The clinical course of cystic fibrosis (CF) lung disease is marked by acute drops of lung function, defined clinically as rapid decline. As such, lung function is monitored routinely through pulmonary function testing, producing hundreds of measurements over the lifespan of an individual patient. Point-of-care technologies aimed at improving detection of rapid decline have been limited. Our aim in this early translational study is to develop and translate a predictive algorithm into a prototype prognostic tool for improved detection of rapid decline. The predictive algorithm was developed, validated and checked for 6-month, 1-year, and 2-year forecast accuracies using data on demographic and clinical characteristics from 30 879 patients aged 6 years and older who were followed in the U.S. Cystic Fibrosis Foundation Patient Registry from 2003 to 2015. Predictions of rapid decline based on the algorithm were compared to a detection algorithm currently being used at a CF center with 212 patients who received care between 2012–2017. The algorithm was translated into a prototype web application using RShiny, which resulted from an iterative development and refinement based on clinician feedback. The study showed that the algorithm had excellent predictive accuracy and earlier detection of rapid decline, compared to the current approach, and yielded a prototype platform with the potential to serve as a viable point-of-care tool. Future work includes implementation of this clinical prototype, which will be evaluated prospectively under real-world settings, with the aim of improving the pre-visit planning process for CF point of care. Likely extensions to other point-of-care settings are discussed.

中文翻译：

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改进对快速囊性纤维化疾病进展的检测——将预测算法早期转化为即时护理工具

囊性纤维化 (CF) 肺病的临床病程以肺功能急性下降为标志，临床定义为快速下降。因此，肺功能通过肺功能测试进行常规监测，在单个患者的整个生命周期内产生数百个测量值。旨在改善快速衰退检测的即时护理技术受到限制。我们在这项早期转化研究中的目标是开发一种预测算法并将其转化为原型预测工具，以改进快速衰退的检测。该预测算法是使用 30 879 名 6 岁及以上在美国接受随访的患者的人口统计学和临床​​特征数据开发、验证和检查的 6 个月、1 年和 2 年预测准确性 囊性纤维化基金会患者登记处（2003 年至 2015 年）。将基于该算法的快速下降预测与 CF 中心目前使用的检测算法进行了比较，该算法在 2012 年至 2017 年间接受了 212 名患者的治疗。该算法使用 RShiny 转换为原型 Web 应用程序，这是基于临床医生反馈的迭代开发和改进的结果。研究表明，与当前方法相比，该算法具有出色的预测准确性和对快速衰退的早期检测，并产生了一个原型平台，具有作为可行的即时护理工具的潜力。未来的工作包括实施该临床原型，该原型将在现实环境中进行前瞻性评估，目的是改进 CF 护理点的访问前计划过程。