carbapenems. They pose a serious global health threat and have become a challenge for health care providers. The aim of this research was to assess the prevalence of extended-spectrum beta-lactamase producing Escherichia coli in University of Nigeria Teaching Hospital Ituku-Ozalla Enugu and to detect the risk factors for acquisition of the resistant organism. To proffer advice on antibiotic stewardship in clinical practice and public health interventions, to curb the spread of the resistant organisms in the hospital. Results Out of the 200 E. coli isolates, 70 (35.00%) were confirmed positive for extended-spectrum beta-lactamase production. Fifty-three (75.7%) were from hospital acquired infections. All the isolates were resistant to ampicillin, tetracycline and chloramphenicol while 68 (97.14%) of the 70 isolates were susceptible to imipenem. BlaTEM, blaSHV and blaTEM were detected in 66 (94%) of the 70 isolates. The ESBL bla genes detected were blaCTX-M (n = 26; 37.14%), blaTEM (n = 7; 10.00%), blaSHV (n = 2; 2.86%), blaCTX-M/TEM (n = 7; 10.0%), blaCTX-M/SHV (n = 14; 20.0%) and blaCTX-M/TEM/SHV (n = 10; 14.29%). The three bla genes were not detected in 4 (5.71%) of the isolates. Recent surgery, previous antibiotic and intensive care unit admission were the associated risk factors to infections caused by extended-spectrum beta-lactamase producing E. coli. Conclusion There is a high rate of infections caused by extended-spectrum beta-lactamase producing E. coli. Recent surgery, previous antibiotic and intensive care unit admission were associated risk factors.

scholarly journals Incidence, Risk Factors, and Outcome of Acute Kidney Injury in the Intensive Care Unit: A Single-Center Study from Jordan

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Ashraf O. Oweis ◽  
Sameeha A. Alshelleh ◽  
Suleiman M. Momany ◽  
Shaher M. Samrah ◽  
Basheer Y. Khassawneh ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
Kidney Injury ◽  
Tertiary Hospital ◽  
Acute Kidney Injury Network ◽  
Apache Ii Score ◽  
Mortality And Morbidity ◽  
Incidence Risk

Background. Acute kidney injury (AKI) is a common serious problem affecting critically ill patients in intensive care unit (ICU). It increases their morbidity, mortality, length of ICU stay, and long-term risk of chronic kidney disease (CKD). Methods. A retrospective study was carried out in a tertiary hospital in Jordan. Medical records of patients admitted to the medical ICU between 2013 and 2015 were reviewed. We aimed to identify the incidence, risk factors, and outcomes of AKI. Acute kidney injury network (AKIN) classification was used to define and stage AKI. Results. 2530 patients were admitted to medical ICU, and the incidence of AKI was 31.6%, mainly in stage 1 (59.4%). In multivariate analysis, increasing age (odds ratio (OR) = 1.2 (95% CI 1.1–1.3), P = 0.0001) and higher APACHE II score (OR = 1.5 (95% CI 1.2–1.7), P = 0.001) were predictors of AKI, with 20.4% of patients started on hemodialysis. At the time of discharge, 58% of patients with AKI died compared to 51.3% of patients without AKI (P = 0.05). 88% of patients with AKIN 3 died by the time of discharge compared to patients with AKIN 2 and 1 (75.3% and 61.2% respectively, P = 0.001). Conclusion. AKI is common in ICU patients, and it increases mortality and morbidity. Close attention for earlier detection and addressing risk factors for AKI is needed to decrease incidence, complications, and mortality.

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