Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach. (May 2019)

• Record Type: Journal Article
• Authors: Cuadrado-Godia, Elisa; Jamthikar, Ankush D.; Gupta, Deep; Khanna, Narendra N.; Araki, Tadashi; Maniruzzaman, Md; Saba, Luca; Nicolaides, Andrew; Sharma, Aditya; Omerzu, Tomaz; Suri, Harman S.; Gupta, Ajay; Mavrogeni, Sophie; Turk, Monika; Laird, John R.; Protogerou, Athanasios; Sfikakis, Petros; Kitas, George D.; Viswanathan, Vijay; Suri, Jasjit S.
• Is Part Of: Computers in biology and medicine. Volume 108(2019)
• Page Start: 182
• Page End: 195
• Publication Date: 2019-05
• Subjects: Cardiovascular risk calculator -- Conventional cardiovascular risk factors -- Covariates -- Image-based phenotypes -- Logistic regression -- Ranking -- Odds ratio -- p-value -- AUC -- Performance; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.03.020
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Purpose: Conventional cardiovascular risk factors (CCVRFs) and carotid ultrasound image-based phenotypes (CUSIP) are independently associated with long-term risk of cardiovascular (CV) disease. In this study, 26 cardiovascular risk (CVR) factors which consisted of a combination of CCVRFs and CUSIP together were ranked. Further, an optimal risk calculator using AtheroEdge composite risk score (AECRS1.0) was designed and benchmarked against seven conventional CV risk (CVR) calculators. Methods: Two types of ranking were performed: (i) ranking of 26 CVR factors and (ii) ranking of eight types of 10-year risk calculators. In the first case, multivariate logistic regression was used to compute the odds ratio (OR) and in the second, receiver operating characteristic curves were used to evaluate the performance of eight types of CVR calculators using SPSS23.0 and MEDCALC12.0 with validation against STATA15.0. Results: The left and right common carotid arteries (CCA) of 202 Japanese patients were examined to obtain 404 ultrasound scans. CUSIP ranked in the top 50% of the 26 covariates. Intima-media thickness variability (IMTV) and IMTV10yr were the most influential carotid phenotypes for left CCA (OR = 250, P < 0.0001 and OR = 207, P < 0.0001 respectively) and right CCA (OR = 1614, P < 0.0001 and OR = 626, P < 0.0001 respectively). However, for the mean CCA, AECRS1.0 and AECRS1.010yr reported the most highly significant OR among all the CVR factors (OR = 1.073, P < 0.0001 and OR = 1.104, P < 0.0001). AECRS1.010yr also reported highest area-under-the-curve (AUC = 0.904, P < 0.0001) compared to seven types of conventional calculators. Age and glycated haemoglobin reported highest OR (1.96, P < 0.0001 and 1.05, P = 0.012) among all other CCVRFs. Conclusion: AECRS1.010yr demonstrated the best performance due to presence of CUSIP and ranked at the first place with highest AUC.