Geoffrey M. Shaw

Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change

IntroductionStress-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.MethodsSPRINT 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).ResultsOverall, 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.ConclusionSPRINT 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.

Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model

Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. This paper introduces a model capable of capturing the essential glucose and insulin kinetics in patients from retrospective data gathered in an intensive care unit (ICU). The model uses two time-varying patient specific parameters for glucose effectiveness and insulin sensitivity. The model is mathematically reformulated in terms of integrals to enable a novel method for identification of patient specific parameters. The method was tested on long-term blood glucose recordings from 17 ICU patients, producing 4% average error, which is within the sensor error. One-hour forward predictions of blood glucose data proved acceptable with an error of 2-11%. All identified parameter values were within reported physiological ranges. The parameter identification method is more accurate and significantly faster computationally than commonly used non-linear, non-convex methods. These results verify the models ability to capture long-term observed glucose-insulin dynamics in hyperglycemic ICU patients, as well as the fitting method developed. Applications of the model and parameter identification method for automated control of blood glucose and medical decision support are discussed.

Improved performance of urinary biomarkers of acute kidney injury in the critically ill by stratification for injury duration and baseline renal function

To better understand the diagnostic and predictive performance of urinary biomarkers of kidney injury, we evaluated γ-glutamyltranspeptidase (GGT), alkaline phosphatase (AP), neutrophil-gelatinase-associated lipocalin (NGAL), cystatin C (CysC), kidney injury molecule-1 (KIM-1), and interleukin-18 (IL-18) in a prospective observational study of 529 patients in 2 general intensive care units (ICUs). Comparisons were made using the area under the receiver operator characteristic curve (AUC) for diagnosis or prediction of acute kidney injury (AKI), dialysis, or death, and reassessed after patient stratification by baseline renal function (estimated glomerular filtration rate, eGFR) and time after renal insult. On ICU entry, no biomarker had an AUC above 0.7 in the diagnosis or prediction of AKI. Several biomarkers (NGAL, CysC, and IL-18) predicted dialysis (AUC over 0.7), and all except KIM-1 predicted death at 7 days (AUC between 0.61 and 0.69). Performance was improved by stratification for eGFR or time or both. With eGFR <60 ml/min, CysC and KIM-1 had AUCs of 0.69 and 0.73, respectively, within 6 h of injury, and between 12 and 36 h, CysC (0.88), NGAL (0.85), and IL-18 (0.94) had utility. With eGFR >60 ml/min, GGT (0.73), CysC (0.68), and NGAL (0.68) had the highest AUCs within 6 h of injury, and between 6 and 12 h, all AUCs except AP were between 0.68 and 0.78. Beyond 12 h, NGAL (0.71) and KIM-1 (0.66) performed best. Thus, the duration of injury and baseline renal function should be considered in evaluating biomarker performance to diagnose AKI.

Early intervention with erythropoietin does not affect the outcome of acute kidney injury (the EARLYARF trial).

We performed a double-blind placebo-controlled trial to study whether early treatment with erythropoietin could prevent the development of acute kidney injury in patients in two general intensive care units. As a guide for choosing the patients for treatment we measured urinary levels of two biomarkers, the proximal tubular brush border enzymes gamma-glutamyl transpeptidase and alkaline phosphatase. Randomization to either placebo or two doses of erythropoietin was triggered by an increase in the biomarker concentration product to levels above 46.3, with a primary outcome of relative average plasma creatinine increase from baseline over 4 to 7 days. Of 529 patients, 162 were randomized within an average of 3.5 h of a positive sample. There was no difference in the incidence of erythropoietin-specific adverse events or in the primary outcome between the placebo and treatment groups. The triggering biomarker concentration product selected patients with more severe illness and at greater risk of acute kidney injury, dialysis, or death; however, the marker elevations were transient. Early intervention with high-dose erythropoietin was safe but did not alter the outcome. Although these two urine biomarkers facilitated our early intervention, their transient increase compromised effective triaging. Further, our study showed that a composite of these two biomarkers was insufficient for risk stratification in a patient population with a heterogeneous onset of injury.

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

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.

Urinary cystatin C is diagnostic of acute kidney injury and sepsis, and predicts mortality in the intensive care unit

IntroductionTo evaluate the utility of urinary cystatin C (uCysC) as a diagnostic marker of acute kidney injury (AKI) and sepsis, and predictor of mortality in critically ill patients.MethodsThis was a two-center, prospective AKI observational study and post hoc sepsis subgroup analysis of 444 general intensive care unit (ICU) patients. uCysC and plasma creatinine were measured at entry to the ICU. AKI was defined as a 50% or 0.3-mg/dL increase in plasma creatinine above baseline. Sepsis was defined clinically. Mortality data were collected up to 30 days. The diagnostic and predictive performances of uCysC were assessed from the area under the receiver operator characteristic curve (AUC) and the odds ratio (OR). Multivariate logistic regression was used to adjust for covariates.ResultsEighty-one (18%) patients had sepsis, 198 (45%) had AKI, and 64 (14%) died within 30 days. AUCs for diagnosis by using uCysC were as follows: sepsis, 0.80, (95% confidence interval (CI), 0.74 to 0.87); AKI, 0.70 (CI, 0.64 to 0.75); and death within 30 days, 0.64 (CI, 0.56 to 0.72). After adjustment for covariates, uCysC remained independently associated with sepsis, AKI, and mortality with odds ratios (CI) of 3.43 (2.46 to 4.78), 1.49 (1.14 to 1.95), and 1.60 (1.16 to 2.21), respectively. Concentrations of uCysC were significantly higher in the presence of sepsis (P < 0.0001) or AKI (P < 0.0001). No interaction was found between sepsis and AKI on the uCysC concentrations (P = 0.53).ConclusionsUrinary cystatin C was independently associated with AKI, sepsis, and death within 30 days.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN012606000032550.

Test Characteristics of Urinary Biomarkers Depend on Quantitation Method in Acute Kidney Injury

The concentration of urine influences the concentration of urinary biomarkers of AKI. Whether normalization to urinary creatinine concentration, as commonly performed to quantitate albuminuria, is the best method to account for variations in urinary biomarker concentration among patients in the intensive care unit is unknown. Here, we compared the diagnostic and prognostic performance of three methods of biomarker quantitation: absolute concentration, biomarker normalized to urinary creatinine concentration, and biomarker excretion rate. We measured urinary concentrations of alkaline phosphatase, γ-glutamyl transpeptidase, cystatin C, neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, and IL-18 in 528 patients on admission and after 12 and 24 hours. Absolute concentration best diagnosed AKI on admission, but normalized concentrations best predicted death, dialysis, or subsequent development of AKI. Excretion rate on admission did not diagnose or predict outcomes better than either absolute or normalized concentration. Estimated 24-hour biomarker excretion associated with AKI severity, and for neutrophil gelatinase-associated lipocalin and cystatin C, with poorer survival. In summary, normalization to urinary creatinine concentration improves the prediction of incipient AKI and outcome but provides no advantage in diagnosing established AKI. The ideal method for quantitating biomarkers of urinary AKI depends on the outcome of interest.

Tight glycemic control in critical care - The leading role of insulin sensitivity and patient variability: A review and model-based analysis

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 75,000h 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.

Model-Based Insulin and Nutrition Administration for Tight Glycaemic Control in Critical Care

OBJECTIVE Present a new model-based tight glycaemic control approach using variable insulin and nutrition administration. BACKGROUND Hyperglycaemia is prevalent in critical care. Current published protocols use insulin alone to reduce blood glucose levels, require significant added clinical effort, and provide highly variable results. None directly address both the practical clinical difficulties and significant patient variation seen in general critical care, while also providing tight control. METHODS The approach presented manages both nutritional inputs and exogenous insulin infusions using tables simplified from a model-based, computerised protocol. Unique delivery aspects include bolus insulin delivery for safety and variable enteral nutrition rates. Unique development aspects include the use of simulated virtual patient trials created from retrospective data. The model, protocol development, and first 50 clinical case results are presented. RESULTS High qualitative correlation to within +/-10% between simulated virtual trials and published clinical results validates the overall approach. Pilot tests covering 7358 patient hours produced an average glucose of 5.9 +/- 1.1 mmol/L. Time in the 4-6.1 mmol/L band was 59%, with 84% in 4.0-7.0 mmol/L, and 92% in 4.0-7.75 mmol/L. The average feed rate was 63% of patient specific goal feed and the average insulin dose was 2.6U/hour. There was one hypoglycaemic measurement of 2.1 mmol/L. No departures from protocol or clinical interventions were required at any time. SUMMARY Modulating both low dose insulin boluses and nutrition input rates challenges the current practice of using only insulin in larger doses to reduce hyperglycaemic levels. Clinical results show very tight control in safe glycaemic bands. The approach could be readily adopted in any typical ICU.

Validation of a Model-based Virtual Trials Method for Tight Glycemic Control in Intensive Care

BackgroundIn-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.MethodsData 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.ResultsModel 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.ConclusionsThis 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.