Huaiwu He

Background: The effect of different spontaneous breathing trial (SBT) methods on lung volume and ventilation distribution has not been well clarified in post-cardiac surgery patients. Methods: In this prospective observational study, patients underwent 30 min of pressure-support ventilation (PSV)-SBT [PS 8 cmH ₂ O, zero positive end-expiratory pressure (ZEEP)], followed by a 30-min T-piece trial if tolerated. Electrical impedance tomography (EIT) was used to continuously monitor regional lung ventilation and end-expiratory lung volume (EELV) at baseline, PSV-SBT 3 min, PSV-SBT 30 min, T-piece SBT 3 min and T-piece SBT 30 min. EELV _loss = [VT _baseline /tidal impedance variation (TIV) _baseline ] × ΔEELI. EELV _{loss PSV} was defined as volume loss at 30 min of PSV-SBT and EELV _{loss T-piece} was defined as volume loss during T-piece SBT. Results: In 60 patients who complied with both SBT steps, 43 succeeded (71.7%) and 17 failed (28.3%) the T-piece SBT. Compared to the success group, the failure group exhibited a higher incidence of pendelluft (52.9% vs. 23.3%, p = 0.045) and significantly greater EELV _loss at T-piece SBT 30 min (623[459,746] ml vs. 511[376,702]ml, p = 0.003). However, the success group showed greater EELV _{loss PSV} than the failure group (322[247,459] ml vs. 199[166, 269] ml, p < 0.001), which was an abnormal pattern. Notably, the failure group had lower TIV (2102[1769,2562] vs. 2742[2153,3872], p = 0.005) and a higher respiratory rate (RR) than baseline at PSV-SBT 30 min (20[17,24] vs. 16[12,18], p < 0.001). Furthermore, we classified all patients into two groups based on the predominant reduction of volume loss: P -volume loss group ( N = 37, EELV _{loss PSV} > EELV _{loss T-piece} ) and T-volume loss group ( N = 23, EELV _{loss T-piece} > EELV _{loss PSV} ). In addition, the T-volume loss group had a higher weaning failure rate than the P-volume loss group (52.2% [12/23] vs. 13.5% [5/37], p < 0.001) and was associated with reduced baseline dorsal ventilation (39%[37%,43%] vs. 44%[41%,50%], p = 0.023). ROC analysis suggested that a dorsal ventilation threshold of 40.5% was associated with T -volume loss. Conclusions: The successful weaning patients had a higher reduction of EELV _{loss PSV} and a lower reduction of EELV _{loss T-piece} . In the weaning failure patients, the paradox of lower EELV _{loss PSV} that was accompanied by a high RR and low VT might be associated with air trapping. Attention should be paid to using EELV _{loss PSV} to identify weaning failure.

Ventilation distribution assessed by electrical impedance tomography (EIT) has great interests in acute respiratory distress syndrome (ARDS). The aim of th

This study aimed to develop and validate a non-breath-holding contrast-enhanced electrical impedance tomography (EIT) method using low-pass filtering for b

Multi-frequency Electrical Impedance Tomography (mfEIT) has demonstrated considerable potential for non-invasive, real-time monitoring of physiological states by reconstructing tissue conductivity at multiple frequencies. However, practical deployment in Internet-of-Things (IoT)-enabled medical applications, such as wearable thoracic sensors, remains challenging due to the heavy reliance on large-scale datasets and poor generalization of current methods. To address this, we present a model-based, dataset-free unsupervised mfEIT reconstruction framework, termed Multi-Branch Attention Image Prior (MAIP). Our approach leverages a specially designed Multi-Branch Attention Network (MBA-Net) to iteratively reconstruct conductivity images at multiple frequencies without the need for training data. By implicitly regularizing the reconstruction process, MBA-Net effectively captures inter- and intra-frequency correlations, ensuring accurate and robust imaging suitable for resource-limited IoT scenarios. Extensive simulations and real-world experiments validate that the proposed method surpasses state-of-the-art approaches in reconstruction accuracy and generalization ability, thus providing a promising solution for IoT-based medical imaging applications.