Background <p>Patients with ARDS have heterogeneous lungs which exposes them to the risk of lung injury exacerbation by mechanical ventilation. Functional lung CT imaging gives a comprehensive description of regional lung mechanical behaviour. Here, we investigated whether CT registration-based regional lung function parameters are associated with survival in patients with COVID-ARDS.</p> Methods <p>We conducted a two-centre prospective observational study of adult COVID-ARDS patients with an indication for CT within 72&#xa0;h of onset. Dual volume CT images were aligned using image-registration. Regional lung functional parameters, and their spatial distributions, were analysed by univariable Cox proportional hazard models with survival as the main outcome. Selected variables based on the univariable analysis were included in a stepwise Cox model adjusted for age, sex, body mass index and SAPSII.</p> Results <p>94 patients were included in the study. Recruitment was associated with a higher (HR = 1.45, <i>p</i> = 0.023) hazard of death, while apical (sΔV<sub>z</sub>) and central (sΔV<sub>x</sub>) displacement of specific volume change centre-of-mass were associated with a lower hazard of death (HR = 0.72, <i>p</i> = 0.041; HR = 0.68, <i>p</i> = 0.031, respectively).</p> Conclusions <p>Our data show that in addition to recruitment, the spatial distribution of specific volume change, a surrogate measure of regional lung ventilation, is associated with the risk of death in mechanically ventilated COVID-19 ARDS patients. Our findings suggest that CT image-registration based functional biomarkers may have prognostic value in COVID-ARDS patients.</p> Trial registration <p>This study was retrospectively registered in Clinical Trials under NCT06113276 (<a href="https://clinicaltrials.gov/study/NCT06113276">https://clinicaltrials.gov/study/NCT06113276</a>) on 27/10/2023.</p>

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Prognostic value of functional CT imaging in COVID-ARDS: a two-centre prospective observational study

  • Mehdi Shekarnabi,
  • Alicia Guillien,
  • Nicolas Terzi,
  • Florian Sigaud,
  • Laurent Bitker,
  • Emmanuel Roux,
  • Touria Ahaouari,
  • Eduardo Enrique Dávila Serrano,
  • Loic Boussel,
  • Gilbert Ferretti,
  • Hodane Yonis,
  • Mehdi Mezidi,
  • Ines Noirot,
  • Louis Chauvelot,
  • François Dhelft,
  • Maxime Gaillet,
  • Valérie Siroux,
  • Maciej Orkisz,
  • Jean-Christophe Richard,
  • Sam Bayat

摘要

Background

Patients with ARDS have heterogeneous lungs which exposes them to the risk of lung injury exacerbation by mechanical ventilation. Functional lung CT imaging gives a comprehensive description of regional lung mechanical behaviour. Here, we investigated whether CT registration-based regional lung function parameters are associated with survival in patients with COVID-ARDS.

Methods

We conducted a two-centre prospective observational study of adult COVID-ARDS patients with an indication for CT within 72 h of onset. Dual volume CT images were aligned using image-registration. Regional lung functional parameters, and their spatial distributions, were analysed by univariable Cox proportional hazard models with survival as the main outcome. Selected variables based on the univariable analysis were included in a stepwise Cox model adjusted for age, sex, body mass index and SAPSII.

Results

94 patients were included in the study. Recruitment was associated with a higher (HR = 1.45, p = 0.023) hazard of death, while apical (sΔVz) and central (sΔVx) displacement of specific volume change centre-of-mass were associated with a lower hazard of death (HR = 0.72, p = 0.041; HR = 0.68, p = 0.031, respectively).

Conclusions

Our data show that in addition to recruitment, the spatial distribution of specific volume change, a surrogate measure of regional lung ventilation, is associated with the risk of death in mechanically ventilated COVID-19 ARDS patients. Our findings suggest that CT image-registration based functional biomarkers may have prognostic value in COVID-ARDS patients.

Trial registration

This study was retrospectively registered in Clinical Trials under NCT06113276 (https://clinicaltrials.gov/study/NCT06113276) on 27/10/2023.