Model-based prediction of expiratory resistance index in patients with asthma

Ofer Barnea, Shimon Abboud, Alexander Guber, Israel Bruderman

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: Develop a s ensitive algorithm and index for detection of asthma patients using forced expiratory flow curves. Methods: A lumped-parameter model of forced expiration was developed. The model can predict the flow-volume curve during forced expiratory maneuver. The flow-volume curves generated by the model depend on values of resistance parameters (FER). Use of flow-volume curves recorded from normal subjects and from patients with asthma before and after ventolin inhalation as inputs for the inverse model, yielded the resistance parameters for each case. These parameters are based on the entire information presented in the flow-volume curve and on the reduction in flow at all lung volumes. Results. Forced Expiratory Resistance (FERN) indices were estimated at different percent of lung volumes using the inverse model. The index was significantly affected by inhalation of ventolin in asthmatic patients and was insensitive to ventolin inhalation in normal patients. In asthmatic patients, the FER index at five lung volumes (out of eight), was two-five times greater than in normal subjects with p < 0.05 (three of them with p < 0.01). Conclusions. The estimated parameters were sensitive indicators of the degree of lung function impairment and were a ble to accurately distinguish between healthy and asthmatic patients.

Original languageEnglish
Pages (from-to)241-245
Number of pages5
JournalJournal of Clinical Monitoring and Computing
Volume18
Issue number4
DOIs
StatePublished - Aug 2004

Keywords

  • Asthma
  • Lung function
  • MEFV
  • Mathematical model

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