scholarly journals Insulin sensitivity and sepsis score: A correlation between model-based metric and sepsis scoring system in critically ill patients

2017 ◽  
Vol 32 ◽  
pp. 112-123
Author(s):  
Fatanah M. Suhaimi ◽  
J. Geoffrey Chase ◽  
Christopher G. Pretty ◽  
Geoffrey M. Shaw ◽  
Normy N. Razak ◽  
...  
Keyword(s):  
Insulin Sensitivity ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Scoring System ◽  
Model Based ◽  
Sepsis Score

scholarly journals Observation of changes in model-based insulin sensitivity during haemodialysis transitions for critically ill patients

2012 ◽  
Vol 45 (18) ◽  
pp. 349-354
Author(s):  
Ummu K. Jamaludin ◽  
Paul D. Docherty ◽  
J. Geoffrey Chase ◽  
Aaron Le Compte ◽  
Geoffrey M. Shaw
Keyword(s):  
Insulin Sensitivity ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Model Based

scholarly journals Model-Based Insulin Sensitivity for Early Diagnosis of Sepsis in Critical Care

2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Wan Fadzlina Wan Muhd Shukeri ◽  
Azrina Md. Ralib ◽  
Ummu Khultum Jamaludin ◽  
Mohd Basri Mat-Nor
Keyword(s):  
Critical Care ◽  
Insulin Sensitivity ◽  
Glycemic Control ◽  
Real Time ◽  
Critically Ill ◽  
Sensitivity And Specificity ◽  
Critically Ill Patients ◽  
Predictive Value ◽  
Sepsis Group ◽  
Model Based

Introduction: Currently, it is almost impossible to diagnose a patient at the onset of sepsis due to the lack of real-time metrics with high sensitivity and specificity. The purpose of the present study is to determine the diagnostic value of model-based insulin sensitivity (SI) as a new sepsis biomarker in critically ill patients, and compare its performance to classical inflammatory parameters. Materials and method: We monitored hourly SI levels in septic (n=19) and non-septic (n=19) critically ill patients in a 24-hour follow-up study. Patients with type I or type II diabetes mellitus were excluded. SI levels were calculated by a validated glycemic control software, STAR TGC (Stochastic TARgeted Tight Glycemic Controller) (Christchurch, NZ). STAR TGC uses a physiological glucose-insulin system model coupled with stochastic models that capture SI variability in real time. Results: The median SI levels were lower in the sepsis group than in the non-sepsis group (1.9 x 10-4 L/mU/min vs 3.7 x 10-4 L/mU/min, P <0.0001). The areas under the receiver operating characteristic curve (AUROC) of the model-based SI for distinguishing non-sepsis from sepsis was 0.911, superior to white cells count (AUROC 0.611) and temperature (AUROC 0.618). The optimal cut-off value of the test was 2.9 x 10-4 L/mU/min. At this cut-off value, the sensitivity and specificity was 88.9% and 84.2%, respectively. The positive predictive value was 84.2%, while the negative predictive value was 88.9%. Conclusion: The early and relevant decrease of SI in sepsis suggests that it might be a promising novel biomarker of sepsis in critical care. Low SI is diagnostic of sepsis, while high SI rules out sepsis, and these may be determined non-invasively in real-time from glycemic control protocol data.

Disseminated Intravascular Coagulopathy in Critically Ill Patients in Amman, Jordan

2021 ◽  
pp. 109980042110172
Author(s):  
Eman Mahmoud Qasim Emleek ◽  
Amani Anwar Khalil
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Scoring System ◽  
Elevated Serum ◽  
Underlying Disease ◽  
Disseminated Intravascular Coagulopathy ◽  
Period Prevalence ◽  
Lower Baseline ◽  
Intravascular Coagulopathy

Background: The disseminated intravascular coagulation (DIC) is under-recognized in critically ill patients. The International Society of Thrombosis and Haemostasis (ISTH; DIC) provides a useful scoring system for accurate DIC identification. The study investigated the period prevalence of ISTH DIC from 2015 to 2017 in critically ill patients. Methods: In this multi-center, retrospective observational study, we included all patients identified with a DIC code or medically diagnosed with DIC during all admissions. Based on ISTH DIC scores ≥ 5, patients were classified with overt DIC. Results: A total of 220 patients were included in this study. The period prevalence of DIC was 4.45%. The point prevalence of DIC has increased from 3.49% to 5.58% from 2015 to 2017 (27.7% female; median age 61.6 years). Based on the ISTH-Overt DIC criteria, 45.2% of the sample had sepsis. Overt DIC patients had significantly lower baseline hemoglobin (HB; t = 2.137, df = 193, p = 0.034), platelet count ( t = 3.591, df = 193, p < 0.001) and elevated serum creatinine level ( M = 2.1, SD = 1.5, t = 2.203, df = 193, p = 0.029) compared to non–Overt DIC. There was a statistically significant elevation in FDPs among Overt DIC compared to non–Overt DIC (χ2 = 30.381, df = 1, p < 0.001). Overt DIC patients had significantly prolonged PT ( U = 2,298, z = 5.7, p < 0.001), PTT ( U = 2,334, z = 2.0, p = 0.045) and INR ( U = 2,541, z = 5.1, p < 0.001) compared to those with non–Overt DIC. Conclusion: The ISTH overt-DIC score can be used in critically ill patients regardless of the underlying disease. Efforts are required to predict and identify overt DIC using a valid scoring system on admission and follow-up of adult patients admitted to ICU.

scholarly journals Laboratory findings and a combined multifactorial approach to predict death in critically ill patients with COVID-19: a retrospective study

2020 ◽  
Vol 148 ◽  
Author(s):  
Q. Liu ◽  
N. C. Song ◽  
Z. K. Zheng ◽  
J. S. Li ◽  
S. K. Li
Keyword(s):  
Logistic Regression ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Scoring System ◽  
Related Factors ◽  
Laboratory Findings ◽  
Medical College ◽  
Multivariable Logistic Regression ◽  
Union Hospital ◽  
Multifactorial Approach

Abstract To describe the laboratory findings of cases of death with coronavirus disease 2019 (COVID-19) and to establish a scoring system for predicting death, we conducted this single-centre, retrospective, observational study including 336 adult patients (≥18 years old) with severe or critically ill COVID-19 admitted in two wards of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology in Wuhan, who had definite outcomes (death or discharge) between 1 February 2020 and 13 March 2020. Single variable and multivariable logistic regression analyses were performed to identify mortality-related factors. We combined multiple factors to predict mortality, which was validated by receiver operating characteristic curves. As a result, in a total of 336 patients, 34 (10.1%) patients died during hospitalisation. Through multivariable logistic regression, we found that decreased lymphocyte ratio (Lymr, %) (odds ratio, OR 0.574, P < 0.001), elevated blood urea nitrogen (BUN) (OR 1.513, P = 0.009), and raised D-dimer (DD) (OR 1.334, P = 0.002) at admission were closely related to death. The combined prediction model was developed by these factors with a sensitivity of 100.0% and specificity of 97.2%. In conclusion, decreased Lymr, elevated BUN, and raised DD were found to be in association with death outcomes in critically ill patients with COVID-19. A scoring system was developed to predict the clinical outcome of these patients.

scholarly journals e0620 APACHE-II scoring system is used in critically ill patients with cardiovascular disease

Heart ◽  
2010 ◽  
Vol 96 (Suppl 3) ◽  
pp. A191-A191
Author(s):  
Y. Wang ◽  
J. Zhu ◽  
H. Tan ◽  
Y. Zhang
Keyword(s):  
Cardiovascular Disease ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Scoring System ◽  
Apache Ii

scholarly journals Validation of a prognostic scoring system for critically ill patients with cirrhosis admitted to ICU

2015 ◽  
Vol 16 (3) ◽  
pp. 240-240
Author(s):  
J Campbell ◽  
J McPeake ◽  
M Shaw ◽  
A Puxty ◽  
P Emerson ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Scoring System
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