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STAR Protocol for Critically Ill Patients in Malaysia: ICU Staff Survey and Human Factor Assessment
Athirah Abdul Razak,Asma Abu-Samah,Normy Razak,Fatanah Suhaimi,Ummu Jamaludin,Azrina Ralib,Christopher Pretty 대한전자공학회 2019 IEIE Transactions on Smart Processing & Computing Vol.8 No.5
Since 2001, various glycemic control (GC) studies have been conducted to reduce dysglycemia in critically ill patients. To prove their effectiveness, each proposed GC approach requires not only patient clinical results, but also users’ assessments. This paper presents International Islamic University Malaysia Medical Centre intensive care unit (ICU) staff perceptions and assessments of human factors of Stochastic Targeted (STAR) protocol usage based on a Malaysian pilot trial to analyze the users’ responses to the protocol in the Malaysian set-up. STAR protocol is a model-based and automated GC that accounts for the individual patient’s metabolic variability. The ICU staff feedback on STAR trial was based on 13 survey questions. The survey demonstrated that 87.5% of ICU staff agreed that STAR protocol improved patient’s outcome, and is user friendly. Human factor assessment quantifies the different interventions recorded from STAR historical and manual bedside records for a total of 31 diabetes mellitus (DM) and non-diabetes mellitus (NDM) patients. During a total of 6168 hours in ICU stays, the percentage of compliance in blood glucose (BG) measurements, insulin infusion, and nutrition administered for DM and NDM cohorts were 97.3%/97.2%, 74.1%/70.3% and 65%/71.2%, respectively.
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Asma Abu-Samah,Normy Norfiza Abdul Razak,Fatanah Mohamad Suhaimi,Ummu Kulthum Jamaludin,James Geoffrey Chase 대한전자공학회 2019 IEIE Transactions on Smart Processing & Computing Vol.8 No.3
Personalized treatment in glycemic control (GC) is a visibly promising research area that requires improved mechanisms providing patient-specific procedures to enable complicated decision support. Available per-patient data must be more than written records, and be fully integrated in this personalization process. This article presents a process for relating the intensive care unit patients’ demographic and admission data to their GC performance. With this objective, a probabilistic Bayesian network was chosen to provide more personalized decisions. As a case study, average daily blood glucose measurements were chosen as the interest target node in order to weigh GC that provides a reduced nursing workload. To test the idea, data from 482 patients, with nine variables from four Malaysian intensive care units with different controls were exploited. The identified steps crucial in building a dependable model are variable selection, continuous state discretization, and unsupervised structure learning. Using a multi-target node evaluation, a network with 80% mean overall classification precision was obtained with a normalized equal distance discretization algorithm and a maximum weight spanning tree technique. Meanwhile, the interest target node scored 90.39% precision. The results from this study, which are complemented with an evaluation of missing data, are proposed as a benchmark for using Bayesian networks in this type of application.
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