Literature DB >> 33138963

New algorithm to quantify cardiopulmonary interaction in patients with atrial fibrillation: a proof-of-concept study.

Piet A H Wyffels1, Stefan De Hert2, Patrick F Wouters2.   

Abstract

BACKGROUND: Traditional formulas to calculate pulse pressure variation (PPV) cannot be used in patients with atrial fibrillation (AF). We have developed a new algorithm that accounts for arrhythmia-induced pulse pressure changes, allowing us to isolate and quantify ventilation-induced pulse pressure variation (VPPV). The robustness of the algorithm was tested in patients subjected to altered loading conditions. We investigated whether changes in VPPV imposed by passive leg raising (PLR) were proportional to the pre-PLR values.
METHODS: Consenting patients with active AF scheduled for an ablation of the pulmonary vein under general anaesthesia and mechanical ventilation were included. Loading conditions were altered by PLR. ECG and invasive pressure data were acquired during 60 s periods before and after PLR. A generalised additive model was constructed for each patient on each observation period. The impact of AF was modelled on the two preceding RR intervals of each beat (RR0 and RR-1). The impact of ventilation and the long-term pulse pressure trends were modelled as separate splines. Ventilation-induced pulse pressure variation was defined as the percentage of the maximal change in pulse pressure during the ventilation cycle.
RESULTS: Nine patients were studied. The predictive abilities of the models had a median r2 of 0.92 (inter-quartile range: 89.2-94.2). Pre-PLR VPPV ranged from 0.1% to 27.9%. After PLR, VPPV decreased to 0-11.3% (P<0.014). The relation between the Pre-PLR values and the magnitude of the changes imposed by the PLR was statistically significant (P<0.001).
CONCLUSIONS: Our algorithm enables quantification of VPPV in patients with AF with the ability to detect changing loading conditions.
Copyright © 2020 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  algorithm; atrial fibrillation; cardiopulmonary interaction; dynamic filling parameter; haemodynamic; mechanical ventilation; pulse pressure variation

Year:  2020        PMID: 33138963     DOI: 10.1016/j.bja.2020.09.039

Source DB:  PubMed          Journal:  Br J Anaesth        ISSN: 0007-0912            Impact factor:   9.166


  2 in total

1.  Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiology.

Authors:  Johannes Enevoldsen; Gavin L Simpson; Simon T Vistisen
Journal:  J Clin Monit Comput       Date:  2022-06-13       Impact factor: 2.502

2.  Diagnostic values of different ECG durations in paroxysmal AF diagnosis.

Authors:  Qian Li; Bing Su; Juan Liu
Journal:  Ann Noninvasive Electrocardiol       Date:  2021-12-16       Impact factor: 1.468

  2 in total

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