Abstract: Slide Presentations |

Correlation of Spirometric Values With the Measured Maximal Voluntary Ventilation (MVV) FREE TO VIEW

Salman S. Razi, MD; William G. Petersen, MD; Mary Yaktus, CPFT; Mark W. Riggs, PhD
Author and Funding Information

Scott & White Memorial Hospital, Temple, TX


Chest. 2003;124(4_MeetingAbstracts):122S. doi:10.1378/chest.124.4_MeetingAbstracts.122S-b
Text Size: A A A
Published online


PURPOSE:  Cardiopulmonary exercise testing is a common tool used to differentiate the numerous causes of dyspnea. Exceeding 70% of the maximal voluntary ventilation (MVV) during maximal exercise is usually viewed as a sign of ventilatory limitation to exercise. Traditionally, the MVV is estimated by multiplying the FEV1 by 40 rather than by direct measurement. Our goal was to determine if including other spirometric variables in a mathematical model could more accurately predict the MVV. We also sought to determine if different models were appropriate for different pathophysiologic subgroups of patients.

METHODS:  Retrospective review of spirometries and MVVs was performed. Data collected included patient demographics, spirometric values, and measured MVVs. Stepwise regression was performed to build mathematical models to correlate the measured MVV with spirometric data of all individuals. Furthermore, additional models were built for patients with normal, obstructive, and restrictive spirometry.

RESULTS:  585 records were reviewed. One third were normal spirometries; 39% had obstructive physiology; 24% had restrictive physiology. A mathematical model including multiple spirometric variables such as the peak expiratory flow rate (PEFR) and the maximal inspiratory flow rate (MIFR) more accurately predicted the measured MVV than did the traditinal formula. Furthermore even better correlation was obtained by constructing different formulas specific for the physiologic subgroups of normal, obstructive, and restrictive.CONCLUSIONS: Including of multiple spirometric variables in a mathematical model improved upon the estimation of MVV compared to the traditional method of multiplying the FEV1 by 40. Specific formulas for different spirometric patterns further improved the accuracy of the model.

CLINICAL IMPLICATIONS:  Specific prediction equations for MVV based on the underlying spirometric physiology lead to a more accurate estimation, thus allowing improved interpretation of cardiopulmonary exercise testing.

DISCLOSURE:  S.S. Razi, None.

Tuesday, October 28, 2003

2:30 PM - 4:00 PM




Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Find Similar Articles
CHEST Journal Articles
PubMed Articles
  • CHEST Journal
    Print ISSN: 0012-3692
    Online ISSN: 1931-3543