Preoperative and postoperative complications as risk factors for delayed gastric emptying following pancreaticoduodenectomy: A single-center retrospective study.

BACKGROUND: Mortality rates after pancreaticoduodenectomy (PD) have significantly decreased in specialized centers. However, postoperative morbidity, particularly delayed gastric emptying (DGE), remains the most frequent complication following PD. AIM: To identify risk factors associated with DGE after the PD procedure. METHODS: In this retrospective, cross-sectional study, clinical data were collected from 114 patients who underwent PD between January 2015 and June 2018. Demographic factors, pre- and perioperative characteristics, and surgical complications were assessed. Univariate and multivariate analyses were performed to identify risk factors for post-PD DGE. RESULTS: The study included 66 males (57.9%) and 48 females (42.1%), aged 33-83 years (mean: 62.5), with a male-to-female ratio of approximately 1.4:1. There were 63 cases (55.3%) of PD and 51 cases (44.7%) of pylorus-preserving pancreatoduodenectomy. Among the 114 patients who underwent PD, 33 (28.9%) developed postoperative DGE. Univariate analysis revealed significant differences in four of the 14 clinical indexes observed: pylorus preservation, retrocolonic anastomosis, postoperative abdominal complications, and early postoperative albumin (ALB). Logistic regression analysis further identified postoperative abdominal complications [odds ratio (OR) = 4.768, P = 0.002], preoperative systemic diseases (OR = 2.516, P = 0.049), and early postoperative ALB (OR = 1.195, P = 0.003) as significant risk factors. CONCLUSION: Postoperative severe abdominal complications, preoperative systemic diseases, and early postoperative ALB are identified as risk factors for post-PD DGE.

Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas.

AIM: To evaluate the prognostic power of different molecular data in liver cancer. METHODS: Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then a heatmap was constructed and gene set enrichment analysis was performed for pathway analysis. RESULTS: The mRNA data were the most informative prognostic variables in all kinds of omics data in liver cancer, with the highest concordance index (C-index) of 0.61. For the copy number variation, methylation and miRNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone (P < 0.05). On the other hand, the combination of clinical data with methylation, miRNA and mRNA data could significantly boost the prediction accuracy of the clinical data itself (P < 0.05). Based on the significant prognostic variables, different prognostic models were built. In addition, the heatmap analysis, survival analysis, and gene set enrichment analysis validated the practicability of the prognostic models. CONCLUSION: In all kinds of omics data in liver cancer, the mRNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction.