Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)
- 494 Downloads
There is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s’ European populations.
The objective of this study was to develop a risk engine with multiple risk equations using a recent patient cohort with type 2 diabetes mellitus reflective of the US population.
A total of 17 risk equations for predicting diabetes-related microvascular and macrovascular events, hypoglycemia, mortality, and progression of diabetes risk factors were estimated using the data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (n = 10,251). Internal and external validation processes were used to assess performance of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine. One-way sensitivity analysis was conducted to examine the impact of risk factors on mortality at the population level.
The BRAVO risk engine added several risk factors including severe hypoglycemia and common US racial/ethnicity categories compared with the UKPDS risk engine. The BRAVO risk engine also modeled mortality escalation associated with intensive glycemic control (i.e., glycosylated hemoglobin < 6.5%). External validation showed a good prediction power on 28 endpoints observed from other clinical trials (slope = 1.071, R2 = 0.86).
The BRAVO risk engine for the US diabetes cohort provides an alternative to the UKPDS risk engine. It can be applied to assist clinical and policy decision making such as cost-effective resource allocation in USA.
Hui Shao analyzed the data for developing the BRAOVO risk engine with both internal and external validation and drafted the manuscript. Vivian Fonseca and Shuqian Liu provided clinical interpretation to support the BRAVO risk engine. Charles Stoecker reviewed the econometrics in the risk engine during the model development. Lizheng Shi as the principal investigator initiated the project and worked with Hui Shao in developing the BRAVO risk engine and the manuscript. All authors are extensively involved in writing the manuscript.
Compliance with Ethical Standards
No sources of funding were received for the preparation of this article.
Conflict of interest
Hui Shao, Vivian Fonseca, Charles Stoecker, Shuqian Liu, and Lizheng Shi have no conflicts of interest directly relevant to the content of this article.
The datasets used for this study are publicly available and can be requested through the National Heart, Lung, and Blood Institute .
- 27.Fishman G. Discrete-event simulation: modeling, programming, and analysis. Berlin: Springer Science & Business Media; 2013.Google Scholar
- 31.Hayes A, Leal J, Gray A, et al. UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia. 2013;56(9):1925–33.CrossRefPubMedGoogle Scholar
- 42.National Heart Lung and Blood Institute. Action to Control Cardiovascular Risk in Diabetes (ACCORD) data. Available from: https://biolincc.nhlbi.nih.gov/studies/accord/. Accessed 9 Apr 2018.