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Efficacy of ventilator waveform observation for detection of patient–ventilator asynchrony during NIV: a multicentre study

Federico Longhini, Davide Colombo, Lara Pisani, Francesco Idone, Pan Chun, Jonne Doorduin, Liu Ling, Moreno Alemani, Andrea Bruni, Jin Zhaochen, Yu Tao, Weihua Lu, Eugenio Garofalo, Luca Carenzo, Salvatore Maurizio Maggiore, Haibo Qiu, Leo Heunks, Massimo Antonelli, Stefano Nava, Paolo Navalesi
ERJ Open Research 2017 3: 00075-2017; DOI: 10.1183/23120541.00075-2017
Federico Longhini
1Anesthesia and Intensive Care, Sant'Andrea Hospital, ASL VC, Vercelli, Italy
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  • ORCID record for Federico Longhini
Davide Colombo
2Anesthesia and Intensive Care, “Maggiore Della Carità” Hospital, Novara, Italy
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Lara Pisani
3Alma Mater University, Dept of Clinical, Integrated and Experimental Medicine (DIMES), Respiratory and Critical Care Unit, S. Orsola-Malpighi Hospital, Bologna, Italy
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Francesco Idone
4Dept of Anesthesiology and Intensive Care, Agostino Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
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Pan Chun
5Dept of Critical Care Medicine, Zhongda Hospital, Southeast University, School of Medicine, Nanjing, China
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Jonne Doorduin
6Dept of Intensive Care Medicine and Neurology, Radboud University Medical Centre, Nijmegen, the Netherlands
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Liu Ling
5Dept of Critical Care Medicine, Zhongda Hospital, Southeast University, School of Medicine, Nanjing, China
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Moreno Alemani
7Dept of Anesthesiology and Intensive Care, Ospedale Civile “G. Fornaroli”, Magenta, Italy
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Andrea Bruni
8Dept of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
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Jin Zhaochen
9Dept of Critical Care Medicine, Zhenjiang First People's Hospital, Zhenjiang, China
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Yu Tao
10Dept of Critical Care Medicine, First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, China
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Weihua Lu
10Dept of Critical Care Medicine, First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, China
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Eugenio Garofalo
8Dept of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
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Luca Carenzo
2Anesthesia and Intensive Care, “Maggiore Della Carità” Hospital, Novara, Italy
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Salvatore Maurizio Maggiore
11Dept of Anesthesiology, Perioperative Care and Intensive Care, “S.S. Annunziata” Hospital, “Gabriele d'Annunzio” University of Chieti-Pescara, Chieti, Italy
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Haibo Qiu
5Dept of Critical Care Medicine, Zhongda Hospital, Southeast University, School of Medicine, Nanjing, China
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Leo Heunks
12Dept of Intensive Care Medicine, VU University Medical Centre, Amsterdam, the Netherlands
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Massimo Antonelli
4Dept of Anesthesiology and Intensive Care, Agostino Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
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Stefano Nava
3Alma Mater University, Dept of Clinical, Integrated and Experimental Medicine (DIMES), Respiratory and Critical Care Unit, S. Orsola-Malpighi Hospital, Bologna, Italy
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  • For correspondence: stefano.nava@aosp.bo.it
Paolo Navalesi
8Dept of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
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  • Article
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  • FIGURE 1
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    FIGURE 1

    Portions of two representative reports, a, c and e) one during noninvasive ventilation through a helmet and b, d and f) the other through mask. Tracings of a and b) airway pressure (Paw), c and d) flow and e and f) diaphragm electrical activity (EAdi) are shown. In a, c and e, two ineffective efforts are depicted, while two double-triggerings are depicted in b, d and f.

  • FIGURE 2
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    FIGURE 2

    The regression lines between the asynchrony index (AI) and the double true index (DTI) shown for overall data, and for mask and helmet separately. DTI inversely correlated with the AI, both overall (r2=0.67, p<0.0001), and separately for mask (r2=0.82, p<0.0001) and helmet (r2=0.64, p<0.0001).

Tables

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  • TABLE 1

    Characteristics and distribution of asynchronies

    Rate of asynchronyOverall (n=40)Mask (n=20)Helmet (n=20)p-value
    AI <10% (tracings)4.0±3.3% (20)4.4±3.3 (9)3.6±3.3 (11)0.752
    AI ≥10% (tracings)18.1±5.8% (20)18.4±5.2% (11)17.7±6.9% (9)
    IE33.3%25.3%44.8%0.047
    AT40.5%42.2%38.0%0.665
    DT26.2%32.5%17.2%0.014

    Data are presented as mean±sd (n), unless otherwise stated. p-values refer to Chi-squared test between interfaces. AI: asynchrony index; IE: ineffective effort; AT: autotriggering; DT: double-triggering.

    • TABLE 2

      Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the breath analysis and report analysis

      ParameterBreath analysisReport analysis
      ExpertNonexpertp-valueExpertNonexpertp-value
      Sensitivity (95% CI)0.20 (0.14–0.29)0.21 (0.12–0.30)0.8370.10 (0.05–0.24)0.10 (0.01–0.24)0.915
      Specificity (95% CI)0.90 (0.85–0.93)0.88 (0.79–0.93)0.4041.00 (1.00–1.00)1.00 (1.00–1.00)0.547
      PPV (95% CI)0.18 (0.12–0.25)0.18 (0.11–0.23)0.8421.00 (1.00–1.00)1.00 (1.00–1.00)0.547
      NPV (95% CI)0.89 (0.88–0.90)0.89 (0.88–0.90)0.6940.53 (0.51–0.57)0.53 (0.50–0.57)0.887
      MaskHelmetp-valueMaskHelmetp-value
      Sensitivity (95% CI)0.28 (0.17–0.37)0.10 (0.05–0.16)<0.00010.18 (0.00–0.36)0.00 (0.00–0.00)<0.0001
      Specificity (95% CI)0.91 (0.86–0.94)0.86 (0.79–0.92)0.0081.00 (1.00–1.00)1.00 (1.00–1.00)0.555
      PPV (95% CI)0.30 (0.15–0.38)0.07 (0.05–0.11)<0.00011.00 (1.00–1.00)1.00 (1.00–1.00)0.553
      NPV (95% CI)0.89 (0.87–0.90)0.89 (0.88–0.90)0.2710.50 (0.45–0.56)0.55 (0.55–0.55)<0.0001
      EuropeAsiap-valueEuropeAsiap-value
      Sensitivity (95% CI)0.23 (0.12–0.39)0.20 (0.14–0.24)0.1760.13 (0.03–0.33)0.10 (0.05–0.15)0.114
      Specificity (95% CI)0.90 (0.78–0.93)0.87 (0.84–0.91)0.9101.00 (1.00–1.00)1.00 (1.00–1.00)0.723
      PPV (95% CI)0.19 (0.10–0.26)0.18 (0.15–0.22)0.7261.00 (1.00–1.00)1.00 (1.00–1.00)0.723
      NPV (95% CI)0.89 (0.88–0.92)0.89 (0.88–0.89)0.3360.54 (0.51–0.60)0.53 (0.51–0.54)0.115
    • TABLE 3

      Influence of support level, breathing pattern and respiratory drive on sensitivity and prevalence of asynchronies

      VariablesRange#Sensitivity ≥0.20Linear trend for sensitivityAI ≥10%Linear trend for prevalence
      PS cmH2O<1022.2%p=0.10233.3%p=0.491
      10–1250.0%57.1%
      >1260.0%50.0%
      VT mL<57060.0%p=0.07250.0%p=0.654
      570–85150.0%55.0%
      >85120.0%40.0%
      RRmec breath·min−1<190.0%p=0.14650.0%p=0.371
      19–2555.0%60.0%
      >2538.9%30.0%
      EAdipeak µV<1030.0%p=0.99960.0%p=0.178
      10–2260.0%55.0%
      >2230.0%30.0%

      AI: asynchrony index; PS: inspiratory pressure support; VT: tidal volume; RRmec: ventilator rate of cycling; EAdipeak: peak electrical activity of the diaphragm. #: defined by percentiles.

      Supplementary Materials

      • Figures
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      • Supplementary Material

        Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.

        Supplementary material 00075-2017_supp

      • Supplementary Material

        M. Antonelli 00075-2017_Antonelli

        L. Heunks 00075-2017_Heunks

        S.M. Maggiore 00075-2017_Maggiore

        P. Navalesi 00075-2017_Navalesi

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      Efficacy of ventilator waveform observation for detection of patient–ventilator asynchrony during NIV: a multicentre study
      Federico Longhini, Davide Colombo, Lara Pisani, Francesco Idone, Pan Chun, Jonne Doorduin, Liu Ling, Moreno Alemani, Andrea Bruni, Jin Zhaochen, Yu Tao, Weihua Lu, Eugenio Garofalo, Luca Carenzo, Salvatore Maurizio Maggiore, Haibo Qiu, Leo Heunks, Massimo Antonelli, Stefano Nava, Paolo Navalesi
      ERJ Open Research Oct 2017, 3 (4) 00075-2017; DOI: 10.1183/23120541.00075-2017

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      Efficacy of ventilator waveform observation for detection of patient–ventilator asynchrony during NIV: a multicentre study
      Federico Longhini, Davide Colombo, Lara Pisani, Francesco Idone, Pan Chun, Jonne Doorduin, Liu Ling, Moreno Alemani, Andrea Bruni, Jin Zhaochen, Yu Tao, Weihua Lu, Eugenio Garofalo, Luca Carenzo, Salvatore Maurizio Maggiore, Haibo Qiu, Leo Heunks, Massimo Antonelli, Stefano Nava, Paolo Navalesi
      ERJ Open Research Oct 2017, 3 (4) 00075-2017; DOI: 10.1183/23120541.00075-2017
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