scholarly journals Logistic regression analysis and nursing interventions for high-risk factors for pressure sores in patients in a surgical intensive care unit

2015 ◽  
Vol 2 (2-3) ◽  
pp. 51-54 ◽  
Author(s):  
Xin-Ran Wang ◽  
Bin-Ru Han
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Intensive Care ◽  
Surgical Intensive Care Unit ◽  
Nursing Interventions ◽  
Pressure Sores ◽  
Surgical Intensive Care ◽  
High Risk Factors

scholarly journals Risk Factors for Carbapenem Resistant Klebsiella pneumoniae Hospital Infection in the Intensive Care Unit

2018 ◽  
Vol 19 (3) ◽  
pp. 255-261
Author(s):  
Zorana M. Djordjevic ◽  
Marko M. Folic ◽  
Nevena Gajovic ◽  
Slobodan M. Jankovic
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Logistic Regression ◽  
Intensive Care ◽  
Klebsiella Pneumoniae ◽  
Surgical Intensive Care Unit ◽  
Multivariate Logistic Regression ◽  
Surgical Intensive Care ◽  
Carbapenem Resistant

Abstract Carbapenem-resistant Klebsiella pneumoniae (CR-Kp) has become a major threat to patients in hospitals, increasing mortality, length of stay and costs. The aim of this study was to discover risk factors for the development of hospital infections (HIs) caused by CR-Kp. A prospective cohort study was conducted in the Medical-Surgical Intensive Care Unit of the Clinical Centre in Kragujevac, Kragujevac, Serbia, from January 1, 2011, to December 31, 2015. The “cases” were patients with HIs caused by CR-Kp, while the “controls” were patients infected with carbapenem-sensitive Klebsiella pneumoniae (CS-Kp). The significance of multiple putative risk factors for HIs caused by CR-Kp was tested using multivariate logistic regression. Although univariate analyses pointed to many risk factors, with a significant influence on the occurrence of hospital CR-Kp infections, the multivariate logistic regression identified five independent risk factors: use of mechanical ventilation (OR=6.090; 95% CI=1.030-36.020; p=0.046); length of antibiotic therapy before HIs (days) (OR=1.080; 95% CI=1.003-1.387; p=0.045); previous use of carbapenems (OR=7.005; 95% CI=1.054-46.572; p=0.044); previous use of ciprofloxacin (OR=20.628; 95% CI=2.292-185.687; p=0.007) and previous use of metronidazole (OR=40.320; 95% CI=2.347-692.795; p=0.011) HIs caused by CR-Kp are strongly associated with the use of mechanical ventilation and the duration of the previous use of certain antibiotics (carbapenems, ciprofloxacin and metronidazole).

Risk factors for poor outcome in older patients admitted in a surgical intensive care unit

2021 ◽  
Author(s):  
Emilie Occhiali ◽  
Pierre Prolange ◽  
Florence Cassiau ◽  
Frédéric Roca ◽  
Benoit Veber ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Older Patients ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Poor Outcome

RISK FACTORS FOR CANDIDEMIA: COMPARISON BETWEEN MEDICAL AND SURGICAL INTENSIVE CARE UNIT PATIENTS

CHEST Journal ◽  
2005 ◽  
Vol 128 (4) ◽  
pp. 379S
Author(s):  
Stephen B. Heitner ◽  
Glenn Eiger ◽  
Robert Fischer ◽  
Emma C. Scott ◽  
Aba Somers
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care

scholarly journals Data Science for Extubation Prediction and Value of Information in Surgical Intensive Care Unit

2019 ◽  
Vol 8 (10) ◽  
pp. 1709 ◽  
Author(s):  
Tsung-Lun Tsai ◽  
Min-Hsin Huang ◽  
Chia-Yen Lee ◽  
Wu-Wei Lai
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Prediction Model ◽  
Value Of Information ◽  
Data Science ◽  
Apache Ii ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Science Framework

Besides the traditional indices such as biochemistry, arterial blood gas, rapid shallow breathing index (RSBI), acute physiology and chronic health evaluation (APACHE) II score, this study suggests a data science framework for extubation prediction in the surgical intensive care unit (SICU) and investigates the value of the information our prediction model provides. A data science framework including variable selection (e.g., multivariate adaptive regression splines, stepwise logistic regression and random forest), prediction models (e.g., support vector machine, boosting logistic regression and backpropagation neural network (BPN)) and decision analysis (e.g., Bayesian method) is proposed to identify the important variables and support the extubation decision. An empirical study of a leading hospital in Taiwan in 2015–2016 is conducted to validate the proposed framework. The results show that APACHE II and white blood cells (WBC) are the two most critical variables, and then the priority sequence is eye opening, heart rate, glucose, sodium and hematocrit. BPN with selected variables shows better prediction performance (sensitivity: 0.830; specificity: 0.890; accuracy 0.860) than that with APACHE II or RSBI. The value of information is further investigated and shows that the expected value of experimentation (EVE), 0.652 days (patient staying in the ICU), is saved when comparing with current clinical experience. Furthermore, the maximal value of information occurs in a failure rate around 7.1% and it reveals the “best applicable condition” of the proposed prediction model. The results validate the decision quality and useful information provided by our predicted model.

scholarly journals Deep Venous Thrombosis in Surgical Intensive Care Unit: Prevalence and Risk Factors

2009 ◽  
Vol 32 (2) ◽  
pp. 85-88 ◽  
Author(s):  
Chumpon Wilasrusmee ◽  
Kidakorn Kiranantawat ◽  
Suthas Horsirimanont ◽  
Panuwat Lertsithichai ◽  
Pinmanee Reodecha ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Venous Thrombosis ◽  
Deep Venous Thrombosis ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care

scholarly journals Readmission to a surgical intensive care unit: incidence, outcome and risk factors

Critical Care ◽  
2008 ◽  
Vol 12 (5) ◽  
pp. R123 ◽  
Author(s):  
Axel Kaben ◽  
Fabiano Corrêa ◽  
Konrad Reinhart ◽  
Utz Settmacher ◽  
Jan Gummert ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care
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