Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data.
Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data.
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DOI：
10.1097/ede.0000000000000920
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发表时间：
2019-01
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影响因子：
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通讯作者：
Szczesniak R
中科院分区：
文献类型：
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作者：
Keogh RH;Seaman SR;Barrett JK;Taylor-Robinson D;Szczesniak R
补充数字内容可在文本中找到。囊性纤维化（CF）是一种遗传性、慢性、进行性疾病，在英国影响约10,000人，在全球影响超过70,000人。近几十年来，CF的生存率有了很大的提高，提供最新的患者预后信息是很重要的。英国囊性纤维化登记是一个安全的中央数据库，它收集了英国几乎所有CF患者的年度数据。从2005年到2015年，6181人的43592份年度记录的数据被用于开发一个动态生存预测模型，该模型使用16个预测因子，根据患者当前的健康状况，提供个性化的生存概率估计。我们使用里程碑式的方法开发了这个模型，给出了从18岁到50岁长达10年的预测生存曲线。我们使用交叉验证比较了几个模型。最终模型判别性好（预测2年、5年和10年生存期的c指数分别为0.873、0.843和0.804），预测误差小（Brier评分分别为0.036、0.076和0.133）。它根据个人目前的状况确定短期和长期死亡风险低和高的个人。例如，对于2013-2015年期间20岁的患者，超过80%的患者2年生存率大于95%，40%的患者预计存活10年或更长时间。动态个性化预测模型可以指导治疗决策，为患者提供个性化信息。我们的应用程序说明了地标方法在充分利用纵向和生存数据方面的效用，并展示了如何根据预测性能定义和比较模型。
Supplemental Digital Content is available in the text. Cystic fibrosis (CF) is an inherited, chronic, progressive condition affecting around 10,000 individuals in the United Kingdom and over 70,000 worldwide. Survival in CF has improved considerably over recent decades, and it is important to provide up-to-date information on patient prognosis. The UK Cystic Fibrosis Registry is a secure centralized database, which collects annual data on almost all CF patients in the United Kingdom. Data from 43,592 annual records from 2005 to 2015 on 6181 individuals were used to develop a dynamic survival prediction model that provides personalized estimates of survival probabilities given a patient’s current health status using 16 predictors. We developed the model using the landmarking approach, giving predicted survival curves up to 10 years from 18 to 50 years of age. We compared several models using cross-validation. The final model has good discrimination (C-indexes: 0.873, 0.843, and 0.804 for 2-, 5-, and 10-year survival prediction) and low prediction error (Brier scores: 0.036, 0.076, and 0.133). It identifies individuals at low and high risk of short- and long-term mortality based on their current status. For patients 20 years of age during 2013–2015, for example, over 80% had a greater than 95% probability of 2-year survival and 40% were predicted to survive 10 years or more. Dynamic personalized prediction models can guide treatment decisions and provide personalized information for patients. Our application illustrates the utility of the landmarking approach for making the best use of longitudinal and survival data and shows how models can be defined and compared in terms of predictive performance.