Журнал аналитических и биоаналитических методов

Абстрактный

Using Digital Filters to Obtain Accurate Trended Urine Glucose Levels from Toilet-Deployable Near-Infrared Spectrometers

Huijae Kim and Dan G Allen

Many over-the-counter glucose measurement systems currently exist but are not widely used by nondiabetic consumers because of the inconvenience. There exists a need for new methods of conveniently detecting early stages of diabetic or prediabetic conditions rather than waiting for the disease to progress to the point that symptoms indicative of physiological damage are present and a user requests medical care. Near-infrared (NIR) spectroscopic urinalysis has shown some promise for use as an unobtrusive measurement system for glucose levels but has required expensive equipment. This paper presents a method of combining a cost-effective, home-deployable NIR system with a non-traditional trend-based data analysis to extract representative glucose levels from patients. By taking multiple measurements over time with an unobtrusive, automatic, in-toilet urinalysis system, limited accuracy samples from each patient can be averaged to obtain an improved accuracy trended value. Data trending is able to predict glucose levels with sufficient accuracy to be clinically relevant in the detection of chronically high glucose conditions. The bandwidth, or averaging window, of the filters can be varied to achieve a target accuracy level, even when the error of individual measurements is large and variable. Urine spectra can be captured from an athome or at-work toilet with a urine capture slot and NIR spectrometer. A new data reporting strategy is proposed for trended measurements, whereby filtered data is reported with a known and acceptable post-filter variance, rather than reporting individual sample measurements. This is in contrast to traditional methods of single-point clinical tests, which may require expensive equipment to achieve sufficient single-point accuracy, be obtrusive or inconvenient, available only on demand, or susceptible to outliers.