5 research outputs found
Higher order moments of bilinear time series processes with symmetrically distributed errors
No full textStatistical Methods
sj-pdf-2-hpi-10.1177_11207000211038550 – Supplemental material for Which hip morphology measures and patient factors are associated with age of onset and symptom severity in femoroacetabular impingement syndrome?
No full textSupplemental material, sj-pdf-2-hpi-10.1177_11207000211038550 for Which hip morphology measures and patient factors are associated with age of onset and symptom severity in femoroacetabular impingement syndrome? by Nicholas J Murphy, Laura E Diamond, Kim L Bennell, Alexander Burns, Edward Dickenson, Jillian Eyles, Camdon Fary, Stuart M Grieve, Damian R Griffin, Young Jo Kim, James M Linklater, David G Lloyd, Robert Molnar, Rachel L O’Connell, John O’Donnell, Sunny Randhawa, Parminder J Singh, Libby Spiers, Phong Tran, Tim Wrigley and David J Hunter in HIP International</p
sj-pdf-1-hpi-10.1177_11207000211038550 – Supplemental material for Which hip morphology measures and patient factors are associated with age of onset and symptom severity in femoroacetabular impingement syndrome?
No full textSupplemental material, sj-pdf-1-hpi-10.1177_11207000211038550 for Which hip morphology measures and patient factors are associated with age of onset and symptom severity in femoroacetabular impingement syndrome? by Nicholas J Murphy, Laura E Diamond, Kim L Bennell, Alexander Burns, Edward Dickenson, Jillian Eyles, Camdon Fary, Stuart M Grieve, Damian R Griffin, Young Jo Kim, James M Linklater, David G Lloyd, Robert Molnar, Rachel L O’Connell, John O’Donnell, Sunny Randhawa, Parminder J Singh, Libby Spiers, Phong Tran, Tim Wrigley and David J Hunter in HIP International</p
Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention
No full textBackground: Accurate risk stratification is vital for primary prevention of cardiovascular disease (CVD). However, traditional tools such as the Framingham Risk Score (FRS) may underperform within the diverse intermediate-risk group, which includes individuals requiring distinct management strategies. Objectives: This study aimed to develop a lipidomic-enhanced risk score (LRS), specifically targeting risk prediction and reclassification within the intermediate group, benchmarked against the FRS. Methods: The LRS was developed via a machine learning workflow using ridge regression on the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab; n = 10,339). It was externally validated with the Busselton Health Study (n = 4,492), and its predictive utility for coronary artery calcium scoring (CACS)–based outcomes was independently validated in the BioHEART cohort (n = 994). Results: LRS significantly improved discrimination metrics for the intermediate-risk group in both AusDiab and Busselton Health Study cohorts (all P < 0.001), increasing the area under the curve for CVD events by 0.114 (95% CI: 0.1123-0.1157) and 0.077 (95% CI: 0.0755-0.0785), with a net reclassification improvement of 0.36 (95% CI: 0.21-0.51) and 0.33 (95% CI: 0.15-0.49), respectively. For CACS-based outcomes in BioHEART, LRS achieved a significant area under the curve improvement of 0.02 over the FRS (0.76 vs 0.74; P < 1.0 × 10-5). A simplified, clinically applicable version of LRS was also created that had comparable performance to the original LRS. Conclusions: LRS, augmenting the FRS, presents potential to improve intermediate-risk stratification and to predict atherosclerotic markers using a simple blood test, suitable for clinical application. This could facilitate the triage of individuals for noninvasive imaging such as CACS, fostering precision medicine in CVD prevention and management
