到期
计算机科学
临床实习
医学
内科学
呼吸系统
家庭医学
作者
Carlotta Hennigs,Franziska Bilda,Jan Graßhoff,Stephan Walterspacher,Philipp Rostalski
标识
DOI:10.1515/auto-2023-0206
摘要
Abstract Expiratory flow limitation (EFL) is an often unrecognized clinical condition with a multitude of negative implications. A mathematical EFL model is proposed to detect flow limitations automatically. The EFL model is a switching one-compartment lung mechanics model with a volume-dependent airway resistance to simulate the dynamic behavior during expiration. The EFL detection is based on a breath-by-breath model parameter identification and validated on clinical data of mechanically ventilated patients. In the severe flow limitation group 93.9 % ± 5 % and in the no limitation group 10.2 % ± 13.7 % of the breaths are detected as EFL. Based on the high detection rate of EFL, these results support the usefulness of the EFL detection. It is a first step toward an automated detection of EFL in clinical applications and may help to reduce underdiagnosis of EFL.
科研通智能强力驱动
Strongly Powered by AbleSci AI