Reliability of Passive Leg Raising, Stroke Volume Variation and Pulse Pressure Variation to Predict Fluid Responsiveness During Weaning From Mechanical Ventilation After Cardiac Surgery: A Prospective, Observational Study

Inferior vena cava distensibility during pressure support ventilation: a prospective study evaluating interchangeability of subcostal and trans‑hepatic views, with both M‑mode and automatic border tracing

The Inferior Vena Cava (IVC) is commonly utilized to evaluate fluid status in the Intensive Care Unit (ICU),with more recent emphasis on the study of venous congestion. It is predominantly measured via subcostal approach (SC) or trans-hepatic (TH) views, and automated border tracking (ABT) software has been introduced to facilitate its assessment. Prospective observational study on patients ventilated in pressure support ventilation (PSV) with 2 × 2 factorial design. Primary outcome was to evaluate interchangeability of measurements of the IVC and the distensibility index (DI) obtained using both M-mode and ABT, across both SC and TH. Statistical analyses comprised Bland-Altman assessments for mean bias, limits of agreement (LoA), and the Spearman correlation coefficients. IVC visualization was 100% successful via SC, while TH view was unattainable in 17.4% of cases. As compared to the M-mode, the IVC-DI obtained through ABT approach showed divergences in both SC (mean bias 5.9%, LoA -18.4% to 30.2%, ICC = 0.52) and TH window (mean bias 6.2%, LoA -8.0% to 20.4%, ICC = 0.67). When comparing the IVC-DI measures obtained in the two anatomical sites, accuracy improved with a mean bias of 1.9% (M-mode) and 1.1% (ABT), but LoA remained wide (M-mode: -13.7% to 17.5%; AI: -19.6% to 21.9%). Correlation was generally suboptimal (r = 0.43 to 0.60). In PSV ventilated patients, we found that IVC-DI calculated with M-mode is not interchangeable with ABT measurements. Moreover, the IVC-DI gathered from SC or TH view produces not comparable results, mainly in terms of precision.

Accuracy of pulse pressure variations for fluid responsiveness prediction in mechanically ventilated patients with biphasic positive airway pressure mode

The accuracy of pulse pressure variation (PPV) to predict fluid responsiveness using pressure-controlled (PC) instead of volume-controlled modes is under debate. To specifically address this issue, we designed a study to evaluate the accuracy of PPV to predict fluid responsiveness in severe septic patients who were mechanically ventilated with biphasic positive airway pressure (BIPAP) PC-ventilation mode. 45 patients with sepsis or septic shock and who were mechanically ventilated with BIPAP mode and a target tidal volume of 7-8 ml/kg were included. PPV was automatically assessed at baseline and after a standard fluid challenge (Ringer's lactate 500 ml). A 15% increase in stroke volume (SV) defined fluid responsiveness. The predictive value of PPV was evaluated through a receiver operating characteristic (ROC) curve analysis and "gray zone" statistical approach. 20 (44%) patients were considered fluid responders. We identified a significant relationship between PPV decrease after volume expansion and SV increase (spearman ρ = - 0.5, p < 0.001). The area under ROC curve for PPV was 0.71 (95%CI 0.56-0.87, p = 0.007). The best cut-off (based on Youden's index) was 8%, with a sensitivity of 80% and specificity of 60%. Using a gray zone approach, we identified that PPV values comprised between 5 and 15% do not allow a reliable fluid responsiveness prediction. In critically ill septic patients ventilated under BIPAP mode, PPV appears to be an accurate method for fluid responsiveness prediction. However, PPV values comprised between 5 and 15% constitute a gray zone that does not allow a reliable fluid responsiveness prediction.

Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study

BACKGROUND

Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although restrictions in mobilising patients often preclude this haemodynamic challenge being used. We investigated whether machine learning applied on transthoracic echocardiography (TTE) data might be used as a tool for predicting fluid responsiveness in critically ill patients.

METHODS

We studied, 100 critically ill patients (mean age: 62 yr [standard deviation: 14]) with severe sepsis or septic shock prospectively over 24 months. Transthoracic echocardiography measurements were performed at baseline, after PLR, and before and after a standardised fluid challenge in learning and test populations (n=50 patients each). A 15% increase in stroke volume defined fluid responsiveness. The machine learning methods used were classification and regression tree (CART), partial least-squares regression (PLS), neural network (NNET), and linear discriminant analysis (LDA). Each method was applied offline to determine whether fluid responsiveness may be predicted from left and right cardiac ventricular physiological changes detected by cardiac ultrasound. Predictive values for fluid responsiveness were compared by receiver operating characteristics (area under the curve [AUC]; mean [95% confidence intervals]).

RESULTS

In the learning sample, the AUC values were PLR 0.76 (0.62-0.89), CART 0.83 (0.73-0.94), PLS 0.97 (0.93-1), NNET 0.93 (0.85-1), and LDA 0.90 (0.81-0.98). In the test sample, the AUC values were PLR 0.77 (0.64-0.91), CART 0.68 (0.54-0.81), PLS 0.83 (0.71-0.96), NNET 0.83 (0.71-0.94), and LDA 0.85 (0.74-0.96) respectively. The PLS model identified inferior vena cava collapsibility, velocity-time integral, S-wave, E/Ea ratio, and E-wave as key echocardiographic parameters.

CONCLUSIONS

Machine learning generated several models for predicting fluid responsiveness that were comparable with the haemodynamic response to PLR.