Cumulative tobacco consumption has a dose-dependent effect on atheromatosis burden and improves severe atheromatosis prediction in asymptomatic middle-aged individuals: The ILERVAS study.

BACKGROUND AND AIMS: Sex-specific impact of cumulative tobacco consumption (CTC) on atheromatosis extension and total plaque area remains unknown. We aimed to determine the impact of CTC in atheromatosis localization and burden. METHODS: We performed a cross-sectional analysis in 8330 asymptomatic middle-aged individuals. 12-territory vascular ultrasounds in carotid and femoral arteries were performed to detect atheromatous plaque presence and to measure total plaque area. Adjusted regressions and conditional predictions by smoking habit or CTC (stratified in terciles as low (≤13.53), medium (13.54-29.3), and high (>29.3 packs-year)) were calculated. Severe atheromatosis (SA, ≥3 territories with atheroma plaque) was predicted with the Systematic COronary Risk Evaluation 2 (SCORE2) model. The improvement of SA prediction after adding CTC was evaluated. RESULTS: CTC was associated with an increased risk of atheromatosis, stronger in femoral than in carotid artery, but similar in both sexes. A dose-dependent effect of CTC on the number of territories with atheroma plaque and total plaque area was observed. Addition of CTC to the SCORE2 showed a higher sensitivity, accuracy, and negative predictive value in males, and a higher specificity and positive predictive value in females. In both sexes, the new SCORE2-CTC model showed a significant increase in AUC (males: 0.033, females: 0.038), and in the integrated discrimination index (males: 0.072; females: 0.058, p < 0.001). Age and CTC were the most important clinical predictors of SA in both sexes. CONCLUSIONS: CTC shows a dose-dependent association with atheromatosis burden, impacts more strongly in femoral arteries, and improves SA prediction.

Development and Validation of a Personalized, Sex-Specific Prediction Algorithm of Severe Atheromatosis in Middle-Aged Asymptomatic Individuals: The ILERVAS Study.

Background: 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&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&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: < 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].