医学
病危
提升(金属加工)
重症监护医学
危重病
几何学
数学
作者
Xavier Monnet,Mario Rienzo,David Osman,Nadia Anguel,Christian Richard,Michael R. Pinsky,Jean‐Louis Teboul
标识
DOI:10.1097/01.ccm.0000215453.11735.06
摘要
Objective: Passive leg raising (PLR) represents a “self-volume challenge” that could predict fluid response and might be useful when the respiratory variation of stroke volume cannot be used for that purpose. We hypothesized that the hemodynamic response to PLR predicts fluid responsiveness in mechanically ventilated patients. Design: Prospective study. Setting: Medical intensive care unit of a university hospital. Patients: We investigated 71 mechanically ventilated patients considered for volume expansion. Thirty-one patients had spontaneous breathing activity and/or arrhythmias. Interventions: We assessed hemodynamic status at baseline, after PLR, and after volume expansion (500 mL NaCl 0.9% infusion over 10 mins). Measurements and Main Results: We recorded aortic blood flow using esophageal Doppler and arterial pulse pressure. We calculated the respiratory variation of pulse pressure in patients without arrhythmias. In 37 patients (responders), aortic blood flow increased by ≥15% after fluid infusion. A PLR increase of aortic blood flow ≥10% predicted fluid responsiveness with a sensitivity of 97% and a specificity of 94%. A PLR increase of pulse pressure ≥12% predicted volume responsiveness with significantly lower sensitivity (60%) and specificity (85%). In 30 patients without arrhythmias or spontaneous breathing, a respiratory variation in pulse pressure ≥12% was of similar predictive value as was PLR increases in aortic blood flow (sensitivity of 88% and specificity of 93%). In patients with spontaneous breathing activity, the specificity of respiratory variations in pulse pressure was poor (46%). Conclusions: The changes in aortic blood flow induced by PLR predict preload responsiveness in ventilated patients, whereas with arrhythmias and spontaneous breathing activity, respiratory variations of arterial pulse pressure poorly predict preload responsiveness.
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