Diabetes mellitus is a metabolic disease that affects millions of people across the world, from different age groups and ethnic backgrounds. It is a chronic disease, so it can not be completely treated; only properly managed. This is why the reliable method of diagnostics, engineered by Huang et al., is a great tool for fighting this particular disease. Thorough, evidence-based research provided in their work “Using Hemoglobin A1C as a Predicting Model for Time Interval from Pre-Diabetes Progressing to Diabetes” already helped various medical institutions in different countries to perform precise diagnostics. The purpose of this paper is to summarize the results of this recent research article and discuss how the presented clinical findings are significant for diagnosing and managing diabetes as well as for improving the nursing practice.
Prediction Model Based on Hemoglobin A1C
Although the first clinical usage of hemoglobin glycation A1C diagnostics dates back to the eighties, it wasn’t widely used in medical practice. There is still a lot of discussion going on how to properly measure HbA1C results. According to Huang et al. (2014), “The HbA1C time interval before diabetes diagnosed such as the interval between HbA1C 5.7% to 6.5% is still unclear”. There is growing interest in the implementation of HbA1C analysis as an alternative to blood glucose tests. HbA1C results are not as severely affected by dietary habits or proper timing, so this kind of diagnostics could potentially be more accurate.
The method developed by Huang et al. uses a linear regression trend line model to evaluate plasma A1C levels. A similar method was already used in different medical studies, mainly for forecasting the development of melanoma metastasis. Huang et al. worked with different kinds of patients, comparing variables with three control groups of different risk levels. As Huang et al. (2014) stated themselves, “A linear regression trend-line was calculated for every HbA1C value of each patient”. After getting enough estimated data, they managed to pinpoint an interval in which pre-diabetes risk levels can go up to severe diabetes risk levels. Evaluating the progression levels of HbA1C before diabetes was diagnosed, Huang et al. decreased the inaccuracy of their measurements down to a couple of days at most. This form of diagnosis also uses data provided by non-intrusive methods. Coupled with its accuracy, which is not diminished by diets or medication, this type of analysis could be used on the young patient population, including children.
Huang et al. conducted their clinical studies in three major Taiwanese hospitals, and they initially worked with data on about fifty thousand patients. While measuring different diabetes risk groups, Huang et al. concluded that in their clinical groups, it would take about two and a half years for a person from the pre-diabetes risk group to advance to dangerous levels of HbA1C, correlating with results gained from people in severe diabetes groups. Their method was quite informative also, able to provide the numbers about the levels of blood sugar over three months. As stated by Huang et al. (2014), the main convenience of HbA1C level measurement is that it provides us with more elaborate data on what is happening over a specific period, and the results do not suffer from inconsistency as much as with blood glucose tests. After further studies and testing, they also tried to apply their model to data provided by medical institutions from different countries; this allowed Huang et al. to forecast the risk levels of diabetes for the population of whole regions.
Relevance to Diabetes and Significance for Nursing Practice
As Huang et al. (2014) state in their work, by using prediction models based on HbA1C measurements it is possible to develop characteristic models of physician decision management, which will allow for operative treatment of pre-diabetes groups of population and also will help to improve patient safety research. HbA1C measurements are easier to standardize, they are more concrete, and convenient to keep and study. Further developments on this prediction model can help to better track records of people diagnosed with diabetes, which in turn will help medical caregivers. Forecasting the progression of diabetes disease will help medical staff to treat people far more efficiently.
Moreover, this prediction model also was used in various research projects and theoretical works. As Malka noted in his studies (2016), the amount of glycated hemoglobin in diabetic patients’ blood provides an accurate estimation, and using the HbA1C prediction model allows us to develop new ways of personalized medication. Whole different methods of diabetes diagnostics are developed based on this prediction model. Focusing on the cost-efficiency of HbA1 tests, various researchers proposed their methods of testing, more affordable than generic reagent usage. According to Coopman et al. (2016), the HbA1C prediction tool allowed us to gather enough insight and to develop our ATR-FTIR spectroscopy method.
Diabetes is a serious disease with which a lot of the population has to cope. Severe symptoms and cruel complications can result even in death. This is why proper diagnostic methods are a must, and Huang et al. with their HbA1C prediction model are one of the pioneers in that field. Their forecasting model allows to accurately predict the development of diabetes disease and can be applied to any group of patients, even to children. Moreover, easily standardized, this model can help both in nursery practice and theoretical studies.
Coopman, R., Van de Vyer, T., Kishabongo, A. S., Katchunga, P., Van Aken, E. H., Cikomola, J…. Delanghe, J. R. (2016). Glycation in Human Fingernail Clippings Using ATR-FTIR Spectrometry, a New Marker for the Diagnosis and Monitoring of Diabetes Mellitus. Clinical Biochemistry, 16(4), 1-6. Web.
Huang, C. L., Iqbal, U., Nguyen, P. A., Chen, Z. F., Clinciu, D. L., Hsu, Y. H. E., … Jian, W. S. (2014). Using Hemoglobin A1C as a Predicting Model for Time Interval from Pre-diabetes Progressing to Diabetes. PloS One, 9(8). Web.
Malka, R., Nathan, D. M., & Higgins, J. M. (2016). Mechanistic Modelling of Hemoglobin Glycation and Red Blood Cell Kinetics Enables Personalized Diabetes Monitoring. Science Translational Medicine, 8(359), 130-159.