Risk and reward: extending stochastic glycaemic control intervals to reduce workload - PubMed (original) (raw)

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

Vincent Uyttendaele et al. Biomed Eng Online. 2020.

Abstract

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.

Keywords: Blood glucose; Glycaemic control; Hyperglycaemia; Insulin resistance; Insulin sensitivity; Insulin therapy; Trade-off; Workload.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1

Fig. 1

Stochastic model representation showing the 5th–95th percentiles prediction range of future 1–6 h SI levels given current identified patient-specific SI_n_

Fig. 2

Fig. 2

Excerpt of virtual trial results for Patient A. Blood glucose (top), insulin rates (middle), and enteral (solid line) and dextrose bolus (bars) nutrition rates (bottom) are compared between STAR-3H (red) and STAR-6H (blue)

Fig. 3

Fig. 3

Excerpt of virtual trial results for Patient B. Blood glucose (top), insulin rates (middle), and enteral (solid line) and dextrose bolus (bars) nutrition rates (bottom) are compared between STAR-3H (red) and STAR-6H (blue)

Fig. 4

Fig. 4

Excerpt of virtual trial results for Patient C. Blood glucose (top), insulin rates (middle), and enteral (solid line) and dextrose bolus (bars) nutrition rates (bottom) are compared between STAR-6H (blue) and STAR-ULC-6H (red). The 4.4–8.0 mmol/L target band is shown as well as the 8.5 mmol/L limit (dashed black)

Fig. 5

Fig. 5

Risk and reward trade-off between STAR Standard (solid) and STAR-ULC (dashed) with increasing measurements intervals

Fig. 6

Fig. 6

GC episode selection from the original 606 patients

Fig. 7

Fig. 7

Risk-based dosing approach of the STAR framework. Current patient-specific identified SI is used to forecast the likely 5th–95th percentile range of future SI. This range is used to calculate the corresponding 5th–95th percentile range of likely future BG outcome for a given insulin and nutrition inputs

Fig. 8

Fig. 8

Stochastic modelling of SI_n_+1 variability. For each SI_n_ value, there exists a conditional probability distribution function (along SI_n_+1 axis) where the area under the curve sums up to 1.0

References

    1. McCowen KC, Malhotra A, Bistrian BR. Stress-induced hyperglycemia. Crit Care Clin. 2001;17(1):107–124. - PubMed
    1. Ali NA, O’Brien JM, Dungan K, Phillips G, Marsh CB, Lemeshow S, Connors AF, Preiser JC. Glucose variability and mortality in patients with sepsis. Crit Care Med. 2008;36(8):2316–2321. - PMC - PubMed
    1. Capes SE, Hunt D, Malmberg K, Gerstein HC. Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview. Lancet. 2000;355(9206):773–778. - PubMed
    1. Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P, Bouillon R. Intensive insulin therapy in critically ill patients. N Engl J Med. 2001;345(19):1359–1367. - PubMed
    1. Finfer S, Chittock DR, Su SY, Blair D, Foster D, Dhingra V, Bellomo R, Cook D, Dodek P, Henderson WR, et al. Intensive versus conventional glucose control in critically ill patients. N Engl J Med. 2009;360(13):1283–1297. - PubMed

MeSH terms

LinkOut - more resources