Surgery – Diagnostic testing
Reexamination Certificate
2001-12-21
2003-06-24
Hindenburg, Max F. (Department: 3736)
Surgery
Diagnostic testing
Reexamination Certificate
active
06582366
ABSTRACT:
BACKGROUND OF INVENTION
In health, all events taking place inside living bodies are controlled by a myriad of minute processes, each having outcomes preordained by their DNA templates. One such process is the blood glucose metabolism, which is optimized to achieve normoglycemia even for wide swings in the predominant effect inputs, food and physical exercise. Illness, on the other hand, is characterized by the body's inability to maintain control over one or more biological processes. Unless correctable by surgery, medication, or other means, the out-of-control condition extends to other processes and results in poor patient prognosis. The only alternative left to people suffering from “incurable” afflictions is disease management, that is, careful monitoring of the parameters of interest, coupled with corrective actions such as insulin injections, dialysis, etc.
For example, diabetes patients have little or no endogenous blood glucose (BG) control capability, and many must inject insulin to compensate for swings outside the acceptable serum glucose range. Similarly, end-stage renal disease patients have lost the ability to control their nitrogen metabolism, and must undergo dialysis, typically for the rest of their lives. The reader will appreciate that many other situations sadly exist, however, because they are representative of this entire class of problems, we will limit the examples presented here to glucose control and diabetes, and their spin-off, long-term weight management.
Diabetes mellitus is a significant chronic disease with no cure, affecting more than 16 million Americans and 100 to 150 million people worldwide. In the U.S. it is the fourth-leading cause of death each year. (National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases: Diabetes in America/Second Edition, NIH Publication No. 95-1468 (1995)) Its incidence rate among children and teenagers in the U.S. increased several fold during the last thirty years, and more than 30,000 new cases are identified each year. It is a $156 billion problem for the US economy.
A problem of this magnitude naturally generates intense efforts to alleviate it, and one landmark study has altered the course of treatment. The results of the Diabetes Control and Complications Trial (DCCT) indicate that tight control of blood glucose levels results in a dramatic reduction of complications, including 76% lower incidence of diabetic retinopathy, 60% less neuropathy, and as much as 56% fewer cases of nephropathy. The costs to society could be drastically reduced, but at the expense of intensive management (more frequent blood sugar measurements, multiple daily injections).
Technological advances have made it possible for patients to administer insulin at home, and to monitor the blood glucose levels (self monitoring of blood glucose, or SMBG) with equipment that costs less than $100; test strips and accessories run about $2,000 per year per patient, but of course this expense will double with intensive management as suggested by the DCCT results. To further improve ease of control, intense product development efforts have led to the introduction of a new, rapid insulin (HUMALOG™), and non-invasive blood glucose measurement equipment could be marketed within the next few years.
However, currently available equipment does not take into account the blood glucose dynamics, and invasive blood glucose measurement techniques discourage frequent testing. The blood glucose values are therefore known only to a minute extent, because even the most compliant patients will only draw blood and test the glucose concentration less than ten times a day. The data available is therefore much too far from the Nyquist sampling rate, and very little in formation results from the individual blood tests. Again, intense product development efforts have led to the introduction of statistical process control concepts in blood glucose meters, and many now can store several hundred measurements, including the time of day and date each value was measured. For a rigid regimen, whereby the patient always eats the same meals at the same time, and injects the same amounts and types of insulin every day at the same time, the statistical information thus gathered helps with the metabolic control and gives the physician the means to assess the patient compliance and adequacy of the regimen. The main draw-back of this methodology is that it is all after the fact, while also severely restricting the patients ' freedom of action in the most basic are as, such as food and exercise, and rest.
This too has a solution, albeit an imperfect one: the “gold standard” in diabetes nutrition is carbohydrate counting, a method that allows the patients with insulin-dependent diabetes to eat any higher carbohydrate meal, so long as the equivalent amount of insulin is administered. There are problems with this also, the two most important being that most patients cannot do the conversion, and that not all carbohydrates are equal in terms of their speed of conversion to blood glucose. (An extensive amount of information exists on the glycemic index of different types of food.)
Intense efforts to introduce non-invasive blood glucose testers will soon bring to market equipment that will make frequent testing convenient, but still no predictive capability will be added by non-invasive SMBG. The burden of understanding the temporal characteristics of the physiological mechanisms of glucose regulation depends on the patients.
Those equipped with the knowledge and willingness to devote time are able to achieve a good overall level of control, but most patients are overwhelmed and find themselves unable to understand what has caused the lack of blood glucose control. For patients who are not equipped with the ability to understand such issues, the only way to stay in control is to adhere to a very stringent schedule of daily insulin, food and exercise that does not vary from one day to another. Such a regimen poses serious compliance and lifestyle problems. For these patients, solutions based on mathematical modeling are available:
The body of a healthy person has the endogenous control capability to minimize the glycemic challenge to the body, but the diabetes patient has little or no endogenous control capability. Maintaining blood glucose levels within normal limits can be treated as a control systems engineering problem, which was, and continues to be the object of much research. Those who published their work in this field (Heinonen, Porumbescu, etc.) considered the body's blood glucose system a classic control system, consisting of a plant whose aim is to maintain control over an output (Blood Glucose), in the presence of zero or non-zero inputs (food, physical exercise, insulin), and of perturbations (emotional status, illness). The euglycemic output is achieved by feedback: continuously measuring the output values, comparing them to the desired state (set point), and initiating compensatory action. By contrast, in diabetes this is an open-loop system, in need of an external feedback loop, therefore attempts were made to provide it through mechanical means.
After more than twenty years of trying, it can be safely said that attempts to devise mathematical models for blood glucose metabolism have failed. The reasons are numerous, but one of the most important is that models that will work properly for an entire population are very complicated and include nonlinear equations. Some degree of success has been reported with developing adaptive individual-specific models, which will work in conjunction with a strict regimen that does not vary from one day to another.
For example, U.S. Pat. No. 5,840,020 (Heinonen et al.) addresses the adaptive model shown in
FIG. 4
, whereby the error between a predicted value and the actually measured value is used to optimize (converge) a dynamical model in a recursive fashion, using Widrow's Adaptive Least Means Square algorithm.
Similarly, Porumbescu et al. (“Patient Specific Expert System For IDDM
Astorino Michael
Hindenburg Max F.
Proactive Metabolics Co.
Saliwanchik Lloyd & Saliwanchik
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