Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression
Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression
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DOI：
10.1002/sim.8443
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发表时间：
2020-03-15
影响因子：
2
通讯作者：
Clancy, John P.
中科院分区：
文献类型：
--
作者：
Szczesniak, Rhonda D.;Su, Weiji;Clancy, John P.
囊性纤维化（CF）是一种进行性遗传性疾病，其特征是肺功能频繁、长期下降。准确预测潜在的肺功能快速下降对于临床决策支持和及时干预至关重要。确定一个人是否正在经历一段时间的快速下降是复杂的，由于其异质性的时间和程度，以及测量的肺功能的误差分量。我们构建个性化的预测概率“nowcasting”快速下降。我们假设每个患者的真实纵向肺功能S（t）遵循非线性、非平稳随机过程，并通过随机效应调节患者间异质性。在时间t时相应的肺功能下降被定义为变化率S '（t）。我们通过将测量的肺功能建模为S（t）的噪声版本，以观察到的协变量和测量历史为条件预测S '（t）。该方法适用于30 879例美国CF登记患者的数据。结果与目前采用的决策规则，使用单中心数据212个人。使用预测概率比该中心目前采用的决策规则更早识别出快速下降（平均差异：0.65年; 95%置信区间（CI）：0.41，0.89）。我们构建了一个自举算法来获得预测概率的CI。我们用R Shiny说明了实时实现。预测精度进行了研究，使用实证模拟，这表明这种方法更准确地检测峰值下降，快速下降的统一阈值相比。ROC曲线下面积估计值（Q1-Q3）的中位数分别为0.817（0.814-0.822）和0.745（0.741-0.747），表明两者的准确性合理。本文演示了如何个性化的变化率估计可以加上概率预测推理和实施一个有用的医疗监测方法。
Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung-function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for "nowcasting" rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between-patient heterogeneity through random effects. Corresponding lung-function decline at time t is defined as the rate of change, S '(t). We predict S '(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single-center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real-time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1-Q3) were 0.817 (0.814-0.822) and 0.745 (0.741-0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical-monitoring approach.