Untangling glycaemia and mortality in critical care Uyttendaele, Vincent; Dickson, Jennifer L; Shaw, Geoffrey M ...
Critical care (London, England),
06/2017, Letnik:
21, Številka:
1
Journal Article, Web Resource
Recenzirano
Odprti dostop
Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to ...replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided.
Clinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant.
SI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed.
Whereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition.
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.
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.
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.
The 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.
In 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.
This 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.
Background
Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher ...insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care.
Methods
Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (
P
< 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal.
Results
Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent.
Conclusion
Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.
•A 3D stochastic model to predict insulin sensitivity is evaluated over 1525 patients.•The model, under cross-validation, matched the underlying data distribution.•The model provided an 18.12% ...narrower 90% credible interval than existing methods.•The model provided narrower 90% credible intervals in 96.35% of cases.•The model avoided some clinically undesirable trends present in existing methods.
Glycaemic control in the intensive care unit is dependent on effective prediction of patient insulin sensitivity (SI). The stochastic targeted (STAR) controller uses a 2D stochastic model for prediction, with current SI as an input and future SI as an output.
This paper develops an extension of the STAR 2D stochastic model into 3D by adding blood glucose (G) as an input. The performance of the 2D and 3D stochastic models is compared over a retrospective cohort of 65,269 data points across 1525 patients.
Under five-fold cross-validation, the 3D model was found to better match the expected potion of data points within, above and below various credible intervals, suggesting it provided a better representation of the underlying probability field. The 3D model was also found to provide an 18.1% narrower 90% credible interval on average, and a narrower 90% credible interval in 96.4% of cases, suggesting it provided more accurate predictions of future SI. Additionally, the 3D stochastic model was found to avoid the undesirable tendency of the 2D model to overestimate SI for patients with high G, and underestimate SI for patients with low G.
Overall, the 3D stochastic model is shown to provide clear potential benefits over the 2D model for minimal clinical cost or effort, though further exploration into whether these improvements in SI prediction translate into improved clinical outcomes is required.
•Performance of a multi-input stochastic model to predict insulin sensitivity is evaluated.•Evaluation involves virtual trials of 1477 retrospective patients from multiple hospitals.•The model ...decreased hyperglycaemic hours from 12.3 % using existing methods to 11.2 %.•The model increased patient nutrition for a negligible increase in computation or work load.•Overall, the model could provide greater personalisation and clinical performance.
Safe, effective glycaemic control (GC) requires accurate prediction of future patient insulin sensitivity (SI), balancing the risk of hyper- and hypo-glycaemia. The stochastic targeted (STAR) protocol combines a clinically validated metabolic model and SI metric with a risk-based stochastic approach to optimise patient specific insulin and feed rates. Validated virtual trials comparing a novel 3D stochastic model for prediction of future patient SI using current patient SI and current blood glucose (BG) to an existing 2D stochastic model for SI prediction were conducted.
The virtual trials involved 1477 retrospective patients across two hospitals and two GC protocols. They were conducted using five-fold cross-validation to build each stochastic model, ensuring independent test data.
The 3D stochastic model shifted BG from the 4.4–8.0 mmol/L target band towards the lower 4.4–6.5 mmol/L band, providing a decrease from 12.31 % to 11.19 % in hyperglycaemic hours (BG > 8.0 mmol/L), but only a 0.24 % increase, from 1.01 % to 1.25 %, in light hypoglycaemic hours (BG < 4.0 mmol/L). Simultaneously, the 3D stochastic model enabled greater patient nutrition, and required negligible increase in computational or clinical workload.
The 3D stochastic model provided greater personalisation and better realised STAR’s design philosophy of minimising hyperglycaemic events for an acceptable clinical risk of 5.0 % BG < 4.4 mmol/L. Thus, this model could provide better clinical conformity to design targets if implemented within the STAR protocol.
In 2009, the NICE-SUGAR study became a reference supporting the use of higher glycaemic target bands for glycaemic control. The important increased risk of hypoglycaemia and mortality associated with ...lower target band in this study contradicted previous studies showing lower target bands improved outcomes. In this analysis, virtual trials of the NICE-SUGAR protocol and the patient-specific model-based STAR protocol are compared to reported clinical results to evaluate the safety and efficacy of the NICE-SUGAR protocol design.
Simulation results show STAR has higher safety and performance than NICE-SUGAR, with higher time in band, lower glycaemic variability, and lower incidence of both hyper- and hypo- glycaemia, which are all associated with improved outcomes. Compared to clinical results, the important difference in workload (9.4 vs 25.0 measurements per day) and insulin administration (50.2 ± 38.1 vs. 154.0 ± 209.2 U/d) shown in the simulations suggest poor clinical compliance to protocol in the NICE-SUGAR study. Thus, the increased clinical incidence of hypoglycaemia in the NICE-SUGAR study may have resulted from low compliance to protocol, and the interpretation of the results could have been biased by a non-compliant glycaemic control protocol design.
In conclusion, NICE-SUGAR protocol design was not clinically feasible, shown in the low compliance, likely resulting in low safety, efficacy, and highly variable glycaemic outcomes. Hence, the use of intensive insulin therapy for glycaemic control targeting lower glycaemic bands has been wrongly blamed for increased hypoglycaemia and mortality. Glycaemic control must be safe and effective for all patient, before any further study can assess potential beneficial clinical outcomes.
•A 3D model to forecast patient-specific insulin sensitivity variability is proposed.•The new model has similar predictive power with much tighter predictive bounds.•Tighter prediction bands allow ...tighter glycaemic control, without compromising safety.
Insulin therapy for glycaemic control (GC) in critically ill patients may improve outcomes by reducing hyperglycaemia and glycaemic variability, which are both associated with increased morbidity and mortality. However, initial positive results have proven difficult to repeat or achieve safely. STAR (Stochastic TARgeted) is a model-based glycaemic control protocol using a risk-based dosing approach. STAR uses a 2D stochastic model to predict distributions of likely future changes in model-based insulin sensitivity (SI) based on its current value, and determines the optimal intervention.
This study investigates the impact of a new 3D stochastic model on the ability to predict more accurate future SI distributions, which would allow more aggressive insulin dosing and improved glycaemic control.
The new 3D stochastic model is built using both current SI and its prior variation to predict future SI distribution from 68,629 h of clinical data (819 GC episodes). The 5th–95th percentile range of predicted SI distribution are calculated and compared with the 2D model.
Results show the 2D model is over-conservative compared to the 3D case for more than 77% of the data, predominantly where SI is stable (|%ΔSI| ≤ 25%). These formerly conservative prediction ranges are now ∼30% narrower with the 3D model, which safely enables more aggressive insulin dosing for these patient hours. In addition, distributions of predicted SI within the 5th–95th percentile range are much closer to the ideal value of 90% for more patients with the 3D model.
The new 3D model better characterises patient specific metabolic variability and patient specific response to insulin, allowing more optimal insulin dosing to increase performance and safety.
Glycemic control (GC) to regulate dysglycemia in critically ill patients is associated with improved outcomes. However, it has been hard to achieve safely due to metabolic variability. Protocol ...design is critical to provide safe, effective control to all patients. STAR is a unique model-based, patient-specific, and risk-based glycemic control framework accounting for inter- and intra- patient variability using a clinically validated digital twin model. This study presents the first clinical trial results of STAR with a new 3D stochastic model, which was shown to improve predictions of patient-specific insulin sensitivity variability and value in validated virtual trials, and can thus potentially improve glycemic control outcomes.
In total, 616 patients are considered in this analysis, totaling 37489 hours of control. Overall, 77% of blood glucose (BG) measurements were in target, with a median BG of 6.7 mmol/L. There was no incidence of severe hypoglycemia (BG < 2.2 mmol/L) and only 0.3% of mild hypoglycemia (BG < 4.0 mmol/L). These results were achieved with a median 25th 75th percentiles of 4.0 2.0 6.0 U/h of insulin, and nutrition rates with median 100 83 100 % of goal feed. These results were consistent when considering per-patient statistics.
Using this new tri-variate stochastic model in the STAR glycemic control framework successfully provided extremely safe, effective GC for all patients, with high nutrition intake, despite targeting normoglycemic ranges. High safety from hypoglycemia, high %BG in normoglycemia, and high nutrition delivery are all associated with improved outcome in ICU patients.
Glycemic control (GC) has improved outcomes for intensive care unit (ICU) patients. However, the increased risk of hypoglycemia and glycemic variability due to inter- and intra- patient variability ...make safe, effective GC difficult. Stochastic TARgeted (STAR) GC framework is a unique, patient-specific, risk-based dosing protocol directly accounting for both inter- and intra- patient variability using a stochastic model of future patient variability. A new tri-variate (3D) stochastic model, developed and validated in virtual trials to provide more accurate future predictions of insulin sensitivity (SI), is clinically evaluated.
STAR-3D was implemented as standard care at the Christchurch Hospital ICU, New Zealand, between April 2019 and January 2021. In total, 567 patients (33276 hours) were treated. The overall median IQR BG achieved was 6.7 6.0 7.8 mmol/L with 76% BG in the 4.4-8.0 mmol/L target band. Importantly, there were only 0.3% BG < 4.0 mmol/L (mild hypoglycemia) and no incidence of severe hypoglycemia (BG < 2.2 mmol/L). These outcomes were achieved with median IQR 4.0 2.0 6.0 U/h insulin and median IQR nutrition delivery of 99 80 100% goal feed (GF). Similar safety and performance BG outcomes were obtained at a per-patient level, suggesting STAR-3D successfully provided safe, effective control for all patients, regardless of patient condition. Compared to the original version of STAR, STAR-3D provided improved safety and efficacy, while achieving higher nutrition delivery.
The new 3D stochastic model in STAR-3D provided higher safety and efficacy for all patients in this large clinical trial, despite using higher insulin rates than its predecessor to provide greater nutrition delivery. STAR-3D thus better captured patient-specific condition and variability to provide improved GC outcomes.
PDF
