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DESCRIPTIONThe Pre-Dx® Diabetes Risk Score (DRS) is a multianalyte assay with algorithmic analysis (MAAA) that is intended to predict the 5-year risk of type 2 diabetes. It is composed of 7 serum biomarkers that are combined via a proprietary algorithm. The proposedv use is to identify patients at greater risk of developing type 2 diabetes, and to potentially target preventive interventions at patients with the highest risk.
Type 2 diabetes mellitus is a highly prevalent disorder that is associated with an extremely high degree of morbidity and mortality. The true prevalence of type 2 diabetes in the U.S. is uncertain due to a lack of population screening, but an estimated prevalence of 8.2% was reported in 2006. The incidence has been increasing rapidly over the last several decades, and current trends indicate that this increase will continue. Projections have estimated that the prevalence in the U.S. will reach 11.5% in 2011, 13.5% in 2021, and 14.5% in 2031.
Therefore, there is an urgent public health need to counter this trend. The potential to improve outcomes and reduce costs by preventing the onset of diabetes is vast. In order to accomplish this, accurate risk prediction methods may be helpful to identify populations with the highest risk of diabetes. Identification of patients at high risk could then be followed by preventive interventions targeted at high-risk individuals.
Predicting Risk of Type 2 Diabetes.
Other risk factors for diabetes include family history, ethnicity, lifestyle factors, dietary patterns, and numerous different laboratory parameters. A history of diabetes in the immediate family has long been recognized as one of the strongest predictors of diabetes. Regarding ethnicity, the risk of diabetes is increased 1.34 times for blacks, 1.86 times for Hispanics, and 2.26 times for Asians. A sedentary lifestyle, cigarette smoking, and dietary patterns that include sweetened foods and beverages have all been positively associated with the development of diabetes. In addition, there are numerous non-glucose laboratory parameters that are associated with the risk of diabetes. These include inflammatory markers, lipid markers, measures of endothelial dysfunction, sex hormones, and many others.
Formal risk prediction instruments have combined clinical, laboratory, and genetic information to improve and refine upon the predictive ability of single factors. Many different formal risk prediction models have been developed. These models vary in the number and type of factors examined, and in the intended use of the instrument. For example, some prediction instruments consider the full range of clinical, biochemical, and genetic factors in order to derive the most accurate predictive model. Others, such as the Indian Risk Score, use easily available clinical information without any laboratory markers in order to facilitate implementation as a widespread screening tool in areas of low resources.
In general, the available models have been shown to have good predictive ability, but most of them have not been externally validated. There is some evidence that directly compares the predictive accuracy of different measures, but there is insufficient comparative research to determine the optimal model. There is evidence that different models have different accuracy depending on the population tested. Also, relatively simple models have performed similarly to more complex models, and genetic information seems to add little over readily available clinical and metabolic parameters.
Interventions to Prevent Type 2 Diabetes
A Cochrane review on the efficacy of lifestyle interventions to prevent type 2 diabetes was published in 2008. This review included eight randomized trials that compared exercise and dietary interventions to standard therapy in patients at high risk for diabetes. There was a 37% reduction in the incidence of diabetes for the intervention cohort when a combined diet/exercise intervention was used, but there were not significant effects noted for an exercise-only or a diet-only intervention.
Another systematic review and meta-analysis evaluated the efficacy of medications for preventing progression to type 2 diabetes. This review included 10 studies of oral hypoglycemic agents and 15 studies of injectable agents. Oral hypoglycemic agents and orlistat were found to be effective in reducing progression to diabetes compared to usual care. In the largest trials with followup of greater than 2 years, metformin (relative risk [RR]: 0.69, 95% confidence interval [CI]: 0.57-0.83), acarbose (0.75, 0.63-0.90), troglitazone (0.45, 0.25-0.83), and orlistat (hazard ratio [HR]: 0.63, 95% CI: 0.46-0.86) were efficacious in decreasing diabetes incidence compared with placebo. Evidence for other medication such as statins, fibrates, antihypertensive agents, and estrogen was inconclusive.
The largest randomized trial of preventive interventions was the Diabetes Prevention Program trial. This trial enrolled 3,234 obese patients with a high risk of diabetes as defined by body mass index (BMI) level, fasting glucose, and 2-hour post-prandial glucose levels. Participants were randomized to one of three groups, an intensive lifestyle intervention, a medication intervention consisting of metformin (850 mg twice per day), or a placebo control with information provided on diet and exercise. After a mean follow-up of 3 years, the incidence of diabetes was significantly reduced by 58% in the intensive lifestyle intervention group, and by 31% in the metformin group. A follow-up observational study concluded that the bulk of the benefit persisted for at least 10 years following completion of the trial.
Pre-Dx® Diabetes Risk Score
The results of these biomarkers are combined with age and gender to produce a quantitative risk score that varies from 0 to 10. Results are reported as the absolute 5-year risk of developing type 2 diabetes and the relative risk compared to age and gender matched controls.
The biomarkers included in the Pre-Dx® Diabetes Risk Score are not subject to U.S. Food and Drug Administration (FDA) approval. Laboratories performing these tests are subject to Clinical Laboratory Improvement Amendment (CLIA) standards for laboratory testing.
POLICYThe use of multianalyte panels with algorithmic analysis (MAAA) for the prediction of type 2 diabetes is considered investigational.
Investigative is defined as the use of any treatment procedure, facility, equipment, drug, device, or supply not yet recognized as a generally accepted standard of good medical practice for the treatment of the condition being treated and; therefore, is not considered medically necessary. For the definition of Investigative, “generally accepted standards of medical practice” means standards that are based on credible scientific evidence published in peer-reviewed medical literature generally recognized by the relevant medical community, and physician specialty society recommendations, and the views of medical practitioners practicing in relevant clinical areas and any other relevant factors. In order for equipment, devices, drugs or supplies [i.e, technologies], to be considered not investigative, the technology must have final approval from the appropriate governmental bodies, and scientific evidence must permit conclusions concerning the effect of the technology on health outcomes, and the technology must improve the net health outcome, and the technology must be as beneficial as any established alternative and the improvement must be attainable outside the testing/investigational setting.
The coverage guidelines outlined in the Medical Policy Manual should not be used in lieu of the Member's specific benefit plan language.
POLICY HISTORY07/18/2013: New policy added. Approved by Medical Policy Advisory Committee.
03/14/2014: Policy reviewed; no changes.
12/31/2014: Added the following new 2015 CPT code to the Code Reference section: 81465.
08/04/2015: Code Reference section updated for ICD-10.
06/07/2016: Policy number added. Investigative definition updated in Policy Guidelines section.
SOURCE(S)Blue Cross and Blue Shield Association Policy # 2.04.90
CODE REFERENCEThis may not be a comprehensive list of procedure codes applicable to this policy.