Aaron Shawn D, Stephenson Anne L, Cameron Donald W, Whitmore George A
Division Head of Respiratory Medicine, Ottawa Hospital Research Institute, University of Ottawa, The Ottawa Hospital, 501 Smyth Road, Ottawa, Ontario, K1H 8L6 Canada.
Department of Medicine, St Michael's Hospital, 30 Bond Street, 6th floor, Bond Wing, Toronto, Ontario, M5B 1W8, Canada.
J Clin Epidemiol. 2015 Nov;68(11):1336-45. doi: 10.1016/j.jclinepi.2014.12.010. Epub 2014 Dec 31.
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.
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.
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 <18.5, number of previous hospitalizations for CF exacerbations in the preceding year, and decline in forced expiratory volume in 1 second in the preceding year were significantly associated with CF exacerbations. When combined in one summative model, the regression functions for CF chronic health and CF exacerbation risk provided a simple clinical scoring tool for assessing 1-year risk of death for an individual CF patient. Goodness-of-fit tests of the model showed very encouraging results. We confirmed predictive validity of the model by comparing actual and estimated deaths in repeated hold-out samples from the data set and showed excellent agreement between estimated and actual mortality.
Our threshold regression model incorporates a composite CF chronic health status index and an exacerbation risk index to produce an accurate clinical scoring tool for prediction of 1-year survival of CF patients. Our tool can be used by clinicians to decide on optimal timing for lung transplant referral.
我们构建了一个统计模型，以评估囊性纤维化（CF）患者在预定的年度临床就诊期间的死亡风险。我们的模型包括一个CF健康指数，该指数显示了风险因素对CF慢性健康以及CF急性加重的严重程度和频率的影响。
我们的研究使用了加拿大CF注册中心的数据，涉及1970年后出生的3794例CF患者。分析了截至2010年的数据，得到44390条年度就诊记录。我们的随机过程模型假定，年度临床就诊期间的CF健康状况是慢性病进展和急性加重冲击流的叠加。当急性加重使CF健康状况跨越临界阈值时，患者死亡。这些数据构成了删失生存数据，因此，使用阈值回归将CF死亡与研究协变量联系起来。采用最大似然估计来确定哪些临床协变量纳入CF慢性健康和CF急性加重的回归函数中。
肺功能、铜绿假单胞菌感染、CF相关糖尿病、体重不足、胰腺功能不全以及ΔF508纯合突变与CF慢性健康状况显著相关。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<18.5、前一年因CF急性加重住院的次数以及前一年第一秒用力呼气量的下降与CF急性加重显著相关。当合并在一个综合模型中时，CF慢性健康和CF急性加重风险的回归函数为评估个体CF患者的1年死亡风险提供了一个简单的临床评分工具。模型的拟合优度检验显示出非常令人鼓舞的结果。我们通过比较数据集中重复留出样本中的实际死亡数和估计死亡数，证实了模型的预测有效性，并显示估计死亡率与实际死亡率之间具有极好的一致性。
我们的阈值回归模型纳入了一个综合的CF慢性健康状况指数和一个急性加重风险指数，以产生一个准确的临床评分工具，用于预测CF患者的1年生存率。我们的工具可供临床医生用于决定肺移植转诊的最佳时机。
