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J Appl Physiol (1985). 2017 Jul 1;123(1):227-242. doi: 10.1152/japplphysiol.00988.2016. Epub 2017 Apr 27.

Data collection, handling, and fitting strategies to optimize accuracy and precision of oxygen uptake kinetics estimation from breath-by-breath measurements.

Author information

1
School of Biomedical Sciences, University of Leeds, Leeds, United Kingdom; a.p.benson@leeds.ac.uk.
2
Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom.
3
Heart Centre, University of Leipzig, Leipzig, Germany.
4
School of Biomedical Sciences, University of Leeds, Leeds, United Kingdom.
5
Neurosciences Intensive Care Unit, Wessex Neurological Centre, University Hospital Southampton, Southampton, United Kingdom; and.
6
Rehabilitation Clinical Trials Center, Division of Respiratory and Critical Care Physiology and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California.

Abstract

Phase 2 pulmonary oxygen uptake kinetics (ϕ2 τV̇o2P) reflect muscle oxygen consumption dynamics and are sensitive to changes in state of training or health. This study identified an unbiased method for data collection, handling, and fitting to optimize V̇o2P kinetics estimation. A validated computational model of V̇o2P kinetics and a Monte Carlo approach simulated 2 × 105 moderate-intensity transitions using a distribution of metabolic and circulatory parameters spanning normal health. Effects of averaging (interpolation, binning, stacking, or separate fitting of up to 10 transitions) and fitting procedures (biexponential fitting, or ϕ2 isolation by time removal, statistical, or derivative methods followed by monoexponential fitting) on accuracy and precision of V̇o2P kinetics estimation were assessed. The optimal strategy to maximize accuracy and precision of τV̇o2P estimation was 1-s interpolation of 4 bouts, ensemble averaged, with the first 20 s of exercise data removed. Contradictory to previous advice, we found optimal fitting procedures removed no more than 20 s of ϕ1 data. Averaging method was less critical: interpolation, binning, and stacking gave similar results, each with greater accuracy compared with analyzing repeated bouts separately. The optimal procedure resulted in ϕ2 τV̇o2P estimates for transitions from an unloaded or loaded baseline that averaged 1.97 ± 2.08 and 1.04 ± 2.30 s from true, but were within 2 s of true in only 47-62% of simulations. Optimized 95% confidence intervals for τV̇o2P ranged from 4.08 to 4.51 s, suggesting a minimally important difference of ~5 s to determine significant changes in τV̇o2P during interventional and comparative studies.NEW & NOTEWORTHY We identified an unbiased method to maximize accuracy and precision of oxygen uptake kinetics (τV̇o2P) estimation. The optimum number of bouts to average was four; interpolation, bin, and stacking averaging methods gave similar results. Contradictory to previous advice, we found that optimal fitting procedures removed no more than 20 s of phase 1 data. Our data suggest a minimally important difference of ~5 s to determine significant changes in τV̇o2P during interventional and comparative studies.

KEYWORDS:

accuracy and precision; computational modeling; data handling; oxygen uptake kinetics

PMID:
28450551
DOI:
10.1152/japplphysiol.00988.2016
[Indexed for MEDLINE]
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