Clinical Compliance in Personalised Model-based Medical Decision Support: Do computers and interfaces yield better compliance?

Dickson, Jennifer; Chase, J. Geoffrey

doi:10.1016/j.ifacol.2019.01.021

Cited by 2 publications

(3 citation statements)

References 48 publications

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“…However, these virtual trials have been validated and shown to well capture the overall potential glycaemic outcomes [55, 56]. In addition, compliance to STAR is very high in regular clinical use [28, 57].…”

Section: Discussionmentioning

confidence: 99%

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation

et al. 2019

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BackgroundThe challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits inter- and intra-patient metabolic variability results in increased hypoglycaemia and glycaemic variability, both increasing morbidity and mortality. Hence, current recommendations for glycaemic control target higher glycaemic ranges, guided by the fear of harm. Lately, studies have proven the ability to provide safe, effective control for lower, normoglycaemic, ranges, using model-based computerised methods. Such methods usually identify patient-specific physiological parameters to personalize titration of insulin and/or nutrition. The Stochastic-Targeted (STAR) glycaemic control framework uses patient-specific insulin sensitivity and a stochastic model of its future variability to directly account for both inter- and intra-patient variability in a risk-based insulin-dosing approach.ResultsIn this study, a more personalized and specific 3D version of the stochastic model used in STAR is compared to the current 2D stochastic model, both built using kernel-density estimation methods. Fivefold cross validation on 681 retrospective patient glycaemic control episodes, totalling over 65,000 h of control, is used to determine whether the 3D model better captures metabolic variability, and the potential gain in glycaemic outcome is assessed using validated virtual trials. Results show that the 3D stochastic model has similar forward predictive power, but provides significantly tighter, more patient-specific, prediction ranges, showing the 2D model over-conservative > 70% of the time. Virtual trial results show that overall glycaemic safety and performance are similar, but the 3D stochastic model reduced median blood glucose levels (6.3 [5.7, 7.0] vs. 6.2 [5.6, 6.9]) with a higher 61% vs. 56% of blood glucose within the 4.4–6.5 mmol/L range.ConclusionsThis improved performance is achieved with higher insulin rates and higher carbohydrate intake, but no loss in safety from hypoglycaemia. Thus, the 3D stochastic model developed better characterises patient-specific future insulin sensitivity dynamics, resulting in improved simulated glycaemic outcomes and a greater level of personalization in control. The results justify inclusion into ongoing clinical use of STAR.

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Section: Discussionmentioning

confidence: 99%

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation

et al. 2019

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“…Compliance needs to be assessed and audited from time to time since compliance may impact GC performances. 1,18 Moreover, given STAR is a clinical practice change, shifting from a sliding-scale to a model-based protocol, it is interesting to see whether the level of trust increases over the years. The magnitude and extent of non-compliance can also provide insight into the reasons for specific clinical choices and/or the lack of trust in new protocol.…”

Section: Compliance Assessmentmentioning

confidence: 99%

“…Whereas manual insulin therapy or ad-hoc protocols used in the ICU patients need to rely on "one size fits all method" for clinical judgement rather than patient-specific variability. 18 Thus, if more advanced technologies treatment is used for an effective GC, all of the challenges rise need to be countered.…”

Section: Introductionmentioning

confidence: 99%

<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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Clinical Compliance in Personalised Model-based Medical Decision Support: Do computers and interfaces yield better compliance?

Cited by 2 publications

References 48 publications

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation

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

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