• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2017 IEEE Healthcare Innovations and Point of Care Technologies >Early detection of rapid cystic fibrosis disease progression tailored to point of care: A proof-of-principle study
【24h】

Early detection of rapid cystic fibrosis disease progression tailored to point of care: A proof-of-principle study

机译：针对护理点量身定制的快速囊性纤维化疾病进展的早期检测：一项原理验证研究

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Slowing cystic fibrosis (CF) lung disease progression is crucial to survival, but point-of-care technologies aimed at early detection - and possibly prevention - of rapid lung function decline are limited. This proof-of-principle study leverages a rich national patient registry and follow-up data on a local CF cohort to build an algorithm and prototype prognostic tool aimed at early detection of rapid lung function decline. The algorithm was developed using a novel longitudinal analysis of lung function (measured as forced expiratory volume in 1 s of % predicted, FEV1). Covariates included clinical and demographic characteristics selected from the registry based on information criterion. Preliminary assessment of algorithm performance suggested excellent predictive accuracy and earlier detection of rapid decline than standard of care being applied at a local center. Graphical displays were presented and evaluated for clinical utility. Predictions from the algorithms and chosen graphical displays were translated into a prototype web application using RShiny and underwent iterative development based on clinician feedback. This paper suggests that the algorithm and its translation could offer a means for earlier detection and treatment of rapid decline, providing clinicians with a viable point-of-care technology to intervene prior to irreversible lung damage.
机译：缓慢的肺囊性纤维化（CF）进展对于生存至关重要，但是旨在早期发现并可能预防肺功能快速下降的即时医疗技术受到限制。这项原理验证研究利用了丰富的国家患者登记信息和本地CF队列的随访数据，以构建旨在早期发现肺功能快速下降的算法和原型预后工具。该算法是使用新型的肺功能纵向分析方法开发的（以预测的FEV1为单位，以1 s的百分比计算的呼气量）。协变量包括基于信息标准从注册表中选择的临床和人口统计学特征。对算法性能的初步评估表明，与本地中心所采用的护理标准相比，该算法具有出色的预测准确性和较早发现的快速下降。呈现图形显示并评估其临床实用性。使用RShiny将算法的预测和选定的图形显示转换为原型Web应用程序，并根据临床医生的反馈进行迭代开发。本文认为，该算法及其翻译可以为早期检测和快速下降提供治疗手段，为临床医生提供可行的即时护理技术，以在不可逆转的肺损伤之前进行干预。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号