Literature DB >> 30865842

The Peak Index: Spirometry Metric for Airflow Obstruction Severity and Heterogeneity.

Surya P Bhatt1,2, Sandeep Bodduluri1,2, Vrishank Raghav3, Nirav R Bhakta4, Carla G Wilson5, Young-Il Kim1,6, Michael Eberlein7, Frank C Sciurba8, MeiLan K Han9, Mark T Dransfield1,2, Arie Nakhmani10.   

Abstract

Rationale: Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation. Spirometry loops are not smooth curves and have undulations and peaks that likely reflect heterogeneity of airflow.
Objectives: To assess whether the Peak Index, the number of peaks adjusted for lung size, is associated with clinical outcomes.
Methods: We analyzed spirometry data of 9,584 participants enrolled in the COPDGene study and counted the number of peaks in the descending part of the expiratory flow-volume curve from the peak expiratory flow to end-expiration. We adjusted the peaks count for the volume of the lungs from peak expiratory flow to end-expiration to derive the Peak Index. Multivariable regression analyses were performed to test associations between the Peak Index and lung function, respiratory morbidity, structural lung disease on computed tomography (CT), forced expiratory volume in 1 second (FEV1) decline, and mortality.
Results: The Peak Index progressively increased from Global Initiative for Chronic Obstructive Lung Disease stage 0 through 4 (P < 0.001). On multivariable analysis, the Peak Index was significantly associated with CT emphysema (adjusted β = 0.906; 95% confidence interval [CI], 0.789 to 1.023; P < 0.001) and small airways disease (adjusted β = 1.367; 95% CI, 1.188 to 1.545; P < 0.001), St. George's Respiratory Questionnaire score (adjusted β = 1.075; 95% CI, 0.807 to 1.342; P < 0.001), 6-minute-walk distance (adjusted β = -1.993; 95% CI, -3.481 to -0.506; P < 0.001), and FEV1 change over time (adjusted β = -1.604; 95% CI, -2.691 to -0.516; P = 0.004), after adjustment for age, sex, race, body mass index, current smoking status, pack-years of smoking, and FEV1. The Peak Index was also associated with the BODE (body mass index, airflow obstruction, dyspnea, and exercise capacity) index and mortality (P < 0.001).Conclusions: The Peak Index is a spirometry metric that is associated with CT measures of lung disease, respiratory morbidity, lung function decline, and mortality.Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

Entities:  

Keywords:  airflow obstruction; chronic obstructive pulmonary disease; heterogeneity; spirometry

Mesh:

Year:  2019        PMID: 30865842      PMCID: PMC6774744          DOI: 10.1513/AnnalsATS.201811-812OC

Source DB:  PubMed          Journal:  Ann Am Thorac Soc        ISSN: 2325-6621


  36 in total

1.  Ventilation Heterogeneity in Never-smokers and COPD:: Comparison of Pulmonary Functional Magnetic Resonance Imaging with the Poorly Communicating Fraction Derived From Plethysmography.

Authors:  Christopher Davis; Khadija Sheikh; Damien Pike; Sarah Svenningsen; David G McCormack; Denis O'Donnell; J Alberto Neder; Grace Parraga
Journal:  Acad Radiol       Date:  2016-01-08       Impact factor: 3.173

2.  Interpretative strategies for lung function tests.

Authors:  R Pellegrino; G Viegi; V Brusasco; R O Crapo; F Burgos; R Casaburi; A Coates; C P M van der Grinten; P Gustafsson; J Hankinson; R Jensen; D C Johnson; N MacIntyre; R McKay; M R Miller; D Navajas; O F Pedersen; J Wanger
Journal:  Eur Respir J       Date:  2005-11       Impact factor: 16.671

3.  Variability of the configuration of maximum expiratory flow-volume curves.

Authors:  Y K Tien; E A Elliott; J Mead
Journal:  J Appl Physiol Respir Environ Exerc Physiol       Date:  1979-03

4.  Interdependent regional lung emptying during forced expiration: a transistor model.

Authors:  J Solway; J J Fredberg; R H Ingram; O F Pedersen; J M Drazen
Journal:  J Appl Physiol (1985)       Date:  1987-05

5.  Evaluation of clinical methods for rating dyspnea.

Authors:  D A Mahler; C K Wells
Journal:  Chest       Date:  1988-03       Impact factor: 9.410

6.  An asymmetrical model of the airways of the dog lung.

Authors:  K Horsfield; W Kemp; S Phillips
Journal:  J Appl Physiol Respir Environ Exerc Physiol       Date:  1982-01

7.  Ventilation Distribution Heterogeneity at Rest as a Marker of Exercise Impairment in Mild-to-Advanced COPD.

Authors:  J Alberto Neder; Conor D J O'Donnell; Julia Cory; Daniel Langer; Casey E Ciavaglia; Y Ling; Katherine A Webb; Denis E O'Donnell
Journal:  COPD       Date:  2014-09-17       Impact factor: 2.409

8.  Anatomy of membranous bronchioles in normal, senile and emphysematous human lungs.

Authors:  E K Verbeken; M Cauberghs; J M Lauweryns; K P van de Woestijne
Journal:  J Appl Physiol (1985)       Date:  1994-10

9.  The detection of sleep apnea in the awake patient. The 'saw-tooth' sign.

Authors:  M H Sanders; R J Martin; B E Pennock; R M Rogers
Journal:  JAMA       Date:  1981-06-19       Impact factor: 56.272

10.  Computer quantification of airway collapse on forced expiration to predict the presence of emphysema.

Authors:  Marko Topalovic; Vasileios Exadaktylos; Anneleen Peeters; Johan Coolen; Walter Dewever; Martijn Hemeryck; Pieter Slagmolen; Karl Janssens; Daniel Berckmans; Marc Decramer; Wim Janssens
Journal:  Respir Res       Date:  2013-11-19
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  2 in total

1.  Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease.

Authors:  Sandeep Bodduluri; Arie Nakhmani; Joseph M Reinhardt; Carla G Wilson; Merry-Lynn McDonald; Ramaraju Rudraraju; Byron C Jaeger; Nirav R Bhakta; Peter J Castaldi; Frank C Sciurba; Chengcui Zhang; Purushotham V Bangalore; Surya P Bhatt
Journal:  JCI Insight       Date:  2020-07-09

Review 2.  Small Airways Disease, Biomarkers and COPD: Where are We?

Authors:  Priyamvada S Chukowry; Daniella A Spittle; Alice M Turner
Journal:  Int J Chron Obstruct Pulmon Dis       Date:  2021-02-18
  2 in total

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