BACKGROUND: Pyrrolidine alkaloid-induced hepatic sinusoidal obstruction syndrome (PA-HSOS) is characterized by rapidly progressive portal hypertension. Hepatic venous pressure gradient (HVPG) is the gold standard for the...BACKGROUND: Pyrrolidine alkaloid-induced hepatic sinusoidal obstruction syndrome (PA-HSOS) is characterized by rapidly progressive portal hypertension. Hepatic venous pressure gradient (HVPG) is the gold standard for the diagnosis of sinusoidal portal hypertension. However, the role of HVPG in evaluating disease severity and treatment choice in patients with PA-HSOS needs further verification. This study aimed to investigate the clinical value of HVPG in patients with PA-HSOS. METHODS: Patients who were diagnosed with PA-HSOS according to the "Nanjing criteria" and received HVPG measurements in our hospital between January 2016 and April 2020 were retrospectively reviewed. RESULTS: Multivariate logistic regression analysis identified HVPG as independently associated with nonresponse to initial anticoagulation (odds ratio [OR], 1.191 [95% CI, 1.004-1.413]; P =.044), and the area under the receiver operating characteristic curve (AUC) was 0.741 (95% CI, 0.626-0.857; P <.001). When HVPG of >20.165 mm Hg was combined with serum total bilirubin, heart rate, and blood urea nitrogen for modeling, the AUC was 0.881 (95% CI, 0.804-0.958; P <.001). In addition, there was a linear correlation between HVPG and the bleeding area of hepatic sinusoids (BAHS) in 76 patients (P =.008 and R =.343). Furthermore, after excluding patients with an onset time of more than 1 month, HVPG improved the efficacy in predicting nonresponse to anticoagulant therapy (AUC, 0.789 [95% CI, 0.654-0.924]; P =.001), and the linear relationship between HVPG and BAHS was enhanced (P =.001 and R =.499). CONCLUSION: HVPG can assist in predicting the outcome of anticoagulant therapy in patients with PA-HSOS, and this prediction is more accurate for patients within 1 month of disease onset.
BACKGROUND: Chronic obstructive pulmonary disease (COPD) increases surgical morbidity and mortality, but its effect on paraesophageal hernia repair (PEHR) is unknown. This study aimed to evaluate the associations between...BACKGROUND: Chronic obstructive pulmonary disease (COPD) increases surgical morbidity and mortality, but its effect on paraesophageal hernia repair (PEHR) is unknown. This study aimed to evaluate the associations between COPD and PEHR outcomes, hypothesizing an increased risk of hernia recurrence in patients with COPD. METHODS: A retrospective cohort study (2011-2022) was conducted on elective patients with PEHR, stratified into COPD and high-risk non-COPD groups (American Society of Anesthesiologists [ASA] class 3 or 4). The primary outcome was early recurrence (<6 months). Propensity score matching (1:1) was used to control for age, gender, and body mass index (BMI). The chi-squared and Mann-Whitney U tests were used to compare demographics and outcomes. Kaplan-Meier curves were used to analyze the recurrence timing, and multivariate logistic regression was used to assess COPD as an independent risk factor, adjusting for age, BMI, ASA class, and smoking status. RESULTS: Among 537 patients, 62 matched pairs were analyzed. COPD was not linked to increased respiratory complications but was associated with higher discharge to advanced care (12.9% vs 1.6%; P =.038) and earlier recurrence (160 vs 652 days; P =.01). Kaplan-Meier curves showed increased early recurrence in patients with COPD (P =.02), although recurrence rates converged later. COPD independently predicted early recurrence (odds ratio, 4.4; P <.001). CONCLUSION: COPD is an independent risk factor for early recurrence after PEHR, although it does not increase the risk of respiratory complications or overall recurrence. Patients with COPD more frequently required higher-level care and experienced earlier recurrence. Our findings may guide shared decision-making and suggest strategies to mitigate recurrence risk, such as routine mesh placement.
BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide. With improved survival of patients with GC after curative resection, there is an urgent need for precise dynamic prognostic assess...BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide. With improved survival of patients with GC after curative resection, there is an urgent need for precise dynamic prognostic assessment to guide individualized follow-up. The American Joint Committee on Cancer TNM staging system and lymph node ratio (LNR) are static prognostic tools that fail to capture time-dependent risk changes and competing noncancer mortality, limiting their utility in long-term survival management. METHODS: A single-center retrospective cohort study was conducted on 2772 patients with stage I to III GCs who underwent R0 gastrectomy. Patients with distant metastasis (stage IV) or those receiving neoadjuvant therapy were strictly excluded. Patients were stratified by LNR (negative LNR, low LNR, and high LNR) and age (early-onset GC [EOGC], conventional-onset GC [COGC], and late-onset GC [LOGC]). Conditional survival and time-varying hazard ratios (HRs) were calculated using landmark Cox models. Competing risk analysis was performed to estimate cumulative incidence functions (CIFs) of GC-specific death (GCSD) and noncancer death (NCD) using the Gray test for group comparisons. Multivariate Fine-Gray models were used to identify independent predictors of NCD. RESULTS: The prognostic disadvantage of high LNR attenuated over time. At 5 years postoperatively, the 5-year conditional survival rates of the high LNR group showed a convergence trend, comparable with those of the negative LNR (98.7%) and low LNR groups (93.0%). The time-varying HRs for high LNR vs negative LNR decreased from 13.67 at the 1-year landmark to 2.94 at the 5-year landmark, indicating a substantial attenuation of prognostic impact over time. The HR attenuated substantially over time but remained clinically relevant. A mortality crossover occurred at approximately 2.17 years (26 months) postoperatively in patients with LOGC. Before this time point, the cumulative incidence of GCSD was slightly higher than that of NCD. By 3 years postoperatively, the CIF of NCD (8.98% [95% CI, 6.54%-11.42%]) exceeded that of GCSD (7.96% [95% CI, 5.62%-10.30%]) (Gray test P =.032). Multivariate analysis showed that age stratification (LOGC vs EOGC: subdistribution HR [SHR] = 16.508; P <.001), LNR (low LNR vs negative LNR: SHR = 1.638; P =.013; high LNR vs negative LNR: SHR = 2.649; P =.002), and adjuvant chemotherapy (SHR = 0.362; P <.001) were independent predictors of NCD. CONCLUSION: The prognostic value of LNR is transient, with a high LNR-associated risk attenuating in long-term survivors. A 3-year mortality crossover in patients with LOGC mandates a shift from cancer-focused surveillance to comorbidity and frailty management. High LNR was identified as a proxy for frailty in the elderly patients, warranting a shift from aggressive oncologic surveillance to holistic geriatric care.
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, in which accurate and faster detection is required in high-risk population to improve prognosis and decrease cancer-associated mortal...BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, in which accurate and faster detection is required in high-risk population to improve prognosis and decrease cancer-associated mortality. Currently, radiomics has emerged as a promising computational approach to address this challenge, reporting increased accuracy in differentiating PDAC from benign lesions. Our study aimed to evaluate radiomics-based models derived from computed tomography, magnetic resonance imaging, positron emission tomography, or ultrasound for the detection of PDAC in patients under surveillance. METHODS: A systematic literature search on PubMed, Embase, Scopus, and Cochrane was followed by a meta-analysis comparing diagnostic performance metrics, including area under the receiver operating characteristic curve, sensitivity, and specificity. The DerSimonian-Laird method was used to estimate the pooled sensitivity, specificity, positive likelihood ratios (PLRs), and negative likelihood ratios (NLRs), with subgroup analysis performed using Cochrane RevMan 5.4.1 software and OpenMetaAnalyst. RESULTS: A total of 15 studies involving 14,688 patients were analyzed, with most studies published between 2019 and 2025. Among these patients, the number of patients with PDAC was 6153 (41.8%), the healthy cases were 7145 (48.6%), and the rest of the patients were unspecified (9.6%). Artificial intelligence (AI)/machine learning (ML) reported a pooled sensitivity of 0.88 (95% CI, 0.84-0.91; I = 87.8%) and a specificity of 0.93 (95% CI, 0.87-0.96; I = 95.0%) in detecting PDAC. The pooled PLR was 12.1 (95% CI, 8.4-21.4; I = 95.5%); however, the NLR was 0.12 (95% CI, 0.09-0.16; I = 83.1%). CONCLUSION: The use of AI and ML along with diagnostic modality presents a promising alternative to conventional diagnostic modality owing to the display of convincing diagnostic metric for detection of PDAC. Further prospective studies are needed to study the efficacy of this new approach, along with its incorporation with genomic, proteomic, and metabolomic data to develop multi-omic predictive frameworks to further improve PDAC detection.