Association between Diabetes Mellitus and Sepsis for the Glycemic Control Outcome of Two Intensive Care Units in Malaysia

Close monitoring and tight glycemic control are required among critically ill patients as they have dynamic metabolism which may precipitate stress-induced hyperglycemia. Clinically, diabetes mellitus (DM) patient with sepsis indicated a high mortality rate. This study investigates the association between DM and non-DM related to sepsis and non-sepsis patients from different insulin infusion therapy management. This study used 128 retrospective data from Hospital A, and 37 retrospective data from Hospital B. ICU patients who received insulin infusion therapy during their stay in the ICU were selected. Both centres implement the sliding scale-based insulin infusion therapy with the target range for blood glucose (BG) level within 6.0 – 10.0 mmol/L. The retrospective clinical data were compared among cohorts for DM and non-DM associated with sepsis and non-sepsis conditions. Findings showed that the DM group had higher insulin sensitivity than non-DM for both cohorts. Meanwhile, cohort B had higher insulin sensitivity than cohort A for all classes. Cohort A (DM+Sepsis) had low insulin sensitivity (66.7 L/(mU.min) and worst condition with sepsis which resulted from the lowest percentage (30.81%) of BG measurement within the target range. The (nonDM+nonSepsis) class had the tightest glycemic control for cohort A (3.4 mmol/L) and cohort B (2.2 mmol/L), as observed by the BG interquartile range. Furthermore, cohort A (nonDM+nonSepsis) had a 41.55% of severe hyperglycemia and 0.12% for severe hypoglycemia. Contrary, cohort B (nonDM+nonSepsis) had the highest percentage within the target range (74.31%) and the lowest percentage of hyperglycemia (18.78%). There was significantly different (p-values <0.05) between cohort A and cohort B in BG level and glucose intake, likewise between sepsis and non-sepsis of non-DM for both cohorts. The findings indicate that a successful glycemic control protocol is much influenced by insulin sensitivity, patient variability, diabetes condition, and patient sepsis status

Insulin Sensitivity and Sepsis Score: A Correlation between Model-based Metric and Sepsis Scoring System in Critically Ill Patients

Sepsis is highly correlated with mortality and morbidity. Sepsis is a clinical condition demarcated as the existence of infection and systemic inflammatory response syndrome, SIRS. Confirmation of infection requires a blood culture test, which requires incubation, and thus results take at least 48 h for a syndrome that requires early direct treatment. Since sepsis has a strong inflammatory component, it is hypothesized that metabolic markers affected by inflammation, such as insulin sensitivity, might provide a metric for more rapid, real-time diagnosis. This study uses clinical data from 30 sepsis patients (7624 h in ICU) of whom 60% are male. Median age and median Apache II score are 63 years and 19, respectively. Model-identified insulin sensitivity (SI) profiles were obtained for each patient, and insulin sensitivity and its hourly changes were correlated with modified hourly sepsis scores (SSH1). SI profiles and values were similar across the cohort. The sepsis score is highly variable and changes rapidly. The modified hourly sepsis score, SSH1, shows a better relation with insulin sensitivity due to less fluctuation in the SIRS element. Median SI and median SI of the cohort is 0.4193e-3 and 0.004253e-3 L/mU.min, respectively. Additionally, median SI are 4.392 × 10−4 L/mU min (SSH1 = 0), 4.153 × 10−4 L/mU min (SSH1 = 1), 3.752 × 10−4 L/mU min (SSH1 = 2) and 2.353 × 10−4 L/mU min (SSH1 = 3). Significant relationship between insulin sensitivity across different SSH1 groups was observed (p < 0.05) even when corrected for multiple comparisons. CDF of SI indicates that insulin sensitivity is more significant when comparing an hourly sepsis score at a very distinguished level

Contact Pattern of Alveolar Consonants in the Malay Consonants of Paralysis Subject using Electropalatography

Place of articulation plays an important part to produce different sounds. Besides the place of articulation, tongue is also an active articulator during a continuous speech. During the speech, the tongue moves around creating different sounds when it is placed at different place of articulation. The movement of tongue is controlled by muscles. The lack of muscle movement will produce inactive tongue movement. Paralysis is an example of the muscle weakness in a person resulting in difficulties to move. Paralysis may occur due to several factors including stroke and spinal cord injury (SCI). One of the indirect effects of paralysis is slurred speech and difficulty in speaking. This study aims to determine the contact pattern of five paralysed subjects during speech production of alveolar consonants in the Malay Language. The subjects had paralysis due to different aetiologies and with different medical history backgrounds. All participants were required to produce five single consonants; /d/, /t/, /l/, /n/ and /s/. The data recording was done in a studio laboratory with a soundproof system. The device used for detecting the tongue and hard palate contact in this study was electropalatography (EPG). Subjects were required to wear the artificial palate consists of 62 sensors to detect the tongue and hard palate contact. The speech contact was analysed using Articulate Assistant 1.18TM. The results were then compared with the average contact pattern of Malay speaker which had been obtained in the previous study. In conclusion, the subjects who had frequent treatments produced better articulation and the subjects with positive attitudes produced better articulation during the treatment process

Performance of glycemic control protocol and virtual trial

Model-based glycemic control offers direct management of patient-specific variability and better adaptive control. Implementation of the model-based glycemic control has the potential to reduce hyperglycemia episodes, mortality and morbidity as seen in some successful TGC. The design of any TGC must consider not only the glycemic target range but also safety and efficacy of the insulin therapy. This paper presents the evaluation of glycemic control protocol adapted in the ICU of Tengku Ampuan Afzan Hospital. Virtual trials method is used to simulate the controller algorithm on a virtual patient with feed variation factor. Data from actual clinical and the virtual trial are compared to analyze the protocol performance concerning blood glucose outcome and insulin efficacy. A stochastic model is also used to indicate metabolic response and metabolic variation of the cohort

Probabilistic glycemic control decision support in icu: proof of concept using Bayesian network

Glycemic control in critically ill 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 automated recommendations. One of the most promising solution for this is the STAR protocol which is based on a clinically validated ICING insulin and nutrition physiological model, however this approach does not consider demographical background such as age, weight, height and ethnicity. This article presents the extension to their personalized care solution by integrating per-patient demographical and upon admission to intensive care conditions to automate decision support for clinical staffs. In this context, a virtual study was conducted on 210 retrospectives critically ill 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 the STAR control’s performance. The proof of concept study shows the feasibility and the clinical potential to employ the probabilistic method as a decision support towards a more personalized care. ************************************************************************************* Kawalan glisemik dalam pesakit kritikal di unit rawatan rapi adalah rumit dari segi tindak balas pesakit terhadap penjagaan dan rawatan. Sifat keberubahan individu dan pencarian hasil terapi insulin yang lebih baik telah membawa kepada penggunaan model matematik fisiologi manusia berdasarkan keadaan metabolik pesakit untuk memberikan cadangan rawatan secara individu. Salah satu penyelesaian yang paling menjanjikan harapan adalah protokol STAR yang berdasarkan kepada model fisiologi insulin-nutrisi-glukosa yang telah disahkan secara klinikal. Namun pendekatan ini tidak mengambil kira latar belakang demografi seperti umur, berat, ketinggian dan etnik. Artikel ini membentangkan lanjutan kepada penyelesaian rawatan secara peribadi mereka dengan mengintegrasikan informasi demografi pesakit dan keadaan mereka semasa dimasukkan ke dalam unit rawatan rapi untuk mengautomasikan sokongan keputusan untuk kakitangan unit. Dalam konteks ini, satu kajian ‘virtual’ dilakukan pada data 210 pesaki. Sebagai kajian kes, konsep integrasi dibentangkan secara kasar, tetapi butiran diberikan dari segi bukti konsep yang menggunakan Rangkaian Bayesian, menghubungkan latar belakang kemasukan ke unit dan prestasi kawalan STAR. Bukti kajian kes menunjukkan 71.43% dan 73.90% ketepatan dan kebolehlaksanaan unjuran masing-masing dengan dataset ujian. Dengan lebih banyak data, rangkaian Bayesian yang lebih baik dipercayai boleh dihasilkan. Walaubagaimanapun, keputusan ini menunjukkan kemungkinan rangkaian ini bertindak sebagai pengelas yang berkesan dengan menggunakan data dari unit rawatan rapi dan prestasi kawalan glisemik untuk menjadi asas sokongan keputusan bersifat probabilistik, peribadi dan automatic dalam unit rawatan rapi

Estimation of plasma insulin and endogenous insulin secretion in critically ill patients using intensive control insulin-nutrition-glucose model

The objective of this study is to estimate total plasma insulin level and endogenous insulin secretion by using Intensive Control Insulin-Nutrition-Glucose (ICING) model and 90 critically ill patients’ data from Hospital Tengku Ampuan Afzan, Kuantan. Integral-based method was applied to solve mathematical equations defined in ICING model to find critical parameters of insulin sensitivity (SI) and results of total endogenous insulin secretion and total plasma insulin level were presented in median and 95% confidence interval (CI). It is reported that the total median plasma insulin is 1.35 × 106 mU while (6.59 × 105, 2.79 × 106) mU is in 95% CI, and the total median endogenous insulin secretion is 12.9% from the total median plasma insulin. The results elucidated the effectiveness of current practice via Intensive Insulin Infusion Therapy (IIT) and also suggest a further study on investigating the incretin mechanism which is strongly believed to contribute to the total plasma insulin level and help to simulate endogenous insulin secretion

Virtual trial of glycaemic control performance and nursing workload assessment in diabetic critically ill patients

Tight glycaemic control in critically ill patients is used to reduce mortality in intensive care units. However, its usage is debatable in reducing hypoglycaemia or accurately maintain normoglycaemia level. This paper presents the assessment for two 'wider' Stochastic TARgeted (STAR) glycemic controllers, namely Controller A (blood glucose (BG) target 4.4-8.0 mmol/L) and Controller B (BG target 4.4-10.0 mmol/L) with 1 to 3 hour nursing interventions. These controllers were assessed to determine the better control on diabetic and non-diabetic patients. 66 diabetic and 66 non-diabetic critically ill patient's data from Hospital Tunku Ampuan Afzan (HTAA) were employed for virtual trial simulations with a clinically validated physiological model. Performance metrics were assessed within the percentage time in band (TIB) of 4.4 to 8.0 mmol/L, 4.4 to 10.0 mmol/L, and 6.0 to 10.0 mmol/L. Controller A shows better performance in normoglycaemic TIB of 4.4 to 10.0 mmol/L where non-diabetic and diabetic patients achieved 92.5% and 83.8% respectively. In conclusion, Controller A is higher in efficiency and safer to be used for both patients cohorts. However, higher clinical interventions in diabetic patients within this control raise the alarm to reduce nursing workload. This is believed to improve clinical interventions burnout and ensure patient's comfortability. © 2018 Authors

Efficacy and safety of SPRINT and STAR protocol on Malaysian critically-ill patients

Abstract—Intensive care unit patients may have a better glycaemic management with the right control protocol. Results of virtual trial performance on Malaysian critically-ill patients adopting a model-derived and model-based control protocol known as SPRINT and STAR are presented in this paper. These ICU patients have been treated by intensive sliding-scale insulin infusion. The effectiveness and safety of glycaemic control are then analysed. Results showed that patient safety improved by 83% with SPRINT and STAR protocol as the number of hypoglycaemic patients significantly reduced (BG<2.2 mmol/L). Percentage of time within desired bands and median BG improves in both SPRINT and STAR. However the improvements are associated with higher number of BG measurements (workload)

Performance of STAR virtual trials for diabetic and non-diabetic in HTAA Intensive Care Unit

Critically ill patients are commonly linked to stress-induced hyperglycaemia which relates to insulin resistance and the risk of per-diagnosed with diabetes and other metabolic illnesses. Thus, it is essential to choose the best practice of blood glucose management in order to reduce morbidity and mortality rates in intensive care unit. This study is focusing on clinical data of 210 critically ill patients in Hospital Tengku Ampuan Afzan (HTAA), Kuantan who underwent Intensive Insulin Therapy which utilized a sliding scale method. Patients were identified in two main groups of diabetic (123) and non-diabetic (87) where stochastic model is generated to observe 90% confidence interval of insulin sensitivity. Blood glucose levels comparison between these two cohorts is conducted to observe the percentage of blood glucose levels within targeted band of 4.4 – 10.0 mmol/L. It is found that 82% of BG levels are within targated band for non-diabetes cohort under stochastic targeted (STAR) glycaemic control protocol. However, only 59.6% and 70.6% BG levels are within targeted band for diabetes cohort for insulin infusion therapy used in HTAA and STAR protocols. Thus, further investigation on blood glucose control protocol for diabetes patients is required to increase the reliability and efficacy of current practice despite of patient safety

Validation of a model-based virtual trials method for tight glycemic control in intensive care

peer reviewedBACKGROUND: In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully validated the independence of virtual patients (or resulting clinical trial predictions) from the data used to create them. This study uses matched cohorts from a TGC clinical trial to validate virtual patients and in-silico virtual trial models and methods. METHODS: Data from a 211 patient subset of the Glucontrol trial in Liege, Belgium. Glucontrol-A (N = 142) targeted 4.4-6.1 mmol/L and Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by APACHE II score, initial BG, age, weight, BMI and sex (p > 0.25). Virtual patients are created by fitting a clinically validated model to clinical data, yielding time varying insulin sensitivity profiles (SI(t)) that drives in-silico patients.Model fit and intra-patient (forward) prediction errors are used to validate individual in-silico virtual patients. Self-validation (tests A protocol on Group-A virtual patients; and B protocol on B virtual patients) and cross-validation (tests A protocol on Group-B virtual patients; and B protocol on A virtual patients) are used in comparison to clinical data to assess ability to predict clinical trial results. RESULTS: Model fit errors were small (<0.25%) for all patients, indicating model fitness. Median forward prediction errors were: 4.3, 2.8 and 3.5% for Group-A, Group-B and Overall (A+B), indicating individual virtual patients were accurate representations of real patients. SI and its variability were similar between cohorts indicating they were metabolically similar.Self and cross validation results were within 1-10% of the clinical data for both Group-A and Group-B. Self-validation indicated clinically insignificant errors due to model and/or clinical compliance. Cross-validation clearly showed that virtual patients enabled by identified patient-specific SI(t) profiles can accurately predict the performance of independent and different TGC protocols. CONCLUSIONS: This study fully validates these virtual patients and in silico virtual trial methods, and clearly shows they can accurately simulate, in advance, the clinical results of a TGC protocol, enabling rapid in silico protocol design and optimization. These outcomes provide the first rigorous validation of a virtual in-silico patient and virtual trials methodology