Open Access

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

  • JGeoffrey Chase1,
  • Geoffrey Shaw2,
  • Aaron Le Compte1Email author,
  • Timothy Lonergan1,
  • Michael Willacy1,
  • Xing-Wei Wong1,
  • Jessica Lin1,
  • Thomas Lotz1,
  • Dominic Lee3 and
  • Christopher Hann1
Critical Care200812:R49

DOI: 10.1186/cc6868

Received: 19 December 2007

Accepted: 16 April 2008

Published: 16 April 2008

Abstract

Introduction

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.

Methods

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).

Results

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.

Conclusion

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.

Introduction

Hyperglycaemia is prevalent in critical care, even with no prior diabetes [14]. Increased secretion of counter-regulatory hormones stimulates endogenous glucose production and increases effective insulin resistance [3, 4]. Studies also indicate that high-glucose-content nutritional regimes can exacerbate hyperglycaemia [510].

Hyperglycaemia worsens outcomes, increasing the risk of severe infection [11], myocardial infarction [1], and critical illnesses such as polyneuropathy and multiple organ failure [2]. Evidence also exists of significant reductions in other therapies such as ventilator support and renal replacement therapy with aggressive glycaemic control [2, 12]. More importantly, van den Berghe and colleagues [2, 13, 14] and Krinsley [15, 16] showed that tight glucose control to limits of 6.1 to 7.75 mmol/L reduced relative intensive care unit (ICU) patient mortality by 18% to 45% for patients with a stay of greater than 3 days. Both sets of studies also showed significant cost savings per patient [17, 18]. Finally, two recent reviews showed that tighter control with less variability provides better outcome [19, 20].

Regulating blood glucose levels in critical care using simple model-based protocols and insulin alone has been moderately successful [2125]. However, no model-based method has been clinically tested to a mortality endpoint. In contrast, clinically tested sliding scales and titration-based methods have not always been effective, due to an inability to customise the control to individual patients [2628]. On the other hand, model-based methods are able to identify evolving patient-specific parameters and tailor therapy appropriately.

The significantly elevated insulin resistance often encountered in broad critical care cohorts challenges the practice of using insulin-only protocols. In the presence of significant insulin resistance, insulin effect saturates at high concentrations of insulin [23, 29, 30], limiting the achievable glycaemic reductions. Hence, despite the potential, many ICUs do not use fixed protocols or necessarily agree on what constitutes acceptable or desirable glycaemic management and performance [4, 12, 3134].

However, tighter glycaemic control is still possible by also controlling the exogenous nutritional inputs exacerbating the original problem [510]. Clinical studies that intentionally lowered carbohydrate nutrition have significantly reduced average blood glucose levels without added insulin [5, 8, 9], and Krishnan and colleagues [10] showed that feeding 33% to 66% of the amount recommended by the American College of Chest Physicians (ACCP) guidelines [35] minimised mortality and hyperglycaemia. The present paper presents the clinical implementation of a protocol, developed from model-based controllers [36, 37], that modulates both nutrition and insulin to provide tight glycaemic control together with easy clinical implementation. The protocol is a simple paper wheel-based system (Specialised Relative Insulin Nutrition Tables, or SPRINT) that modulates both insulin and nutritional inputs based on hourly or 2-hourly blood glucose measurements for tight glycaemic control. The objectives of this study were to measure the effect of the SPRINT protocol on glycaemic control compared with previous ICU control methods and to evaluate the effect the implementation of the protocol has had on mortality outcomes.

Materials and methods

Protocol

Model-based tight blood glucose control is possible with a validated patient-specific glucose-insulin regulatory system model that captures the fundamental dynamics. Chase and colleagues [21, 23, 38] and Hann and colleagues [38] used a model that captured the rate of insulin utilisation, insulin losses, and saturation dynamics and that has been validated using retrospective data [3840], clamp data [41], and several short-term (not longer than 24 hours) clinical control trials [36, 37]. The model thus captures the metabolic status of the highly dynamic ICU patient and uses it to provide tight control. However, computational resources are not available in some critical care units for effective computerised control methods, and their complexity can limit easy large-scale implementation required to test overall safety and efficacy. Hence, a simpler paper-based method was developed to mimic this protocol. SPRINT was implemented as a clinical practice change at the Christchurch Hospital Department of Intensive Care in August 2005. Further details on SPRINT, its development, and initial pilot study can be found in [27, 28, 42].

The entry criterion for the SPRINT protocol was a blood glucose measurement of greater than 8 mmol/L on two occasions during standard patient monitoring, where the 8 mmol/L represents the upper limit of clinically desirable glycaemic control in the Christchurch ICU. Patients were occasionally put on SPRINT at the discretion of the clinician if the blood glucose levels were consistently greater than 7 mmol/L in severe critical illness. Patients were not put on the protocol if they were not expected to remain in the ICU for more than 24 hours. Data were collected for all blood glucose measurements, insulin administered, and nutrition given to the patient. The Upper South Regional Ethics Committee, New Zealand, granted ethics approval for the audit, analysis, and publication of these data.

Hourly blood glucose measurements are used to ensure tight control [27]. Two-hourly measurements are used when the patient is stable, defined as three consecutive 1-hourly measurements in the 4.0 to 6.0 mmol/L band [27, 42], or when an arterial line is not present. SPRINT is stopped when the patient is adequately self-regulating, defined as 6 or more hours (three 2-hourly measurements) in the 4.0 to 6.0 mmol/L band with over 80% of the goal feed rate and a maximum of 2 U/hour of insulin [27, 42].

Total insulin prescribed by SPRINT is limited to 6 U/hour to minimise saturation and the administration of ineffective insulin [23, 29, 30, 43]. Insulin is given predominantly in bolus form for safety, avoiding infusions being left on at levels inappropriate for evolving patient condition. Occasionally, doctors prescribed a background insulin infusion rate of 0.5 to 2 U/hour, primarily for patients known to have type II diabetes, and the insulin bolus recommendations from SPRINT were added to this background rate. A background rate of 0.5 to 1.0 U/hour, to which SPRINT bolus insulin is added, is mandated in patients with type I diabetes.

Goal enteral nutrition rates are approximately 25 kcal/kg per day of RESOURCE Diabetic (Novartis Medical Nutrition, Minneapolis, MN, USA) or Glucerna (Abbott Laboratories, Abbott Park, IL, USA) with 34% to 36% of calories from carbohydrates [44]. Minimum and maximum nutrition rates are 7.5 to 25 kcal/kg per day, with 2.7 to 9 kcal/kg per day from carbohydrates. Thus, an 80-kg male would receive a maximum of 2,000 kcal/day and a minimum of 600 kcal/day, with 216 to 640 kcal/day from carbohydrates, exceeding the minimum level below which there is an increased risk of bloodstream infections [45]. These guidelines are detailed by Shaw and colleagues [26] and are approximately equivalent to the ACCP guidelines [35].

Statistical analysis

Baseline variables were compared using the two-tailed Mann-Whitney U test or chi-square test. Change in mortality was compared between the SPRINT and historical cohorts by means of the chi-square test. The Mann-Whitney and chi-square tests were used to compare blood glucose metrics between survivors and non-survivors. MINITAB® Release 14.1 (Minitab Inc., State College, PA, USA) was used for statistical comparisons, and for all statistical tests, P values of less than 0.05 were considered significant.

Log-normal statistics were used to provide an accurate description of blood glucose control results as negative blood glucose concentrations are not possible and typical distributions of blood glucose measurements are asymmetric and show a skew toward higher concentrations. The design of the protocol was that, for periods outside the ideal target range, short periods of higher blood glucose levels were preferred over hypoglycaemic events. Thus, the distributions for blood glucose are right-skewed and log-normal.

Cohorts

SPRINT was implemented as a clinical practice change and thus was the sole method of treatment for hyperglycaemia. A retrospective cohort has been used to infer changes in patient outcome due to SPRINT. This cohort was extracted from all intensive care patients for the 20-month period of January 2003 to August 2005. Figure 1 shows the selection of patients into the SPRINT and retrospective patient cohorts. Entry criteria into the retrospective cohort were an ICU length of stay of at least 1 day and at least two blood glucose measurements of more than 8 mmol/L spaced not more than 24 hours apart. Patients were excluded where there were insufficient clinical data available to compute an Acute Physiology and Chronic Health Evaluation (APACHE) II score. There was no set protocol for treating hyperglycaemia in the Christchurch ICU during the retrospective period, and clinicians often used a variety of insulin sliding scales.
https://static-content.springer.com/image/art%3A10.1186%2Fcc6868/MediaObjects/13054_2007_Article_6301_Fig1_HTML.jpg
Figure 1

Method of cohort selection for the Specialised Relative Insulin Nutrition Tables (SPRINT) and retrospective patient groups. APACHE, Acute Physiology And Chronic Health Evaluation; BG, blood glucose concentration.

The retrospective patient pool had a larger proportion of operative cardiovascular patients, and the SPRINT patient pool had a larger proportion of gastrointestinal patients. Changes in the economics of health care caused changes in the types of patients admitted to the Christchurch ICU over the 4-year period encompassed by the SPRINT and retrospective data. The difference in cardiothoracic patients between the patient pools may have resulted from less case throughput and better pre-intensive care glycaemic control. Thus, to provide better-matched cohorts, retrospective operative cardiovascular patients and SPRINT gastrointestinal patients were randomly eliminated from the patient pools to create the cohorts used for analysis, as shown in Figure 1. The patient elimination procedure was repeated 100 times to create 100 cohorts. To present the data clearly, the median cohort results are presented based on mortality outcome for analysis in this article. The major results and outcomes were unaffected by the specific cohort iteration.

Results

Patient cohorts

The clinical details of this retrospective cohort are compared with the SPRINT cohort by means of baseline variables, APACHE II scores, and APACHE III diagnosis codes in Table 1.
Table 1

Comparison of SPRINT and retrospective cohort baseline variables

 

Overall

 

Retrospective

SPRINT

P value

Total patients

413

 

371

  

Age, years

64 (53–74)

 

65 (49–74)

 

0.53

Percentage of males

59.1%

 

63.6%

 

0.19

APACHE II score

18 (15–23)

 

18 (15–24)

 

0.50

APACHE II risk of death

28.5% (14.2%-49.7%)

 

25.7% (13.1%-49.4%)

 

0.39

Diabetic history

71 (17.2%)

 

62 (16.7%)

 

0.86

APACHE III diagnosis

     

Operative

Number of patients

Percentage

Number of patients

Percentage

P value

   Cardiovascular

99

24%

76

20%

0.24

   Respiratory

10

2%

9

2%

1.00

   Gastrointestinal

53

13%

60

16%

0.18

   Neurological

9

2%

7

2%

0.77

   Trauma

8

2%

14

4%

0.12

   Other (renal, metabolic, orthopaedic)

4

1%

4

1%

0.88

Non-operative

Number of patients

Percentage

Number of patients

Percentage

P value

   Cardiovascular

41

10%

39

11%

0.79

   Respiratory

77

19%

66

18%

0.76

   Gastrointestinal

7

2%

10

3%

0.34

   Neurological

33

8%

20

5%

0.15

   Trauma

29

7%

32

9%

0.40

   Sepsis

29

7%

17

5%

0.15

   Other (renal, metabolic, orthopaedic)

14

3%

17

5%

0.39

Data are expressed as median (interquartile range) where appropriate. P values computed using chi-square and Mann-Whitney U tests where appropriate. APACHE, Acute Physiology And Chronic Health Evaluation; SPRINT, Specialised Relative Insulin Nutrition Tables.

Glycaemic control

Table 2 presents a comparison of glycaemic control for the 371 SPRINT protocol patients against the 413 patients from the retrospective cohort. Measurements (27,664) were recorded for more than 44,769 hours of patient control on SPRINT compared with 13,162 measurements for 43,447 recorded hours of retrospective data. Patients on SPRINT had their blood glucose measured every hour during 24% of their time on the protocol and every 2 hours over the remaining 76% where there was improved glycaemic stability. Log-normal mean blood glucose levels in the SPRINT cohort for hourly and 2-hourly measurements were 6.3 mmol/L (standard deviation 1.6 mmol/L) and 5.6 mmol/L (standard deviation 1.1 mmol/L), respectively. The mean time between measurements in the SPRINT cohort was 1 hour 36 minutes compared with 3 hours 18 minutes for the retrospective cohort. The precision of the recordkeeping system in the Christchurch ICU is to the nearest hour, and nursing staff typically measured blood glucose and used the protocol on the hour.
Table 2

Summary comparison of SPRINT and retrospective glycaemic control

Overall cohort data

Retrospective

SPRINT

P value

   Number of patients

413

371

 

   Hours of control

43,447

44,769

 

   Total BG measurements

13,162

27,664

 

   BG mean (log-normal), mmol/L

7.2

6.0

<0.01

   BG standard deviation (log-normal), mmol/L

2.4

1.5

 

   Percentage of measurements between

   

4.4 and 6.1 mmol/L

30.0%

53.9%

<0.01

   Percentage of measurements less than

   

4.4 mmol/L

6.5%

9.0%

<0.01

4.0 mmol/L

3.8%

3.8%

0.97

2.2 mmol/L

0.2%

0.1%

<0.01

   Mean insulin usage, U/hour

1.2

2.8

<0.01

   Mean nutrition rate

   

During periods of feeding, kcal/day

1,599

1,283

<0.01

Entire duration of SPRINT usage, kcal/day

-

1,014

 

   Mean percentage of goal feed

-

66.1%

 

Per-patient data

   

   Hours of control

49 (19–140)

53 (19–146)

0.24

   Number of BG measurements

15 (6–40)

37 (17–97)

<0.01

   BG mean (log-normal), mmol/L

7.4 (6.6–8.3)

6.0 (5.5–6.6)

<0.01

   BG standard deviation (log-normal), mmol/L

1.6 (1.2–2.4)

1.3 (1.0–1.8)

<0.01

   Percentage of patients <6.1 mmol/L

74.3%

96.0%

<0.01

   Insulin usage, U/hour

0.9 (0.1–1.6)

2.6 (2.1–3.3)

<0.01

   Nutrition rate

   

During periods of feeding, kcal/day

908 (0–1,608)

936 (0–1,308)

0.68

Entire duration of SPRINT usage, kcal/day

-

709 (0–1,167)

 

   Percentage of goal feed

-

49.7 (0.0–70.8)

 

Per-patient data are expressed as median (interquartile range) as appropriate. BG, blood glucose concentration; SPRINT, Specialised Relative Insulin Nutrition Tables.

The percentage time in the 4.4 to 6.1 mmol/L band defined by van den Berghe and colleagues [2, 13] was 53.9% compared with 30.0% in the retrospective cohort. Hypoglycaemia was comparable to the retrospective cohort, with only 0.1% of measurements less than 2.2 mmol/L. SPRINT had a higher proportion of measurements below the 4.4 mmol/L limit; however, the two cohorts were comparable for measurements below the 4.0 mmol/L lower limit of the SPRINT target band. Per-patient results show that the mean and standard deviation of blood glucose for SPRINT are lower. Additionally, the interquartile range for both metrics amongst patients is tighter and thus there is less variability in glycaemic control performance between patients. Figure 2 shows a tightly controlled distribution of blood glucose measurements for all patients along with the 4.4–6.1 mmol/L range.
https://static-content.springer.com/image/art%3A10.1186%2Fcc6868/MediaObjects/13054_2007_Article_6301_Fig2_HTML.jpg
Figure 2

Comparison of distribution of all blood glucose measurements. (a, b) Histogram and empirical cumulative distribution function of all blood glucose measurements for all Specialised Relative Insulin Nutrition Tables (SPRINT) patients (shaded, solid line) and retrospective cohort patients (dashed line), respectively. BG, blood glucose concentration.

The mean overall hourly insulin usage on SPRINT was 2.8 U/hour, which is a level that avoids insulin saturation effects [29, 30, 43]. The median feed level recommended by SPRINT was 66.1% of the patient-specific goal feed [42]. The mean overall nutrition rate was 1,283 kcal/day on SPRINT during periods when the patients was being fed, including via the parenteral route, compared with 1,599 kcal/day for the retrospective cohort. The mean nutrition rate over the entire length of stay, including periods in which feed was stopped for reasons outside glycaemic control, was 1,014 kcal/day on SPRINT. When no enteral or parenteral nutrition was recorded in the retrospective cohort data, it was not clear whether the nutrition administration was halted for clinical reasons or because the patient had begun eating meals. Thus, a nutrition comparison with the retrospective cohort was possible only for periods when the patient was receiving enteral or parenteral alimentation.

Figures 3 to 5 show the average percentage of measurements in the 4.4 to 6.1 mmol/L band, the average blood glucose concentration, and the average blood glucose standard deviation for patients grouped by starting blood glucose level and APACHE II score. The percentage of measurements in the target band was 66% to 203% higher and the blood glucose standard deviation was 6% to 30% lower on SPRINT compared with the retrospective cohort.
https://static-content.springer.com/image/art%3A10.1186%2Fcc6868/MediaObjects/13054_2007_Article_6301_Fig3_HTML.jpg
Figure 3

Grouped comparison of percentage of measurements in the 4.4 to 6.1 mmol/L band. (a) Measurements grouped by first blood glucose measurement. (b) Measurements grouped by Acute Physiology And Chronic Health Evaluation (APACHE) II score. *P < 0.05 (Mann-Whitney test). BG, blood glucose concentration; SPRINT, Specialised Relative Insulin Nutrition Tables.

https://static-content.springer.com/image/art%3A10.1186%2Fcc6868/MediaObjects/13054_2007_Article_6301_Fig4_HTML.jpg
Figure 4

Grouped comparison of average blood glucose level (log-normal). (a) Measurements grouped by first blood glucose measurement. (b) Measurements grouped by Acute Physiology And Chronic Health Evaluation (APACHE) II score. *P < 0.05 (Mann-Whitney test). BG, blood glucose concentration; SPRINT, Specialised Relative Insulin Nutrition Tables.

https://static-content.springer.com/image/art%3A10.1186%2Fcc6868/MediaObjects/13054_2007_Article_6301_Fig5_HTML.jpg
Figure 5

Grouped comparison of blood glucose standard deviation (log-normal). (a) Measurements grouped by first blood glucose measurement. (b) Measurements grouped by Acute Physiology And Chronic Health Evaluation (APACHE) II score. *P < 0.05 (Mann-Whitney test). BG, blood glucose concentration; SPRINT, Specialised Relative Insulin Nutrition Tables.

Figure 6 shows the box-and-whisker plot of hourly blood glucose concentration for all patients over first 48 hours on SPRINT. After approximately 7 hours, the blood glucose median and spread reach their average levels. This level of control is essentially maintained for the remainder of the period. Table 3 shows that 96% of SPRINT patients reached the 6.1 mmol/L band from the initial hyperglycaemic state compared with only 74% of the retrospective hyperglycaemic patients. SPRINT, therefore, brings a patient under control within 7 to 8 hours and maintains a constant level of performance.
https://static-content.springer.com/image/art%3A10.1186%2Fcc6868/MediaObjects/13054_2007_Article_6301_Fig6_HTML.jpg
Figure 6

Hourly blood glucose average values for all patients on Specialised Relative Insulin Nutrition Tables (SPRINT). Boxes represent the interquartile range (IQR) containing the median, whiskers represent 1.5 times the IQR, and crosses represent outlying measurements beyond this range. BG, blood glucose concentration.

Table 3

Significance of mortality difference between SPRINT and retrospective cohorts grouped by length of intensive care unit stay

 

Intensive care unit mortality

 

Retrospective

SPRINT

Percentage change (relative)

P value

LOS ≥ 1 day

18.6%

16.8%

-10%

0.523

LOS ≥ 2 days

20.7%

17.9%

-14%

0.403

LOS ≥ 3 days

22.0%

16.9%

-23%

0.177

LOS ≥ 4 days

22.7%

16.6%

-27%

0.130

LOS ≥ 5 days

21.1%

13.9%

-34%

0.087

 

Hospital mortality

 

Retrospective

SPRINT

Percentage change (relative)

P value

LOS ≥ 1 day

27.4%

24.9%

-9%

0.457

LOS ≥ 2 days

31.6%

26.3%

-17%

0.170

LOS ≥ 3 days

34.1%

25.4%

-26%

0.045

LOS ≥ 4 days

34.3%

23.5%

-32%

0.020

LOS ≥ 5 days

31.9%

20.6%

-35%

0.019

The P values test the median mortality result using the chi-square contingency table test. LOS, length of stay; SPRINT, Specialised Relative Insulin Nutrition Tables.

Figure 7 shows the average nutrition intake and insulin administration rate for the first 7 days on the SPRINT protocol. The average nutrition intake is lower and the average insulin rate is higher during the initial phase of controlling hyperglycaemia. Once hyperglycaemia has been controlled, the average nutrition rate recommended by the protocol increases, generally as patient condition improves and carbohydrate tolerance increases, whilst average insulin administration rate remains relatively constant.
https://static-content.springer.com/image/art%3A10.1186%2Fcc6868/MediaObjects/13054_2007_Article_6301_Fig7_HTML.jpg
Figure 7

Average nutrition and insulin administration rates for the first 7 days on Specialised Relative Insulin Nutrition Tables (SPRINT). Nutrition rates are represented by solid lines, and insulin rates are represented by dashed lines with crosses.

Clinical outcomes

Figure 8 shows the percentage mortality for both the SPRINT and retrospective patients for both in-hospital and ICU mortality, grouped by length of ICU stay, for several iterations of the cohort selection procedure described in Figure 1.
https://static-content.springer.com/image/art%3A10.1186%2Fcc6868/MediaObjects/13054_2007_Article_6301_Fig8_HTML.jpg
Figure 8

Comparison of intensive care unit (ICU) mortality and in-hospital mortality between Specialised Relative Insulin Nutrition Tables (SPRINT) and retrospective patients for several iterations of the patient selection procedure detailed in Figure 1. The top row of plots shows ICU mortality, and the bottom row of plots shows hospital mortality. The retrospective mortality average is shown by the horizontal line, and mortality since initiation of SPRINT is shown by the variable line. The horizontal axis represents the total number of patients treated with the SPRINT protocol. The plots are grouped by length of ICU stay (LOS). The spread of lines results from the random patient selection process.

Table 3 shows the change in mortality, both in-ICU and in-hospital, for patients with lengths of stay of at least 1 to 5 days, compared with the retrospective cohort using the chi-square test, for the median iteration of the cohort selection procedure. As length of ICU stay increases, the reduction in mortality becomes statistically stronger. Statistical significance (P < 0.05) is achieved for an ICU stay of 3 days or longer for in-hospital mortality.

Several recent studies have identified hyperglycaemia as a risk factor for mortality in critical care [1, 2, 19, 4648]. Table 4 compares average blood glucose, maximum blood glucose, and range of blood glucose between SPRINT ICU survivors and non-survivors by means of the Mann-Whitney test. There is no statistically significant difference between survivors and non-survivors for any of these glycaemic metrics.
Table 4

Comparison of statistical significance for patient risk variables between survivors and non-survivors

 

Survivors

Non-survivors

P value

SPRINT cohort

n = 310

n = 61

 

Average BG, mmol/L

6.1 (5.6–6.6)

5.9 (5.5–6.5)

0.55a

Maximum BG, mmol/L

9.9 (8.6–11.8)

10.4 (8.5–12.1)

0.62a

BG range, mmol/L

6.1 (4.7–8.1)

6.5 (5.0–8.8)

0.35a

APACHE II score

18.0 (14.0–22.0)

25.0 (18.0–30.3)

<0.01a

APACHE II risk of death

23% (12%-41%)

50% (26%-71%)

<0.01a

Average percentage goal feed

48% (0%-71%)

54% (29%-68%)

0.42a

Average hourly insulin, U/hour

2.7 (2.2–3.3)

2.5 (2.1–3.2)

0.65a

Diabetic history

56 (18.1%)

6 (9.8%)

0.12b

Retrospective cohort

n = 336

n = 77

 

Average BG, mmol/L

7.5 (6.6–8.4)

7.3 (6.6–8.3)

0.83a

Maximum BG, mmol/L

10.5 (9.4–12.6)

11.3 (10.0–13.6)

0.04a

BG range, mmol/L

5.8 (3.8–8.6)

6.8 (4.7–9.5)

0.05a

APACHE II score

18.0 (15.0–22.0)

24.0 (18.8–29.0)

<0.01a

APACHE II risk of death

25% (13%-43%)

51% (23%-67%)

<0.01a

Average hourly insulin, U/hour

0.8 (0.0–1.7)

1.0 (0.5–1.5)

0.33a

Diabetic history

58 (17.3%)

13 (16.9%)

0.94b

Statistical significance was tested for average, maximum and range of blood glucose, APACHE II score, average feed rate, and diabetic history between SPRINT intensive care unit survivors and mortalities. Data are expressed as number (percentage) or per-patient median (interquartile range) where appropriate. aMann-Whitney U test; bchi-square test. APACHE, Acute Physiology And Chronic Health Evaluation; BG, blood glucose concentration; SPRINT, Specialised Relative Insulin Nutrition Tables.

Discussion

High levels of control were achieved on a patient cohort with relatively severe medical conditions compared with other studies. The median APACHE II score was 18, which is higher than some previous intensive insulin clinical studies whose APACHE II medians or averages were 9 [2, 13] and 16.9 [15]. Higher APACHE II scores are a general indicator of increased insulin resistance [15].

The overall mean of 6.0 mmol/L with a standard deviation of 1.5 mmol/L compares well with the 5.7 ± 1.0 mmol/L value of van den Berghe and colleagues [2], who studied a much less ill cohort. Similarly, it is lower than the 7.3 ± 3.4 mmol/L result reported by Krinsley [15] for a less critically ill cohort. Finally, it is similar to the 6.0 ± 1.3 mmol/L reported by van den Berghe and colleagues [14], who reported an average APACHE II score of 24. It is also important to note that van den Berghe and colleagues [14] reported only mean morning glucose, which a recent large study showed to be significantly lower than other daily measurements [49], thus possibly minimising the variation reported.

SPRINT was implemented as a clinical practice change and thus only a comparison with retrospective data was possible. A limitation of this study is that the SPRINT and retrospective groups were comparable only on a cohort-wide basis. However, as shown in Figure 8, there is a strong signal for the reduction in mortality following the introduction of a high-performance glycaemic control protocol. Mortality reductions were statistically stronger for patients who stayed in the ICU for increasingly longer periods. The plots of Figure 8 indicate a trend toward a steady-state reduction in mortality, particularly for patients with longer ICU stays, and show that greater statistical significance may be reached with a larger cohort, which agrees with the results of van den Berghe and colleagues [2].

Figures 2 and 6 show that as mean blood glucose decreases the lower 5% limit of measurements does not appreciably drop, indicating that control is not simply shifting blood glucose lower but is also tightening the spread and thus minimising the risk of hypoglycaemic events. Table 2 shows that 0.1% (n = 24) of measurements were less than or equal to 2.2 mmol/L, with only 20 (5.2%) patients experiencing one or more such measurements. Thus, lower and tighter glycaemia was achieved without increasing the risk of hypoglycaemia.

Time in a relevant glycaemic band can provide a more robust description of control performance than an average glycaemic value. This result is consistent across all starting blood glucose ranges and APACHE II scores shown in Figures 3 to 5 and emphasises the consistency of control achieved under varying patient conditions. Grouping patients by these metrics enables comparisons between more similar groups.

The mean nutrition rate puts the caloric intake of 1,283 kcal/day on SPRINT in the middle tertile of the ACCP guidelines reported by Krishnan and colleagues [10] to be optimal for mortality outcome. The median per-patient average nutrition rates were similar for the SPRINT and the retrospective cohorts.

Table 4 shows that SPRINT has removed glycaemia as a statistically correlated risk factor of ICU mortality. In the retrospective cohort, maximum blood glucose and range of blood glucose were still associated with mortality. Additionally, APACHE II score was significantly higher in non-survivors for both cohorts. The APACHE II risk of death (which depends upon diagnosis) was also higher in non-survivors than survivors, with a median risk of death of 50% versus 23% in non-survivors versus survivors on SPRINT. There was no significant difference in percentage of goal feed rates between survivors and non-survivors for the SPRINT cohort.

A recent review of published insulin-based glycaemic control protocols in intensive care identified protocols that adjusted insulin infusion based on frequently measured changes in blood glucose concentration as providing the best control [50]. SPRINT is derived from a model-based controller that incorporates non-linear insulin transport and glucose removal kinetics. Thus, it was identified in simulation that, in addition to insulin usage, feed modulation is required for ideal control [27]. SPRINT uses the absolute blood glucose level, change in blood glucose level, and current insulin and nutrition administration rates to identify effectively the patient's insulin sensitivity and respond accordingly. The unique wheel-based design of SPRINT allows all of these metrics to be incorporated into an essentially nurse-automated protocol.

The paper-based design of the SPRINT system allowed for relatively easy adoption into the Christchurch ICU, which does not typically have suitable bedside computing resources available. Computerised methods can have several advantages over paper-based systems. The SPRINT wheels are discretised to increments of 1 U of insulin and 10% of the goal feed to provide a design that is compact and easy-to-use, whereas a computer implementation could allow more refined dosing recommendations. Complete electronic recordkeeping is also possible on computerised systems, which can assist in measuring protocol compliance.

SPRINT is fully implemented by nursing staff without additional clinical intervention or modification. We believe that the frequent blood glucose measurement required by SPRINT is justified as the protocol prescribes definitive actions and provides tight safe results. Hence, it outlines what to do under most or all conceivable scenarios, limiting the need for the clinician intervention or modification seen in other protocols.

Conclusion

SPRINT was implemented as a clinical practice change and achieved an average blood glucose level of 6.0 ± 1.5 mmol/L, with 53.9% of measurements in the 4.4 to 6.1 mmol/L band over a general ICU cohort. Reductions in ICU and hospital mortality rates were observed in comparison with a retrospective cohort. Risk factors such as maximum blood glucose and range of blood glucose were no longer associated with survival under SPRINT.

Key messages

  • High-performance glycaemic control can be achieved with a simple nurse-automated protocol.

  • Modulating both insulin and nutrition in tandem can achieve tight consistent glucose control.

  • Reductions in mortality for longer-stay (>3 days) patients suggest that tight control improves outcomes.

  • The maximum and range of blood glucose measurements were no longer correlated with mortality, due to tight control removing these glucose metrics as indicators of outcome.

  • Comparing glucose performance metrics on a per-patient basis can give important information on the variation of protocol performance between patients.

Abbreviations

ACCP: 

ACCP = American College of Chest Physicians

APACHE: 

APACHE = Acute Physiology And Chronic Health Evaluation

ICU: 

ICU = intensive care unit

SPRINT: 

SPRINT = Specialised Relative Insulin Nutrition Tables.

Declarations

Acknowledgements

GS and JGC received financial support from the Christchurch ICU Trust and have provided personal funding support. ALC received financial support from the New Zealand Tertiary Education Commission, a University of Otago Internal Studentship Grant, and the Canterbury Intensive Care Education and Research Trust. T Lonergan received financial support from a University of Otago Internal Studentship Grant and the Canterbury Intensive Care Education and Research Trust. MW received financial support from the Canterbury Intensive Care Education and Research Trust. XWW received financial support from the New Zealand Tertiary Education Commission. JL received financial support from the New Zealand Tertiary Education Commission and a University of Otago Internal Studentship Grant. T Lotz received financial support from the Canterbury Intensive Care Education and Research Trust and a University of Canterbury Doctoral Scholarship.

Authors’ Affiliations

(1)
Department of Mechanical Engineering, University of Canterbury
(2)
Department of Intensive Care, University of Otago, Christchurch Hospital, Christchurch School of Medicine and Health Science
(3)
Department of Mathematics and Statistics, University of Canterbury

References

  1. Capes SE, Hunt D, Malmberg K, Gerstein HC: Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview. Lancet 2000, 355: 773-778. 10.1016/S0140-6736(99)08415-9PubMedView ArticleGoogle Scholar
  2. Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P, Bouillon R: Intensive insulin therapy in the critically ill patients. N Engl J Med 2001, 345: 1359-1367. 10.1056/NEJMoa011300PubMedView ArticleGoogle Scholar
  3. Mizock BA: Alterations in fuel metabolism in critical illness: hyperglycaemia. Best Pract Res Clin Endocrinol Metab 2001, 15: 533-551. 10.1053/beem.2001.0168PubMedView ArticleGoogle Scholar
  4. McCowen KC, Malhotra A, Bistrian BR: Stress-induced hyperglycemia. Crit Care Clin 2001, 17: 107-124. 10.1016/S0749-0704(05)70154-8PubMedView ArticleGoogle Scholar
  5. Patino JF, de Pimiento SE, Vergara A, Savino P, Rodriguez M, Escallon J: Hypocaloric support in the critically ill. World J Surg 1999, 23: 553-559. 10.1007/PL00012346PubMedView ArticleGoogle Scholar
  6. Weissman C: Nutrition in the intensive care unit. Crit Care 1999, 3: R67-75. 10.1186/cc360PubMedPubMed CentralView ArticleGoogle Scholar
  7. Woolfson AM: Control of blood glucose during nutritional support in ill patients. Intensive Care Med 1980, 7: 11-14. 10.1007/BF01692915PubMedView ArticleGoogle Scholar
  8. Ahrens CL, Barletta JF, Kanji S, Tyburski JG, Wilson RF, Janisse JJ, Devlin JW: Effect of low-calorie parenteral nutrition on the incidence and severity of hyperglycemia in surgical patients: a randomized, controlled trial. Crit Care Med 2005, 33: 2507-2512. 10.1097/01.CCM.0000186746.64572.8APubMedView ArticleGoogle Scholar
  9. Kim H, Son E, Kim J, Choi K, Kim C, Shin W, Suh O: Association of hyperglycemia and markers of hepatic dysfunction with dextrose infusion rates in Korean patients receiving total parenteral nutrition. Am J Health Syst Pharm 2003, 60: 1760-1766.PubMedGoogle Scholar
  10. Krishnan JA, Parce PB, Martinez A, Diette GB, Brower RG: Caloric intake in medical ICU patients: consistency of care with guidelines and relationship to clinical outcomes. Chest 2003, 124: 297-305. 10.1378/chest.124.1.297PubMedView ArticleGoogle Scholar
  11. Bistrian BR: Hyperglycemia and infection: which is the chicken and which is the egg? JPEN J Parenter Enteral Nutr 2001, 25: 180-181. 10.1177/0148607101025004180PubMedView ArticleGoogle Scholar
  12. Diringer MN: Improved outcome with aggressive treatment of hyperglycemia – Hype or hope? Neurology 2005, 64: 1330-1331.PubMedView ArticleGoogle Scholar
  13. Berghe G, Wouters PJ, Bouillon R, Weekers F, Verwaest C, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P: Outcome benefit of intensive insulin therapy in the critically ill: Insulin dose versus glycemic control. Crit Care Med 2003, 31: 359-366. 10.1097/01.CCM.0000045568.12881.10PubMedView ArticleGoogle Scholar
  14. Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, Milants I, Van Wijngaerden E, Bobbaers H, Bouillon R: Intensive insulin therapy in the medical ICU. N Engl J Med 2006, 354: 449-461. 10.1056/NEJMoa052521PubMedView ArticleGoogle Scholar
  15. Krinsley JS: Decreased mortality of critically ill patients with the use of an intensive glycemic management protocol. Crit Care Med 2003, 31: A19.Google Scholar
  16. Krinsley JS: Effect of an intensive glucose management protocol on the mortality of critically ill adult patients. Mayo Clin Proc 2004, 79: 992-1000.PubMedView ArticleGoogle Scholar
  17. Krinsley JS, Jones RL: Cost analysis of intensive glycemic control in critically ill adult patients. Chest 2006, 129: 644-650. 10.1378/chest.129.3.644PubMedView ArticleGoogle Scholar
  18. Berghe G, Wouters PJ, Kesteloot K, Hilleman DE: Analysis of healthcare resource utilization with intensive insulin therapy in critically ill patients. Crit Care Med 2006, 34: 612-616.PubMedGoogle Scholar
  19. Egi M, Bellomo R, Stachowski E, French CJ, Hart G: Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology 2006, 105: 244-252. 10.1097/00000542-200608000-00006PubMedView ArticleGoogle Scholar
  20. Chase JG, Hann CE, Shaw GM, Wong XW, Lin J, Lotz T, Le Compte AJ, Lonergan T: An overview of glycemic control in critical care – relating performance and clinical results. Journal of Diabetes Science and Technology 2007, 1: 82-91.View ArticleGoogle Scholar
  21. Chase JG, Shaw GM, Lin J, Doran CV, Hann C, Robertson MB, Browne PM, Lotz T, Wake GC, Broughton B: Adaptive bolus-based targeted glucose regulation of hyperglycaemia in critical care. Med Eng Phys 2005, 27: 1-11. 10.1016/j.medengphy.2004.08.006PubMedView ArticleGoogle Scholar
  22. Plank J, Blaha J, Cordingley J, Wilinska ME, Chassin LJ, Morgan C, Squire S, Haluzik M, Kremen J, Svacina S, et al.: Multicentric, randomized, controlled trial to evaluate blood glucose control by the model predictive control algorithm versus routine glucose management protocols in intensive care unit patients. Diabetes Care 2006, 29: 271-276. 10.2337/diacare.29.02.06.dc05-1689PubMedView ArticleGoogle Scholar
  23. Chase JG, Shaw GM, Lin J, Doran CV, Hann C, Lotz T, Wake GC, Broughton B: Targeted glycemic reduction in critical care using closed-loop control. Diabetes Technol Ther 2005, 7: 274-282. 10.1089/dia.2005.7.274PubMedView ArticleGoogle Scholar
  24. Chee F, Fernando TL, Savkin AV, van Heeden V: Expert PID control system for blood glucose control in critically ill patients. IEEE Trans Inf Technol Biomed 2003, 7: 419-425. 10.1109/TITB.2003.821326PubMedView ArticleGoogle Scholar
  25. Chee F, Fernando T, van Heerden PV: Closed-loop control of blood glucose levels in critically ill patients. Anaesth Intensive Care 2002, 30: 295-307.PubMedGoogle Scholar
  26. Shaw GM, Chase JG, Wong J, Lin J, Lotz T, Le Compte AJ, Lonergan TR, Willacy MB, Hann CE: Rethinking glycaemic control in critical illness – from concept to clinical practice change. Crit Care Resusc 2006, 8: 90-99.PubMedGoogle Scholar
  27. Lonergan T, LeCompte A, Willacy M, Chase JG, Shaw GM, Wong XW, Lotz T, Lin J, Hann CE: A simple insulin-nutrition protocol for tight glycemic control in critical illness: development and protocol comparison. Diabetes Technol Ther 2006, 8: 191-206. 10.1089/dia.2006.8.191PubMedView ArticleGoogle Scholar
  28. Chase J, Shaw GM, Wong XW, Lotz T, Lin J, Hann CE: Model-based glycaemic control in critical care – a review of the state of the possible. Biomedical Signal Processing and Control 2006, 1: 3-21. 10.1016/j.bspc.2006.03.002View ArticleGoogle Scholar
  29. Prigeon RL, Roder ME, Porte D Jr, Kahn SE: The effect of insulin dose on the measurement of insulin sensitivity by the minimal model technique. Evidence for saturable insulin transport in humans. J Clin Invest 1996, 97: 501-507. 10.1172/JCI118441PubMedPubMed CentralView ArticleGoogle Scholar
  30. Natali A, Gastaldelli A, Camastra S, Sironi AM, Toschi E, Masoni A, Ferrannini E, Mari A: Dose-response characteristics of insulin action on glucose metabolism: a non-steady-state approach. Am J Physiol Endocrinol Metab 2000, 278: E794-801.PubMedGoogle Scholar
  31. Bland DK, Fankhanel Y, Langford E, Lee M, Lee SW, Maloney C, Rogers M, Zimmerman G: Intensive versus modified conventional control of blood glucose level in medical intensive care patients: a pilot study. Am J Crit Care 2005, 14: 370-376.PubMedGoogle Scholar
  32. Gale SC, Gracias VH: Glycemic control needs a standard reference point. Critical care medicine 2006, 34: 1856-1857. 10.1097/01.CCM.0000220201.72591.43PubMedView ArticleGoogle Scholar
  33. Mackenzie I, Ingle S, Zaidi S, Buczaski S: Tight glycaemic control: a survey of intensive care practice in large English hospitals. Intensive Care Med 2005, 31: 1136. 10.1007/s00134-005-2677-2PubMedView ArticleGoogle Scholar
  34. Schultz MJ, Spronk PE, Moeniralam HS: Tight glycaemic control: a survey of intensive care practice in the Netherlands. Intensive Care Med 2006, 32: 618-619. 10.1007/s00134-006-0099-4PubMedView ArticleGoogle Scholar
  35. Cerra FB, Benitez MR, Blackburn GL, Irwin RS, Jeejeebhoy K, Katz DP, Pingleton SK, Pomposelli J, Rombeau JL, Shronts E, et al.: Applied nutrition in ICU patients. A consensus statement of the American College of Chest Physicians. Chest 1997, 111: 769-778. 10.1378/chest.111.3.769PubMedView ArticleGoogle Scholar
  36. Wong XW, Singh-Levett I, Hollingsworth LJ, Shaw GM, Hann CE, Lotz T, Lin J, Wong OS, Chase JG: A novel, model-based insulin and nutrition delivery controller for glycemic regulation in critically ill patients. Diabetes Technol Ther 2006, 8: 174-190. 10.1089/dia.2006.8.174PubMedView ArticleGoogle Scholar
  37. Wong XW, Chase JG, Shaw GM, Hann CE, Lotz T, Lin J, Singh-Levett I, Hollingsworth LJ, Wong OS, Andreassen S: Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study. Med Eng Phys 2006, 28: 665-681. 10.1016/j.medengphy.2005.10.015PubMedView ArticleGoogle Scholar
  38. Hann CE, Chase JG, Lin J, Lotz T, Doran CV, Shaw GM: Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model. Comput Methods Programs Biomed 2005, 77: 259-270. 10.1016/j.cmpb.2004.10.006PubMedView ArticleGoogle Scholar
  39. Doran CV: Modelling and control of hyperglycemia in critical care patients. In Master's thesis. University of Canterbury, Mechanical Engineering; 2004.Google Scholar
  40. Shaw GM, Chase JG, Lee DS, Bloomfield M, Doran CV, Lin J, Lotz T: Peak and range of blood glucose are also associated with ICU mortality. Critical Care Medicine 2005, 32: A125.Google Scholar
  41. Lotz T, Chase J, McAuley K, Lee D, Lin J, Hann C, Mann JI: Transient and steady state euglycemic clamp validation of a model for glycemic control and insulin sensitivity testing. Diabetes Technol Ther 2006, 8: 338-346. 10.1089/dia.2006.8.338PubMedView ArticleGoogle Scholar
  42. Lonergan T, Le Compte A, Willacy M, Chase JG, Shaw GM, Hann CE, Lotz T, Lin J, Wong XW: A pilot study of the SPRINT protocol for tight glycemic control in critically Ill patients. Diabetes Technol Ther 2006, 8: 449-462. 10.1089/dia.2006.8.449PubMedView ArticleGoogle Scholar
  43. Chase JG, Shaw GM, Lin J, Doran CV, Bloomfield M, Wake GC, Broughton B, Hann C, Lotz T: Impact of insulin-stimulated glucose removal saturation on dynamic modelling and control of hyperglycaemia. International Journal of Intelligent Systems Technologies and Applications (IJISTA) 2004, 1: 79-94. 10.1504/IJISTA.2005.007308View ArticleGoogle Scholar
  44. RESOURCE ® Diabetic nutrition information[http://www.novartisnutrition.com/au/productDetail?id=439&source=summary]
  45. Rubinson L, Diette GB, Song X, Brower RG, Krishnan JA: Low caloric intake is associated with nosocomial bloodstream infections in patients in the medical intensive care unit. Crit Care Med 2004, 32: 350-357. 10.1097/01.CCM.0000089641.06306.68PubMedView ArticleGoogle Scholar
  46. Krinsley JS: Association between hyperglycemia and increased hospital mortality in a heterogeneous population of critically ill patients. Mayo Clin Proc 2003, 78: 1471-1478.PubMedView ArticleGoogle Scholar
  47. Finney SJ, Zekveld C, Elia A, Evans TW: Glucose control and mortality in critically ill patients. Jama 2003, 290: 2041-2047. 10.1001/jama.290.15.2041PubMedView ArticleGoogle Scholar
  48. Laird AM, Miller PR, Kilgo PD, Meredith JW, Chang MC: Relationship of early hyperglycemia to mortality in trauma patients. J Trauma 2004, 56: 1058-1062.PubMedView ArticleGoogle Scholar
  49. Egi M, Bellomo R, Stachowski E, French CJ, Hart G, Stow P: Circadian rhythm of blood glucose values in critically ill patients. Crit Care Med 2007, 35: 416-421. 10.1097/01.CCM.0000253814.78836.43PubMedView ArticleGoogle Scholar
  50. Meijering S, Corstjens AM, Tulleken JE, Meertens JH, Zijlstra JG, Ligtenberg JJ: Towards a feasible algorithm for tight glycaemic control in critically ill patients: a systematic review of the literature. Crit Care 2006, 10: R19. 10.1186/cc3981PubMedPubMed CentralView ArticleGoogle Scholar

Copyright

© Chase et al.; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.