A statistical model to predict one-year risk of death in patients with cystic fibrosis
A statistical model to predict one-year risk of death in patients with cystic fibrosis
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DOI：
10.1016/j.jclinepi.2014.12.010
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发表时间：
2015-11-01
影响因子：
7.2
通讯作者：
Whitmore, George A.
中科院分区：
文献类型：
--
作者：
Aaron, Shawn D.;Stephenson, Anne L.;Whitmore, George A.
目的：我们构建了一个统计模型来评估囊性纤维化（CF）患者在计划的年度临床访视之间的死亡风险。我们的模型包括一个CF健康指数，显示CF慢性健康的危险因素的影响和CF accelerations.Study设计和设置的严重程度和频率：我们的研究使用加拿大CF注册表数据的3,794 CF患者出生于1970年后。分析了截至2010年的数据，产生了44，390份年度访问记录。我们的随机过程模型假设，CF健康之间的年度临床访问是一个叠加的慢性疾病进展和急性发作休克流。当病情加重使CF健康状况超过临界阈值时，会发生死亡。数据构成删失生存数据，因此，使用阈值回归将CF死亡与研究协变量联系起来。最大似然估计被用来确定哪些临床协变量包括在两个CF慢性健康和CF accelerations.Results回归函数：肺功能，铜绿假单胞菌感染，CF相关的糖尿病，体重不足，胰腺功能不全，和deltaF 508纯合子突变与CF慢性健康状态显着相关。肺功能、年龄、性别、CF诊断时的年龄、铜绿假单胞菌感染、体重指数
Objectives: We constructed a statistical model to assess the risk of death for cystic fibrosis (CF) patients between scheduled annual clinical visits. Our model includes a CF health index that shows the influence of risk factors on CF chronic health and on the severity and frequency of CF exacerbations.Study Design and Setting: Our study used Canadian CF registry data for 3,794 CF patients born after 1970. Data up to 2010 were analyzed, yielding 44,390 annual visit records. Our stochastic process model postulates that CF health between annual clinical visits is a superposition of chronic disease progression and an exacerbation shock stream. Death occurs when an exacerbation carries CF health across a critical threshold. The data constitute censored survival data, and hence, threshold regression was used to connect CF death to study covariates. Maximum likelihood estimates were used to determine which clinical covariates were included within the regression functions for both CF chronic health and CF exacerbations.Results: Lung function, Pseudomonas aeruginosa infection, CF-related diabetes, weight deficiency, pancreatic insufficiency, and the deltaF508 homozygous mutation were significantly associated with CF chronic health status. Lung function, age, gender, age at CF diagnosis, P aeruginosa infection, body mass index