The glucogram: A new quantitative tool for glycemic analysis in the surgical intensive care unit.

BACKGROUND: Glycemic control is an important aspect of patient care in the surgical intensive care unit (SICU). This is a pilot study of a novel glycemic analysis tool - the glucogram. We hypothesize that the glucogram may be helpful in quantifying the clinical significance of acute hyperglycemic states (AHS) and in describing glycemic variability (GV) in critically ill patients.
MATERIALS AND METHODS: Serial glucose measurements were analyzed in SICU patients with lengths of stay (LOS) >30 days. Glucose data were formatted into 12-hour epochs and graphically analyzed using stochastic and momentum indicators. Recorded clinical events were classified as major or minor (control). Examples of major events include cardiogenic shock, acute respiratory failure, major hemorrhage, infection/sepsis, etc. Examples of minor (control) events include non-emergent bedside procedures, blood transfusion given to a hemodynamically stable patient, etc. Positive/negative indicator status was then correlated with AHS and associated clinical events. The conjunction of positive indicator/major clinical event or negative indicator/minor clinical event was defined as clinical "match". GV was determined by averaging glucose fluctuations (maximal - minimal value within each 12-hour epoch) over time. In addition, event-specific glucose excursion (ESGE) associated with each positive indicator/AHS match (final minus initial value for each occurrence) was calculated. Descriptive statistics, sensitivity/specificity determination, and student's t-test were used in data analysis.
RESULTS: Glycemic and clinical data were reviewed for 11 patients (mean SICU LOS 74.5 days; 7 men/4 women; mean age 54.9 years; APACHE II of 17.7 ± 6.44; mortality 36%). A total of 4354 glucose data points (1254 epochs) were analyzed. There were 354 major clinical events and 93 minor (control) events. The glucogram identified AHS/indicator/clinical event "matches" with overall sensitivity of 84% and specificity of 65%. We noted that while the mean GV was greater for non-survivors than for survivors (19.3 mg/dL vs. 10.3 mg/dL, P = 0.02), there was no difference in mean ESGE between survivors (154.7) and non-survivors (160.8, P = 0.67).
CONCLUSIONS: The glucogram was able to quantify the correlation between AHS and major clinical events with a sensitivity of 84% and a specificity of 65%. In addition, mean GV was nearly two times higher for non-survivors. The glucogram may be useful both clinically (i.e., in the electronic ICU or other "early warning" systems) and as a research tool (i.e., in model development and standardization). Results of this study provide a foundation for further, larger-scale, multi-parametric, prospective evaluations of the glucogram.

INTRODUCTION

The importance of hyperglycemia in the intensive care unit (ICU) is well established.[12] While numerous studies report on the impact of hyperglycemia and glycemic variability (GV) on outcomes in critically ill patients,[3-5] little is known regarding the clinical predictive value of isolated acute hyperglycemic events or “spikes”.[67] This article describes the glucogram, a novel graphical model that may be helpful in quantifying the relationship between acute hyperglycemic states (AHS), momentum/stochastic indicator “spikes”, and clinical events in chronic ICU patients. The glucogram is based on previously described principles of graphical representation of biomedical parameters.[8-10] After determining the feasibility of this model on a limited basis,[8] we set out to examine its utility on a larger sample of patients. We hypothesize that AHS are associated with major clinical events and that the model described herein may help better characterize and quantify this relationship. We also hypothesize that the glucogram could serve as a novel candidate tool for GV assessment.

MATERIALS AND METHODS

We describe the use of a graphical representation of glucose levels, along with associated momentum and stochastic indicators, in order to better quantify the relationship between AHS and associated clinical events. This model is called the glucogram.[8] For the purposes of this study, medical record review of 11 critically ill ICU patients with lengths of stay of >30 days was performed. Study variables included patient age, gender, APACHE II scores, length of surgical intensive care unit (SICU) stay, clinical event data (see below), and mortality. Serial glucose measurements were formatted into 12-hour epochs, with initial, maximal, minimal, and final values incorporated for each time period (see “Data organization” Section). Epochs were then organized into sequential bar graphs [Figure 1] and plotted alongside momentum and stochastic indicators to form a glucogram [Figure 2].

Figure 1

Schematic representation of the open-high-low-close (O-H-L-C) system. All glucose values within each 12-hour period (or epoch) were “compressed” into this simplifi ed, structured graphical form, with the only values of importance being the O-H-L-C data. This format is subsequently utilized to construct secondary graphs, including moving averages and specialized indicators

Figure 2

Glucogram example. Red squares represent 24 major clinical events correctly matching with corresponding indicator spikes. Green squares represent seven minor clinical events that correctly correlated with lack of indicator spike. In terms of mismatches, there were three instances of a major event not correlating with an indicator spike (empty squares) and four indicator spikes incorrectly correlating with minor events or no apparent event (gray squares). Of note, the number of events associated with each occurrence/spike is listed within the corresponding square. The uppermost window shows the momentum indicator (MoIR). In this case, positive MoIR spikes were considered to have values of 200% or greater (areas shaded in yellow). The middle window shows the stochastic indicator (StIR). In this case, values of 60 or above were considered to represent positive indicator spikes (areas shaded in yellow). The bottom window shows the glucose levels represented in the O-H-L-C format

Correlations between clinical events and positive indicator status (i.e., indicator “spike”) were then made. Clinical events were divided into major and minor [Table 1]. Our goal was to select well-defined, distinct event categories (i.e., blood transfusion, respiratory failure, surgical procedure), with major events being traditionally associated with increased physiologic stress. The selection of minor events was aimed at identifying discreet occurrences that normally would not be associated with increased physiologic stress. Each clinical event group (major/minor) was further divided into sub-classes/sub-types of clinical events [Table 1]. Indicator spikes correlating with major events, and minor (control) events correlating with absence of indicator spike were termed clinical “matches”.

Table 1

Definitions and types of clinical events

The determination of clinical event–indicator relationship was performed by single evaluator (Stawicki) and reviewed independently on three different occasions. The initial analysis identified a total of 372 major events and 89 minor events. The second review identified 362 major events and 90 minor events. There were 21 discrepancies between the first and the second event–indicator analysis. These were further reviewed at the time of the third analysis, resulting in 15 events being removed. Among these, all constituted redundant listings (i.e., patient with urosepsis being simultaneously listed as having sepsis). In addition, three major events were re-classified as minor (two transfusion events for preoperative correction of “inadequate” hemoglobin levels, one central line placement event that was reviewed initially as emergent and subsequently re-classified as non-emergent). Thus, a total of 354 major events and 93 minor events were included in the final analysis.

Descriptive statistics, sensitivity/specificity determination, and student's t-test were used in data analysis. PASW Statistics 18 software (SPSS, Inc., Chicago, IL, USA) was utilized. Statistical significance was set at α < 0.05. Institutional approval was granted for the conduct of this study.

Glycemic data organization

Serial glucose (both serum and fingerstick) determinations were obtained from a centralized database (The Ohio State University Medical Center Information Warehouse). Glucose measurements were recorded over a range of time frequencies (from 60 minutes to 8 hours) for each patient's entire ICU stay. Glucose information was then transformed into the open-high-low-close (O-H-L-C) format [Figure 1] by arranging the data into equal 12-hour intervals (epochs). For each epoch, the opening value (the first value in the epoch), the highest and the lowest values, as well as the closing value (the last value) were recorded/graphed. Momentum and stochastic indicators were then plotted for each patient based on aggregate O-H-L-C glucose data.

The momentum indicator

The momentum indicator (MoIR) measures the rate of change (ROC) in a particular parameter and is used to detect rapid changes in trends as well as likely trend reversal points. High MoIR readings tend to occur when a trend is at its strongest. Lower MoIR readings are generally found at the start/end of a trend, or between trends.[8] When the MoIR peaks and begins to descend, it usually marks the beginning of a new descending trend. Conversely, when the indicator begins to rise following a trough, it usually marks the beginning of an ascending trend.[8] Detailed description of MoIR calculations is beyond the scope of this manuscript, and the reader is referred to the source article describing this technique for more information.[8]

The stochastic indicator

The stochastic indicator (StIR) compares the closing value of a parameter with a range of that parameter's values during a pre-determined time period.[811] When a parameter's value is rising, it tends to have closing values near the high value for the respective epoch and a falling parameter closes near the epoch's low value.[8-12] The StIR is plotted on a scale from 0 to 100. In general, readings >60–80 are considered “strong” and indicate that the trend is nearing an interim high. Readings <20–30 are classified as “weak” and indicate that the trend is nearing an interim low. Detailed description of StIR calculations is beyond the scope of this manuscript, and the reader is referred to the source article describing this technique for further information.[8]

The glucogram

A sample glucogram can be seen in Figure 2. The patient in this example had a total of 27 major clinical events. Of these, 24 were associated with positive indicator spike(s)and 3 did not have an associated indicator spike. There were also four instances where an indicator spike did not have a matching major event and seven cases of minor events correctly correlating with absence of an indicator spike. In this example, the glucogram's sensitivity for major clinical events was 89% and specificity was 64%.

Event-specific glucose excursion

Event-specific glucose excursion (ESGE) was calculated for every positive clinical event/AHS “match”. The ESGE was defined as the difference between the highest and the initial value for each glucose upswing. The initial value was defined as the lowest glucose value associated with the appearance of an indicator spike. The highest value was defined as the maximal glucose value encountered within the dataset corresponding to the respective indicator spike. This methodology has been adapted from Hermanides et al.[13] and applied to O-H-L-C epochs.

Glycemic management protocol

According to the standard practice in the SICU at the Ohio State University Medical Center, all patients are placed on an insulin sliding scale regimen administered every 6 hours for treatment of blood glucose values >120 mg/dL. A continuous infusion of insulin, titrated per nursing-driven protocol, may be initiated if a patient has two consecutive blood glucose values >200 mg/dL or at the discretion of the SICU physician in the setting of continued/persistent hyperglycemia. The insulin drip is titrated hourly per protocol based on the current and previous blood glucose values, with goal range of 110–150 mg/dL.

RESULTS

Clinical information and glucose data were reviewed for 11 patients with an average SICU length of stay of 74.5 days. There were 7 men and 4 women. Mean patient age was 54.9 ± 13.8 years. The mean APACHE II score for the study group was 17.7 ± 6.44. Admitting diagnoses included burns (n = 2), postoperative emergent abdominal surgery (n = 2), severe pancreatitis (n = 3), and bacteremia/sepsis (n = 4).

Aggregate glycemic data included 4354 distinct measurements. After O-H-L-C formatting, these measurements were consolidated into 1254 epochs (each epoch of 12 hours duration). The median number of glucose measurements per patient per day was 9 (range 4–20). A total of 354 major clinical events were observed in the study group. Of these, 297 (sensitivity of 83.9%) appropriately correlated with indicator spikes. There also were 93 minor events, of which 60 (specificity of 64.5%) correctly matched with lack of indicator spike. The overall positive predictive value of glucogram was 90.0%, with a negative predictive value of 51.3%. When ESGE data for survivors were compared to data for non-survivors, no significant differences were noted in terms of mean maximal glucose excursions. The ESGE was 154.7 ± 59.2 mg/dL for survivors and 160.8 ± 76.8 mg/dL for non-survivors (P = 0.67).

Overall patient mortality in this study was 36% (4/11 patients). GV was significantly greater for patients who died than for survivors (19.3 ± 4.59 mg/dL vs. 10.3 ± 4.66 mg/dL, P = 0.02). Further review of the composite 100-epoch pre-SICU discharge (defined as mortality vs. transfer out alive) GV graph for survivors versus non-survivors demonstrates that (a) the raw variability [Figure 3A] is noticeably greater for non-survivors and (b) the 10-epoch moving average lines clearly separate the two groups and interface only once during the entire 100-epoch period [Figure 3B].

Figure 3

Composite graphs of glycemic variability (GV) during the last 100 pre-discharge 12-hour epochs. Discharge was defi ned as either ICU mortality or discharge alive from the ICU. Dashed red line represents GV for non-survivors and black solid line represents GV for survivors. Composite raw GV data are shown in (A) while composite 10-epoch moving average for GV is shown in (B). Note that the 10-epoch GV moving averages interact only once during the entire 100-epoch period (black circle). This fi nding may be pertinent to daily patient care because the GV, represented as a moving average, could serve as a clinical “barometer”. GV is defi ned as the maximal–minimal glucose value for each epoch, with glucose levels expressed in mg/dL

DISCUSSION

The use of graphical indicators as described in this article represents a novel way of displaying and interpreting glycemic data.[8] The patient sample in this study exemplifies a group that may benefit the most from clinical use of such indicators. Specifically, chronic ICU patients who experience multiple, often simultaneous clinical events are very susceptible to adverse consequences of inadequate monitoring, especially when only subtle early warning signs of impending clinical deterioration are present.[14]

Financial indicators are well described, have been used for quite some time in various non-biomedical applications, and are relatively easy to learn, understand, and interpret.[15-17] There are several potential advantages of using financial analysis tools in biomedical applications. The greatest advantage of utilizing financial indicators in the ICU is their ability to accentuate subtle changes in underlying variables and demonstrate trends even when detailed inspection of raw data does not point to any acute findings.[12] Financial indicators provide a more objective method of evaluating clinical parameters and scenarios where traditionally subjective interpretations are used (i.e., quantifying the clinical importance of trends such as AHS).[810] Lastly, graphical indicators may be useful in standardizing trend interpretation and minimizing inter-observer variability in trend analysis.[818] In this capacity, the glucogram may be useful as a standardization/communication tool when conducting research (i.e., as a proposed platform for glycemic analysis comparisons between studies/investigators).

This study utilizes the O-H-L-C format to display glycemic data. This methodology allows the user to convert fragmentary, seemingly random data into well-organized, time-based units that can then be further examined using secondary analyses/indicators.[8-1012] We analyzed 4354 separate glucose measurements that were “compressed” into ~1250 epochs (each epoch is 12 hours in duration). Due to the very nature of the O-H-L-C system, the behavior of the variable itself, and not its absolute value, is more predictive of new trend identification.[8-1012] For example, a patient could have daily glucose ranges that seem “stable” to an abbreviated visual inspection. However, because sequential daily “opening” and “closing” glucose values may progressively trend higher within each fixed glucose range, a new trend can be identified using these subtle differences in “opening” and “closing” glucose values.[8-1012]

While it is well established that AHS are associated with worse patient outcomes,[19] tools available to quantify this relationship are still lacking. The glucogram is an attempt to fill this void. Although not perfect, the glucogram demonstrated reasonable overall sensitivity (84%) and specificity (65%) for major clinical events in the context of AHS. Given the broad range of clinical events examined [Tables 1–3], as well as an element of subjectivity involved in clinical event review, these sensitivity/specificity figures are not unexpected. We would also like to point out that results achieved by the glucogram seem more focused than ad-hoc glucose analyses during typical ICU rounds. In a way, AHS can serve as a “barometer” of acute inflammatory states in the ICU.

Table 3

Occurrence of minor (control) clinical events (overall number/percentage of events, with up to three most common sub-groups listed for each event category)

We found that overall GV was approximately two times higher in patients who died when compared to patients who survived to hospital discharge [Figure 3]. The glucogram may be well suited as a candidate for measuring and tracking GV. In fact, each epoch of the glucogram could potentially be thought of as a compact GV measurement tool, where the difference between the highest and the lowest glucose value for that particular epoch can be used to calculate mean glucose values, standard deviations, or moving averages. Previous research demonstrated that increased GV is associated with higher patient mortality.[520] While the results of this study do not come as a surprise, the authors are pleased that the glucogram, in addition to serving as a proxy for identification of acute clinical events, could also be identified as a candidate tool for assessment of GV. Of note, 10-epoch GV moving average composite graphs help differentiate survivors from non-survivors. When looking at the composite graph of the 100 pre-discharge (defined as death vs. transfer alive out of the SICU) epochs in survivors versus non-survivors, we found that 10-epoch GV moving averages interfaced directly only once during the entire 50-day period plotted [Figure 3].

While it appears to be a promising tool for analysis of biomedical data trends, the authors do not recommend using the glucogram as the sole predictor of significant clinical events at this time.[8] As more data emerge regarding the clinical utility of the glucogram, its bedside applications will become more defined. Lastly, the authors chose to use glucose and not other biomedical parameters (i.e., white blood cell count, vital signs, urine output, etc.) in this analysis for two basic reasons: (a) although glucose values tend to fluctuate, these fluctuations tend to be less volatile than fluctuations in traditional vital signs and (b) glucose measurements are collected frequently throughout the day, as compared to other laboratory parameters (i.e., white blood cell count, platelet count, other serum laboratory determinations) which are obtained less frequently beyond the most acute care periods. Our future endeavors may involve multi-parametric models that include glucose in conjunction with other biomedical parameters.

Weaknesses of this study include its retrospective nature, inability to determine causal relationships, and inherent subjectivity associated with utilizing arbitrary clinical events and their subsequent interpretation. The authors recognize that a sample of 11 patients is small. However, the major goal of our investigation was to determine basic characteristics and behaviors of the glucogram in a large sample of clinical events. Thus, the glucogram should be thought of as an event-specific indicator system, with the sensitivity/specificity affected mainly by the number and character of events and not the number of patients. Our future investigations will include a greater number of both patients and clinical events. In addition, because our methodology has not been previously used in the clinical setting, we anticipate that clinical event lists will likely be modified as the glucogram continues to evolve. In terms of data organization, we chose the 12-hour epoch duration in order to standardize our glycemic assessment so that two or more glucose measurements per epoch would be available for each patient throughout their respective SICU stay. Because patients tended to have fewer glucose measurements as their clinical status improved and glycemic surveillance was de-escalated, the compromise in the form of a 12-hour epoch was necessary to achieve our measurement frequency goals. This limitation also prevented direct comparisons between our study and other studies. In subsequent endeavors, we plan to employ more frequent (optimally hourly) glucose checks throughout the study interval.

Due to the retrospective nature of this study, it was not possible to precisely determine the temporal sequence of clinical event–indicator spikes. However, we did anecdotally notice a number of significant clinical events that were clearly preceded by an AHS-associated indicator spike. Building on findings of the current study, we plan to include in our future investigations a blinded, prospective evaluation of the glucogram that will specifically examine the temporal event–indicator relationship and further refine clinical event classifications. Because this preliminary study represents a largely phenomenological effort to examine the relationship between indicator spikes and clinical events, we chose to include “insulin drip requirement” as a major clinical event, keeping in tune with the contention that the need for escalation of glycemic control constitutes a “barometer” of other clinically significant events. It is possible that in subsequent analyses, this inclusion may not be required, but definitive answers to this question will not be possible until prospective evaluation of the glucogram is undertaken. It is also likely that some glycemic spikes were associated with active titration of insulin therapy. However, it is important to note that initiation/escalation of insulin drip constitutes <5% of clinical events in this study. Although we did not specifically look at hypoglycemia in this study, we noted a number of hypoglycemic events that occurred right before hyperglycemic spikes. Most of these events were associated with subsequent sepsis. We recognize the harmful nature of hypoglycemia in the ICU, and our planned prospective evaluation will include a reverse-glucogram analysis of hypoglycemic events. Because precise incidences of most recorded events are not actively tracked by our ICU, their prevalence was not reported.

Strengths of this study include the availability of high-quality glucose data, the use of novel techniques to characterize clinical significance of AHS, as well as the demonstration of the glucogram as a candidate method for GV assessment. Directions for future research in this area include: (a) multi-parametric studies; (b) incorporating parametric corrections for therapeutic interventions, i.e., accounting for insulin dosage; (c) incorporating other types of indicators into the model;[8] (d) prospective evaluation and validation of retrospectively identified parameter–model combination(s) with the highest degree of cross-correlation; (e) comparisons between the glucogram and other GV measurement tools; and (f) design of devices that allow real-time analysis of various bio-medical parameters using glucogram-based techniques. In addition to its potential use as a “barometer” in clinical predictive applications, the glucogram may also be useful as a standardized measure of the efficacy of various glycemic control regimens in the ICU. In this role, as a type of a “score card”, the glucogram could provide a real-time evaluation of how well each particular ICU is doing, both in terms of AHS and GV.

CONCLUSIONS

Minimizing the subjective component of patient data interpretation and maximizing the objective component may provide a better way of assessing patients and, when correlated with clinical data, may have useful adjunctive confirmatory or possibly predictive value. Momentum/stochastic indicator spikes on glucograms appear to have reasonable sensitivity for major clinical events. Specificity appears to be somewhat lower. Future research in this area is certainly warranted, with focus on absolute deviation from normal values, time to normoglycemia, and multi-parametric modeling.

Table 2

Occurrence of major clinical events (overall number/percentage of events, with up to three most common subgroups listed for each event category)