Glycaemic control (GC) has been associated with improved outcomes in critically ill patients. However, inter- and intra- patient metabolic variability significantly increase the risk of hypoglycaemia ...when using insulin to control glycaemia. Model-based protocols often identify key physiological parameters from patient data, and demonstrated safe and effective GC. Based on recent studies showing gender difference in insulin secretion, this study uses retrospective data to identify whether there exists a difference in sexes in metabolic stress response, and thus in how personalised GC is given.
Retrospective data from 145 ICU patients under GC who started GC in the first 12 hours of ICU stay are used. Insulin sensitivity (SI) is identified hourly, as well as the hour-to-hour percentage change in SI (%ΔSI). Differences between males and females SI and %ΔSI over 6-h blocks are compared using hypothesis and equivalence testing. A difference in SI levels would suggest a difference in metabolic stress response to insult, while a difference in %ΔSI levels would suggest a resulting difference in the difficulty to control.
Results show females are significantly more insulin resistant than males and not equivalent, suggesting stronger stress response to insult induced stress. Metabolic variability is equivalent in both groups, advocating GC safety and efficacy should be similar between males and females, despite potential higher insulin rates required for females.
This study is the first to suggest potential gender differences in the metabolic stress response.
While the benefits of glycemic control for critically ill patients are increasingly demonstrated, the ability to deliver safe, effective control to intermediate target ranges is widely debated due to ...the increased risk of hypoglycemia. This study analyzes interim clinical trial results of the fully computerized model-based Stochastic TARgeted (STAR) glycemic control framework at the University Hospital of Liège, Belgium. Patients with dysglycemia were randomly assigned to the full version of STAR, modulating both insulin and nutrition inputs, or STAR-IO, an insulin only version of STAR. Both arms target the normoglycemic 80-145 mg/dL (4.4-8.0 mmol/L) band. Results are further compared to retrospective data from 20 patients under the standard unit protocol targeting a higher 100-150 mg/dL (5.6-8.3 mmol/L) band. Much higher time in target band is provided under the full version of STAR, with similar safety and significantly lower incidence of mild hyperglycemia (blood glucose > 145 mg/dL or 8.0 mmol/L) and severe hyperglycemia (blood glucose > 180 mg/dL or 10.0 mmol/L). As a result, lower median blood glucose levels are safely and consistently achieved with lower glycemic variability, suggesting STAR's potential to improve clinical outcomes. These interim results show the possibility to achieve safe, effective control for all patients using STAR, and suggest glycemic control to lower targets could be beneficial.
Hypoglycaemia, hyperglycaemia and blood glucose (BG) variability are associated with worsened outcomes in critical care. However, NICE-SUGAR trial showed no clinical benefit from intensive insulin ...therapy. This study compares the table-based NICE-SUGAR and model-based STAR protocols to assess their relative capability to achieve safe, effective control for all patients. Validated virtual patients (n=443) were used to simulate glycaemic outcomes of the NICE-SUGAR and STAR protocols. Key outcomes evaluate tightness and safety of control for all patients: %BG in 80–144 mg/dL range (PTR); Per-Patient Mean BG (PPM_BG); and Incidence of Hypoglycaemia (BG<40 mg/dL). These metrics determine performance overall, for each patient, and safety. Results are assessed for NICE-SUGAR measuring per-protocol (~24/day) and at reported average rate (~3-hourly; ~8/day). STAR measures 1-3-hourly, averaging 12/day.
Per-protocol, STAR provided tight control, with higher PTR (90.7% vs. 78.3%) and tighter median IQR PPM_BG (112106-119 vs. 117106–137 mg/dL), and greater safety from hypoglycaemia (5 (1%) vs. 10 patients (2.5%)) compared to NICE-SUGAR simulations as per protocol. The 5-95th percentile range PPM_BG for NICE-SUGAR (97–185 mg/dL) showed ~5% of NICE-SUGAR patients had mean BG above 180mg/dL matching clinically reported performance. STAR’s 5th-90th PPM_BG percentile range was (97–146 mg/dL). Measuring as recorded clinically, NICE-SUGAR had PTR of 77%, PPM_BG of 122 110-140 mg/dL and 24(6%) of patients experienced hypoglycaemia. These results match clinically reported values well (mean BG 115 vs. 118 mg/dL clinically vs. simulation, clinically 7% of patients had a hypoglycaemic event).
Glycaemic control protocols need to be both safe and effective for all patients before potential clinical benefits can be assessed. NICE-SUGAR clinical results do not match results expected from their protocol, and show reduced safety and performance in comparison to STAR.
Glycaemic control has been shown to improve outcome in critically ill patients, but hard to achieve in a safe and effective manner. This paper presents the preliminary results of 8 patients ...controlled at the University Hospital of Liège under STAR-Liège, an insulin-only version of the model-based STAR glycaemic controller framework. Clinical data is compared with virtual trial simulations of the glycaemic control outcomes for the STAR-Liège protocol, and with the standard of care protocol of this intensive care unit, to assess safety, performance, and compliance of the new protocol.
Results show 78% of clinical blood glucose measurements in target band. Only 3% of blood glucose measurements were below 4.4 mmol/L (79 mg/dL), with only 1% mild hypoglycaemia and no severe hypoglycaemia. These results are similar to simulation of the protocol, but slightly higher workload is observed clinically due to nursing choice. Compared to standard protocol virtual trial simulations, STAR-Liège achieved tighter and less variable control with similar safety, and less percentage time in higher blood glucose levels. Clinically, 14% of insulin intervention were increased or decreased from recommendation with median IQR change of 1 1, 2 or -2 -3, -2 U/hr respectively.
Clinical and simulation results show STAR-Liège better controls glycaemia to lower ranges compared to the standard protocol, while ensuring safety. Lower time in higher blood glucose ranges potentially improves patient outcomes. Compliance analysis shows potential nurse fears in protocol changes and different insulin dosing. These results are encouraging for the continuation of the clinical trial realised in this medical intensive care unit and its extension to insulin and nutrition control.
Glycaemic control has shown beneficial outcomes for critically ill patients, but has been proven hard to achieve safely, increasing risk of hypoglycaemia. Patient metabolic variability is one of the ...main factor influencing glycaemic control safety and efficacy. STAR is a model-based glycaemic controller using a unique patient-specific risk-based dosing approach. STAR uses a 2D stochastic model, built from population data using kernel density methods, to determine potential forward future evolution in patient-specific insulin sensitivity (SIn+1), based on its current value (SIn).
This study uses virtual trial to compare the current 2D stochastic model used in STAR, with a new 3D stochastic model. The new 3D model also uses prior insulin sensitivity value (SIn-1) to determine distribution of likely future SIn+1. A total of 587 virtual patient glycaemic control episodes longer than 24 hours from three different studies are used here. Safety (% blood glucose (BG) measurements < 4.0 and < 2.2 mmol/L), performance (% time in the target 4.4-8.0 mmol/L band), insulin administration and nutrition delivery (% goal feed) are compared.
Results show similar performance (90% BG in 4.4-8.0 mmol/L), and similar safety, with slightly higher % BG < 4.0 mmol/L (0.9 vs. 1.4%) and % BG < 2.2 mmol/L (0.02 vs. 0.03%) for the 3D model, was achieved for similar workload. The slightly lower median BG level (6.3 vs. 6.0 mmol/L) for the 3D stochastic model is explained by the higher median insulin rate administered (2.5 vs. 3.0 U/hr). More importantly, simulation results showed higher nutrition delivery using the 3D stochastic model (92 vs. 99 % goal feed).
The new 3D stochastic model achieved similar safety and performance than the 2D stochastic model in these virtual simulations, while increasing the total calorific intake. This higher nutritional intake is potentially associated with improved outcome in intensive care units. The 3D stochastic model thus better characterises patient-specific metabolic variability, allowing more optimal insulin and nutritional dosing. Therefore, a pilot clinical trial using the new 3D stochastic model could be realised to assess and compared clinical outcomes using the new stochastic model.
Hyperglycaemia, hypoglycaemia and glycaemic variability in critically ill patients are associated with increased mortality and adverse outcomes. Some studies have shown insulin therapy to control ...glycaemia has improved outcomes, but have proven difficult to repeat or achieve safely. STAR (Stochastic Targeted) is a model-based glycaemic control protocol using a stochastic model to forecast future distributions of insulin sensitivity (SI) based on its current value, to predict the range of future blood glucose outcomes for a given intervention. This study presents an improved 3D stochastic model, forecasting future distributions of SI based on its current value and prior variation. The percentage difference in the 5th, 50th, and 95th percentiles between the current 2D and new 3D models are compared. Results show the original 2D stochastic model is over-conservative for around 77% of the data, predominantly where prior variability was low. For higher prior variation (more than ±25% change in SI), the 3D stochastic model prediction range of future SI is wider. The new 3D model was found to have overall narrower 5th – 95th prediction ranges in SI, but to retain a similar per-patient (60 – 100%) and overall (92%) percentage of SI outcomes correctly predicted within these ranges. These results suggest the new 3D model is more patient-specific and will enable more optimal dosing, to increase both safety and performance. This improvement in forecasting may result in tighter and safer glycaemic control, improving performance within the STAR framework.
Glycaemic control in intensive care unit has been associated with improved outcomes. Metabolic variability is one of the main factors making glycaemic control hard to achieve safely. STAR (Stochastic ...Targeted) is a model-based glycaemic control protocol using a stochastic model to predict likely distributions of future insulin sensitivity based on current patient-specific insulin sensitivity, enabling unique risk-based dosing. This study aims to improve insulin sensitivity forecasting by presenting a new 3D stochastic model, using current and previous insulin sensitivity levels. The predictive power and the percentage difference in the 5th-95th percentile prediction width are compared between the two models. Results show the new model accurately predicts insulin sensitivity variability, while having a median 21.7% reduction of the prediction range for more than 73% of the data, which will safely enable tighter control. The new model also shows trends in insulin sensitivity variability. For previous stable or low insulin sensitivity changes, future insulin sensitivity tends to remain more stable (tighter prediction ranges), whereas for higher previous variation of insulin sensitivity, higher potential future variation of insulin sensitivity is more likely (wider prediction ranges). These results offer the opportunity to better assess and predict future evolution of insulin sensitivity, enabling more optimal risk-based dosing approach, potentially resulting in tighter and safer glycaemic control using the STAR framework.
Glycaemic control (GC) in the intensive care unit (ICU) has been widely debated over the last 20 years. While many studies showed benefits, many others failed to replicate the results, blaming the ...increased related risk of hypoglycaemia. Current ICU guidelines thus often suggest higher glycaemic target ranges, led by the fear of hypoglycaemia – permissive hyperglycaemia. However, recent studies have shown improved safety and performance in GC outcome, using model-based computerised methods. The Stochastic-Targeted (STAR) framework is a patient-specific risk-based dosing protocol modulating insulin and nutrition. This study presents recent intermediate results of the STAR-Liège clinical trial, targeting 4.4-8.0 mmol/L glycaemic band. Clinical data from patients controlled under STAR and STAR insulin only (STAR-IO) are compared to retrospective data under the standard protocol (SP), targeting higher 5.6-8.3 mmol/L glycaemic ranges.
Overall, STAR performance was significantly higher (88% blood glucose measurements in the 4.4-8.0 mmol/L or 80-145 mg/dL target band) compared to STAR-IO (78%) and SP (55%). Incidence of hypoglycaemia was similar (1% below target), while hyperglycaemia was much higher for SP (31% above target) compared to STAR (9%) and STAR-IO (11%). The resulting lower median blood glucose (BG) levels in STAR (6.5 mmol/L), compared to STAR-IO (6.7 mmol/L) and SP (7.7 mmol/L), was achieved with less variability, but required higher clinical workload for STAR (12 measurements per day) compared to SP (7 measurements per day). Compliance to protocol was higher for STAR (98%) compared to STAR-IO (90%) and SP (79%).
Although targeting lower glycaemic ranges, STAR provided better GC compared to the SP. Typically, the full version of STAR also modulating nutrition, was able to better control extremely insulin resistant patients, further improving glycaemic control results. The results of this clinical trial indicate the capability to provide the safe, effective control for all patients required to improve outcomes.
Exposing the human nude phenotype Frank, Jorge; Pignata, Claudio; Panteleyev, Andrei A ...
Nature (London),
04/1999, Letnik:
398, Številka:
6727
Journal Article
Recenzirano
Odprti dostop
The recent discovery of the human counterpart of the hairless mouse phenotype has helped our understanding of the molecular genetics of hair growth. But there are no reports of a defect in the human ...homologue of the best known of the 'bald' mouse phenotypes, the nude mouse. This may be because affected individuals are so gravely ill from the accompanying immunodeficiency that their baldness goes unnoticed. We have carried out a genetic analysis that reveals a human homologue of the nude mouse.
PDF
