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.

Combined Thoracic Ultrasound Assessment during a Successful Weaning Trial Predicts Postextubation Distress.

BACKGROUND: Recent studies suggest that isolated sonographic assessment of the respiratory, cardiac, or neuromuscular functions in mechanically ventilated patients may assist in identifying patients at risk of postextubation distress. The aim of the present study was to prospectively investigate the value of an integrated thoracic ultrasound evaluation, encompassing bedside respiratory, cardiac, and diaphragm sonographic data in predicting postextubation distress. METHODS: Longitudinal ultrasound data from 136 patients who were extubated after passing a trial of pressure support ventilation were measured immediately after the start and at the end of this trial. In case of postextubation distress (31 of 136 patients), an additional combined ultrasound assessment was performed while the patient was still in acute respiratory failure. We applied machine-learning methods to improve the accuracy of the related predictive assessments. RESULTS: Overall, integrated thoracic ultrasound models accurately predict postextubation distress when applied to thoracic ultrasound data immediately recorded before the start and at the end of the trial of pressure support ventilation (learning sample area under the curve: start, 0.921; end, 0.951; test sample area under the curve: start, 0.972; end, 0.920). Among integrated thoracic ultrasound data, the recognition of lung interstitial edema and the increased telediastolic left ventricular pressure were the most relevant predictive factors. In addition, the use of thoracic ultrasound appeared to be highly accurate in identifying the causes of postextubation distress. CONCLUSIONS: The decision to attempt extubation could be significantly assisted by an integrative, dynamic, and fully bedside ultrasonographic assessment of cardiac, lung, and diaphragm functions.

Ultrasound-guided bilateral stellate ganglion blockade to treat digital ischemia in a patient with sepsis: a case report.

PURPOSE: This case report describes the use of ultrasound-guided stellate ganglion blockade to treat sepsis-related digital ischemia in the intensive care unit (ICU). CLINICAL FEATURES: A 71-yr-old female was admitted to the ICU with septic shock and acute respiratory distress syndrome (ARDS) following an initial right hemicolectomy complicated by an anastomotic leak and peritonitis. The patient's condition was further complicated by an abdominal abscess 22 days later. She had type-2 diabetes mellitus and hypertension but no history of vascular disease. With continuing sepsis from the abscess and requiring mechanical ventilation due to ARDS, she developed upper limb digital ischemia refractory to treatment with a low dose of dobutamine and isosorbide dinitrate. We subsequently performed ultrasound-guided bilateral stellate ganglion blockade with the intent of restoring perfusion to her fingers before digital necrosis developed. One hour after each stellate ganglion block, the symptoms of digital ischemia completely resolved. The benefit persisted for two days, and then a repeat block was performed with similar results. CONCLUSIONS: This case illustrates the potential advantages of ultrasound-guided stellate ganglion blockade for the treatment of sepsis-related digital ischemia refractory to standard therapy.

Accuracy of ultrasound B-lines score and E/Ea ratio to estimate extravascular lung water and its variations in patients with acute respiratory distress syndrome.

Extravascular lung water (EVLW) could increase by permeability pulmonary oedema, cardiogenic oedema, or both. Transthoracic echocardiography examination of a patient allows quantifying B-lines, originating from water-thickened interlobular septa, and the E/Ea ratio, related to pulmonary capillary wedge pressure. The aim of our study was to assess the correlation and the trending ability between EVLW measured by transpulmonary thermodilution and the B-lines score or the E/Ea ratio in patients with ARDS. Twenty-six intensive care unit patients were prospectively included. B-lines score was obtained from four ultrasound zones (anterior and lateral chest on left and right hemithorax). E/Ea was measured from the apical four-chamber view. EVLW was compared with the B-lines score and the E/Ea ratio. A linear mixed effect model was used to take account the repeated measurements. A p value<0.05 was considered significant. A total of 73 measurements were collected. The correlation coefficient between EVLW and B-lines score was 0.66 (EVLW=0.71 B-lines+7.64, R2=0.44, p=0.001), versus 0.31 for E/Ea (p=0.06). The correlation between EVLW changes and B-lines variations was significant (R2=0.26, p<0.01), with a concordance rate of 74%. A B-lines score≥6 had a sensitivity of 82% and a specificity of 77% to predict EVLW>10 ml/kg, with an AUC equal to 0.86 (0.76-0.93). The gray zone approach identified a range of B-lines between four and seven for which EVLW>10 ml/kg could not be predicted reliably. The correlation between ultrasound B-lines and EVLW was significant, but the B-lines score was not able to track EVLW changes reliably.