Search

Postoperative morbidity following hepatectomy for hepatocellular carcinoma (HCC) is common and its impact on long-term oncological outcome remains unclear. This study aimed to investigate if postoperative morbidity impacts long-term survival and recurrence following hepatectomy for HCC. The data from a multicenter Chinese database of curative-intent hepatectomy for HCC were analyzed, and independent risks of postoperative 30-day morbidity were identified. After excluding patients with postoperative early deaths (≤90 days), early (≤2 years) and late (>2 years) recurrence rates, overall survival (OS), and time-to-recurrence (TTR) were compared between patients with and without postoperative morbidity. Among 2,161 patients eligible for the study, 758 (35.1%) had postoperative 30-day morbidity. Multivariable logistic regression analysis showed that diabetes mellitus, obesity, Child-Pugh grade B, cirrhosis, and intraoperative blood transfusion were independent risks of postoperative morbidity. The rates of early and late recurrence among patients with postoperative morbidity were higher than those without (50.7% vs. 38.8%, P < 0.001; and 41.7% vs. 34.1%, P = 0.017). Postoperative morbidity was associated with decreased OS (median: 48.1 vs. 91.6 months, P < 0.001) and TTR (median: 19.8 vs. 46.1 months; P < 0.001). After adjustment of confounding factors, multivariable Cox-regression analyses revealed that postoperative morbidity was associated with a 27.8% and 18.7% greater likelihood of mortality (hazard ratio 1.278; 95% confidence interval: 1.126–1.451; P < 0.001) and recurrence (1.187; 1.058–1.331; P = 0.004). This large multicenter study provides strong evidence that postoperative morbidity adversely impacts long-term oncologic prognosis after hepatectomy for HCC. The prevention and management of postoperative morbidity may be oncologically important. [ABSTRACT FROM AUTHOR]

Introduction: Studies have proven that the crash possibility and crash type are not the same among different expressway segment types. However, few studies have conducted real-time safety analysis considering different segment types. This study aimed to explore the crash mechanism's heterogeneity for different segment types (i.e., merge, diverge, weaving, and basic segments). Method: To enable in-depth exploration, this study used detailed traffic data, which were 0–10 min before crash, at 1-min intervals, and from five detectors of both the upstream and downstream to the target segment. This study analyzed the crash mechanism's heterogeneity from the following aspects: crash characteristics, significant crash contributing variables, and variables' importance. Based on this, a variables selection method was proposed to solve the huge dimension scale in modeling. Then, a nested logit model was built, which could consider the crash mechanism's heterogeneity, to quantitatively analyze the impact of crash contributing factors on the crash risk. Results: The results revealed that there are statistically significant differences in crash characteristics between each segment type. Additionally, the sources of most crash contributing factors were found to be significantly different in the spatial–temporal dimension between each segment type. Moreover, this study found that the weather parameter, indicating pavement's wet condition, had a similar effect on crash risk between different segment types. However, the geometry and traffic parameters had significantly different impacts between different segment types. Moreover, when the number of target segments' upstream ramps increases or when the distance between ramps and the target segment decreases, the crash risk would increase. Practical Applications: This study can be applied in the intelligent transportation system to improve traffic safety performance, especially in active traffic management systems. [ABSTRACT FROM AUTHOR]