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BACKGROUND Endotracheal intubation (ETI) is a crucial skill for airway management in anesthesia and emergency. The classic ETI based on laryngoscopy have limitations in learning curve, respiratory exposure risk and difficult airway. Magnetic-guided technique was based on the non-contact force and has been successfully used in laparoscopy and endoscopy to simplify operation and improve effectiveness. Therefore, we introduce magnetic-guided device of ETI for the first time, developing a magnetic-guided ETI method. METHODS The magnetic-guided ETI device consisted of a magnetic guidewire and an external magnet (EM). For the novel device, the control parameter (working distance) is determined by force and anatomic parameters. The former was investigated by test bench, and the later was measured through CT graph. Then 30 undergraduates without prior ETI training divided into medical and non-medical group according the participant’s major. Both subgroups underwent ETI training with classic and magnetic-guided methods. Magnetic-guided ETI also be tested in difficult airway module. The first-attempt success rate, total intubation time and 5-point Likert scale of difficulty were recorded for assessments. RESULTS We obtained the magnetic force and the tip deflection angle-distance curves of magnetic-guided ETI device. In addition, the surface distance is 32.45 ± 5.24mm, and the deflection angle is 35.4 ± 7.6°. Thus, the working distance for the novel device is 40–60 mm. Magnetic-guided ETI was completed without close and direct exposure to patient’s oral cavity. Compared with classic method, it got a higher first-attempt success rate (magnetic-guided vs classic: 80.0% vs. 66.7%, p<0.05) and less total intubation time (magnetic-guided vs classic: 42.5 ± 2.7s vs 49.4 ± 5.7s, p<0.01) in normal module. In addition, most subjects indicated magnetic-guided ETI is easier than classic method. This is more evident in the Non-medical Group (magnetic-guided vs classic: 2.8 ± 0.8 vs 3.3 ± 0.7, p<0.01). Even in difficult airway, magnetic-guided method still got a higher first-attempt success rate (magnetic-guided vs classic: 73.3% vs 53.3%, p<0.05) and less total intubation time (magnetic-guided vs classic: 45.3 ± 3.7s vs 53.4 ± 3.5s, p<0.01) than classic method. CONCLUSION Magnetic-guided ETI was a simple, safe and effective method. Compared with former work, it is friendlier to non-medical persons and effective for difficult airway. It also avoids direct and close respiratory exposure during operation. The use of the magnetic-guided ETI device can enhance the safety and efficiency of airway management, making it an effective tool for non-medical persons to rapidly perform ETI.
BACKGROUND Endotracheal intubation (ETI) is a crucial skill for airway management in anesthesia and emergency. The classic ETI based on laryngoscopy have limitations in learning curve, respiratory exposure risk and difficult airway. Magnetic-guided technique was based on the non-contact force and has been successfully used in laparoscopy and endoscopy to simplify operation and improve effectiveness. Therefore, we introduce magnetic-guided device of ETI for the first time, developing a magnetic-guided ETI method. METHODS The magnetic-guided ETI device consisted of a magnetic guidewire and an external magnet (EM). For the novel device, the control parameter (working distance) is determined by force and anatomic parameters. The former was investigated by test bench, and the later was measured through CT graph. Then 30 undergraduates without prior ETI training divided into medical and non-medical group according the participant’s major. Both subgroups underwent ETI training with classic and magnetic-guided methods. Magnetic-guided ETI also be tested in difficult airway module. The first-attempt success rate, total intubation time and 5-point Likert scale of difficulty were recorded for assessments. RESULTS We obtained the magnetic force and the tip deflection angle-distance curves of magnetic-guided ETI device. In addition, the surface distance is 32.45 ± 5.24mm, and the deflection angle is 35.4 ± 7.6°. Thus, the working distance for the novel device is 40–60 mm. Magnetic-guided ETI was completed without close and direct exposure to patient’s oral cavity. Compared with classic method, it got a higher first-attempt success rate (magnetic-guided vs classic: 80.0% vs. 66.7%, p<0.05) and less total intubation time (magnetic-guided vs classic: 42.5 ± 2.7s vs 49.4 ± 5.7s, p<0.01) in normal module. In addition, most subjects indicated magnetic-guided ETI is easier than classic method. This is more evident in the Non-medical Group (magnetic-guided vs classic: 2.8 ± 0.8 vs 3.3 ± 0.7, p<0.01). Even in difficult airway, magnetic-guided method still got a higher first-attempt success rate (magnetic-guided vs classic: 73.3% vs 53.3%, p<0.05) and less total intubation time (magnetic-guided vs classic: 45.3 ± 3.7s vs 53.4 ± 3.5s, p<0.01) than classic method. CONCLUSION Magnetic-guided ETI was a simple, safe and effective method. Compared with former work, it is friendlier to non-medical persons and effective for difficult airway. It also avoids direct and close respiratory exposure during operation. The use of the magnetic-guided ETI device can enhance the safety and efficiency of airway management, making it an effective tool for non-medical persons to rapidly perform ETI.
Background/Objectives: Non-invasive ventilation (NIV) has emerged as a possible first-step treatment to avoid invasive intubation in pediatric intensive care units (PICUs) due to its advantages in reducing intubation-associated risks. However, the timely identification of NIV failure is crucial to prevent adverse outcomes. This study aims to identify predictors of first-attempt NIV failure in PICU patients by testing various machine learning techniques and comparing their predictive abilities. Methods: Data were sourced from the TIPNet registry, which comprised patients admitted to 23 Italian Paediatric Intensive Care Units (PICUs). We selected patients between January 2010 and January 2024 who received non-invasive ventilation (NIV) as their initial approach to respiratory support. The study aimed to develop a predictive model for NIV failure, selecting the best Machine Learning technique, including Generalized Linear Models, Random Forest, Extreme Gradient Boosting, and Neural Networks. Additionally, an ensemble approach was implemented. Model performances were measured using sensitivity, specificity, AUROC, and predictive values. Moreover, the model calibration was evaluated. Results: Out of 43,794 records, 1861 admissions met the inclusion criteria, with 678 complete cases and 97 NIV failures. The RF model demonstrated the highest AUROC and sensitivity equal to 0.83 (0.64, 0.94). Base excess, weight, age, systolic blood pressure, and fraction of inspired oxygen were identified as the most predictive features. A check for model calibration ensured the model’s reliability in predicting NIV failure probabilities. Conclusions: This study identified highly sensitive models for predicting NIV failure in PICU patients, with RF as a robust option.
Background/Objectives: Hypercapnic respiratory failure (HRF) is a primary cause of admittance to the intensive care unit (ICU). This study aimed to investigate the factors that affect the length of hospital stay in HRF patients. Methods: This study was designed as a retrospective, cross-sectional analysis of patients who were admitted to the ICU because of HRF between 2022 and 2024. The demographic and clinical characteristics of the patients and laboratory results were recorded. The Charlson Comorbidity Index (CCI) was calculated. The relationship between these parameters and the length of hospital stay was assessed. Results: A total of 138 patients were included in the study. The average length of hospital stay was 11.45 days, and 37% of the patients were included in the long-term hospitalization group. The degree of hypercapnia was not associated with the length of hospital stay. It was determined that the patients’ albumin levels and CCI were significant determinants of the length of hospital stay. The combined assessment of these two parameters was found to be superior compared to their separate evaluations. Conclusions: In our study, hypoalbuminemia and a higher CCI were identified as predictors of a prolonged ICU stay in HRF patients. Albumin levels of <3.25 g/dL and CCI scores of ≥5 were linked to longer stays, with this combined evaluation offering greater predictive value. These factors can guide patient management.
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