Comorbidity Profiles and Their Effect on Treatment Selection and Survival among Patients with Lung Cancer
Comorbidity Profiles and Their Effect on Treatment Selection and Survival among Patients with Lung Cancer
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DOI：
10.1513/annalsats.201701-030oc
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发表时间：
2017-10-01
影响因子：
8.3
通讯作者：
Shen, Ernest
中科院分区：
文献类型：
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作者：
Gould, Michael K.;Munoz-Plaza, Corrine E.;Shen, Ernest
基本原理：先前的研究表明，肺癌患者的合并症负担很高，但合并症的模式还没有系统地确定。目的：我们的目的是在大样本肺癌患者中确定不同的合并症模式，并检查合并症模式对治疗和生存的影响。方法：在这项回顾性队列研究中，我们使用潜在类别分析来确定2008年至2013年期间诊断的6，662例支气管肺癌患者的人群样本中的合并症特征（或类别）。我们纳入了Charlson合并症指数中的特定合并症。我们使用考克斯比例风险分析来检查合并症类别对生存的影响。结果：患者的平均年龄为70岁，其中50%为女性，34%为非白人，17%为从不吸烟者。大多数患者患有III期（21%）或IV期（53%）疾病。超过一半（51%）的患者至少有一种合并症，而18%的患者至少有四种合并症。潜在的类分析确定了五个不同的共患病类。分类是通过逐渐增加的Charlson合并症指数评分来定义的，并通过是否存在特定类型的血管疾病和糖尿病来进一步区分。合并症分类与治疗选择（P < 0.001）和生存期（P < 0.0001）独立相关，尤其是在0-II期患者中（P < 0.0001）。结论：肺癌患者可以通过不同的合并症特征来描述，这些特征是治疗和生存期的独立预测因子。这些特征提供了对肺癌患者中合并症如何聚集以及如何将其应用于描述性目的或研究的更细致的理解。
Rationale: Prior work has shown that the comorbidity burden is high among patients with lung cancer, but patterns of comorbid conditions have not been systematically identified.Objectives: We aimed to identify distinct comorbidity profiles in a large sample of patients with lung cancer and to examine the effect of comorbidity profiles on treatment and survival.Methods: In this retrospective cohort study, we used latent class analysis to identify comorbidity profiles (or classes) in a populationbased sample of 6,662 patients with bronchogenic carcinoma diagnosed between 2008 and 2013. We included specific comorbid conditions from the Charlson comorbidity index. We used Cox proportional hazards analysis to examine the effect of comorbidity class on survival.Results: The mean age of the patients was 70 years, and 50% were female, 34% were nonwhite, and 17% were never-smokers. Most patients had stage III (21%) or IV (53%) disease. Over half (51%) had at least one comorbid condition, whereas 18% had at least four comorbidities. Latent class analysis identified five distinct comorbidity classes. Classes were defined by progressively greater Charlson comorbidity index scores and were further distinguished by the presence or absence of specific types of vascular disease and diabetes. Comorbidity class was independently associated with treatment selection (P < 0.001) and survival (P < 0.0001), especially among patients with stages 0-II disease (P < 0.0001).Conclusions: Patients with lung cancer can be described by distinct comorbidity profiles that are independent predictors of treatment and survival. These profiles provide a more nuanced understanding of how comorbidities cluster within patients with lung cancer and how they can be applied for descriptive purposes or in research.