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Purpose <p>Identifying physiological clusters in acute hypoxemic respiratory failure (AHRF) may help to personalize non-invasive respiratory support (NIRS). Electrical impedance tomography (EIT) provides real-time, regional information on tidal ventilation, but its value for clustering AHRF patients undergoing NIRS has not been established.</p> Methods <p>We conducted a single-center observational study including adults with AHRF monitored with EIT during NIRS. Tidal ventilation images were pre-processed, normalized, and embedded into a 2-dimensional space using t-SNE. Spectral clustering was applied to identify distinct imaging patterns. Clinical, physiological and laboratory variables were compared across clusters. The association between cluster membership and intubation at 7&#xa0;days was assessed using penalized Cox regression adjusted for age, BMI, PaCO₂ and ROX index.</p> Results <p>Thirty-two patients were enrolled. Spectral clustering identified three distinct clusters of tidal images. Clusters differed in clinical severity and physiological profile: Cluster 1 was characterized by shorter stature and higher SAPS II; Cluster 2 showed the highest pendelluft; Cluster 3 exhibited symmetric ventilation with low pendelluft. These phenotypes also differed in hemodynamics, including heart rate and shock index. Cluster membership was independently associated with intubation at 7&#xa0;days. Compared with Cluster 3, both Cluster 1 and Cluster 2 showed a significantly lower hazard of intubation (HR 0.115, <i>p</i> = 0.017 and 0.042, <i>p</i> = 0.002, respectively).</p> Conclusions <p>Unsupervised clustering of EIT tidal images is feasible in AHRF and identifies distinct physiological clusters with different short-term outcomes. These findings support the potential role of EIT-based imaging patterns for early stratification of patients undergoing NIRS.</p>

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Identification of physiological clusters in acute hypoxemic respiratory failure patients undergoing non-invasive respiratory support using EIT-based t-SNE and spectral clustering

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Gaetano Scaramuzzo,
  • Valentina Bellini,
  • Matteo Trevisani,
  • Francesca Cinquegrana,
  • Marta Bonanni,
  • Marta Ciniero,
  • Matteo Riccardo,
  • Pierluigi Ferrara,
  • Alessandro Trentini,
  • Tiziana Bellini,
  • Giulia Tini,
  • Sara Uboldi,
  • Danila Azzolina,
  • Savino Spadaro,
  • Carlo Alberto Volta,
  • Elena Giovanna Bignami

摘要

Purpose

Identifying physiological clusters in acute hypoxemic respiratory failure (AHRF) may help to personalize non-invasive respiratory support (NIRS). Electrical impedance tomography (EIT) provides real-time, regional information on tidal ventilation, but its value for clustering AHRF patients undergoing NIRS has not been established.

Methods

We conducted a single-center observational study including adults with AHRF monitored with EIT during NIRS. Tidal ventilation images were pre-processed, normalized, and embedded into a 2-dimensional space using t-SNE. Spectral clustering was applied to identify distinct imaging patterns. Clinical, physiological and laboratory variables were compared across clusters. The association between cluster membership and intubation at 7 days was assessed using penalized Cox regression adjusted for age, BMI, PaCO₂ and ROX index.

Results

Thirty-two patients were enrolled. Spectral clustering identified three distinct clusters of tidal images. Clusters differed in clinical severity and physiological profile: Cluster 1 was characterized by shorter stature and higher SAPS II; Cluster 2 showed the highest pendelluft; Cluster 3 exhibited symmetric ventilation with low pendelluft. These phenotypes also differed in hemodynamics, including heart rate and shock index. Cluster membership was independently associated with intubation at 7 days. Compared with Cluster 3, both Cluster 1 and Cluster 2 showed a significantly lower hazard of intubation (HR 0.115, p = 0.017 and 0.042, p = 0.002, respectively).

Conclusions

Unsupervised clustering of EIT tidal images is feasible in AHRF and identifies distinct physiological clusters with different short-term outcomes. These findings support the potential role of EIT-based imaging patterns for early stratification of patients undergoing NIRS.