template design © 2008 the impact of mc error (with and without shown): figure 5: a sample fit...

TEMPLATE DESIGN © 2008 www.PosterPresentations.com The impact of MC error (with and without shown): Figure 5: A sample fit comparison with and without MC error. Table 1: Comparison of performance measurement with and without Monte Carlo error in SPRINT+Glargine protocol. Monte Carlo Analysis of a Glycaemic Control Protocol for Less Acute Wards Normy N. Razak, Jessica Lin, Geoff Chase, Geoff Shaw. Department of Mechanical Engineering, University of Canterbury, Department of Medicine, University of Otago, Department of Intensive Care, Christchurch Hospital Tight glycaemic control (TGC) benefits medical and surgical intensive care unit (ICU) patients by reducing complications associated with hyperglycemia. However, when patients transfer to less acute wards, continuing the same level of TGC is difficult and they get “rebound hyperglycemia” and may return to ICU. Primarily due to a lack of nursing resources. The SPRINT+Glargine protocol was developed to support the transition of patients from ICU to less acute wards. Glargine is injected 1-2x/day, so it can potentially reduce the workload to match clinical resources. Figure 1: Glycaemic control from Clinical and Glargine Alone, respectively. TGC is not achieved in protocol using Glargine only. Analysis: Monte Carlo analysis using clinically validated PK/PD models for insulin-glucose and glargine PKs to capture the impact of sensor error and known physiological variability on performance and robustness. Cohort: 25 patient in silico INTRODUCTION METHOD Glucose-Insulin Physiology Model : Glucose Absorption Model : Glargine Compartmental Model : Physiological Variability Models : Figure 2: Variability in glargine model: kp, k1 and αgla. Fig.3: Cmax for 32U RESULTS 0 2000 4000 6000 8000 10000 2 3 4 5 6 7 8 9 Tim e (m ins) B lo o d G lucose,B [m m ol/L] Blood G lucose M C Blood G lucose Similar performance (vs clinical) confirms validity of the protocol (SPRINT+Glargine) and approach. Figure 4: Patient profile comparison between SPRINT and SPRINT+Glargine protocol. RESULTS METHOD CONCLUSION An effective, robust and safe subcutaneous transition protocol is presented. In silico analysis allowed accurate quantification of nursing effort and the impact of the time for insulin glargine to reach full effectiveness, which may thus define the time required for a safe subcutaneous insulin transition across a diverse range of patients. The results justify a clinical pilot study to fully validate these in silico results. These variations produce a lognormal distribution of maximum plasma insulin. Dotted line represents reported variations in literature. Virtual Trials : Compares clinical data to virtual trials with (robustness) and w/o (performance) MC error using models B rain O ther cells Insulin losses (liver,kidneys) G lucose Insulin Liver B lood G lucose Liver Effective insulin Plasm a Insulin Pancreas B rain O ther cells Insulin losses (liver,kidneys) G lucose Insulin Liver B lood G lucose Liver Effective insulin Plasm a Insulin Pancreas GLUCOSE INSULIN LOSSES local interstitiu m loss Precipi tate from injecti on Precipitate state Hexameric state Dimer/ Monomer Local (injection) interstitium Hexamer from injection Dimer/ Monomer from injection diffusi ve loss diffusi ve loss Hexamer dissociation Precipitate dissociation Subcutaneous Glargine Injection

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www.PosterPresentations.com

The impact of MC error (with and without shown):

Figure 5: A sample fit comparison with and without MC error.

Table 1: Comparison of performance measurement with and without Monte Carlo error in SPRINT+Glargine protocol.

Monte Carlo Analysis of a Glycaemic Control Protocol for Less Acute Wards

Normy N. Razak, Jessica Lin, Geoff Chase, Geoff Shaw. Department of Mechanical Engineering, University of Canterbury, Department of Medicine, University of Otago, Department of Intensive Care, Christchurch Hospital

Tight glycaemic control (TGC) benefits medical and surgical intensive care unit (ICU) patients by reducing complications associated with hyperglycemia. However, when patients transfer to less acute wards, continuing the same level of TGC is difficult and they get “rebound hyperglycemia” and may return to ICU. Primarily due to a lack of nursing resources.

The SPRINT+Glargine protocol was developed to support the transition of patients from ICU to less acute wards. Glargine is injected 1-2x/day, so it can potentially reduce the workload to match clinical resources.

Figure 1: Glycaemic control from Clinical and Glargine Alone, respectively. TGC is not achieved in protocol using Glargine only.

Analysis: Monte Carlo analysis using clinically validated PK/PD models for insulin-glucose and glargine PKs to capture the impact of sensor error and known physiological variability on performance and robustness.

Cohort: 25 patient in silico cohort created from SPRINT clinical data based on periods of long term stability (30 hours) and low insulin requirements to match those who might utilise this protocol.

INTRODUCTION METHOD

Glucose-Insulin Physiology Model:

Glucose Absorption Model:

Glargine Compartmental Model:

Physiological Variability Models:

Figure 2: Variability in glargine model: kp, k1 and αgla.

Fig.3: Cmax for 32U Glargine

RESULTS

0 2000 4000 6000 8000 100002

Time (mins)

Blo

Glu

se,

[mm

ol/

Blood Glucose MCBlood Glucose

Similar performance (vs clinical) confirms validity of the protocol (SPRINT+Glargine) and approach.

Figure 4: Patient profile comparison between SPRINT and SPRINT+Glargine protocol.

RESULTSMETHOD

CONCLUSION

An effective, robust and safe subcutaneous transition protocol is presented. In silico analysis allowed accurate quantification of nursing effort and the impact of the time for insulin glargine to reach full effectiveness, which may thus define the time required for a safe subcutaneous insulin transition across a diverse range of patients. The results justify a clinical pilot study to fully validate these in silico results.

Acknowledgements: Universiti Tenaga Nasional for the support.

These variations produce a lognormal distribution of maximum plasma insulin. Dotted line represents reported variations in literature.

Virtual Trials: Compares clinical data to virtual trials with (robustness) and w/o (performance) MC error using models

Brain

Othercells

Insulin losses (liver, kidneys)

Glucose

Insulin

Liver

BloodGlucose

Liver

Effective insulin