scholarly journals Elevated lactate level predicts intensive care unit admissions, endoscopies and transfusions in patients with acute gastrointestinal bleeding

2018 ◽  
Vol Volume 11 ◽  
pp. 185-192 ◽  
Author(s):  
Manish Shrestha ◽  
Mark Borgstrom ◽  
Eugene Trowers
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Gastrointestinal Bleeding ◽  
Lactate Level ◽  
Acute Gastrointestinal Bleeding ◽  
Elevated Lactate ◽  
Elevated Lactate Level

scholarly journals Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dennis Shung ◽  
Jessie Huang ◽  
Egbert Castro ◽  
J. Kenneth Tay ◽  
Michael Simonov ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Gastrointestinal Bleeding ◽  
Blood Cells ◽  
External Validation ◽  
Red Blood Cell Transfusion ◽  
Internal Validation ◽  
Acute Gastrointestinal Bleeding ◽  
Risk Patients

AbstractAcute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding. A patient cohort admitted for acute gastrointestinal bleeding (N = 2,524) was identified from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database and separated into training (N = 2,032) and internal validation (N = 492) sets. The external validation patient cohort was identified from the eICU collaborative database of patients admitted for acute gastrointestinal bleeding presenting to large urban hospitals (N = 1,526). 62 demographic, clinical, and laboratory test features were consolidated into 4-h time intervals over the first 24 h from admission. The outcome measure was the transfusion of red blood cells during each 4-h time interval. A long short-term memory (LSTM) model, a type of Recurrent Neural Network, was compared to a regression-based models on time-updated data. The LSTM model performed better than discrete time regression-based models for both internal validation (AUROC 0.81 vs 0.75 vs 0.75; P < 0.001) and external validation (AUROC 0.65 vs 0.56 vs 0.56; P < 0.001). A LSTM model can be used to predict the need for transfusion of packed red blood cells over the first 24 h from admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.

Clinical Significance of Elevated Troponin I Level in Patients With Acute Gastrointestinal Bleeding Admitted to the Intensive Care Unit

CHEST Journal ◽  
2014 ◽  
Vol 146 (4) ◽  
pp. 222A
Author(s):  
Rahul Sinha ◽  
Udit Chaddha ◽  
Karim El Kersh ◽  
Matthew Rayner-Lawren Woodford ◽  
Juan Guardiola ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Gastrointestinal Bleeding ◽  
Clinical Significance ◽  
Troponin I ◽  
Elevated Troponin ◽  
Acute Gastrointestinal Bleeding

IMPORTANCE OF ELEVATED LACTATE LEVEL WITHOUT CLEAR SEPSIS

CHEST Journal ◽  
2019 ◽  
Vol 156 (4) ◽  
pp. A1700
Author(s):  
Niranjana Chellappa ◽  
Momena Sohail ◽  
Amy Vandenberg ◽  
Anupam Suneja
Keyword(s):  
Lactate Level ◽  
Elevated Lactate ◽  
Elevated Lactate Level

Cause of an Elevated Lactate Level

JAMA ◽  
2015 ◽  
Vol 313 (23) ◽  
pp. 2381
Author(s):  
Kai E. Swenson ◽  
Charles R. Wira
Keyword(s):  
Lactate Level ◽  
Elevated Lactate ◽  
Elevated Lactate Level

Cause of an Elevated Lactate Level—Reply

JAMA ◽  
2015 ◽  
Vol 313 (23) ◽  
pp. 2381
Author(s):  
Meng Chen ◽  
Tiffany Y. Kim ◽  
Antonio M. Pessegueiro
Keyword(s):  
Lactate Level ◽  
Elevated Lactate ◽  
Elevated Lactate Level
Sign in / Sign up

Export Citation Format

Share Document