Obesity Does Not Affect Mortality after Trauma

2006 ◽  
Vol 72 (10) ◽  
pp. 966-969 ◽  
Author(s):  
Rodrigo F. Alban ◽  
Sergey Lyass ◽  
Daniel R. Margulies ◽  
M. Michael Shabot
Keyword(s):  
Intensive Care Unit ◽  
Risk Factor ◽  
Intensive Care ◽  
Adverse Outcomes ◽  
Apache Ii ◽  
Surgical Intensive Care Unit ◽  
Apache Ii Score ◽  
Surgical Intensive Care ◽  
Significant Difference ◽  
Nonobese Patients

Although obesity has been proposed as a risk factor for adverse outcomes after trauma, numerous studies report conflicting results. The objective of this study was to compare outcomes of obese and nonobese patients after trauma. The study population consisted of all trauma patients admitted to a surgical intensive care unit in a Level I trauma center from January 1999 to December 2002. Admission data, demographics, injury severity score (ISS), severity of illness, hospital course, complications, and outcomes were compared between obese (OB; body mass index [BMI] ≥ 30), and nonobese patients (NOB; BMI ≤ 29). A total of 918 patients was included in the study, 135 OB (14.7%) and 783 NOB (85.3%). There was no significant difference in demographic data, ISS, APACHE II score, and hospital stay. Intensive care unit stay was longer for OB patients (6.8 vs 4.8 days, P = 0.04). Overall mortality was 5.9 per cent for OB and 8.0 per cent for NOB patients (P = 0.48). Mortality by mechanism of injury was 3.4 per cent OB versus 7.4 per cent NOB (P = 0.26) for blunt and 10.6 per cent OB versus 10.2 per cent NOB (P = 0.9) for penetrating injury. The three most common complications associated with death were pulmonary, cardiovascular, and neurological deterioration. Using logistic regression analysis, age and ISS and APACHE II scores were associated with mortality, but BMI was not. We conclude that obesity does not appear to be a risk factor for adverse outcomes after blunt or penetrating trauma. Further research is warranted to uncover the reason for discrepant findings between centers.

Interhospital Transfer: An Independent Risk Factor for Mortality in the Surgical Intensive Care Unit

2013 ◽  
Vol 79 (9) ◽  
pp. 909-913 ◽  
Author(s):  
Katherine R. Arthur ◽  
Rachel R. Kelz ◽  
Angela M. Mills ◽  
Caroline E. Reinke ◽  
Mathew P. Robertson ◽  
...  
Keyword(s):  
Risk Factor ◽  
Intensive Care ◽  
Independent Risk Factor ◽  
Interquartile Range ◽  
Apache Ii ◽  
Surgical Intensive Care Unit ◽  
Apache Ii Score ◽  
Icu Mortality ◽  
Surgical Intensive Care ◽  
Interhospital Transfer

Interhospital transfer (IHT) is associated with mortality in medical and mixed intensive care units (ICUs), but few studies have examined this relationship in a surgical ICU (SICU) setting. We hypothesized that IHT is associated with increased mortality in SICU patients relative to ICU patients admitted within the hospital. We reviewed SICU and transfer center databases from a tertiary academic center over a 2-year period. Inclusion criteria included age 18 years or older and SICU admission 24 hours or greater. Demographic data, admission service, and Acute Physiology and Chronic Health Evaluation (APACHE) II scores were captured. The primary end point was ICU mortality. Univariate logistic regression was used to test the association between variables and mortality. Factors found to be associated with mortality at P < 0.1 were entered into a multivariable model. Of 4542 admissions, 416 arrived by IHT. Compared with the non-IHT group, the IHT group was older (age 58.3 years [interquartile range, 47.8 to 70.6] vs 57.8 years [interquartile range, 44.1 to 68.8] years, P = 0.036), sicker (APACHE II score 16.5 [interquartile range, 12 to 23] vs 14 [interquartile range, 10 to 20], P < 0.001), and more likely to be white (82% [n = 341] vs 69% [n = 2865], P < 0.001). Mortality rates in IHT patients were highest on the emergency surgery (18%), transplant surgery (16%), and gastrointestinal surgery (8%) services. After adjusting for age and APACHE II score, IHT remained a risk factor for ICU mortality (odds ratio, 1.60; 95% confidence interval, 1.04 to 2.45; P = 0.032) in SICU patients. Interhospital transfer is an independent risk factor for mortality in the SICU population; this risk is unevenly distributed through service lines. Further efforts to determine the cause of this association are warranted.

APACHE II SCORE AND OUTCOME IN THE SURGICAL INTENSIVE CARE UNIT (SICU)

1990 ◽  
Vol 18 (Supplement) ◽  
pp. S271
Author(s):  
Robert Rutledge ◽  
Edmund Rutherford ◽  
Samir Fakhry ◽  
Farid Muakkassa ◽  
Christopher Baker ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Apache Ii ◽  
Surgical Intensive Care Unit ◽  
Apache Ii Score ◽  
Surgical Intensive Care

Acute Physiology and Chronic Health Evaluation (APACHE II) score and outcome in the surgical intensive care unit

1991 ◽  
Vol 19 (8) ◽  
pp. 1048-1053 ◽  
Author(s):  
ROBERT RUTLEDGE ◽  
SAMIR M. FAKHRY ◽  
EDMUND J. RUTHERFORD ◽  
FARID MUAKKASSA ◽  
CHRISTOPHER C. BAKER ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Chronic Health Evaluation ◽  
Apache Ii ◽  
Surgical Intensive Care Unit ◽  
Apache Ii Score ◽  
Health Evaluation ◽  
Surgical Intensive Care ◽  
Chronic Health

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.

Interhospital transfer: An independent risk factor for mortality in the surgical intensive care unit

2012 ◽  
Vol 215 (3) ◽  
pp. S51
Author(s):  
Katherine R. Arthur ◽  
Rachel R. Kelz ◽  
Matthew Robertson ◽  
Carrie A. Sims ◽  
Jose L. Pascual ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Risk Factor ◽  
Intensive Care ◽  
Independent Risk Factor ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Interhospital Transfer

Acquired Muscle Weakness in the Surgical Intensive Care Unit

2016 ◽  
Vol 124 (1) ◽  
pp. 207-234 ◽  
Author(s):  
Hassan Farhan ◽  
Ingrid Moreno-Duarte ◽  
Nicola Latronico ◽  
Ross Zafonte ◽  
Matthias Eikermann
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Muscle Weakness ◽  
Prolonged Mechanical Ventilation ◽  
Underlying Mechanism ◽  
Time Frame ◽  
Adverse Outcomes ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Drug Induced

Abstract Muscle weakness is common in the surgical intensive care unit (ICU). Low muscle mass at ICU admission is a significant predictor of adverse outcomes. The consequences of ICU-acquired muscle weakness depend on the underlying mechanism. Temporary drug-induced weakness when properly managed may not affect outcome. Severe perioperative acquired weakness that is associated with adverse outcomes (prolonged mechanical ventilation, increases in ICU length of stay, and mortality) occurs with persistent (time frame: days) activation of protein degradation pathways, decreases in the drive to the skeletal muscle, and impaired muscular homeostasis. ICU-acquired muscle weakness can be prevented by early treatment of the underlying disease, goal-directed therapy, restrictive use of immobilizing medications, optimal nutrition, activating ventilatory modes, early rehabilitation, and preventive drug therapy. In this article, the authors review the nosology, epidemiology, diagnosis, and prevention of ICU-acquired weakness in surgical ICU patients.

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