Of all measures of hyperglycaemia evaluated, HGI correlated best with 30-day mortality in this population of critically ill patients. This supports our hypothesis that HGI is a useful index for quantifying glucose control. Therefore, assuming that normoglycaemia is the aim, the goal of a glucose–insulin algorithm is clear. The algorithm should obtain an HGI as close to zero as possible.
Both in the univariate analysis and in the multivariate binary logistic regression analysis – in which severity of illness, age and sex were included – HGI emerged as the best indicator of hyperglycaemia. We assume this reflects the fact that HGI takes better account of the variation in glucose concentrations over time, and avoids the possibility that alternating high and low values will average out to yield a normal value.
The principle of calculating the area under the glucose curve is not new. Brown and Dodek [8
] determined the area under the curve above a glucose threshold of 11.5 mmol/l. The area under the curve was used to assess the speed of initial normalization of glucose with an insulin algorithm. Other recent studies have also used glucose thresholds above 10.0 mmol/l to separate good control from poor control [20
]. However, following the publication of the Leuven study findings [4
] such thresholds are now considered high, because that study demonstrated improved outcome if normoglycaemia (4.4–6.1 mmol/l) was pursued. Because the vast majority of glucose concentrations in the patients studied here were below 10 mmol/l, crucial information would have been lost if a cutoff of 10.0 mmol/l has been used, as reflected by a decreased area under the ROC curve at a cutoff value for calculation of HGI of 10.0 mmol/l (Fig. ). The cutoff of 6.0 mmol/l was based on the upper limit of the group of patients with strict regulation in the Leuven study [4
]. Recently, Finney and colleagues [19
] found that glucose regulation below 8.3 mmol/l was not related to a better outcome. This is in accord with our observations; Fig. shows that the optimal cutoff for calculation of HGI lies between 6.0 and 8.0 mmol/l.
Some limitations of the present study should be mentioned. The study is a retrospective, single ICU study that covers a period when strict glucose control was not a major issue. Mortality at 30 days was used as an outcome measure to identify the best glucose index. HGI was the best measure of glucose control, but with a ROC area of 0.64 HGI alone cannot serve as a useful predictor of mortality. The relative contributions of endogenous glucose production, exogenous glucose supply and insulin to HGI and other some other measures could not be identified because our study did not include glucose infusion or (par)enteral feeding, and neither did it include intensive treatment with insulin.
It should be stressed that HGI was designed to quantify hyperglycaemia and not hypoglycaemia. Prevention of hypoglycaemia is a critical requirement of any algorithm for glucose control [5
]. However, unlike hyperglycaemia, hypoglycaemia is a phenomenon that tends to be relatively short-lived, as our results show, and could be quantified using more straightforward measures such as the lowest glucose concentration.
An elevated admission glucose level is associated with a worse outcome; this has been found by many investigators in various patient categories, and was also found in the present study [1
]. In our study, however, the area under the ROC curves was smaller for conventional measures of glucose control than it was for HGI.
Like other indices of glucose control, HGI is related to outcome. In contrast to admission glucose, however, HGI is also amenable to therapy. HGI involves additional computation (Fig. ) as compared with more straightforward indices but it does not require more information. Calculating HGI should be feasible in ICUs that possess a patient database management system that can provide automated input for the HGI calculation. The fact that HGI expresses glucose regulation as a single value has methodological advantages. The performance of glucose–insulin algorithms could be compared with HGI, and therefore it is important to measure glucose regularly. A major advantage of HGI is that periods of very frequent sampling (e.g. during hyperglycaemia or hypoglycaemia) are compensated for because HGI is based on an average over time.
HGI must be reassessed in the era of tighter glucose control. Moreover, the value of HGI needs confirmation in other ICUs. Because HGI has not been used by other investigators, it would be of interest to determine how HGI compares with other glucose indices in observational or intervention studies. Existing glucose patient databases could be reanalyzed to determine HGI. The use of glucose measures to predict outcome independently of other parameters such as age and severity scores is interesting but lacks power, as is shown by the area under the ROC. In general, HGI may be more useful for relating hyperglycaemia to organ failure scores such as the Sequential Organ Failure Assessment score [31
] or parameters of systemic inflammation.
Continuous measurements of blood glucose will allow us to calculate and compare HGI and the value of other glucose measures to a degree that is not possible with intermittent measurements [32
]. Currently available glucose sensors are promising but have not yet proven to be sufficiently reliable in critically ill patients and do not allow continuous measurements over prolonged periods [32