Intensive care medicine experimental · 2026
Identification of physiological clusters in acute hypoxemic respiratory failure patients undergoing non-invasive respiratory support using EIT-based t-SNE and spectral clustering.
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Scaramuzzo Gaetano, Bellini Valentina, Trevisani Matteo, Cinquegrana Francesca, Bonanni Marta, Ciniero Marta, Riccardo Matteo, Ferrara Pierluigi, Trentini Alessandro, Bellini Tiziana, Tini Giulia, Uboldi Sara, Azzolina Danila, Spadaro Savino, Volta Carlo Alberto, Bignami Elena Giovanna
Аннотация
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.
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.