Efficacy of ventilator waveform observation for detection of patient–ventilator asynchrony during NIV: a multicentre study. Issue 4 (4th October 2017)

• Record Type: Journal Article
• Authors: Longhini, Federico; Colombo, Davide; Pisani, Lara; Idone, Francesco; Chun, Pan; Doorduin, Jonne; Ling, Liu; Alemani, Moreno; Bruni, Andrea; Zhaochen, Jin; Tao, Yu; Lu, Weihua; Garofalo, Eugenio; Carenzo, Luca; Maggiore, Salvatore Maurizio; Qiu, Haibo; Heunks, Leo; Antonelli, Massimo; Nava, Stefano; Navalesi, Paolo
• Is Part Of: ERJ open research. Volume 3:Issue 4(2017)
• Publication Date: 2017-10-04
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiration -- Periodicals; Respiration; Respiratory organs -- Diseases; Respiratory organs -- Diseases -- Treatment; Respiratory Tract Diseases; Electronic journals; Fulltext; Internet Resources; Periodicals; Periodical; 616.2005
• DOI: 10.1183/23120541.00075-2017
• Languages: English
• ISSNs: 2312-0541

Abstract

The objective of this study was to assess ability to identify asynchronies during noninvasive ventilation (NIV) through ventilator waveforms according to experience and interface, and to ascertain the influence of breathing pattern and respiratory drive on sensitivity and prevalence of asynchronies. 35 expert and 35 nonexpert physicians evaluated 40 5-min NIV reports displaying flow–time and airway pressure–time tracings; identified asynchronies were compared with those ascertained by three examiners who evaluated the same reports displaying, additionally, tracings of diaphragm electrical activity. We determined: 1) sensitivity, specificity, and positive and negative predictive values; 2) the correlation between the double true index (DTI) of each report ( i.e., the ratio between the sum of true positives and true negatives, and the overall breath count) and the corresponding asynchrony index (AI); and 3) the influence of breathing pattern and respiratory drive on both AI and sensitivity. Sensitivities to detect asynchronies were low either according to experience (0.20 (95% CI 0.14–0.29) for expert versus 0.21 (95% CI 0.12–0.30) for nonexpert, p=0.837) or interface (0.28 (95% CI 0.17–0.37) for mask versus 0.10 (95% CI 0.05–0.16) for helmet, p<0.0001). DTI inversely correlated with the AI (r 2 =0.67, p<0.0001). Breathing pattern and respiratory drive did not affect prevalence of asynchronies and sensitivity. Patient–ventilator asynchrony during NIV is difficult to recognise solely by visual inspection of ventilator waveforms. Detection of patient–ventilator asynchrony during NIV by visual inspection of ventilator waveforms is difficult http://ow.ly/3ce930eGdn6