Literature DB >> 441531

A comprehensive on-line computerised lung function screening test.

A E Bunn, J C Vermaak, M A De Kock.   

Abstract

An on-line computerised lung function screening test is described which prints out 22 lung function parameters and a functional diagnosis. The complete test with print-out data takes less than 8 min per patient which implies that more than 60 out-patients can be 'screened' per day. Furthermore, for patients about to undergo major surgery a lung function operative risk grading is also available. The developed computer system is comparatively inexpensive, simple to operate and can be immediately on-lined to most apparatus without special interfacing. The total screening system can be operated by a single technologist and the required respiratory manoeuvres can be performed by almost all patients irrespective of the degree of lung function impairment. Although comprehensive in itself the screening test has been invaluable in deciding upon which patients require more intensive and time-consuming lung function investigations.

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Year:  1979        PMID: 441531     DOI: 10.1159/000194006

Source DB:  PubMed          Journal:  Respiration        ISSN: 0025-7931            Impact factor:   3.580


  4 in total

1.  Computerized single-breath nitrogen washout: predicted values in a rural French community.

Authors:  D B Teculescu; M C Damel; E Costantino; O Buhler; A B Bohadana; M Marchand; Q T Pham
Journal:  Lung       Date:  1996       Impact factor: 2.584

2.  The effect of epinephrine by nebulization on measures of airway obstruction in patients with acute severe croup.

Authors:  A C Argent; M Hatherill; C J L Newth; M Klein
Journal:  Intensive Care Med       Date:  2007-10-03       Impact factor: 17.440

3.  Area under the expiratory flow-volume curve: normative values in the National Health and Nutrition Survey (NHANES) study.

Authors:  Octavian C Ioachimescu; Kevin McCarthy; James K Stoller
Journal:  J Investig Med       Date:  2022-02-21       Impact factor: 3.235

4.  Area under the expiratory flow-volume curve: predicted values by regression and deep learning methods and recommendations for clinical practice.

Authors:  Octavian C Ioachimescu; José A Ramos; Michael Hoffman; James K Stoller
Journal:  BMJ Open Respir Res       Date:  2021-04
  4 in total

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