Clinical Research Directory

Comparison of PPV and LVOT VTI During Passive Leg Raising to Predict Fluid Responsiveness

Hemodynamic instability remains one of the leading causes of morbidity and mortality among critically ill patients in intensive care units. One major contributor to this instability is intravascular fluid deficit. Although fluid administration is often preferred as an initial intervention, inaccurate estimation of fluid requirements carries significant risks. Inadequate resuscitation may lead to tissue hypoperfusion and organ dysfunction, whereas excessive fluid loading is associated with pulmonary edema, increased intra-abdominal pressure, multi-organ dysfunction, and increased mortality. Consequently, reliable prediction of fluid responsiveness is considered a critical determinant in modern intensive care management. The limited reliability of static parameters in predicting fluid responsiveness and the fact that PPV retains its validity only under specific clinical conditions highlight the need for more effective methods. In recent years, hemodynamic changes assessed during the passive leg raising (PLR) maneuver have gained prominence; particularly, left ventricular outflow tract velocity time integral (LVOT VTI)-based cardiac output measurements have been identified as a strong parameter for predicting fluid responsiveness . Moreover, changes in pulse pressure variation (PPV) during PLR have also been reported as a potential predictor; however, the number of studies prospectively comparing PPV variation and LVOT VTI-based cardiac output change within the same patient population remains limited . Therefore, the proposed study aims to fill this gap in the literature and contribute to fluid management in the intensive care setting. The aim of this study is to compare the performance of PPV variation and LVOT VTI-based cardiac output change after the PLR maneuver in predicting fluid responsiveness among mechanically ventilated critically ill patients, and to determine the diagnostic value of both methods. The findings of the present study are expected to support more reliable decision-making in fluid therapy and provide clinical evidence toward the individualization of fluid management in intensive care practice.

Peripheral Venous Pressure Variation, Pulse Pressure Variation and Pleth Variability Index for Fluid Responsiveness

Pulse pressure variation (PPV) and pleth variability index (PVI) are widely used in clinical practice as indicators of the responsiveness to fluid therapy in patients receiving mechanical ventilation. PPV, which measures changes in arterial pressure, requires arterial puncture, which is invasive, and PVI, which detects subtle changes in oxygen saturation, requires an expensive, commercial monitoring equipment. In this study, we aimed to measure peripheral venous pressure variation using less invasive waveform variation in peripheral veins and to determine whether this indicator can be clinically used to predict the responsiveness to fluid therapy. In addition, the investigators aimed to confirm the superiority of the indicators by comparing them with the responsiveness to fluid therapy of the PPV and PVI.

Deep Learning Model for Predicting a Peripheral Venous Waveform-based Pulse Pressure Variation

Pulse pressure variation is a monitoring index that indicates the response to fluid therapy in patients receiving mechanical ventilation, and is used as a reference for patients with unstable hemodynamic conditions. However, it is invasive because it requires arterial puncture to collect it. In a previous study by the investigators, the investigators developed and verified an artificial intelligence model that predicts stroke volume variation, in real time using only the central venous pressure waveform. However, since a large vein such as the jugular vein must be punctured to collect the central venous pressure waveform, it is still invasive, and its clinical utility is low. Therefore, in this study, the investigators collected waveforms from peripheral veins that are less invasive and can be a wide range of applications because all surgical patients have them. The investigators aimed to develop and verify an artificial intelligence model that predicts pulse pressure variation obtained from peripheral venous waveforms .