Strand Matthew, Austin Erin, Moll Matthew, Pratte Katherine A, Regan Elizabeth A, Hayden Lystra P, Bhatt Surya P, Boriek Aladin M, Casaburi Richard, Silverman Edwin K, Fortis Spyridon, Ruczinski Ingo, Koegler Harald, Rossiter Harry B, Occhipinti Mariaelena, Hanania Nicola A, Gebrekristos Hirut T, Lynch David A, Kunisaki Ken M, Young Kendra A, Sieren Jessica C, Ragland Margaret, Hokanson John E, Lutz Sharon M, Make Barry J, Kinney Gregory L, Cho Michael H, Pistolesi Massimo, DeMeo Dawn L, Sciurba Frank C, Comellas Alejandro P, Diaz Alejandro A, Barjaktarevic Igor, Bowler Russell P, Kanner Richard E, Peters Stephen P, Ortega Victor E, Dransfield Mark T, Crapo James D
National Jewish Health, Denver, Colorado.
University of Colorado at Denver.
Chronic Obstr Pulm Dis. 2020 Oct;7(4):346-361. doi: 10.15326/jcopdf.7.4.2020.0146.
Risk factor identification is a proven strategy in advancing treatments and preventive therapy for many chronic conditions. Quantifying the impact of those risk factors on health outcomes can consolidate and focus efforts on individuals with specific high-risk profiles. Using multiple risk factors and longitudinal outcomes in 2 independent cohorts, we developed and validated a risk score model to predict mortality in current and former cigarette smokers.
We obtained extensive data on current and former smokers from the COPD Genetic Epidemiology (COPDGene) study at enrollment. Based on physician input and model goodness-of-fit measures, a subset of variables was selected to fit final Weibull survival models separately for men and women. Coefficients and predictors were translated into a point system, allowing for easy computation of mortality risk scores and probabilities. We then used the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) cohort for external validation of our model.
Of 9867 COPDGene participants with standard baseline data, 17.6% died over 10 years of follow-up, and 9074 of these participants had the full set of baseline predictors (standard plus 6-minute walk distance and computed tomography variables) available for full model fits. The average age of participants in the cohort was 60 for both men and women, and the average predicted 10-year mortality risk was 18% for women and 25% for men. Model time-integrated area under the receiver operating characteristic curve statistics demonstrated good predictive model accuracy (0.797 average), validated in the external cohort (0.756 average). Risk of mortality was impacted most by 6-minute walk distance, forced expiratory volume in 1 second and age, for both men and women.
Current and former smokers exhibited a wide range of mortality risk over a 10- year period. Our models can identify higher risk individuals who can be targeted for interventions to reduce risk of mortality, for participants with or without chronic obstructive pulmonary disease (COPD) using current Global initiative for obstructive Lung Disease (GOLD) criteria.
风险因素识别是推进许多慢性病治疗和预防性治疗的一项行之有效的策略。量化这些风险因素对健康结果的影响可以集中精力关注具有特定高风险特征的个体。我们利用两个独立队列中的多个风险因素和纵向结果,开发并验证了一个风险评分模型,以预测当前吸烟者和既往吸烟者的死亡率。
我们从慢性阻塞性肺疾病基因流行病学(COPDGene)研究中获取了当前吸烟者和既往吸烟者在入组时的大量数据。基于医生的意见和模型拟合优度测量,选择了一组变量,分别为男性和女性拟合最终的威布尔生存模型。系数和预测因子被转换为一个评分系统,便于计算死亡风险分数和概率。然后,我们使用慢性阻塞性肺疾病研究中的亚组和中间结果测量(SPIROMICS)队列对我们的模型进行外部验证。
在9867名具有标准基线数据的COPDGene参与者中,17.6%在10年随访期内死亡,其中9074名参与者拥有完整的基线预测因子(标准加上6分钟步行距离和计算机断层扫描变量),可用于完整模型拟合。该队列中参与者的平均年龄,男性和女性均为60岁,平均预测10年死亡风险,女性为18%,男性为25%。模型在受试者工作特征曲线统计下的时间积分面积显示出良好的预测模型准确性(平均0.797),在外部队列中得到验证(平均0.756)。对于男性和女性而言,6分钟步行距离、1秒用力呼气量和年龄对死亡风险的影响最大。
当前吸烟者和既往吸烟者在10年期间表现出广泛的死亡风险。我们的模型可以识别出较高风险的个体,对于符合当前慢性阻塞性肺疾病全球倡议(GOLD)标准的有或无慢性阻塞性肺疾病(COPD)的参与者,可针对这些个体进行干预以降低死亡风险。