The effect of fractal-like mechanical ventilation on vital signs in a rat model of acute-on-chronic liver failure.

OBJECTIVE: The network of interactions between different organs is impaired in liver cirrhosis. Liver cirrhosis is associated with multi-system involvement, which eventually leads to multiple organ failure. This process is accelerated by a precipitating factor such as bacterial infection, which leads to respiratory distress, circulatory shock, neural dysfunction and very high mortality. Cirrhotic patients often have blunted respiratory sinus arrhythmia and impaired cardio-respiratory variability. Fractal-like mechanical ventilation is reported to enhance respiratory sinus arrhythmia and attenuate respiratory distress in experimental models. In the present study we hypothesise that fractal-like mechanical ventilation may improve the outcome of cirrhotic rats with multiple organ failure. APPROACH: Cirrhosis was induced by chronic biliary obstruction in rats. Acute multiple organ failure was induced by intraperitoneal injection of bacterial endotoxin in cirrhotic rats. The effect of conventional mechanical ventilation (with constant tidal volume and respiratory rate) or fractal-like ventilation (with the same average but variable tidal volume and respiratory rate) were assessed on vital signs, oxygen saturation and plasma alanine aminotransferase in anaesthetised cirrhotic rats. MAIN RESULTS: We demonstrated that fractal-like mechanical ventilation was accompanied by improved oxygen saturation, reduced heart rate and decreased liver injury following injection of bacterial endotoxin. Moreover, variable mechanical ventilation in cirrhotic rats reduced mortality and prevented a fall in short-term heart rate variability following endotoxin challenge in comparison with rats with constant mechanical ventilation. SIGNIFICANCE: We suggest further investigations into the beneficial effects of fractal-like ventilation strategy in critically ill patients with liver failure requiring organ support and mechanical ventilation.

Discriminating Tuberculous Pleural Effusion from Malignant Pleural Effusion Based on Routine Pleural Fluid Biomarkers, Using Mathematical Methods.

BACKGROUND: The differential diagnosis of tuberculous pleural effusion (TPE) and malignant pleural effusion (MPE) is difficult because the biochemical profiles are similar. The present study aimed to differentiate TPE from MPE, using a decision tree and a weighted sparse representation-based classification (WSRC) method, based on the best combination of routine pleural effusion fluid biomarkers. MATERIALS AND METHODS: The routine biomarkers of pleural fluid, including differential cell count, lactate dehydrogenase (LDH), protein, glucose and adenosine deaminase (ADA), were measured in 236 patients (100 with TPE and 136 with MPE). A Sequential Forward Selection (SFS) algorithm was employed to obtain the best combination of parameters for the classification of pleural effusions. Moreover, WSRC was compared to the standard sparse representation-based classification (SRC) and the Support Vector Machine (SVM) methods for classification accuracy. RESULTS: ADA provided the highest diagnostic performance in differentiating TPE from MPE, with 91.91% sensitivity and 74.0% specificity. The best combination of parameters for discriminating TPE from MPE included age, ADA, polynuclear leukocytes and lymphocytes. WSRC outperformed the SRC and SVM methods, with an area under the curve of 0.877, sensitivity of 93.38%, and specificity of 82.0%. The generated flowchart of the decision tree demonstrated 87.2% accuracy for discriminating TPE from MPE. CONCLUSION: This study indicates that a decision tree and a WSRC are novel, noninvasive, and inexpensive methods, which can be useful in discriminating between TPE and MPE, based on the combination of routine pleural fluid biomarkers.