Intensive Care Unit Admission of Decompensated Cirrhotic Patients: Prognostic Scoring Systems

2011 ◽  
Vol 43 (4) ◽  
pp. 1079-1084 ◽  
Author(s):  
P. Feltracco ◽  
M. Brezzi ◽  
S. Barbieri ◽  
M. Milevoj ◽  
H. Galligioni ◽  
...  
Author(s):  
Shao-Chun Wu ◽  
Sheng-En Chou ◽  
Hang-Tsung Liu ◽  
Ting-Min Hsieh ◽  
Wei-Ti Su ◽  
...  

Background: Prediction of mortality outcomes in trauma patients in the intensive care unit (ICU) is important for patient care and quality improvement. We aimed to measure the performance of 11 prognostic scoring systems for predicting mortality outcomes in trauma patients in the ICU. Methods: Prospectively registered data in the Trauma Registry System from 1 January 2016 to 31 December 2018 were used to extract scores from prognostic scoring systems for 1554 trauma patients in the ICU. The following systems were used: the Trauma and Injury Severity Score (TRISS); the Acute Physiology and Chronic Health Evaluation (APACHE II); the Simplified Acute Physiology Score (SAPS II); mortality prediction models (MPM II) at admission, 24, 48, and 72 h; the Multiple Organ Dysfunction Score (MODS); the Sequential Organ Failure Assessment (SOFA); the Logistic Organ Dysfunction Score (LODS); and the Three Days Recalibrated ICU Outcome Score (TRIOS). Predictive performance was determined according to the area under the receiver operator characteristic curve (AUC). Results: MPM II at 24 h had the highest AUC (0.9213), followed by MPM II at 48 h (AUC: 0.9105). MPM II at 24, 48, and 72 h (0.8956) had a significantly higher AUC than the TRISS (AUC: 0.8814), APACHE II (AUC: 0.8923), SAPS II (AUC: 0.9044), MPM II at admission (AUC: 0.9063), MODS (AUC: 0.8179), SOFA (AUC: 0.7073), LODS (AUC: 0.9013), and TRIOS (AUC: 0.8701). There was no significant difference in the predictive performance of MPM II at 24 and 48 h (p = 0.37) or at 72 h (p = 0.10). Conclusions: We compared 11 prognostic scoring systems and demonstrated that MPM II at 24 h had the best predictive performance for 1554 trauma patients in the ICU.


2019 ◽  
Vol 26 (2) ◽  
pp. 1043-1059 ◽  
Author(s):  
Aya Awad ◽  
Mohamed Bader-El-Den ◽  
James McNicholas ◽  
Jim Briggs ◽  
Yasser El-Sonbaty

Current mortality prediction models and scoring systems for intensive care unit patients are generally usable only after at least 24 or 48 h of admission, as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of intensive care unit admission. This study aims to investigate how early hospital mortality can be predicted for intensive care unit patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 h of intensive care unit admission. The results showed that the discrimination power of the machine-learning classification methods after 6 h of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 h of admission.


1993 ◽  
Vol 2 (2) ◽  
pp. 185-191 ◽  
Author(s):  
Kent Sasse

In the United States, at least 6% of all hospital beds are in the intensive care unit (ICU) or coronary care unit. The cost of treating a patient in an intensive care unit averages from $2,000 to $3,500 per day. At least 10–40% of intensive care patients will not survive to hospital discharge. Today, every major category of disease may be found in the modern ICU; common diagnoses are septicemia, postsurgical complications, cerebrovascular accidents, gastrointestinal bleeding, neoplasia, and respiratory failure. ICUs employ some of the most sophisticated medical technology, routinely monitoring the cardiopulmonary performance of patients and often providing assisted ventilation. ICUs are high intensity in terms of their staffing, involving 24-hour physician supervision and nurse:patient ratios from 1:3 to 1:1.


1998 ◽  
Vol 26 (11) ◽  
pp. 1842-1849 ◽  
Author(s):  
Laurent G. Glance ◽  
Turner Osler ◽  
Tamotsu Shinozaki

2021 ◽  
Vol 14 (4) ◽  
pp. 1941-1953
Author(s):  
Nahla A. Mohamed ◽  
Eman Refaat Youness

Sepsis is a systemic inflammatory disorder that may be associated with higher rate of morbidity and mortality in pediatric patients admitted to intensive care unit with sepsis. Usage of different biomarkers may be helpful for early detection and appropriate management of sepsis. Our objectives was to investigate the role of serum lactate dehydrogenase in prediction of sepsis in critical pediatric patients, and its relation with prognostic scoring systems. A prospective cohort study was conducted at El Galaa teaching hospital between January 2020 and December 2020. A total of 168 pediatric patients were divided into the septic group (84) critically ill patients with sepsis from the pediatric intensive care unit (PICU)] and control group (84 stable patients admitted to the inpatient word). Demographic and clinical data were collected, routine laboratory investigation including LDH on admission and after 24 hours were performed. Pediatric Risk of Mortality III (PRISMIII) and Sequential Organ Failure Assessment (pSOFA) were assessed. Serum LDH level was significantly higher in septic group than control (P=0.000) and in non-survivor than survivor group (P=0.000). Also there was statistically significant correlation between survivor and non-survivor as regarding length of hospitality, pSOFA score and PRISMIII score. There was statistically significant positive correlation between LDH, PRISMIII (r=0.842, P<0.001) and pSOFA (r=0.785, P<0.001). We concluded that LDH is a useful marker in predicting of sepsis in critically ill pediatric patients especially when combined with prognostic scoring systems.


2014 ◽  
Vol 29 (6) ◽  
pp. 1131.e1-1131.e6 ◽  
Author(s):  
Philip Emerson ◽  
Joanne McPeake ◽  
Anna O’Neill ◽  
Harper Gilmour ◽  
Ewan Forrest ◽  
...  

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