In:
Frontiers in Cardiovascular Medicine, Frontiers Media SA, Vol. 9 ( 2022-7-14)
Abstract:
Although European guidelines recommend vascular ultrasound for the assessment of cardiovascular risk in low-to-moderate risk individuals, no algorithm properly identifies patients who could benefit from it. The aim of this study is to develop a sex-specific algorithm to identify those patients, especially women who are usually underdiagnosed. Methods Clinical, anthropometrical, and biochemical data were combined with a 12-territory vascular ultrasound to predict severe atheromatosis (SA: ≥ 3 territories with plaque). A Personalized Algorithm for Severe Atheromatosis Prediction (PASAP-ILERVAS) was obtained by machine learning. Models were trained in the ILERVAS cohort ( n = 8,330; 51% women) and validated in the control subpopulation of the NEFRONA cohort ( n = 559; 47% women). Performance was compared to the Systematic COronary Risk Evaluation (SCORE) model. Results The PASAP-ILERVAS is a sex-specific, easy-to-interpret predictive model that stratifies individuals according to their risk of SA in low, intermediate, or high risk. New clinical predictors beyond traditional factors were uncovered. In low- and high-risk (L & amp;H-risk) men, the net reclassification index (NRI) was 0.044 (95% CI: 0.020–0.068), and the integrated discrimination index (IDI) was 0.038 (95% CI: 0.029–0.048) compared to the SCORE. In L & amp;H-risk women, PASAP-ILERVAS showed a significant increase in the area under the curve (AUC, 0.074 (95% CI: 0.062–0.087), p -value: & lt; 0.001), an NRI of 0.193 (95% CI: 0.162–0.224), and an IDI of 0.119 (95% CI: 0.109–0.129). Conclusion The PASAP-ILERVAS improves SA prediction, especially in women. Thus, it could reduce the number of unnecessary complementary explorations selecting patients for a further imaging study within the intermediate risk group, increasing cost-effectiveness and optimizing health resources. Clinical Trial Registration [ www.ClinicalTrials.gov ], identifier [NCT03228459] .
Type of Medium:
Online Resource
ISSN:
2297-055X
DOI:
10.3389/fcvm.2022.895917
DOI:
10.3389/fcvm.2022.895917.s001
DOI:
10.3389/fcvm.2022.895917.s002
DOI:
10.3389/fcvm.2022.895917.s003
DOI:
10.3389/fcvm.2022.895917.s004
DOI:
10.3389/fcvm.2022.895917.s005
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2022
detail.hit.zdb_id:
2781496-8
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