医学
机械通风
重症监护室
肺超声
呼吸衰竭
充氧
肺
曲线下面积
通风(建筑)
麻醉
前瞻性队列研究
重症监护
内科学
重症监护医学
机械工程
工程类
作者
Daniele Guerino Biasucci,Danilo Buonsenso,A. Piano,Nicola Bonadia,Joel Vargas,Donatella Settanni,Maria Grazia Bocci,Domenico Luca Grieco,Annamaria Carnicelli,Giancarlo Scoppettuolo,Davide Eleuteri,Gennaro De Pascale,Mariano Alberto Pennisi,Francesco Franceschi,Massimo Antonelli
出处
期刊:Minerva Anestesiologica
[Edizioni Minerva Medica]
日期:2021-07-15
卷期号:87 (9)
被引量:35
标识
DOI:10.23736/s0375-9393.21.15188-0
摘要
The aim of this study is to determine relationships between lung aeration assessed by lung ultrasound (LUS) with non-invasive ventilation (NIMV) outcome, intensive care unit (ICU) admission and mechanical ventilation (MV) needs in COVID-19 respiratory failure.A cohort of adult patients with COVID-19 respiratory failure underwent LUS during initial assessment. A simplified LUS protocol consisting in scanning six areas, three for each side, was adopted. A score from 0 to 3 was assigned to each area. Comprehensive LUS score (LUSsc) was calculated as the sum of the score in all areas. LUSsc, the amount of involved sonographic lung areas (LUSq), the number of lung quadrants radiographically infiltrated and the degree of oxygenation impairment at admission (SpO2/FiO2 ratio) were compared to NIMV Outcome, MV needs and ICU admission.Among 85 patients prospectively included in the analysis, 49 of 61 needed MV. LUSsc and LUSq were higher in patients who required MV (median 12 [IQR 8-14] and median 6 [IQR 4-6], respectively) than in those who did not (6 [IQR 2-9] and 3 [IQR 1-5], respectively), both P<0.001. NIMV trial failed in 26 patients out 36. LUSsc and LUSq were significantly higher in patients who failed NIMV than in those who did not. From ROC analysis, LUSsc ≥12 and LUSq ≥5 gave the best cut-off values for NIMV failure prediction (AUC=0.95, 95%CI 0.83-0.99 and AUC=0.81, 95% CI 0.65-0.91, respectively).Our data suggest LUS as a possible tool for identifying patients who are likely to require MV and ICU admission or to fail a NIMV trial.
科研通智能强力驱动
Strongly Powered by AbleSci AI