Articles: mechanical-ventilation.
-
Randomized Controlled Trial Multicenter Study
Individualized PEEP to optimise respiratory mechanics during abdominal surgery: a pilot randomised controlled trial.
Higher intraoperative driving pressures (ΔP) are associated with increased postoperative pulmonary complications (PPC). We hypothesised that dynamic adjustment of PEEP throughout abdominal surgery reduces ΔP, maintains positive end-expiratory transpulmonary pressures (Ptp_ee) and increases respiratory system static compliance (Crs) with PEEP levels that are variable between and within patients. ⋯ NCT02671721.
-
Zhonghua nei ke za zhi · Sep 2020
[A preliminary study on the evaluation of diaphragm function by ultrasound in patients with invasive mechanical ventilation].
Objectives: To study the feasibility of using ultrasound to evaluate diaphragm function in patients with invasive mechanical ventilation. Methods: From March to December 2017, 40 adult patients with acute respiratory distress syndrome who were admitted to the Department of Critical Care Medicine, Xiangya Hospital, Central South University for more than 48 hours were included. Diaphragmatic excursion and thickness of bilateral anterior, middle and posterior parts were measured by ultrasound for 5 consecutive days. ⋯ Bilateral anterior, middle and posterior diaphragmatic excursion recovered on day 5, and was higher than the baseline levels on day 1, with the left middle and posterior diaphragmatic excursion changing most significantly. (2) Compared with day 1, 2, 3, the thickening fraction of bilateral anterior, middle and posterior diaphragm were significantly decreased on day 4, with the left middle part [day 1: (33.87±14.34)%; day 2: (37.26±13.91)%; day 3: (30.56±14.27)%; day 4: (15.53±5.68)%] and the left posterior part [day 1: (35.50±15.69)%; day 2: (39.84±15.32)%; day 3: (29.06±14.96)%; day 4: (13.30±5.79)%] changing most significantly (P<0.05). The thickening fractions of left anterior, middle and posterior diaphragm recovered on day 5 compared with that on day 4, but still lower than those on day 1 (P<0.05). Conclusions: It is feasible to evaluate the diaphragm function in patients with invasive mechanical ventilation by ultrasound, which can provide guidance for preventing diaphragmatic atrophy and withdrawing from mechanical ventilation.
-
Am. J. Respir. Crit. Care Med. · Sep 2020
Observational StudyPulmonary Angiopathy in Severe COVID-19: Physiologic, Imaging and Hematologic Observations.
Rationale: Clinical and epidemiologic data in coronavirus disease (COVID-19) have accrued rapidly since the outbreak, but few address the underlying pathophysiology. Objectives: To ascertain the physiologic, hematologic, and imaging basis of lung injury in severe COVID-19 pneumonia. Methods: Clinical, physiologic, and laboratory data were collated. ⋯ Dilated peripheral vessels were present in 21/33 (63.6%) patients with at least two assessable lobes (including 10/21 [47.6%] with no evidence of acute pulmonary emboli). Perfusion defects on DECT (assessable in 18/20 [90%]) were present in all patients (wedge-shaped, n = 3; mottled, n = 9; mixed pattern, n = 6). Conclusions: Physiologic, hematologic, and imaging data show not only the presence of a hypercoagulable phenotype in severe COVID-19 pneumonia but also markedly impaired pulmonary perfusion likely caused by pulmonary angiopathy and thrombosis.
-
Multicenter Study Clinical Trial
Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.
Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks. ⋯ In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.