Is intensive insulin therapy the scapegoat for or cause of hypoglycaemia and poor outcome?

Uyttendaele, Vincent; Knopp, Jennifer L.; Desaive, Thomas; Chase, J. Geoffrey

doi:10.1016/j.ifacsc.2019.100063

Cited by 12 publications

(10 citation statements)

References 86 publications

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“…Recent studies have shown safe, effective control for almost all patients is critical to improve outcomes, [19][20][21] validating other analyses showing time in band is associated with survival. [22][23][24][25][26] For better, more personalised control, fixed insulin protocols have been replaced by automated patient-specific model-based glycemic control.…”

Section: Introductionmentioning

confidence: 63%

<p>Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients</p>

Abu-Samah¹,

Razak²,

Jamaludin³

et al. 2020

MDER

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This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage. Patients and Methods: STAR proposes 1-3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017-quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed. Results: The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance. Conclusion: The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.

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Section: Introductionmentioning

confidence: 63%

<p>Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients</p>

Abu-Samah¹,

Razak²,

Jamaludin³

et al. 2020

MDER

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“…A recent analysis suggests GC to lower glycaemic ranges was wrongly blamed for increased hypoglycaemia [18]. In this analysis, poor protocol compliance was pointed to as the most likely cause of hypoglycaemia.…”

Section: Introductionmentioning

confidence: 69%

“…Therefore, this result emphasises the importance of identifying key physiological parameters, such as SI here, and assessing potential variability to provide safe, and effective control for all, which is critical to improving outcomes [18,36]. In addition, compliance to protocol is essential to ensure any clinical judgement bias in results outcomes and conclusions [18], where longer intervals may improve compliance [37].…”

Section: Discussionmentioning

confidence: 99%

“…In many ICUs, protocols are often 4-hourly based once BG levels are stabilised in the target band. In practice, this interval can quickly become 5-or 6-hourly, given clinical judgement and excessive clinical workload [18,21,[38][39][40]. Usually, the higher the target band, the greater the permissive hyperglycaemia, and, indirectly, the lower the risk of hypoglycaemia.…”

Section: Discussionmentioning

confidence: 99%

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Risk and reward: extending stochastic glycaemic control intervals to reduce workload

et al. 2020

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Background: STAR is a model-based, personalised, risk-based dosing approach for glycaemic control (GC) in critically ill patients. STAR provides safe, effective control to nearly all patients, using 1-3 hourly measurement and intervention intervals. However, the average 11-12 measurements per day required can be a clinical burden in many intensive care units. This study aims to significantly reduce workload by extending STAR 1-3 hourly intervals to 1 to 4-, 5-, and 6-hourly intervals, and evaluate the impact of these longer intervals on GC safety and efficacy, using validated in silico virtual patients and trials methods. A Standard STAR approach was used which allowed more hyperglycaemia over extended intervals, and a STAR Upper Limit Controlled approach limited nutrition to mitigate hyperglycaemia over longer intervention intervals. Results: Extending STAR from 1-3 hourly to 1-6 hourly provided high safety and efficacy for nearly all patients in both approaches. For STAR Standard, virtual trial results showed lower % blood glucose (BG) in the safe 4.4-8.0 mmol/L target band (from 83 to 80%) as treatment intervals increased. Longer intervals resulted in increased risks of hyper-(15% to 18% BG > 8.0 mmol/L) and hypo-(2.1% to 2.8% of patients with min. BG < 2.2 mmol/L) glycaemia. These results were achieved with slightly reduced insulin (3.2 [2.0 5.0] to 2.5 [1.5 3.0] U/h) and nutrition (100 [85 100] to 90 [75 100] % goal feed) rates, but most importantly, with significantly reduced workload (12 to 8 measurements per day). The STAR Upper Limit Controlled approach mitigated hyperglycaemia and had lower insulin and significantly lower nutrition administration rates. Conclusions: The modest increased risk of hyper-and hypo-glycaemia, and the reduction in nutrition delivery associated with longer treatment intervals represent a significant risk and reward trade-off in GC. However, STAR still provided highly safe, effective control for nearly all patients regardless of treatment intervals and approach, showing this unique risk-based dosing approach, modulating both insulin and nutrition, to be robust in its design. Clinical pilot trials using STAR with different measurement timeframes should be undertaken to confirm these results clinically.

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“…2.1 Fundamental Dynamics: Figure 1 shows the fundamental metabolic physiological dynamics considered [45,46], including primary routes of glucose appearance and delivery, insulin appearance and delivery, and their uptake and use leading to a net blood glucose level. A directly related mathematical model in [46] is validated in clinical glycemic control [47][48][49][50], insulin sensitivity testing [51][52][53][54], and virtual patients [55][56][57]. For this analysis, time-varying and steady state levels of insulin (plasma I(t) leading to interstitial Q(t) à Q SS in steady state), total nutrition (P(t) à P SS ) and glucose level (G(t) à G SS ) are used.…”

Section: Methodsmentioning

confidence: 99%

Understanding insulin and nutrition administration in glycemic control with the right side of the brain

Chase¹,

Knopp²

2020

Preprint

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Background Critically ill patients frequently experience stress-induced hyperglycaemia, leading to increased morbidity and mortality. Glycaemic control (GC) with insulin therapy alone has proven difficult, due to significant inter- and intra- patient variability in response to insulin therapy. This study reviews the problem and analyses the impact of physiological dynamics and patient variability on outcome glycemia. Methods A graphical model of metabolic dynamics is used to analyse the impact of fundamental glucose flux dynamics on insulin and nutrition administration in the context of maintaining a glycemic goal. It is used to delineate the limits of ability in controlling insulin and/or nutrition administration to achieve safe, effective glycemic control in critical illness in the presence of low insulin sensitivity and high insulin sensitivity variability. Results Insulin saturation limits insulin-mediated glucose uptake. At low insulin sensitivity, maintaining a glycemic target level requires reduced nutrition administration due to saturated insulin-mediated glucose uptake. Metabolic insulin sensitivity variability leads to insulin-mediated glucose uptake variability, requiring reduced nutrition administration at low insulin sensitivity and higher insulin doses to mitigate the risk of hypo- and hyper- glycemia. Conclusions This work reviews the clinical glycemic control problem using a graphically-based physiological analysis to show the need to control nutrition administration, along with insulin, to achieve safe, effective control. These reductions are necessary for highly insulin resistant patients, a condition typically occurring early in ICU stay. Glycemic control should directly control nutrition in addition to insulin to optimise all avenues of glucose flux and thus ensure safe, effective glycemic control.

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Is intensive insulin therapy the scapegoat for or cause of hypoglycaemia and poor outcome?

Cited by 12 publications

References 86 publications

<p>Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients</p>

<p>Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients</p>

Risk and reward: extending stochastic glycaemic control intervals to reduce workload

Understanding insulin and nutrition administration in glycemic control with the right side of the brain

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