Identify a research or evidence-based article that focuses comprehensively on a specific intervention or new diagnostic tool for the treatment of diabetes in adults or children.
In a paper of 750-1,000 words, summarize the main idea of the research findings for a specific patient population. Research must include clinical findings that are current, thorough, and relevant to diabetes and the nursing practice.
The AIC Tool as the New Diagnostic Tool for Diabetes
Often, an AIC test is sometimes known as the glycated hemoglobin, hemoglobin AIC, HbAlc, or the glycol-hemoglobin test. Oxygen is transported to the red blood cells through hemoglobin. Majorly, blood hemoglobin in red cells bind with glucose, and the new diagnostic tool –AIC- is based on this glucose-hemoglobin attachment (Sumner et al., 2016). The greater the amount of glucose in the bloodstream, the higher the glucose amount gets attached to the hemoglobin. Therefore, an AIC test was basically introduced to assess the hemoglobin amount directly attached on glucose, which works to monitor the level of blood glucose over a period of three months (Ang et al., 2015). In the end, the test results are given in percentage form, and a normal AIC level should read below 5.7%. Over time, hemoglobin cut off levels (AIC) have been established and proposed for diabetes screening, although it is important to put into consideration the consensus of best levels for varying ethnicities. This paper will assess an evidence-based article that comprehensively focused on the AIC tests as a diagnostic tool for the treatment of diabetes in both adults and children, and includes clinical findings that are current, thorough, and relevant to diabetes and the nursing practice.
A certain research-riented project evaluated the convenience of using AIC levels during the analysis for unknown diabetes, and as a meter for assessing a 6-year diabetic incidence within a populace-related sub study. A group of about 10,038 persons from Ansung-Ansan accomplice study was registered as study participants for the research. All the members were subjected to a 75-g oral glucose resistance test as a gauging tool at every biennial development. The illustrative accuracy of the AIC level limit was assessed using the beneficiary working trademark bend, especially for the barring subjects who had recorded the medical history of diabetes. The Cox conforming perils tool was utilized to predict the possibility of diabetic development at 6 years. By means of the Cox relative risks tool, it was much enabled to assess and monitor the commencement of diabetes risks as designated by AIC limit tool, in the wake of making age adjustments while using the variables with P ≤ 0.25 within the age-balanced correlation of both diabetic and non-diabetic clusters (Ang et al., 2015).
The investigators reviewed the age-balanced effects of AIC cutoff on the 6-year diabetic regularity. Model B was included with model A with an added conformism for anthropometric as well as the collective constraints. Model C was the well-adjusted form of model B but in count to HDL cholesterol, HOMA-β, and HOMA-IR, triglyceride, as well as the hsCRP, focuses. The final Cox models met the established comparative menaces presupposition. The study further examined the FPG focus and the prescient AIC level execution (Ang et al., 2015). Basically, the study findings indicated that doctors can use the AIC test tool for diagnosis by sending the blood sample to a laboratory with the National Glycohemoglobin Standardization Program, to provide consistent an comparable results as those utilized in the Diabetes Control and Complications Trial (Ang et al., 2015).
With regards to integration into practice, the AIC tests tool is of great relevance to healthcare. The tool enables healthcare professionals to adequately locate pre-diabetes symptoms or possibilities and comprehensively counsel the patients regarding lifestyle patterns and possible strategies to incorporate in order to prevent the type 2 diabetes. The tool also gives detailed results which enables the healthcare physicians to provide a detailed analysis of the diabetic condition or work with patients much closely as they monitor the disease’ development or suppression progress. Ideally, healthcare providers are generally enabled to allocate the most effective treatment options and lifestyle decisions and choices to inhibit the chronic complications of the disease (Ang et al., 2015). Doctors can as well use the diagnostic tool purposely to detect any related chronic infections whose treatment can begin immediately.
According to the American Diabetes Association (2018), the AIC tests also makes it possible to determine the amount of glucose present in blood, and enables the doctor assess the possibility of diabetes development so that both adults and children are enlightened on the need to avoid certain foods, avoiding the consumption of excess sugars, the need to adopt an exercising culture, shifting lifestyle choices to healthy practices among other concerns. Nevertheless, it is important to note that the test is not a good option for persons from the Southeast Asia nr Mediterranean because they are people with different hemoglobin-hemoglobin variant (Sumner et al., 2016). Hence, it tends to affect some AIC tests because a good number of the group lack diabetic symptoms. Scholars have therefore established that it is important for healthcare professionals to pay attention to these interferences when using the AIC tool purposely to limit the possibility of unrealistic diagnoses.
In conclusion, the AIC test as a diagnostic tool for diabetes has proved efficiency over time. It is important for healthcare professionals to venture much into evidence-based research when using the tool to assess its efficiency to practice. They must also use clinical trials to assess more on other aspects of care, including improving the quality of life for adults and children with chronic illness.
American Diabetes Association. 2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes—2018. Diabetes Care. 2018; 41(suppl 1):S13–S27.
Ang, S. H., Thevarajah, M., Alias, Y., & Khor, S. M. (2015). Current aspects in hemoglobin A1C detection: a review. Clinica Chimica Acta, 439, 202-211.
Sumner, A. E., Duong, M. T., Aldana, P. C., Ricks, M., Tulloch-Reid, M. K., Lozier, J. N., & Sacks, D. B. (2016). A1C combined with glycated albumin improves detection of prediabetes in Africans: the Africans in America study. Diabetes Care, 39(2), 271-277.