INTRODUCTION: Early goal-directed therapy increases survival in persons with sepsis but requires placement of a central line. We evaluate alternative methods to measuring central venous pressure (CVP) to assess volume status, including peripheral venous pressure (PVP) and stroke volume variation (SVV), which may facilitate nurse-driven resuscitation protocols. METHODS: Patients were enrolled in the emergency department or ICU of an academic medical center. Measurements of CVP, PVP, SVV, shoulder and elbow position, and dichotomous variables Awake, Movement, and Vented were measured and recorded 7 times during a 1-hour period. Regression analysis was used to predict CVP from PVP and/or SVV, shoulder/elbow position, and dichotomous variables. RESULTS: Twenty patients were enrolled, of which 20 had PVP measurements and 11 also had SVV measurements. Multiple regression analysis demonstrated significant predictive relationships for CVP using PVP (CVP = 6.7701 + 0.2312 × PVP - 0.1288 × Shoulder + 12.127 × Movement - 4.4805 × Neck line), SVV (CVP = 14.578 - 0.3951 × SVV + 18.113 × Movement), and SVV and PVP (CVP = 4.2997 - 1.1675 × SVV + 0.3866 × PVP + 18.246 × Awake + 0.1467 × Shoulder = 0.4525 × Elbow + 15.472 × Foot line + 10.202 × Arm line). DISCUSSION: PVP and SVV are moderately good predictors of CVP. Combining PVP and SVV and adding variables related to body position, movement, ventilation, and sleep/wake state further improves the predictive value of the model. The models illustrate the importance of standardizing patient position, minimizing movement, and placing intravenous lines proximally in the upper extremity or neck.
INTRODUCTION: Early goal-directed therapy increases survival in persons with sepsis but requires placement of a central line. We evaluate alternative methods to measuring central venous pressure (CVP) to assess volume status, including peripheral venous pressure (PVP) and stroke volume variation (SVV), which may facilitate nurse-driven resuscitation protocols. METHODS:Patients were enrolled in the emergency department or ICU of an academic medical center. Measurements of CVP, PVP, SVV, shoulder and elbow position, and dichotomous variables Awake, Movement, and Vented were measured and recorded 7 times during a 1-hour period. Regression analysis was used to predict CVP from PVP and/or SVV, shoulder/elbow position, and dichotomous variables. RESULTS: Twenty patients were enrolled, of which 20 had PVP measurements and 11 also had SVV measurements. Multiple regression analysis demonstrated significant predictive relationships for CVP using PVP (CVP = 6.7701 + 0.2312 × PVP - 0.1288 × Shoulder + 12.127 × Movement - 4.4805 × Neck line), SVV (CVP = 14.578 - 0.3951 × SVV + 18.113 × Movement), and SVV and PVP (CVP = 4.2997 - 1.1675 × SVV + 0.3866 × PVP + 18.246 × Awake + 0.1467 × Shoulder = 0.4525 × Elbow + 15.472 × Foot line + 10.202 × Arm line). DISCUSSION: PVP and SVV are moderately good predictors of CVP. Combining PVP and SVV and adding variables related to body position, movement, ventilation, and sleep/wake state further improves the predictive value of the model. The models illustrate the importance of standardizing patient position, minimizing movement, and placing intravenous lines proximally in the upper extremity or neck.