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

Insulin Infusion Protocols for Blood Glucose Management in Critically Ill Patients: A Scoping Review

BACKGROUND

Continuous insulin infusion is a method for maintaining blood glucose stability in critically ill patients with hyperglycemia. Many insulin infusion protocols have been applied in intensive care units. Understanding the content of these protocols can help clinical staff choose the most appropriate and convenient protocol and promote best practices in managing glucose levels in critically ill adult patients.

OBJECTIVE

To examine the types of insulin infusion therapies performed for blood glucose management in critically ill patients.

METHODS

For this scoping review, 3 Chinese-language and 8 English-language databases were searched for articles published from May 25, 2016, to October 25, 2022.

RESULTS

Twenty-one articles met the inclusion criteria. Twenty-one insulin infusion protocols were examined. Most of the insulin infusion protocols were paper protocols. Fourteen glucose management indicators were included in the 21 protocols. The glucose target range for all 21 protocols ranged from 70 to 180 mg/dL (3.9-10.0 mmol/L). Nurses were primarily responsible for protocol implementation in most protocol development processes. The roles of nurses differed in nurse-led insulin infusion protocols and non-nurse-led insulin infusion protocols.

DISCUSSION

This scoping review indicates an urgent need for more comprehensive glycemic control guidelines for patients receiving critical care. Because insulin infusion protocols are core aspects of blood glucose management guidelines, different population subgroups should also be considered.

CONCLUSIONS

Nurse-led guidelines must be based on the best available evidence and should include other variables related to glucose management (eg, patient disease type, medication, and nutrition) in addition to insulin infusion.

Staff knowledge, attitudes and practices regarding glycaemic management in adult intensive care units: A national survey

BACKGROUND

Hyperglycaemia is common in critically ill adult patients. Many studies have identified the content, methods, and effects of glycaemic control but have not explored the effects of knowledge, attitudes, and practices (KAP) on glycaemic control in critically ill adults. Various factors also influence the KAP of intensive care unit (ICU) staff.

AIMS

To assess KAP regarding glucose management for critically ill adults among nurses and medical professionals and identify the factors that influence their KAP in ICUs.

METHODS

A multicentre cross-sectional survey.

RESULTS

In total, 403/459 (response rate: 87.8%) participants from ICUs in nine tertiary hospitals in China participated in this study, 82.4% of whom were female and 93.4% of whom were nurses. The mean work experience was 8.88 years, and the mean critical care experience was 6.59 years. The scoring rate for the three dimensions of knowledge, attitudes, and practices were 82.35%, 87.69%, and 76%, respectively. We did not find any other factors affecting the KAP scores except for the level of knowledge awareness (p < 0.001), awareness of the importance (p < 0.001), and training for glucose control (p = 0.004).

CONCLUSION

ICU staff KAP regarding glycaemic control in critically ill adults among ICU professionals were acceptable in China. However, ICU professionals' current knowledge regarding nutrition, glucose variability, and skills related to glucose management could be improved.

RELEVANCE TO CLINICAL PRACTICE

ICU educators should provide more skills-related training for healthcare professionals in the glycaemic management of critically ill adults. Moreover, the process of managing blood glucose in adult ICU patients is a collaborative, multidisciplinary team effort, with monitoring and feedback required during implementation.

Predicting mechanically ventilated patients future respiratory system elastance - A stochastic modelling approach

BACKGROUND AND OBJECTIVE

Respiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering improved clinical utility in MV treatment.

METHODS

Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (E_rs) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves.

RESULTS

Clinical validation shows all three models captured more than 98% (median) of future E_rs data within the 5th - 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 < E_rs (cmH₂O/L) < 85, suggesting similar predictive capabilities within this clinically relevant E_rs range.

CONCLUSION

The new stochastic models significantly improve prediction, clinical utility, and thus feasibility for synchronisation with clinical interventions. Paired with other MV protocols, the stochastic models developed can potentially form part of decision support systems, providing guided, personalised, and safe MV treatment.

Delay-induced uncertainty for a paradigmatic glucose-insulin model

Medical practice in the intensive care unit is based on the assumption that physiological systems such as the human glucose-insulin system are predictable. We demonstrate that delay within the glucose-insulin system can induce sustained temporal chaos, rendering the system unpredictable. Specifically, we exhibit such chaos for the ultradian glucose-insulin model. This well-validated, finite-dimensional model represents feedback delay as a three-stage filter. Using the theory of rank one maps from smooth dynamical systems, we precisely explain the nature of the resulting delay-induced uncertainty (DIU). We develop a framework one may use to diagnose DIU in a general oscillatory dynamical system. For infinite-dimensional delay systems, no analog of the theory of rank one maps exists. Nevertheless, we show that the geometric principles encoded in our DIU framework apply to such systems by exhibiting sustained temporal chaos for a linear shear flow. Our results are potentially broadly applicable because delay is ubiquitous throughout mathematical physiology.