Department of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Section of Radiology, Department of Medicine and Surgery (DiMeC), University Hospital of Parma, Parma, Italy.
PLoS One. 2023 May 16;18(5):e0285593. doi: 10.1371/journal.pone.0285593. eCollection 2023.
Coronary artery calcium (CAC) is a known risk factor for cardiovascular (CV) events and mortality but is not yet routinely evaluated in low-dose computed tomography (LDCT)-based lung cancer screening (LCS). The present analysis explored the capacity of a fully automated CAC scoring to predict 12-year mortality in the Multicentric Italian Lung Detection (MILD) LCS trial. The study included 2239 volunteers of the MILD trial who underwent a baseline LDCT from September 2005 to January 2011, with a median follow-up of 190 months. The CAC score was measured by a commercially available fully automated artificial intelligence (AI) software and stratified into five strata: 0, 1-10, 11-100, 101-400, and > 400. Twelve-year all-cause mortality was 8.5% (191/2239) overall, 3.2% with CAC = 0, 4.9% with CAC = 1-10, 8.0% with CAC = 11-100, 11.5% with CAC = 101-400, and 17% with CAC > 400. In Cox proportional hazards regression analysis, CAC > 400 was associated with a higher 12-year all-cause mortality both in a univariate model (hazard ratio, HR, 5.75 [95% confidence interval, CI, 2.08-15.92] compared to CAC = 0) and after adjustment for baseline confounders (HR, 3.80 [95%CI, 1.35-10.74] compared to CAC = 0). All-cause mortality significantly increased with increasing CAC (7% in CAC ≤ 400 vs. 17% in CAC > 400, Log-Rank p-value <0.001). Non-cancer at 12 years mortality was 3% (67/2239) overall, 0.8% with CAC = 0, 1.0% with CAC = 1-10, 2.9% with CAC = 11-100, 3.6% with CAC = 101-400, and 8.2% with CAC > 400 (Grey's test p < 0.001). In Fine and Gray's competing risk model, CAC > 400 predicted 12-year non-cancer mortality in a univariate model (sub-distribution hazard ratio, SHR, 10.62 [95% confidence interval, CI, 1.43-78.98] compared to CAC = 0), but the association was no longer significant after adjustment for baseline confounders. In conclusion, fully automated CAC scoring was effective in predicting all-cause mortality at 12 years in a LCS setting.
冠状动脉钙（CAC）是心血管（CV）事件和死亡率的已知危险因素，但在基于低剂量计算机断层扫描（LDCT）的肺癌筛查（LCS）中尚未常规评估。本分析探讨了完全自动化 CAC 评分在多中心意大利肺癌检测（MILD）LCS 试验中预测 12 年死亡率的能力。该研究纳入了 MILD 试验的 2239 名志愿者，他们在 2005 年 9 月至 2011 年 1 月期间进行了基线 LDCT，中位随访时间为 190 个月。CAC 评分通过市售的全自动人工智能（AI）软件进行测量，并分为五个层次：0、1-10、11-100、101-400 和 > 400。总的来说，12 年全因死亡率为 8.5%（2239 例中的 191 例），CAC = 0 的患者为 3.2%，CAC = 1-10 的患者为 4.9%，CAC = 11-100 的患者为 8.0%，CAC = 101-400 的患者为 11.5%，CAC > 400 的患者为 17%。在 Cox 比例风险回归分析中，与 CAC = 0 相比，CAC > 400 在单变量模型（风险比，HR，5.75 [95%置信区间，CI，2.08-15.92]）和调整基线混杂因素后（HR，3.80 [95%CI，1.35-10.74]）均与 12 年全因死亡率相关。随着 CAC 的增加，全因死亡率显著增加（CAC ≤ 400 为 7%，CAC > 400 为 17%，对数秩检验 p 值<0.001）。12 年非癌症死亡率为 3%（2239 例中的 67 例），CAC = 0 的患者为 0.8%，CAC = 1-10 的患者为 1.0%，CAC = 11-100 的患者为 2.9%，CAC = 101-400 的患者为 3.6%，CAC > 400 的患者为 8.2%（Grey 检验 p < 0.001）。在 Fine 和 Gray 的竞争风险模型中，与 CAC = 0 相比，CAC > 400 在单变量模型中预测 12 年非癌症死亡率（亚分布风险比，SHR，10.62 [95%CI，1.43-78.98]），但在调整基线混杂因素后，这种关联不再显著。总之，在 LCS 环境中，全自动 CAC 评分能有效预测 12 年全因死亡率。
