Cardin, Sylvain

The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to compare for the first time the discriminative ability of two machine learning (ML) algorithms based on real-time feature analysis of arterial waveforms obtained from a non-invasive continuous blood pressure system (Finometer®) signal to predict the onset of decompensated shock: the compensatory reserve index (CRI) and the compensatory reserve metric (CRM). One hundred ninety-one healthy volunteers underwent progressive simulated hemorrhage using lower body negative pressure (LBNP). The least squares means and standard deviations for each measure were assessed by LBNP level and stratified by tolerance status (high vs. low tolerance to central hypovolemia). Generalized Linear Mixed Models were used to perform repeated measures logistic regression analysis by regressing the onset of decompensated shock on CRI and CRM. Sensitivity and specificity were assessed by calculation of receiver-operating characteristic (ROC) area under the curve (AUC) for CRI and CRM. Values for CRI and CRM were not distinguishable across levels of LBNP independent of LBNP tolerance classification, with CRM ROC AUC (0.9268) being statistically similar (p = 0.134) to CRI ROC AUC (0.9164). Both CRI and CRM ML algorithms displayed discriminative ability to predict decompensated shock to include individual subjects with varying levels of tolerance to central hypovolemia. Arterial waveform feature analysis provides a highly sensitive and specific monitoring approach for the detection of ongoing hemorrhage, particularly for those patients at greatest risk for early onset of decompensated shock and requirement for implementation of life-saving interventions.

These studies were conducted to assess the relationship between visceral adipose tissue free fatty acid (FFA) release and splanchnic FFA release. Steady-state splanchnic bed palmitate ([9,10-3H]palmitate) kinetics were determined from 14 sampling intervals from eight dogs with chronic indwelling arterial, portal vein, and hepatic vein catheters. We tested a model designed to predict the proportion of FFAs delivered to the liver from visceral fat by use of hepatic vein data. The model predicted that 15 ± 2% of hepatic palmitate delivery originated from visceral lipolysis, which was greater ( P = 0.004) than the 11 ± 2% actually observed. There was a good relationship ( r 2 = 0.63) between the predicted and observed hepatic palmitate delivery values, but the model overestimated visceral FFA release more at lower than at higher palmitate concentrations. The discrepancy could be due to differential uptake of FFAs arriving from the arterial vs. the portal vein or to release of FFAs in the hepatic circulatory bed. Splanchnic FFA release measured using hepatic vein samples was strongly related to visceral adipose tissue FFA release into the portal vein. This finding suggests that splanchnic FFA release is a good indicator of visceral adipose tissue lipolysis.