Clarkson professor has research published by American Society of Mechanical Engineers
Thursday, March 22, 2018 - 11:53 am

POTSDAM -- Clarkson University Associate Professor of Mechanical & Aeronautical Engineering Ronald LaFleur recently had his research on the uncertainty of cardiovascular disease (CVD) risk modeling published by the American Society of Mechanical Engineers.

The technology of error and uncertainty calculation was developed as part of an independent research effort by LaFleur leading to the founding of CertainError LLC with support from Clarkson's Shipley Center for Innovation. There are currently two patents pending filed by Clarkson.

CertainError provides smartphone apps and computer software for automatic error accounting and uncertainty analysis in the educational, research and business domains.

The CVD risk research was initiated by former Clarkson Clinical Assistant Professor of Physician Assistant Studies Laura S. Goshko and was the basis for an initial research project completed by two physician assistant studies students. This was expanded into a larger research effort that examined historical and geographic differences in CVD risk calculation.

Cardiovascular disease continues to be a leading cause of death. Accordingly, risk models that depend on health measures, such as blood pressure and cholesterol levels, are used to predict an individual’s probability of developing the disease.

However, there is significant variability in the published models and this means the CVD risk could be different for the same patient, depending on what model is used. The research project analyzed 35 risk calculators and then generalized them into one ‘Super Risk Formula’, with 208 numbers to calculate one number: the risk of developing CVD within 10 years’ time.

Adding to CVD risk model variability is the fact that a patient’s health numbers, such as blood pressure and cholesterol levels, are an imperfect representation of a person’s health status and each of the 208 numbers has an error. Acknowledgement of uncertainties must be addressed for reliable clinical decision making.

Instead of using traditional arithmetic, a novel alternative, the duals method, was used to account for the uncertainties and report the quality of the risk information. This allowed each of the different CVD risk models to be judged according to their uncertainty, with the lowest uncertainty being the winner and the preferred model for that patient.