Biology Reference
In-Depth Information
A. SMBG and Average BG Levels
As already mentioned, the value of HbA
1c
accurately reflects the
average BG over the preceding five to six weeks. A natural, but
incorrect, conclusion would be that the mean BG derived from SMBG
readings would also accurately reflect the real BG values. In reality,
the mean SMBG can deviate substantially from the real mean
BG value since a patient could measure at fixed times of day when
his or her BG is at its extreme high or low. In this case, the average of the
SMBG readings will be an overestimate or underestimate of
the real mean BG.
B. SMBG and Prediction of Hypoglycemia
Little attention was paid to the evaluation of the risk for hypoglycemia
from SMBG before the first announcement in 1994 that SH could be
predicted from SMBG (Cox et al. [1994]). The reason is that, theoretically,
routine SMBG three to four times a day would rarely capture rapidly
developing events, such as descent into hypoglycemia. This is one
incentive behind the development of systems for continuous BG
monitoring. However, we found that specific analysis of SMBG data can
capture trends towards increased risk for hypoglycemia and can identify
periods of increased risk for hypoglycemia.
In this chapter, we present the mathematics behind these methods for
analysis of SMBG data. The mathematical foundation of our techniques
is based on the following general biomathematical concept: The struggle
for tight glycemic control often results in great BG fluctuations over
time. This process is influenced by many external factors, including the
timing and amount of insulin injected, food eaten, physical activity, etc.
In other words, fluctuations of the BG level over time are the measurable
result of the action of a complex dynamic system, influenced by many
internal and external factors. Observed over short periods of time, this
system is nearly deterministic, and its fluctuations can be predicted by
knowing the state of its components and their interaction. Over longer
periods of time, the system has nearly random behavior that includes
extreme transitions, such as SH episodes. Consequently, different
analytical strategies would quantify long-term characteristics of diabetes,
such as HbA
1c
, long-term risk for SH, and patient behavior, and short-
term characteristics such as imminent moderate or severe hypoglycemia.
Following this concept, this chapter offers a system of quantitative
methods simultaneously evaluating three important components of
glycemic control: HbA
1c
and long-term and short-term risk for
hypoglycemia. In order to be clinically useful, these methods utilize
readily available SMBG data and relatively simple algorithms. In order
to prove clinical relevance, the results are correlated with established
measures of glycemic control, such as HbA
1c
, and risk for upcoming
hypoglycemia.
