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Background <p>Non-invasive assessment of respiratory drive and effort in spontaneously breathing ARDS patients is challenging, yet clinically relevant. We explored whether hierarchical clustering applied to electrical impedance tomography (EIT– a radiation-free non-invasive lung imaging technique) identifies ARDS sub-phenotypes with increased drive and effort.</p> Results <p>Thirty intubated patients with ARDS on assisted mechanical ventilation were monitored by EIT and esophageal pressure during a decremental positive end-expiratory pressure (PEEP) trial. A comprehensive EIT assessment was made (computed variables n = 180) during tidal breathing at different PEEP levels. Agglomerative nesting was applied to scaled data distances. Three clusters of ARDS were identified: <i>inhomogeneous ventilation</i>, <i>unmatched V’/Q</i>, and <i>mismatched V’/Q</i>. The <i>unmatched V’/Q</i> cluster had the highest respiratory drive (<i>p</i> = 0.045) and effort (<i>p</i> = 0.021) at lower PEEP, and experienced longer length of ICU stay (<i>p</i> = 0.019).</p> Conclusions <p>Higher PEEP levels reduced drive of the <i>unmatched V’/Q</i> cluster, mitigating the physiological differences. Clustering approaches to EIT data identify physiologically and clinically relevant sub-phenotypes of ARDS.</p>

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Omics approach to chest electrical impedance tomography reveals physiological cluster of ARDS characterised by increased respiratory drive and effort

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月8日

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  • Tommaso Mauri,
  • Marco Leali,
  • Elena Spinelli,
  • Gaetano Scaramuzzo,
  • Massimo Antonelli,
  • Domenico L. Grieco,
  • Savino Spadaro,
  • Giacomo Grasselli

摘要

Background

Non-invasive assessment of respiratory drive and effort in spontaneously breathing ARDS patients is challenging, yet clinically relevant. We explored whether hierarchical clustering applied to electrical impedance tomography (EIT– a radiation-free non-invasive lung imaging technique) identifies ARDS sub-phenotypes with increased drive and effort.

Results

Thirty intubated patients with ARDS on assisted mechanical ventilation were monitored by EIT and esophageal pressure during a decremental positive end-expiratory pressure (PEEP) trial. A comprehensive EIT assessment was made (computed variables n = 180) during tidal breathing at different PEEP levels. Agglomerative nesting was applied to scaled data distances. Three clusters of ARDS were identified: inhomogeneous ventilation, unmatched V’/Q, and mismatched V’/Q. The unmatched V’/Q cluster had the highest respiratory drive (p = 0.045) and effort (p = 0.021) at lower PEEP, and experienced longer length of ICU stay (p = 0.019).

Conclusions

Higher PEEP levels reduced drive of the unmatched V’/Q cluster, mitigating the physiological differences. Clustering approaches to EIT data identify physiologically and clinically relevant sub-phenotypes of ARDS.