Multi-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data

Davidson, Shaun; Pretty, Christopher G.; Uyttendaele, Vincent; Knopp, Jennifer L.; Desaive, Thomas; Chase, J. Geoffrey

doi:10.1016/j.cmpb.2019.105043

Cited by 10 publications

(11 citation statements)

References 46 publications

(67 reference statements)

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“…Clinically, it is possible to quantify and thus account for this variability, creating an objective means to reduce hypoglycemic and hyperglycemic risk [89,105,[107][108][109][110].…”

Section: Main Results: the Impact Of Metabolic Variabilitymentioning

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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“…Clinically, it is possible to quantify and thus account for this variability, creating an objective means to reduce hypoglycemic and hyperglycemic risk [89,105,[107][108][109][110].…”

Section: Main Results: the Impact Of Metabolic Variabilitymentioning

confidence: 99%

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

Chase¹,

Knopp²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…This outcome also emphasises the importance of accurately characterising intrapatient variability, where improved predictions would improve GC outcome. Ongoing studies are assessing the benefits of using more complex stochastic models [35,[43][44][45], and are currently being tested in clinical trials to validate the results. However, as this is a first study analysing longer treatment intervals, the well-proven original stochastic model approach is used here.…”

Section: Discussionmentioning

confidence: 99%

“…The 5th-95th percentile of predicted SI is thus wider (Fig. 1), and STAR will consistently be more conservative in insulin dosing [43,45], typically providing lower insulin rates to ensure safety (Table 1). While it is a safe approach, performance is affected due to the higher predicted risk, increasing BG levels ( Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…In this study, 1 to 3-, 4-, 5-, and 6-hourly versions of STAR are simulated to better capture the effect of increased measurement intervals on STAR GC safety and performance. These stochastic models are built from retrospective patient data, using kernel-density methods [34,43,45,75], where SI is identified hourly from BG, insulin, and nutrition clinical data [71,76]. SI pairs (SI n , SI n+i ) for i = 1,2,…,6 are created and used to build each stochastic models.…”

Section: Star 1-6 Hourly Extensionmentioning

confidence: 99%

“…There is thus no strict condition on the resulting 95th predicted percentile BG in the treatment decision-making, and a treatment can be considered by STAR despite potentially leading to mild hyperglycaemia. However, as measurement interval increases, wider 5th-95th percentile prediction range of BG is more likely to be larger induced by higher potential variability [35,[43][44][45], resulting in predicted 95th percentile BG potentially much higher than 8.5 mmol/L. To mitigate this impact of rising hyperglycaemia over longer intervention intervals, a second version of the protocol is implemented.…”

Section: Star Upper Limit Controlledmentioning

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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Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

et al. 2021

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While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, E rs , to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future E rs values using retrospective patient data to develop and validate a model indicating future intra-patient variability of E rs . Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5–95% and the 25–75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future E rs values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

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Multi-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data

Cited by 10 publications

References 46 publications

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

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

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

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

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