In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully validated ...the independence of virtual patients (or resulting clinical trial predictions) from the data used to create them. This study uses matched cohorts from a TGC clinical trial to validate virtual patients and in-silico virtual trial models and methods.
Data from a 211 patient subset of the Glucontrol trial in Liege, Belgium. Glucontrol-A (N = 142) targeted 4.4-6.1 mmol/L and Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by APACHE II score, initial BG, age, weight, BMI and sex (p > 0.25). Virtual patients are created by fitting a clinically validated model to clinical data, yielding time varying insulin sensitivity profiles (SI(t)) that drives in-silico patients.Model fit and intra-patient (forward) prediction errors are used to validate individual in-silico virtual patients. Self-validation (tests A protocol on Group-A virtual patients; and B protocol on B virtual patients) and cross-validation (tests A protocol on Group-B virtual patients; and B protocol on A virtual patients) are used in comparison to clinical data to assess ability to predict clinical trial results.
Model fit errors were small (<0.25%) for all patients, indicating model fitness. Median forward prediction errors were: 4.3, 2.8 and 3.5% for Group-A, Group-B and Overall (A+B), indicating individual virtual patients were accurate representations of real patients. SI and its variability were similar between cohorts indicating they were metabolically similar.Self and cross validation results were within 1-10% of the clinical data for both Group-A and Group-B. Self-validation indicated clinically insignificant errors due to model and/or clinical compliance. Cross-validation clearly showed that virtual patients enabled by identified patient-specific SI(t) profiles can accurately predict the performance of independent and different TGC protocols.
This study fully validates these virtual patients and in silico virtual trial methods, and clearly shows they can accurately simulate, in advance, the clinical results of a TGC protocol, enabling rapid in silico protocol design and optimization. These outcomes provide the first rigorous validation of a virtual in-silico patient and virtual trials methodology.
STAR Development and Protocol Comparison Fisk, Liam M.; Le Compte, Aaron J.; Shaw, Geoffrey M. ...
IEEE transactions on biomedical engineering,
12/2012, Letnik:
59, Številka:
12
Journal Article, Web Resource
Recenzirano
Odprti dostop
Accurate glycemic control (AGC) is difficult due to excessive hypoglycemia risk. Stochastic TARgeted (STAR) glycemic control forecasts changes in insulin sensitivity to calculate a range of glycemic ...outcomes for an insulin intervention, creating a risk framework to improve safety and performance. An improved, simplified STAR framework was developed to reduce light hypoglycemia and clinical effort, while improving nutrition rates and performance. Blood glucose (BG) levels are targeted to 80-145 mg/dL, using insulin and nutrition control for 1-3 h interventions. Insulin changes are limited to +3U/h and nutrition to ±30% of goal rate (minimum 30%). All targets and rate change limits are clinically specified and generalizable. Clinically validated virtual trials were run on using clinical data from 371 patients (39841 h) from the Specialized Relative Insulin and Nutrition Tables (SPRINT) cohort. Cohort and per-patient results are compared to clinical SPRINT data, and virtual trials of three published protocols. Performance was measured as time within glycemic bands, and safety by patients with severe (BG <; 40 mg/dL) and mild (%BG <; 72 mg/dL) hypoglycemia. Pilot trial results from the first ten patients (1486 h) are included to support the in-silico findings. In both virtual and clinical trials, mild hypoglycemia was below 2% versus 4% for SPRINT. Severe hypoglycemia was reduced from 14 (SPRINT) to 6 (STAR), and 0 in the pilot trial. AGC was tighter than both SPRINT clinical data and in-silico comparison protocols, with 91% BG within the specified target (80-145 mg/dL) in virtual trials and 89.4% in pilot trials. Clinical effort (measurements) was reduced from 16.2/day to 11.8/day (13.5/day in pilot trials). This STAR framework provides safe AGC with significant reductions in hypoglycemia and clinical effort due to stochastic forecasting of patient variation - a unique risk-based approach. Initial pilot trials validate the in-silico design methods and resulting protocol, all of which can be generalized to suit any given clinical environment.
Dextrose gel is used to treat neonatal hypoglycaemia, but later effects are unknown.
Follow-up of participants in a randomised trial recruited in a tertiary centre and assessed in a research clinic.
...Children who were hypoglycaemic (<2.6 mmol/L) recruited to the Sugar Babies Study (
35 weeks, <48 hours old) and randomised to treatment with 40% dextrose or placebo gel.
Assessment of neurological status, cognitive ability (Weschler Preschool and Primary Scale of Intelligence), executive function (five tasks), motor function (Movement Assessment Battery for Children-2 (MABC-2)), vision, visual processing (Beery-Buktenica Development Test of Visual Motor Integration (Beery VMI) and motion coherence thresholds) and growth at 2 years.
Neurosensory impairment (cerebral palsy; visual impairment; deafness; intelligence quotient <85; Beery VMI <85; MABC-2 score <15th centile; low performance on executive function or motion coherence).
Of 237 babies randomised, 185 (78%) were assessed; 96 randomised to dextrose and 89 to placebo gel. Neurosensory impairment was similar in both groups (dextrose 36/96 (38%) vs placebo 34/87 (39%), relative risk 0.96, 95% CI 0.66 to 1.34, p=0.83). Secondary outcomes were also similar, except children randomised to dextrose had worse visual processing scores (mean (SD) 94.5 (15.9) vs 99.8 (15.9), p=0.02) but no differences in the proportion with visual processing scores <85 or other visual test scores. Children randomised to dextrose gel were taller (z-scores 0.18 (0.97) vs -0.17 (1.01), p=0.001) and heavier (0.57 (1.07) vs 0.29 (0.92), p=0.01).
Treatment of neonatal hypoglycaemia (<2.6 mol/L) with dextrose gel does not alter neurosensory impairment at 4.5 years. However, further assessment of visual processing and growth may be warranted.
ACTRN1260800062392.
Abstract Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial ...successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000 h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric ( S I ). Variation in S I provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/ S I ) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.
Background
Effective tight glycemic control (TGC) can improve outcomes in critical care patients, but it is difficult to achieve consistently. Insulin sensitivity defines the metabolic balance ...between insulin concentration and insulin-mediated glucose disposal. Hence, variability of insulin sensitivity can cause variable glycemia. This study quantifies and compares the daily evolution of insulin sensitivity level and variability for critical care patients receiving TGC.
Methods
This is a retrospective analysis of data from the SPRINT TGC study involving patients admitted to a mixed medical-surgical ICU between August 2005 and May 2007. Only patients who commenced TGC within 12 hours of ICU admission and spent at least 24 hours on the SPRINT protocol were included (N = 164). Model-based insulin sensitivity (
SI
) was identified each hour. Absolute level and hour-to-hour percent changes in
SI
were assessed on cohort and per-patient bases. Levels and variability of
SI
were compared over time on 24-hour and 6-hour timescales for the first 4 days of ICU stay.
Results
Cohort and per-patient median
SI
levels increased by 34% and 33% (
p
< 0.001) between days 1 and 2 of ICU stay. Concomitantly, cohort and per-patient
SI
variability decreased by 32% and 36% (
p
< 0.001). For 72% of the cohort, median
SI
on day 2 was higher than on day 1. The day 1–2 results are the only clear, statistically significant trends across both analyses. Analysis of the first 24 hours using 6-hour blocks of
SI
data showed that most of the improvement in insulin sensitivity level and variability seen between days 1 and 2 occurred during the first 12–18 hours of day 1.
Conclusions
Critically ill patients have significantly lower and more variable insulin sensitivity on day 1 than later in their ICU stay and particularly during the first 12 hours. This rapid improvement is likely due to the decline of counter-regulatory hormones as the acute phase of critical illness progresses. Clinically, these results suggest that while using TGC protocols with patients during their first few days of ICU stay, extra care should be afforded. Increased measurement frequency, higher target glycemic bands, conservative insulin dosing, and modulation of carbohydrate nutrition should be considered to minimize safely the outcome glycemic variability and reduce the risk of hypoglycemia.
Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is used to evaluate the impact of a successful tight ...glycemic control (TGC) intervention (SPRINT) on organ failure, morbidity, and thus mortality.
A retrospective analysis of 371 patients (3,356 days) on SPRINT (August 2005 - April 2007) and 413 retrospective patients (3,211 days) from two years prior, matched by Acute Physiology and Chronic Health Evaluation (APACHE) III. SOFA is calculated daily for each patient. The effect of the SPRINT TGC intervention is assessed by comparing the percentage of patients with SOFA ≤5 each day and its trends over time and cohort/group. Organ-failure free days (all SOFA components ≤2) and number of organ failures (SOFA components >2) are also compared. Cumulative time in 4.0 to 7.0 mmol/L band (cTIB) was evaluated daily to link tightness and consistency of TGC (cTIB ≥0.5) to SOFA ≤5 using conditional and joint probabilities.
Admission and maximum SOFA scores were similar (P = 0.20; P = 0.76), with similar time to maximum (median: one day; IQR: 13 days; P = 0.99). Median length of stay was similar (4.1 days SPRINT and 3.8 days Pre-SPRINT; P = 0.94). The percentage of patients with SOFA ≤5 is different over the first 14 days (P = 0.016), rising to approximately 75% for Pre-SPRINT and approximately 85% for SPRINT, with clear separation after two days. Organ-failure-free days were different (SPRINT = 41.6%; Pre-SPRINT = 36.5%; P < 0.0001) as were the percent of total possible organ failures (SPRINT = 16.0%; Pre-SPRINT = 19.0%; P < 0.0001). By Day 3 over 90% of SPRINT patients had cTIB ≥0.5 (37% Pre-SPRINT) reaching 100% by Day 7 (50% Pre-SPRINT). Conditional and joint probabilities indicate tighter, more consistent TGC under SPRINT (cTIB ≥0.5) increased the likelihood SOFA ≤5.
SPRINT TGC resolved organ failure faster, and for more patients, from similar admission and maximum SOFA scores, than conventional control. These reductions mirror the reduced mortality with SPRINT. The cTIB ≥0.5 metric provides a first benchmark linking TGC quality to organ failure. These results support other physiological and clinical results indicating the role tight, consistent TGC can play in reducing organ failure, morbidity and mortality, and should be validated on data from randomised trials.
Stress-induced hyperglycaemia is prevalent in critical care. Control of blood glucose levels to within a 4.4 to 6.1 mmol/L range or below 7.75 mmol/L can reduce mortality and improve clinical ...outcomes. The Specialised Relative Insulin Nutrition Tables (SPRINT) protocol is a simple wheel-based system that modulates insulin and nutritional inputs for tight glycaemic control.
SPRINT was implemented as a clinical practice change in a general intensive care unit (ICU). The objective of this study was to measure the effect of the SPRINT protocol on glycaemic control and mortality compared with previous ICU control methods. Glycaemic control and mortality outcomes for 371 SPRINT patients with a median Acute Physiology And Chronic Health Evaluation (APACHE) II score of 18 (interquartile range IQR 15 to 24) are compared with a 413-patient retrospective cohort with a median APACHE II score of 18 (IQR 15 to 23).
Overall, 53.9% of all measurements were in the 4.4 to 6.1 mmol/L band. Blood glucose concentrations were found to be log-normal and thus log-normal statistics are used throughout to describe the data. The average log-normal glycaemia was 6.0 mmol/L (standard deviation 1.5 mmol/L). Only 9.0% of all measurements were below 4.4 mmol/L, with 3.8% below 4 mmol/L and 0.1% of measurements below 2.2 mmol/L. On SPRINT, 80% more measurements were in the 4.4 to 6.1 mmol/L band and standard deviation of blood glucose was 38% lower compared with the retrospective control. The range and peak of blood glucose were not correlated with mortality for SPRINT patients (P >0.30). For ICU length of stay (LoS) of greater than or equal to 3 days, hospital mortality was reduced from 34.1% to 25.4% (-26%) (P = 0.05). For ICU LoS of greater than or equal to 4 days, hospital mortality was reduced from 34.3% to 23.5% (-32%) (P = 0.02). For ICU LoS of greater than or equal to 5 days, hospital mortality was reduced from 31.9% to 20.6% (-35%) (P = 0.02). ICU mortality was also reduced but the P value was less than 0.13 for ICU LoS of greater than or equal to 4 and 5 days.
SPRINT achieved a high level of glycaemic control on a severely ill critical cohort population. Reductions in mortality were observed compared with a retrospective hyperglycaemic cohort. Range and peak blood glucose metrics were no longer correlated with mortality outcome under SPRINT.
Hyperglycemia is a common metabolic problem in premature, low-birth-weight infants. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the ...stress of their condition in intensive care. A dynamic model capturing the fundamental dynamics of the glucose regulatory system provides a measure of insulin sensitivity (S I ). Forecasting the most probable future S I can significantly enhance real-time glucose control by providing a clinically validated/proven level of confidence on the outcome of an intervention, and thus, increased safety against hypoglycemia. A 2-D kernel model of S I is fitted to 3567 h of identified, time-varying S I from retrospective clinical data of 25 neonatal patients with birth gestational age 23 to 28.9 weeks. Conditional probability estimates are used to determine S I probability intervals. A lag-2 stochastic model and adjustments of the variance estimator are used to explore the bias-variance tradeoff in the hour-to-hour variation of S I . The model captured 62.6% and 93.4% of in-sample S I predictions within the (25th-75th) and (5th-95th) probability forecast intervals. This overconservative result is also present on the cross-validation cohorts and in the lag-2 model. Adjustments to the variance estimator found a reduction to 10%-50% of the original value provided optimal coverage with 54.7% and 90.9% in the (25th-75th) and (5th-95th) intervals. A stochastic model of SI provided conservative forecasts, which can add a layer of safety to real-time control. Adjusting the variance estimator provides a more accurate, cohort-specific stochastic model of SI dynamics in the neonate.
Abstract Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. ...Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose–insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S I , the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S I only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients ( N = 42,941 h in total) who received insulin while in the ICU and stayed for ≥72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% IQR 1.18, 6.41%. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7–12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose–insulin models render this article a mini-review of the state of model-based TGC in critical care.
Critically ill patients are highly variable in their response to care and treatment. This variability and the search for improved outcomes have led to a significant increase in the use of ...protocolized care to reduce variability in care. However, protocolized care does not address the variability of outcome due to inter- and intra-patient variability, both in physiological state, and the response to disease and treatment. This lack of patient-specificity defines the opportunity for patient-specific approaches to diagnosis, care, and patient management, which are complementary to, and fit within, protocolized approaches.
Computational models of human physiology offer the potential, with clinical data, to create patient-specific models that capture a patient's physiological status. Such models can provide new insights into patient condition by turning a series of sometimes confusing clinical data into a clear physiological picture. More directly, they can track patient-specific conditions and thus provide new means of diagnosis and opportunities for optimising therapy.
This article presents the concept of model-based therapeutics, the use of computational models in clinical medicine and critical care in specific, as well as its potential clinical advantages, in a format designed for the clinical perspective. The review is presented in terms of a series of questions and answers. These aspects directly address questions concerning what makes a model, how it is made patient-specific, what it can be used for, its limitations and, importantly, what constitutes sufficient validation.
To provide a concrete foundation, the concepts are presented broadly, but the details are given in terms of a specific case example. Specifically, tight glycemic control (TGC) is an area where inter- and intra-patient variability can dominate the quality of care control and care received from any given protocol. The overall review clearly shows the concept and significant clinical potential of using computational models in critical care medicine.
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