Episodes of Severe Hypoglycemia in Type 1 Diabetes Are Preceded ...

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1.1%) used Lifescan One Touch BG meters for self-monitoring three to five times daily and recorded the date and time of SH episodes in diaries. For each ...
0021-972X/00/$03.00/0 The Journal of Clinical Endocrinology & Metabolism Copyright © 2000 by The Endocrine Society

Vol. 85, No. 11 Printed in U.S.A.

Episodes of Severe Hypoglycemia in Type 1 Diabetes Are Preceded and Followed within 48 Hours by Measurable Disturbances in Blood Glucose* BORIS P. KOVATCHEV, DANIEL J. COX, LEON S. FARHY, MARTIN STRAUME, LINDA GONDER-FREDERICK, AND WILLIAM L. CLARKE University of Virginia Health System and National Science Foundation Center for Biological Timing, University of Virginia (M.S.), Charlottesville, Virginia 22908 ABSTRACT This study quantifies blood glucose (BG) disturbances occurring before and after episodes of severe hypoglycemia (SH). For 6 – 8 months, 85 individuals with type 1 diabetes and a history of SH (age, 44 ⫾ 10 yr; 41 women and 44 men; duration of diabetes, 26 ⫾ 11 yr; hemoglobin A1c, 7.7 ⫾ 1.1%) used Lifescan One Touch BG meters for self-monitoring three to five times daily and recorded the date and time of SH episodes in diaries. For each subject, the timing of SH episodes was located in the temporal stream of SMBG readings recorded by the meter, and characteristics, including the Low BG index (LBGI), were computed in 24-h increments. In the 24-h period before

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XTENSIVE STUDIES, including the Diabetes Control and Complications Trial (DCCT) (1), the Stockholm Diabetes Intervention Study (2), and the United Kingdom Prospective Diabetes Study (3), have repeatedly demonstrated that the maintenance of blood glucose (BG) levels approximating the normal range reduces long-term complications of diabetes. However, the same studies also documented adverse effects of intensive insulin therapy, the most acute of which is the increased risk for frequent severe hypoglycemia (SH), a condition identified as low BG resulting in stupor, seizure, or unconsciousness that precludes selftreatment (4, 5). As SH could result in accidents, coma, and even death, it discourages patients and health care providers from pursuing intensive therapy. Consequently, hypoglycemia has been identified as a major barrier to improved glycemic control (6, 7). Thus, patients with type I diabetes mellitus (T1DM) face a life-long optimization problem: to maintain strict glycemic control without increasing risk for hypoglycemia. A recent review presents in detail the current clinical status of this problem and the options for prevention of SH available to patients and their health care provider (8). A conclusion of this review is that “. . . prevention of SH is only possible if hypoglycemia unawareness is prevented, i.e. if mild, self-treated episodes of hypoglycemia are prevented. Failure to do so creates a vicious circle where repeated epReceived April 25, 2000. Revision received July 20, 2000. Accepted August 8, 2000. Address all correspondence and requests for reprints to: Dr. Boris P. Kovatchev, University of Virginia Health System, Box 800137, Charlottesville, Virginia 22908. * This work was supported by NIH Grants RO1-DK-51562 and RO1DK-28288 and a grant from Lifescan Corp. (Milpitas, CA).

the SH episode LBGI rose (P ⬍ 0.001), average BG was lower (P ⫽ 0.001), and BG variance increased (P ⫽ 0.001). In the 24 h after SH, LBGI and BG variance remained elevated (P ⬍ 0.001), but average BG returned to baseline. These disturbances disappeared in 48 h. On the basis of LBGI we identified subjects at low, moderate, and high risk of SH, who reported, on the average, 1.7, 3.4, and 7.4 SH episodes (P ⬍ 0.005) during the study. In addition, we designed an algorithm that predicted 50% of all SH episodes that occurred in this subject group. We conclude that episodes of SH are preceded and followed by quantifiable BG disturbances, which could be used to devise warnings of imminent SH. (J Clin Endocrinol Metab 85: 4287– 4292, 2000)

isodes of hypoglycemia ultimately result in unawareness, which in turn increases the risk of SH” (8). A major challenge of breaking this “vicious circle” is the detection of hypoglycemia before development of neuroglycopenia. Numerous studies have investigated the occurrence of hypoglycemia-related symptoms and generally found that such warning signs occur, but may be recognized by patients in less than 50% of all hypoglycemic episodes with low BG levels of 3.9 mmol/L and below (9 –12). This means that up to half of all hypoglycemic episodes may be asymptomatic or unrecognized, and even if recognized, in many cases recognition occurs at a BG too low to permit self-treatment. On the other hand, contemporary BG monitors provide the means for frequent BG determinations and eventual prediction of imminent hypoglycemia that is independent of symptoms. The problem with self-monitoring of BG (SMBG) is that currently there is no reliable method for recognition of imminent hypoglycemia based on SMBG readings (13). Indeed, there is no reliable prediction of patients’ immediate risk for SH from any data. Various approaches to assess the risk of SH have been tested using history of SH, low hemoglobin A1c (HbA1c), hypoglycemia unawareness, etc. (4, 5, 14). The DCCT concluded that only about 8% of future SH could be predicted from known variables (4), and a recent structural equation model accounted for 18% of the variance in SH using history of SH, hypoglycemia awareness, and autonomic symptom score (14). As we previously reported (15), one reason for such poor prediction is purely mathematical. The problem is that the BG scale is substantially asymmetric, and the hypoglycemic range (⬍3.9 mmol/L) is numerically much smaller than the hyperglycemic range (⬎10 mmol/L). As a result, standard

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statistics, such as the mean and sd, tend to underestimate patients’ risk for hypoglycemia. To correct that, we introduced and validated the low BG index (LBGI), a measure of the risk of SH in patients with T1DM, that takes into account the specific distribution of BG data (15–17). Using this new approach we were able to account for 40% of the variance in SH episodes in the subsequent 6 months on the basis of history of SH and SMBG data (17) and later to enhance this prediction to 46% (18) by introducing a temporal component into our model. In addition, we documented three ranges for LBGI (below 2.5, 2.5–5, and above 5) that identified three categories of subjects at low, moderate, and high risk of subsequent SH (17). The subjects in the high risk category reported 5.2 SH episodes in the following 6 months, compared with 0.4 and 2.3 for the low and moderate risk categories (17). A recent report presents the mathematical foundation of this technique in detail (19). The purpose of this study is to extend these findings by investigating the relative short-term changes in LBGI and other BG parameters associated with episodes of SH. We hypothesized that SH episodes are preceded and followed by measurable BG disturbances. Further, we hypothesized such disturbances can be quantified from SMBG data, which, in turn, would allow identification of subjects at risk for SH and for on-line prediction of imminent SH in individual patients. Experimental Subjects Eighty-five individuals were recruited through advertisement in newsletters and diabetes clinics and by direct referrals. All participants attended introductory meetings in groups of 6 –10, were informed about the study, and signed consent forms. The initial screening included a collection of background data and a determination of HbA1c. The inclusion criteria were: 1) age of 21– 60 yr, 2) T1DM of at least 2-yr duration and insulin use since the time of diagnosis, 3) at least 2 documented SH episodes in the past year, and 4) routine use of SMBG devices for diabetes monitoring. Table 1 presents the demographic characteristics of the participants;

Materials and Methods The participants’ usual BG meters were replaced by Lifescan OneTouch Profile memory meters (Lifescan Inc., Milpitas, CA) that can store up to 250 BG readings together with the date and time of each reading. The participants were instructed to use the meter 3–5 times a day and to record in monthly diaries any SH episodes, with the exact date and time. SH was defined as severe neuroglycopenia that results in stupor or unconsciousness and precludes self-treatment (4, 5), and its occurrence was confirmed by the research team in telephone interviews after SH episodes. For each subject the study continued 6 – 8 months, and every month his/her meter was downloaded, and the SH diary was collected. With this memory capacity of the meter and this frequency of TABLE 1. Demographic characteristics of the participants Characteristic (at the timeof recruitment)

Age (yr) Gender (female/male) Duration of diabetes (yr) Daily insulin dose (U/kg) No. of insulin injections/day (for nonpump users) HbA1c (%; nondiabetic range for this laboratory, ⬍6.9%) At the beginning of study At the end of study No. of SH episodes during the previous yr

Mean ⫾

SD

44.3 ⫾ 10 41/44 26.4 ⫾ 10.7 0.6 ⫾ 0.2 2.7 ⫾ 0.9

7.6 ⫾ 1.1 7.4 ⫾ 1.0 9.4 ⫾ 6.3

downloading, no BG data were lost. No changes in the participants’ diabetes management routine and/or additional treatment were administered during the study. As shown in Table 1, no significant change in HbA1c was observed before vs. after the study (pre-post; t ⫽ 1.25; P ⫽ 0.2); the pre-post HbA1c values were highly correlated (r ⫽ 0.83; P ⬍ 0.001).

The timing of SMBG and SH The memory meter stores SMBG readings together with the date and exact time (hour, minute, and second) of each reading. Thus, for each subject we had a 6- to 8-month temporal sequence of SMBG records, and from subjects’ monthly diaries, we had the date and time of SH episodes that had occurred.1 Specialized software was developed for preprocessing of the data. This included 1) assembling the memory meter data for each subject into a continuous 6- to 8-month sequence of BG readings and scanning/cleaning of the data from test trials and artifacts, such as battery failures; and 2) matching of each subject’s records of SH with this sequence by date and time. The latter was performed as follows. For each SMBG reading the time (hours/minutes) until the nearest SH episode and the time elapsed from the last SH episode were computed. Thus, it was possible to 1) time 24-h periods backward and forward from each SH episode, and 2) time 24-h periods backward from each SMBG reading. As explained in Results, these two methods were used to 1) identify BG disturbances preceding and following SH, and 2) design an algorithm tracking risk of imminent SH on the basis of SMBG. For any 24-h period we computed average, minimum, maximum, and sd of BG and the LBGI. Due to the nature of SH (stupor, unconsciousness), no SMBG was performed exactly at the time of SH, but in a few cases SMBG was performed shortly before SH. The average per SH episode minimum elapsed time between SH and the nearest preceding SMBG reading was 5.2 ⫾ 4.1 h; 29 SH episodes (7%) were preceded by a SMBG reading within 15 min. The LBGI is a previously introduced predictor of SH, based on a logarithmic transformation of the BG scale (15) and a previously published risk analysis theory (19). In general, the LBGI value can be computed on a single BG reading or derived from any set of BG readings. For this study we computed the LBGI of each subject for various 24-h periods and for the entire duration of the study. The computation of the LBGI follows these steps. First, symmetrization of the BG scale is performed using the transformation f(BG, ␣,␤, ␥) ⫽ ␥[(ln(BG))␣ ⫺ ␤]. The parameters were ␣ ⫽ 1.026, ␤ ⫽ 1.861, and ␥ ⫽ 1.794 derived from clinical assumptions [if BG is measured in milligrams per dL, the parameters of f(BG) are as follows; ␣ ⫽ 1.084, ␤ ⫽ 5.381, and ␥ ⫽ 1.509]. Next, computation of the BG risk function r(BG) ⫽ 10.f(BG) is performed. The function r(BG) ranges from 0 –100. Its minimum value of zero is achieved at BG ⫽ 6.25 mmol/L, a safe euglycemic BG reading, whereas its maximum is reached at the extreme ends of the BG scale. Thus, r(BG) can be interpreted as a measure of the risk associated with a certain BG level. Let x1, x2, .. xn be a series of n BG readings, and let rl(BG) ⫽ r(BG) if f(BG)⬍0 and 0 otherwise. The low BG risk index is then defined as LBGI ⫽ n (1/n)⌺i⫽1 rl(xi). In summary, the LBGI is a nonnegative quantity that increases when the number and/or the absolute extent (not relative to the mean extent, as it would be in a sd computation) of low BG readings increases. The advantage of computing the LBGI, as opposed to simply taking the mean and sd of BG, is that the LBGI is not influenced by hyperglycemia (all readings above 6.25 mmol/L have zero loads). In addition, the LBGI was designed as a risk measure targeting a specific condition (hypoglycemia) and has been proven to predict SH better than any other standard statistic (17, 18).

Results

During the study a total of 75,495 SMBG readings (on the average 4.0 ⫾ 1.5/subject䡠day) were downloaded from the 1 These diary records were naturally less precise; approximately 15% of all SH episodes were recorded by date only. To restore the missing hour/minute of such SH episode, in the follow-up interview the subject was asked to identify in his/her meter the SMBG reading immediately preceding such a SH episode.

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participants’ memory meters, and 399 (4.7 ⫾ 6.0/subject) SH episodes were recorded in diaries. The average BG per subject was 9.0 ⫾ 1.7 mmol/L. Sixty-eight (80%) of the participants experienced 1 or more episodes of SH. These subjects did not differ from those who did not experience SH (the remaining 20% of the subjects) in terms of any of their demographic characteristics. Recurrent severe hypoglycemia

During the study 20 subjects experienced recurrent (within 48 h) SH. Fifty-three sequences of 2 and 9 sequences of 3 recurrent SH episodes were observed. Thirty-two SH episodes were followed by a single SH episode within 24 h, and 21 SH episodes were followed by another SH between 24 and 48 h later. Nine SH episodes were followed by SH within 24 h and another SH on the day after. In general, 18% of all SH episodes were followed within 48 h by a recurrent SH. BG disturbances before and after severe hypoglycemia

This analysis uses SMBG characteristics computed within 24-h intervals timed from a SH episode. Figure 1 presents the typical picture of BG disturbances observed before and after an episode of severe hypoglycemia. In the period 48 to 24 h before SH, the average BG level decreased, and the BG variance (assessed as sd of BG) increased. In the 24-h period immediately preceding SH, the average BG level dropped further, and the variance in BG continued to increase. In the 24-h period after SH, the average BG level normalized; however, the BG variance remained greatly increased. Both the average BG and its variance returned to baseline levels within 48 h after SH. In the following, we use the LBGI to quantify these disturbances, specifically emphasizing hypoglycemia. Figure 2 is a three-panel plot of the BG disturbances observed at the group level in three variables in the periods ⫺48 to ⫺24 h, ⫺24 to 0 h, 0 to 24 h, and 24 to 48 h. from a SH. The 24-h LBGI is presented along with the 24-h average BG level and BG variance. Repeated measures ANOVA was used to

FIG. 2. Average BG, BG variance, and LBGI in 24-h increments preceding and following SH. All three characteristics are significantly disturbed before and after SH. The LBGI increased sharply in the 24 h before SH and returned slowly to its baseline value in 2 days.

compare each 24-h period to a baseline computed on all 24-h periods that were at least 48 h away from SH. Figure 2 includes t values and P levels for all contrasts2 between a 24-h period and the baseline. We can conclude that 1) the average BG level was significantly lower ⫺24 to 0 h before SH (P ⫽ 0.001), with a trend toward decrease in the ⫺48 to ⫺24 h period (P ⫽ 0.04); and 2) the variance in BG increased steadily ⫺48 to 0 h before SH and was even larger on the day after SH (P ⬍ 0.001). The LBGI increased sharply in the 24 h before SH (P ⬍ 0.001) and remained elevated for 24 h after SH. The subjects who experienced recurrent SH did not differ from the subjects without recurrent SH in terms of any of their post-SH SMBG characteristics. The LBGI, average BG, and BG variance in the 24 h after SH were similar in these two groups (all P ⬎ 0.4). Identifying subjects at risk for SH

The average LBGI computed for the entire study was 4.75 ⫾ 2.7. As stated in the introduction, on the basis of the LBGI we previously identified 3 categories of subjects with LBGI of 2.5 or less, between 2.5–5, and greater than 5 who FIG. 1. Typical picture of BG disturbances escorting SH episodes. During the 48-h period before SH, the BG level decreases, and its variance increases. After the SH episode, the mean BG level returns to its normal values, but its variance remains increased for 24 h.

2 The contrasts that were used were comparisons of each variable to a reference (the baseline). In the statistical package SPSS used for this analysis this is referred to as simple contrast.

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were at low, moderate, and high risk for SH, respectively. According to this previous classification, during this study the participants were, on the average, at moderate to high risk of SH, which reflects the selective inclusion of subjects with a history of multiple SH episodes. Specifically, judging from the LBGI during this study, 11 subjects were in the low risk, 42 were in the moderate risk, and 32 were in the high risk category. Table 2 presents the distribution of SH episodes across these 3 categories together with a group comparison showing significant differences in SH frequency (P ⬍ 0.005). Table 3 illustrates this classification with data for two subjects A and B, who reported 31 and 3 SH episodes during the study, respectively. Despite his higher frequency of SH, subject A had a higher average BG and a higher HbA1c than subject B. The LBGI, however, classified subject A in the high risk category and subject B in the moderate risk category.

subject B there are 3 of 4 high risk periods that do not include SH. This indicated that subject B was better controlling his SH risk, which is also evident by his overall data (Table 3). Across the studied group of subjects, 44% of all recorded SH episodes fell within the 24-h risk periods identified by the algorithm. When the duration of a risk period was extended from 24 to 48 h, 50% of all SH episodes were predicted. If the analysis was restricted to include only days when subjects performed at least three or at least four SMBG measurements per day, the accuracy of the latter prediction increased to 53% and 57%, respectively. Finally, we analyzed BG levels occurring during high risk periods that did not contain SH, i.e. after false alarms. The average nadir of such BG levels was 2.3 ⫾ 0.2 vs. 5.9 ⫾ 1.7 mmol/L (t ⫽ 19.5; P ⬍ 0.0001) for all nonrisk periods, including all SH episodes that remained unaccounted for. This indicates that although SH did not occur, BG levels within high risk periods were notably low.

Identifying idiosyncratic risk periods for SH

For this analysis we designed an algorithm that at each SMBG reading retrieves the SMBG data in the preceding 24-h period of time and judges whether these data are likely to indicate upcoming SH. The judgment was made on the basis of a risk value derived from the current LBGI value and the average LBGI in the preceding 24 h. The algorithm simulated the action of a hypothetical SMBG device that at each reading computes current LBGI and average LBGI from the previous 24 h and decides whether these values exceed a certain threshold. If the threshold was exceeded, the algorithm identified the following 24 h as a period of high risk for SH. An optimal threshold was derived from an optimization along the following restrictions: 1) the algorithm had to predict a maximum percentage of SH episodes, and 2) the algorithm had to identify as risky no more than 15% of the total time of the study (1 day a week on the average) to prevent overestimation of the risk. The optimal threshold was held constant for all subjects. In short, 24 h of SMBG timed back from a SMBG reading were used to judge whether the next 24-h period is risky for SH. Figure 3 illustrated the action of the algorithm over 10 weeks of data (approximately one third of the study) for the 2 subjects, A and B, whose data were used in Table 3. The SH episodes are marked by triangles; subject A reported 9 SH episodes, whereas subject B reported 1 SH episode during that time. The risk values, derived from SMBG, are presented by a black line. Every time a risk value exceeds the threshold, the algorithm would declare the next 24-h period as high risk for SH. These high risk periods are marked by gray bars. SH episodes that occur within the gray area are predicted by the algorithm. High risk periods that do not include a SH episode indicate that the subject avoided potential SH. For subject A, there are 5 of 10 high risk periods that do not include SH; for

Discussion

This study identifies BG disturbances accompanying episodes of SH and offers quantitative tools for anticipation of imminent SH on the basis of routine SMBG data. This methodology could assist patients with T1DM with their chronic and important dilemma: improving glycemic control without increasing the risk of SH. In general, there are two mathematical approaches to this problem. The first approach is to build a deterministic model of insulin-glucose dynamics and evaluate idiosyncratic parameters with the goal of forecasting as precisely as possible each subject’s BG fluctuations. Computer freeware for interactive simulation of insulin and BG profiles, such as AIDA, has been developed on the basis of a simple insulin-glucose model (20). However, this approach is quite demanding for the patient, requiring continuous input of insulin, food, and physical activity data. The second approach is to observe, without reference to specific underlying mechanisms, subjects’ BG departures from a normal BG level, record multiple BG readings, and establish BG patterns relevant to SH through statistical modeling. In essence, this combines routine SMBG and intelligent processing of the collected SMBG data. As routine SMBG is commonly available, the problem that remains is to develop statistical methods capable of extracting from SMBG information relevant to SH. Our first, previously reported, steps TABLE 3. Example, comparison of subjects at different risk for SH

Subject A Subject B

Average BG (mmol/L)

HbA1c (%)

LBGI

No. of SH episodes

10.2 9.3

8.3 7.9

5.15 3.14

31 3

TABLE 2. Risk categories for severe hypoglycemia identified by the low BG index Category (no. of subjects)

Mean no. of SH

95% confidence interval for the no. of SH episodes

Subjects with no SH (%)

ANOVA

Low SH risk, LBGI ⬍2.5 (n ⫽ 11) Moderate SH risk, 2.5 ⱕ LBGI ⬍5 (n ⫽ 42) High SH risk, LBGI ⱖ5 (n ⫽ 32)

1.7 3.4 7.4

0.5–3.0 2.1– 4.6 4.5–10.3

36 21 15

F ⫽ 6.3 P ⬍ 0.005

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FIG. 3. Ten weeks of data for subject A (upper panel) and subject B (lower panel). SH episodes are marked by a triangle; a black line presents the risk value. When the risk threshold is crossed, the algorithm indicates a subsequent high risk period (gray bar). For subject A, seven of nine SH episodes are predicted, and there are five false alarms, e.g. high risk periods that did not result in SH. For subject B there are three false alarms, and the only SH episode was predicted.

in that direction, were to derive a symmetrization of the BG scale (15) and on that basis to introduce and validate the LBGI, which has been repeatedly proven to be the most powerful predictor of long-term risk of SH (16 –18). This present study offers new data that clearly demonstrate that, contrary to widespread belief, SH is not an entirely random event occurring abruptly and unpredictably. Specifically, we found that SH is predictable at two levels; first, SH occurs most frequently in an identifiable subgroup of subjects, and second, for each subject, at least half of the SH episodes occur within identifiable high risk periods. Both levels of prediction are based on the LBGI derived from routine SMBG data. Level 1: subjects at risk for severe hypoglycemia

Previous studies demonstrated that SH usually occurs in subjects with a history of SH (4). All subjects in this study were selected as having a history of multiple SH, but not all subjects experienced prospective SH to the same degree. Confirming our previous report (17), our current data demonstrate that subjects at high risk for SH can be identified as having elevated LBGI. Indeed, subjects with LBGI of 5 or greater during the study reported more than 4 times as many SH episodes compared with subjects in the low risk category (LBGI ⬍2.5). This high risk group consisted of 38% of the subjects and accounted for 60% of all SH episodes. Post-hoc analysis demonstrated that a classification of the subjects with respect to their risk for SH was not possible on the basis of known SMBG variables, other than the LBGI. For example, our attempts to develop a three-group classification based on the subjects’ average BG yielded no result; the subjects in the three groups had 4.1, 4.8, and 5.2 SH episodes respectively (P ⫽ .77). This analysis generalizes the illustrative results presented in Table 3.

Level 2: idiosyncratic risk periods for SH

Our data demonstrate that SH episodes are accompanied by measurable BG disturbances, generally reflected by a steady lowering of BG, an increase in its variance that begins 48 h before SH, and a normalizing of average BG, but a further increase in its variance, immediately after SH. A markedly sharp increase in the LBGI was observed in the 24-h periods preceding SH, and the LBGI remained elevated in the 24 h after SH. Although normalization of the average BG and the increase in BG variance immediately post-SH could be explained by changes in treatment after severe hypoglycemia, the fact that the LBGI remained elevated for 24 h indicates that the risk for SH remains high, after a SH episode. This is confirmed by our observation that 18% of all SH episodes were followed by recurrent SH. Thus, patients with a history of SH should be advised about the likelihood of recurrent SH episodes. On the basis of the changes in the LBGI before SH, we devised an algorithm for identification of idiosyncratic periods of high risk for SH. Attempts to use in the algorithm other variables, such as average BG and sd of BG, did not yield better results. Overall, in this group of subjects, who were specially selected as having a history of SH, the algorithm predicted 50% of all SH episodes. However, this prediction was not equally good for all subjects; 67% of the SH episodes were predicted for the 32 subjects in the high risk category (Table 2), whereas 25% of SH episodes were predicted for the remaining 53 subjects. This indicates that although this approach could potentially eliminate half of all SH episodes in the studied cohort, the patients who would benefit most are those at highest risk for SH. The limitations of this means of data collection do not allow for a traditional optimization of the decision-making algorithm in terms of a true and false positives. The main

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reason for that is that a false positive cannot be objectively identified; a false positive could be a risk period not including SH because of glucose counterregulation or because of successful prevention through treatment. Therefore, we were forced to optimize our algorithm by imposing a somewhat arbitrary, but reasonable, assumption on the frequency of risk alerts. We imposed a requirement for no more than 15% of all 24-h time windows (or no more than 1 day a week) to be declared risky. Post-hoc analysis indicated that if the optimization criterion was loosened to allow for 23% high risk periods, the prediction of SH increased to 70%. Finally, even if not leading to SH, a high risk period is associated with dangerously low BG readings (an average nadir of 2.3 mmol/L), substantially lower that the nadir of low risk periods (P ⬍ 0.0001). In conclusion, the proposed quantitative approach identified measurable BG disturbances associated with SH. It also demonstrated that subjects at high risk for SH can be identified, and more than 50% of SH episodes can be anticipated on-line from SMBG data and therefore potentially avoided. Although sensitive to SH and low BG, this method is still not sufficiently refined to be directly incorporated into automated devices for the prediction of imminent SH due to the limitations of this data collection method. Further data collection and theoretical research are needed to enhance its specificity and its predictive capabilities. References 1. DCCT Research Group. 1993 The effect of intensive treatment of diabetes on the development and progression of long-term complications of insulindependent diabetes mellitus. N Engl J Med. 329:978 –986. 2. Reichard P, Phil M. 1994 Mortality and treatment side effects during long-term intensified conventional insulin treatment in the Stockholm Diabetes Intervention study. Diabetes. 43:313–317. 3. UK Prospective Diabetes Study Group. 1991 Effect of intensive blood glucose control with metformin on complications in patients with type 2 diabetes (UKPDS 34). Lancet. 352:837– 853.

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4. DCCT Research Group. 1991 Epidemiology of severe hypoglycemia in the diabetes control and complications trial. Am J Med. 90:450 – 459. 5. DCCT Research Group. 1997 Hypoglycemia in the Diabetes Control and Complications Trial. Diabetes. 46:271–286. 6. Cryer PE, Fisher JN, Shamoon H. 1994 Hypoglycemia. Diabetes Care. 17:734 –755. 7. Cryer PE. 1999 Hypoglycemia is the limiting factor in the management of diabetes. Diabetes Metab Res Rev. 15:42– 46. 8. Bolli, GB. 1999 How to ameliorate the problem of hypoglycemia in intensive as well as nonintensive treatment of type I diabetes. Diabetes Care. 22(Suppl 2):B43–B52. 9. Clarke WL, Gonder-Frederick LA, Richards FE, Cryer PE. 1991 Multifactorial origin of hypoglycemic symptom unawareness in T1DM: association with defective glucose counterregulation and better glycemic control. Diabetes. 40:680 – 685. 10. Clarke WL, Cox DJ, Gonder-Frederick LA, Julian D, Schlundt D, Polonsky W. 1995 Reduced awareness of hypoglycemia in IDDM adults: a prospective study of hypoglycemia frequency and associated symptoms. Diabetes Care. 18:517–522. 11. Cox DJ, Gonder-Frederick LA, Antoun B, Cryer P, Clarke WL. 1993 Perceived symptoms in the recognition of hypoglycemia. Diabetes Care. 16:519 –527. 12. Cox DJ, Gonder-Frederick LA, Cryer P, Clarke WL. 1996 Sex differences in BG thresholds for counterregulatory hormone release and low blood glucose symptom perception. Diabetes Care. 19:269 –270. 13. Bremer T, Gough DA. 1999 Is blood glucose predictable from previous values? A solicitation for data. Diabetes. 48:445– 451. 14. Gold AE, Frier BM, MacLeod KM, Deary IJ. 1997 A structural equation model for predictors of severe hypoglycaemia in patients with insulin-dependent diabetes mellitus. Diabetes Med. 14:309 –315. 15. Kovatchev BP, Cox DJ, Gonder-Frederick LA, Clarke WL. 1997 Symmetrization of the blood glucose measurement scale and its applications. Diabetes Care. 20:1655–1658. 16. Cox DJ, Kovatchev BP, Julian DM, et al. 1994 Frequency of severe hypoglycemia in IDDM can be predicted from self-monitoring blood glucose data. J Clin Endocrinol Metab. 79:1659 –1662. 17. Kovatchev BP, Cox DJ, Gonder-Frederick LA Young-Hyman D, Schlundt D, Clarke WL. 1998 Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index. Diabetes Care. 21:1870 –1875. 18. Kovatchev BP, Straume M, Farhy LS, Cox DJ. 1999 Estimating the speed of blood glucose transitions and its relationship with severe hypoglycemia. Diabetes. 48(Suppl 1):A363. 19. Kovatchev BP, Straume M, Cox DJ, Farhy LS. Risk analysis of blood glucose data: a quantitative approach to optimizing the control of insulin dependent diabetes. J Theoret Med. In press. 20. Lehmann ED. 1999 Experience with the internet release of AIDA v4.0 – http: //www.diabetic.org.uk/aida.htm–an interactive educational diabetes simulator. Diabetes Technol Therapeut. 1:41–54.