scholarly journals PROBABILISTIC GLYCEMIC CONTROL DECISION SUPPORT IN ICU: PROOF OF CONCEPT USING BAYESIAN NETWORK

2019 ◽  
Vol 81 (2) ◽  
Author(s):  
Asma Abu-Samah ◽  
Normy Norfiza Abdul Razak ◽  
Fatanah Mohamad Suhaimi ◽  
Ummu Kulthum Jamaludin ◽  
Azrina Md. Ralib
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
Glycemic Control ◽  
Bayesian Network ◽  
Intensive Care Units ◽  
Physiological Model ◽  
Proof Of Concept ◽  
Therapy Outcomes ◽  
Intensive Care Patients ◽  
And Performance

Glycemic control in intensive care patients is complex in terms of patients’ response to care and treatment. The variability and the search for improved insulin therapy outcomes have led to the use of human physiology model based on per-patient metabolic condition to provide personalized automated recommendations. One of the most promising solutions for this is the STAR protocol, which is based on a clinically validated insulin-nutrition-glucose physiological model. However, this approach does not consider demographical background such as age, weight, height, and ethnicity. This article presents the extension to intensive care personalized solution by integrating per-patient demographical, and upon admission information to intensive care conditions to automate decision support for clinical staff. In this context, a virtual study was conducted on 210 retrospectives intensive care patients’ data. To provide a ground, the integration concept is presented roughly, but the details are given in terms of a proof of concept using Bayesian Network, linking the admission background and performance of the STAR control. The proof of concept shows 71.43% and 73.90% overall inference precision, and reliability, respectively, on the test dataset. With more data, improved Bayesian Network is believed to be reproduced. These results, nevertheless, points at the feasibility of the network to act as an effective classifier using intensive care units data, and glycemic control performance to be the basis of a probabilistic, personalized, and automated decision support in the intensive care units.

scholarly journals Towards Personalized Intensive Care Decision Support Using a Bayesian Network: A Multicenter Glycemic Control Study

2019 ◽  
Vol 8 (3) ◽  
pp. 202-209
Author(s):  
Asma Abu-Samah ◽  
Normy Norfiza Abdul Razak ◽  
Fatanah Mohamad Suhaimi ◽  
Ummu Kulthum Jamaludin ◽  
James Geoffrey Chase
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
Glycemic Control ◽  
Bayesian Network ◽  
Care Decision ◽  
Control Study

Linking Bayesian Network and Intensive Care Units Data: A Glycemic Control Study

2018 ◽  
Author(s):  
Asma Abu-Samah ◽  
Normy Norfiza Abdul Razak ◽  
Fatanah Mohamad Suhaimi ◽  
Ummu Kulthum Jamaludin ◽  
Geoffrey Chase
Keyword(s):  
Intensive Care ◽  
Glycemic Control ◽  
Bayesian Network ◽  
Intensive Care Units ◽  
Control Study

An Efficiency Evaluation of Protocols for Tight Glycemic Control in Intensive Care Units

2007 ◽  
Vol 16 (6) ◽  
pp. 589-598 ◽  
Author(s):  
Mark A. Malesker ◽  
Pamela A. Foral ◽  
Ann C. McPhillips ◽  
Keith J. Christensen ◽  
Julie A. Chang ◽  
...  
Keyword(s):  
Intensive Care ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Intensive Care Units ◽  
Tight Glycemic Control ◽  
Third Party ◽  
Full Time ◽  
Motion Data ◽  
Time Motion ◽  
The Impact

Background The efficiency of protocols for tight glycemic control is uncertain despite their adoption in hospitals. Objectives To evaluate the efficiency of protocols for tight glycemic control used in intensive care units. Methods Three separate studies were performed: (1) a third-party observer used a stopwatch to do a time-motion analysis of patients being treated with a protocol for tight glycemic control in 3 intensive care units, (2) charts were retrospectively reviewed to determine the frequency of deviations from the protocol, and (3) a survey assessing satisfaction with and knowledge of the protocol was administered to full-time nurses. Results Time-motion data were collected for 454 blood glucose determinations from 38 patients cared for by 47 nurses. Mean elapsed times from blood glucose result to therapeutic action were 2.24 (SD, 1.67) minutes for hypoglycemia and 10.65 (SD, 3.24) minutes for hyperglycemia. Mean elapsed time to initiate an insulin infusion was 32.56 (SD, 12.83) minutes. Chart review revealed 734 deviations from the protocol in 75 patients; 57% (n = 418) were deviations from scheduled times for blood glucose measurements. The mean number of deviations was approximately 9 per patient. Of 60 nurses who responded to the workload survey, 42 (70%) indicated that the protocol increased their workload; frequency of blood glucose determinations was the most common reason. Conclusions Nurses spend substantial time administering protocols for tight glycemic control, and considerable numbers of deviations occur during that process. Further educational efforts and ongoing assessment of the impact of such protocols are needed.

How Low Should You Go? The Limbo of Glycemic Control in Intensive Care Units

2011 ◽  
Vol 31 (4) ◽  
pp. e9-e18 ◽  
Author(s):  
A. C. Faust ◽  
R. L. Attridge ◽  
L. Ryan
Keyword(s):  
Intensive Care ◽  
Glycemic Control ◽  
Intensive Care Units

Using a Decision Wheel to Reduce Use of Restraints in a Medical-Surgical Intensive Care Unit

2016 ◽  
Vol 25 (6) ◽  
pp. 479-486 ◽  
Author(s):  
Stacy Hevener ◽  
Barbara Rickabaugh ◽  
Toby Marsh
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Intensive Care Units ◽  
Decision Support Tool ◽  
Educational Activity ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Support Tool ◽  
Unplanned Extubations

Background Little information is available on the use of tools in intensive care units to help nurses determine when to restrain a patient. Patients in medical-surgical intensive care units are often restrained for their safety to prevent them from removing therapeutic devices. Research indicates that restraints do not necessarily prevent injuries or removal of devices by patients. Objectives To decrease use of restraints in a medical-surgical intensive care unit and to determine if a decision support tool is useful in helping bedside nurses determine whether or not to restrain a patient. Methods A quasi-experimental study design was used for this pilot study. Data were collected for each patient each shift indicating if therapeutic devices were removed and if restraints were used. An online educational activity supplemented by 1-on-1 discussion about proper use of restraints, alternatives, and use of a restraint decision tool was provided. Frequency of restraint use was determined. Descriptive statistics and thematic analysis were used to examine nurses’ perceptions of the decision support tool. Results Use of restraints was reduced 32%. No unplanned extubations or disruption of life-threatening therapeutic devices by unrestrained patients occurred. Conclusions With implementation of the decision support tool, nurses decreased their use of restraints yet maintained patients’ safety. A decision support tool may help nurses who are undecided or who need reassurance on their decision to restrain or not restrain a patient.

Effectiveness of a multidimensional approach for prevention of ventilator-associated pneumonia in 21 adult intensive-care units from 10 cities in India: findings of the International Nosocomial Infection Control Consortium (INICC)

2013 ◽  
Vol 141 (12) ◽  
pp. 2483-2491 ◽  
Author(s):  
Y. MEHTA ◽  
N. JAGGI ◽  
V. D. ROSENTHAL ◽  
C. RODRIGUES ◽  
S. K. TODI ◽  
...  
Keyword(s):  
Intensive Care ◽  
Nosocomial Infection ◽  
Infection Control ◽  
Intensive Care Units ◽  
Ventilator Associated Pneumonia ◽  
Multidimensional Approach ◽  
Time Period ◽  
Control And Prevention ◽  
Quasi Experimental ◽  
And Performance

SUMMARYWe report on the effect of the International Nosocomial Infection Control Consortium's (INICC) multidimensional approach for the reduction of ventilator-associated pneumonia (VAP) in adult patients hospitalized in 21 intensive-care units (ICUs), from 14 hospitals in 10 Indian cities. A quasi-experimental study was conducted, which was divided into baseline and intervention periods. During baseline, prospective surveillance of VAP was performed applying the Centers for Disease Control and Prevention/National Healthcare Safety Network definitions and INICC methods. During intervention, our approach in each ICU included a bundle of interventions, education, outcome and process surveillance, and feedback of VAP rates and performance. Crude stratified rates were calculated, and by using random-effects Poisson regression to allow for clustering by ICU, the incidence rate ratio for each time period compared with the 3-month baseline was determined. The VAP rate was 17·43/1000 mechanical ventilator days during baseline, and 10·81 for intervention, showing a 38% VAP rate reduction (relative risk 0·62, 95% confidence interval 0·5–0·78, P = 0·0001).

scholarly journals Survey of Glycemic Control Protocols in Cardiac Surgery Intensive Care Units

2017 ◽  
Vol 2 (1) ◽  
pp. 24-25
Author(s):  
Vaishali S Badge ◽  
FM Ashiqe
Keyword(s):  
Mechanical Ventilation ◽  
Cardiac Surgery ◽  
Intensive Care ◽  
Glycemic Control ◽  
Glycaemic Control ◽  
Hospital Stay ◽  
Intensive Care Units ◽  
Cardiac Arrhythmias ◽  
Prolonged Mechanical Ventilation ◽  
Control Protocols

ABSTRACT Perioperative Hyperglycaemia can lead to sepsis, mediastinitis, prolonged mechanical ventilation, cardiac arrhythmias, increased ICU and hospital stay. The different centres follow different protocols to treat hyperglycaemia and still there is a controversy regarding the tight sugar control protocol. This survey was carried out to find the appropriate protocol regarding glycaemic control in various centres in UK. How to cite this article Badge VS, Ashiqe FM. Survey of Glycemic Control Protocols in Cardiac Surgery Intensive Care Units. Res Inno in Anesth 2017;2(1):24-25.

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