Sympathetic withdrawal is associated with hypotension after hepatic reperfusion

Objective: Post-reperfusion syndrome (PRS), severe hypotension after graft reperfusion during liver transplantation, is an adverse clinical event associated with poorer patient outcomes. The purpose of this study was to determine whether alterations in autonomic control in liver transplant recipients prior to graft reperfusion are associated with the subsequent development of PRS. Methods: Heart rate variability (HRV), systolic arterial blood pressure (SBP) variability, and baroreflex sensitivity of 218 liver transplant recipients were evaluated using 5 min of ECG and arterial blood pressure signals 10 min before graft reperfusion along with other clinical parameters. Logistic regression analyses were performed to assess predictors of PRS occurrence. Results: Seventy-seven patients (35 %) developed PRS while 141 did not. There were significant differences in SBP (110 ± 16 vs. 119 ± 16 mmHg, P < 0.001) and the ratio of low frequency power to high frequency power (LF/HF) of HRV (1.0 ± 1.4 vs. 2.1 ± 3.7, P = 0.003) between the PRS group and No-PRS group. In multivariate logistic regression analysis, predictors were LF/HF (odds ratio 0.817, P = 0.028) and SBP (odds ratio 0.966, P < 0.001). Interpretation: Low LF/HF and SBP measured before hepatic graft reperfusion were significantly correlated with subsequent PRS occurrence, suggesting that sympathovagal imbalance and depressed SBP may be key factors predisposing to reperfusion-related severe hypotension in liver transplant recipients

Prediction of severe accident occurrence time using support vector machines

AbstractIf a transient occurs in a nuclear power plant (NPP), operators will try to protect the NPP by estimating the kind of abnormality and mitigating it based on recommended procedures. Similarly, operators take actions based on severe accident management guidelines when there is the possibility of a severe accident occurrence in an NPP. In any such situation, information about the occurrence time of severe accident-related events can be very important to operators to set up severe accident management strategies. Therefore, support systems that can quickly provide this kind of information will be very useful when operators try to manage severe accidents. In this research, the occurrence times of several events that could happen during a severe accident were predicted using support vector machines with short time variations of plant status variables inputs. For the preliminary step, the break location and size of a loss of coolant accident (LOCA) were identified. Training and testing data sets were obtained using the MAAP5 code. The results show that the proposed algorithm can correctly classify the break location of the LOCA and can estimate the break size of the LOCA very accurately. In addition, the occurrence times of severe accident major events were predicted under various severe accident paths, with reasonable error. With these results, it is expected that it will be possible to apply the proposed algorithm to real NPPs because the algorithm uses only the early phase data after the reactor SCRAM, which can be obtained accurately for accident simulations