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ImportancePostoperative pulmonary complications and cardiovascular complications are major causes of morbidity, mortality, and resource utilization in cardiac surgery patients.ObjectivesTo investigate the effects of airway pressure release ventilation (APRV) on respiration and hemodynamics in post cardiac surgery patients.Main Outcomes and MeasuresA single-center randomized control trial was performed. In total, 138 patients undergoing cardiopulmonary bypass were prospectively screened. Ultimately 39 patients met the inclusion criteria and were randomized into two groups: 19 patients were managed with pressure control ventilation (PCV) and 20 patients were managed with APRV. Respiratory mechanics after 4 h, hemodynamics within the first day, and Chest radiograph score (CRS) and blood gasses within the first three days were recorded and compared.ResultsA higher cardiac index (3.1 ± 0.7 vs. 2.8 ± 0.8 L⋅min–1⋅m2; p < 0.05), and shock volume index (35.4 ± 9.2 vs. 33.1 ± 9.7 ml m–2; p < 0.05) were also observed in the APRV group after 4 h as well as within the first day (p < 0.05). Compared to the PCV group, the PaO2/FiO2 was significantly higher after 4 h in patients of APRV group (340 ± 97 vs. 301 ± 82, p < 0.05) and within the first three days (p < 0.05) in the APRV group. CRS revealed less overall lung injury in the APRV group (p < 0.001). The duration of mechanical ventilation and ICU length of stay were not significantly (p = 0.248 and 0.424, respectively).Conclusions and RelevanceCompared to PCV, APRV may be associated with increased cardiac output improved oxygenation, and decreased lung injury in postoperative cardiac surgery patients.

Background and objective Mismatch between invasive mechanical ventilation and the requirements of patients results in patient-ventilator asynchrony (PVA), which is associated with a series of adverse clinical outcomes. Although the efficiency of the available approaches for automatically detecting various types of PVA from the ventilator waveforms is unsatisfactory, the feasibility of powerful deep learning approaches in addressing this problem has not been investigated. Methods We propose a 2-layer long short-term memory (LSTM) network to detect two most frequently encountered types of PVA, namely, double triggering (DT) and ineffective inspiratory effort during expiration (IEE), on two datasets. The performance of the networks is evaluated first using cross-validation on the combined dataset, and then using a cross testing scheme, in which the LSTM networks are established on one dataset and tested on the other. Results Compared with the reported rule-based algorithms and the machine learning models, the proposed 2-layer LSTM network exhibits the best overall performance, with the F1 scores of 0.983 and 0.979 for DT and IEE detection, respectively, on the combined dataset. Furthermore, it outperforms the other approaches in cross testing. Conclusions The findings suggest that LSTM is an excellent technique for accurate recognition of PVA in clinics. Such a technique can help detect and correct PVA for a better patient ventilator interaction.

By expanding the LP01 mode into a linear superposition of Laguerre-Gaussian modes and using Collins formula, the analytical propagation equation of wave-guide modes in a single-mode fiber through paraxial ABCD optical systems is derived. The free-space propagation and focusing properties of the LP01 mode are studied and compared with those of the fundamental Gaussian mode. It is shown that the Gaussian mode in place of the LP01 mode may introduce some errors. In the focused field of the LP01 mode there exists the focal shift which increases with decreasing Fresnel number. By a suitable choice of the Fresnel number, a long focusing depth may appear for the LP01 mode which is quite different from that of the focused Gaussian mode.