Journal of clinical monitoring and computing
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J Clin Monit Comput · Apr 2024
Comparison of automated and manual control methods in minimal flow anesthesia.
New-generation anesthesia machines administer inhalation anesthetics and automatically control the fresh gas flow (FGF) rate. This study compared the administration of minimal flow anesthesia (MFA) using the automatically controlled anesthesia (ACA) module of the Mindray A9 (Shenzhen, China) anesthesia machine versus manual control by an anesthesiologist. ⋯ The ACA mode was more advantageous than the MCA mode, reaching target AA concentrations faster and requiring fewer adjustments to achieve a constant depth of anesthesia.
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J Clin Monit Comput · Apr 2024
ReviewIs EIT-guided positive end-expiratory pressure titration for optimizing PEEP in ARDS the white elephant in the room? A systematic review with meta-analysis and trial sequential analysis.
Electrical Impedance Tomography (EIT) is a novel real-time lung imaging technology for personalized ventilation adjustments, indicating promising results in animals and humans. The present study aimed to assess its clinical utility for improved ventilation and oxygenation compared to traditional protocols. Comprehensive electronic database screening was done until 30th November, 2023. ⋯ Our search retrieved five controlled cohort studies and two RCTs with 515 patients and overall reduced risk of mortality [RR = 0.68; 95% CI: 0.49 to 0.95; I2 = 0%], better dynamic compliance [MD = 3.46; 95% CI: 1.59 to 5.34; I2 = 0%] with no significant difference in PaO2/FiO2 ratio [MD = 6.5; 95%CI -13.86 to 26.76; I2 = 74%]. The required information size except PaO2/FiO2 was achieved for a power of 95% based on the 50% reduction in risk of mortality, 10% improved compliance as the cumulative Z-score of the said outcomes crossed the alpha spending boundary and did not dip below the inner wedge of futility. EIT-guided individualized PEEP titration is a novel modality; further well-designed studies are needed to substantiate its utility.
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J Clin Monit Comput · Apr 2024
Effect of vertical stopcock position on start-up fluid delivery in syringe pumps used for microinfusions.
The purpose of this in vitro study was to evaluate the impact of the vertical level of the stopcock connecting the infusion line to the central venous catheter on start-up fluid delivery in microinfusions. Start-up fluid delivery was measured under standardized conditions with the syringe outlet and liquid flow sensors positioned at heart level (0 cm) and exposed to a simulated CVP of 10 mmHg at a set flow rate of 1 ml/h. Flow and intraluminal pressures were measured with the infusion line connected to the stopcock primarily placed at vertical levels of 0 cm, + 30 cm and - 30 cm or primarily placed at 0 cm and secondarily, after connecting the infusion line, displaced to + 30 cm and - 30 cm. Start-up fluid delivery 10 s after opening the stopcock placed at zero level and after opening the stopcock primarily connected at zero level and secondary displaced to vertical levels of + 30 cm and - 30 cm were similar (- 10.52 [- 13.85 to - 7.19] µL; - 8.84 [- 12.34 to - 5.33] µL and - 11.19 [- 13.71 to - 8.67] µL (p = 0.469)). ⋯ Start-up fluid delivery with the stopcock primarily placed at + 30 cm and - 30 cm resulted in large anterograde and retrograde fluid volumes of 34.39 [33.43 to 35.34] µL and - 24.90 [- 27.79 to - 22.01] µL at 10 s, respectively (p < 0.0001). Fluid delivered with the stopcock primarily placed at + 30 cm and - 30 cm resulted in 140% and 35% of calculated volume at 360 s, respectively (p < 0.0001). Syringe infusion pumps should ideally be connected to the stopcock positioned at heart level in order to minimize the amounts of anterograde and retrograde fluid volumes after opening of the stopcock.
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J Clin Monit Comput · Apr 2024
A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring.
Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. ⋯ This study presents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts.