Association of computed tomography-derived left ventricular size with major cardiovascular events in the general population: The Heinz Nixdorf recall study. Issue 1 (May 2015) - Dykun, Iryna; Geisel, Marie H.; Kälsch, Hagen; Lehmann, Nils; Bauer, Marcus; Moebus, Susanne; Jöckel, Karl-Heinz; Möhlenkamp, Stefan; Erbel, Raimund; Mahabadi, Amir A.
Abstract Objective To investigate the relationship between LV size as determined by non-contrast enhanced cardiac CT with incident cardiovascular disease in the general population free of clinical cardiovascular disease. Methods LV axial area was quantified from non-contrast CT in axial, end-diastolic images at a mid-ventricular slice in participants from the population-based Heinz Nixdorf recall study, free of cardiovascular disease (n = 3926, 59±8years, 53%female). LV size index (LVI) was defined as the quotient of LV area and body surface area. Major CV events (coronary events, stroke, CV death) were assessed during follow-up. Association of LVI with events was assessed using Cox regression analysis in unadjusted and multivariable adjusted models. Results During 8.0 ± 1.5years of follow-up, 219 subjects developed a major CV event. Those with events had larger LVI at baseline (2258 ± 352 vs. 2149 ± 276 mm2/m2, p < 0.0001). In univariate analysis, increase of LVI by 1 standard deviation was associated with 40% higher risk of events (HR(95%CI):1.41(1.26–1.59), p < 0.0001). Associations remained statistically significant after adjustment for CV risk factors (1.24(1.10–1.40), p = 0.0007) and when further adjusting for CAC (1.21(1.07–1.37), p = 0.003). There was a trend towards stronger association for subjects with low CAC-score (CAC<100:1.41(1.16–1.71), p = 0.0005, CAC≥100:1.24(1.06–1.44), p = 0.006) in univariate analysis which persisted after multivariable adjustment (CAC<100: 1.41(1.14–1.73), p = 0.001, CAC≥100: 1.12(0.96–1.31), p = 0.16). Conclusion CT-derived LV size is associated with incident major CV events independent of traditional risk factors and CAC-score in a population-based cohort and may improve the prediction of hard events especially in subjects with low CAC-scores.