Background/Aims: Necrotizing pancreatitis (NP) presents a more severe clinical trajectory and increased mortality compared to edematous pancreatitis. Prompt identification of NP is vital for patient prognosis. A risk prediction model for NP among Chinese patients has been developed and validated to aid in early detection. Methods: A retrospective analysis was performed on 218 patients with acute pancreatitis (AP) to examine the association of various clinical variables with NP. The least absolute shrinkage and selection operator (LASSO) regression was utilized to refine variables and select predictors. Subsequently, a multivariate logistic regression was employed to construct a predictive nomogram. The model's accuracy was validated using bootstrap resampling (n=500) and its calibration assessed via a calibration curve. The model's clinical utility was evaluated through decision curve analysis. Results: Of the 28 potential predictors analyzed in 218 AP patients, the incidence of NP was 25.2%. LASSO regression identified 14 variables, with procalcitonin, triglyceride, white blood cell count at 48 hours post-admission, calcium at 48 hours post-admission, and hematocrit at 48 hours post-admission emerging as independent risk factors for NP. The resulting nomogram accurately predicted NP risk with an area under the curve of 0.822, sensitivity of 82.8%, and specificity of 76.4%. The bootstrap-validated area under the curve remained at 0.822 (95% confidence interval, 0.737 to 0.892). This model exhibited excellent calibration and demonstrated greater predictive efficacy and clinical utility for NP than APACHE II, Ranson, and BISAP. Conclusions We have developed a prediction nomogram of NP that is of great value in guiding clinical decision.

Background/Aims: Necrotizing pancreatitis (NP) presents a more severe clinical trajectory and increased mortality compared to edematous pancreatitis. Prompt identification of NP is vital for patient prognosis. A risk prediction model for NP among Chinese patients has been developed and validated to aid in early detection. Methods: A retrospective analysis was performed on 218 patients with acute pancreatitis (AP) to examine the association of various clinical variables with NP. The least absolute shrinkage and selection operator (LASSO) regression was utilized to refine variables and select predictors. Subsequently, a multivariate logistic regression was employed to construct a predictive nomogram. The model's accuracy was validated using bootstrap resampling (n=500) and its calibration assessed via a calibration curve. The model's clinical utility was evaluated through decision curve analysis. Results: Of the 28 potential predictors analyzed in 218 AP patients, the incidence of NP was 25.2%. LASSO regression identified 14 variables, with procalcitonin, triglyceride, white blood cell count at 48 hours post-admission, calcium at 48 hours post-admission, and hematocrit at 48 hours post-admission emerging as independent risk factors for NP. The resulting nomogram accurately predicted NP risk with an area under the curve of 0.822, sensitivity of 82.8%, and specificity of 76.4%. The bootstrap-validated area under the curve remained at 0.822 (95% confidence interval, 0.737 to 0.892). This model exhibited excellent calibration and demonstrated greater predictive efficacy and clinical utility for NP than APACHE II, Ranson, and BISAP. Conclusions We have developed a prediction nomogram of NP that is of great value in guiding clinical decision.

Background/Aims: Necrotizing pancreatitis (NP) presents a more severe clinical trajectory and increased mortality compared to edematous pancreatitis. Prompt identification of NP is vital for patient prognosis. A risk prediction model for NP among Chinese patients has been developed and validated to aid in early detection. Methods: A retrospective analysis was performed on 218 patients with acute pancreatitis (AP) to examine the association of various clinical variables with NP. The least absolute shrinkage and selection operator (LASSO) regression was utilized to refine variables and select predictors. Subsequently, a multivariate logistic regression was employed to construct a predictive nomogram. The model's accuracy was validated using bootstrap resampling (n=500) and its calibration assessed via a calibration curve. The model's clinical utility was evaluated through decision curve analysis. Results: Of the 28 potential predictors analyzed in 218 AP patients, the incidence of NP was 25.2%. LASSO regression identified 14 variables, with procalcitonin, triglyceride, white blood cell count at 48 hours post-admission, calcium at 48 hours post-admission, and hematocrit at 48 hours post-admission emerging as independent risk factors for NP. The resulting nomogram accurately predicted NP risk with an area under the curve of 0.822, sensitivity of 82.8%, and specificity of 76.4%. The bootstrap-validated area under the curve remained at 0.822 (95% confidence interval, 0.737 to 0.892). This model exhibited excellent calibration and demonstrated greater predictive efficacy and clinical utility for NP than APACHE II, Ranson, and BISAP. Conclusions We have developed a prediction nomogram of NP that is of great value in guiding clinical decision.

Background/Aims: Necrotizing pancreatitis (NP) presents a more severe clinical trajectory and increased mortality compared to edematous pancreatitis. Prompt identification of NP is vital for patient prognosis. A risk prediction model for NP among Chinese patients has been developed and validated to aid in early detection. Methods: A retrospective analysis was performed on 218 patients with acute pancreatitis (AP) to examine the association of various clinical variables with NP. The least absolute shrinkage and selection operator (LASSO) regression was utilized to refine variables and select predictors. Subsequently, a multivariate logistic regression was employed to construct a predictive nomogram. The model's accuracy was validated using bootstrap resampling (n=500) and its calibration assessed via a calibration curve. The model's clinical utility was evaluated through decision curve analysis. Results: Of the 28 potential predictors analyzed in 218 AP patients, the incidence of NP was 25.2%. LASSO regression identified 14 variables, with procalcitonin, triglyceride, white blood cell count at 48 hours post-admission, calcium at 48 hours post-admission, and hematocrit at 48 hours post-admission emerging as independent risk factors for NP. The resulting nomogram accurately predicted NP risk with an area under the curve of 0.822, sensitivity of 82.8%, and specificity of 76.4%. The bootstrap-validated area under the curve remained at 0.822 (95% confidence interval, 0.737 to 0.892). This model exhibited excellent calibration and demonstrated greater predictive efficacy and clinical utility for NP than APACHE II, Ranson, and BISAP. Conclusions We have developed a prediction nomogram of NP that is of great value in guiding clinical decision.

Objective:To evaluate the efficacy of plasma exchange and drugs in the treatment of hypertriglyceridemic pancreatitis (HTGP).Methods:Plasma exchange, exchange plasma,exchanges plasma, plasma or plasma exchanges, acute pancreatitis were used as keywords for research search, and the network English database such as PubMed, Embase, Cochrane Library were searched from database establishment to May 31, 2022. The prospective and retrospective cohort studies on plasma exchange and drugs in the treatment of HTGP were retrieved. The papers were screened and the quality was evaluated according to preset inclusion and exclusion criteria; and the important data were extracted. Meta-analysis was performed using RevMan5.3 software.Results:A total of 11 papers with 819 patients were included. Among them, 285 patients received plasma exchange, and 534 patients received drug treatment. The results of meta-analysis showed that plasma exchange decreased triglyceride faster than drug therapy ( OR=5.28, 95% CI 0.92-9.63, P<0.05), but plasma exchange was comparable to drug therapy on the incidence of pancreatic pseudocysts, pancreatic necrosis, acute renal failure, acute respiratory failure and shock, and mortality ( OR=0.54, 95% CI 0.23-1.29; OR=1.23, 95% CI 0.62-2.43; OR=0.85, 95% CI 0.25-2.91; OR=0.84, 95% CI 0.25-2.79; OR=0.64, 95% CI 0.31-1.34, OR=1.29, 95% CI 0.72-2.30; all P value >0.05), and patients with plasma exchange had longer hospital stays ( OR=2.09, 95% CI 0.10-4.08, P<0.05). Conclusions:Compared with drug therapy, plasma exchange can not reduce the mortality and complications of HTGP patients.

Objective:To evaluate the accuracy and practicability of hepatocellular carcinoma prediction score (PAGE-B) and modified hepatocellular carcinoma prediction score (mPAGE-B) in predicting the development of hepatocellular carcinoma in patients with hepatitis B virus (HBV)-associated liver cirrhosis and received nucleos(t)ide analogue (NA) treatment.Methods:From June 2009 to December 2014, at Department of Hepatology, the First Affiliated Hospital of Fujian Medical University, the clinical data of 707 patients with HBV-associated liver cirrhosis and received NA treatment were retrospectively collected, and the patients were followed up. The risk factors of development of hepatocellular carcinoma were analyzed. PAGE-B (including platelet count, age, gender), mPAGE-B (including platelet count, age, gender and albumin), Child-Turcotte-Pugh (CTP) score and aspartate aminotransferase to platelet ratio index (APRI) were compared in area under receiver operator characteristic curve (AUROC) for predicting the occurrence of hepatocellular carcinoma within 5 years. Risk stratification analysis was carried out for mPAGE-B and PAGE-B. Multivariate Cox regression analysis, receiver operator characteristic curve, Mann-Whitney U test and Kaplan-Meier method were used for statistical analysis. Results:The age of 707 patients was (46.7±12.2) years old, including 567 males (80.2%) and 140 females (19.8%). The positive rate of hepatitis B e antigen was 56.4% (399/707). The scores of PAGE-B, mPAGE-B, CTP and APRI were 15.90±4.24, 12.39±3.58, 6.88±2.15 and 1.80 (0.85, 3.79), respectively. The overall follow up time was (38.14±20.97) months and the incidence of hepatocellular carcinoma was 8.1% (57/707). The results of multivariate Cox regression analysis showed that advanced age, low platelet count and quantitative reduction of HBV DNA were independent risk factors of development of hepatocellular carcinoma (Wald=20.44, 5.64 and 9.25; HR(95% confidence interval (95% CI) 1.056(1.031 to 1.081), 0.994(0.989 to 0.999) and 0.769(0.649 to 0.911); P<0.001, =0.018 and 0.002). The AUROCs (95% CI) of PAGE-B, mPAGE-B, CTP score and APRI for predicting the occurrence of hepatocellular carcinoma within 5 years were 0.708 (0.639 to 0.778), 0.724 (0.657 to 0.778), 0.576 (0.500 to 0.652) and 0.516 (0.443 to 0.589), respectively. There were no statistically significant differences in AUROCs for predicting the occurrence of hepatocellular carcinoma within 5 years between mPAGE-B and PAGE-B, between APRI and CTP score (both P>0.05). The AUROC for predicting the occurrence of hepatocellular carcinoma within 5 years of CTP score was less than those of PAGE-B and mPAGE-B, and the differences were statistically significant ( Z=3.00 and 3.79; P=0.003, <0.001). The AUROC for predicting the occurrence of hepatocellular carcinoma within 5 years of APRI was less than those of PAGE-B and mPAGE-B, and the differences were statistically significant ( Z=4.75 and 5.46, both P<0.001). There were 51 cases (7.2%), 394 cases (55.7%) and 262 cases (37.1%) in the low-risk (<10) group, medium-risk (10 to 17) group and high-risk (>17) group as assessed by PAGE-B. The incidence of hepatocellular carcinoma was 0(0/51), 4.8% (19/394) and 14.5% (38/262), respectively the annual average incidence of hepatocellular carcinoma was 0, 1.6% and 5.5%, respectively, the 5-year cumulative incidence of hepatocellular carcinoma was 0, 7.3% and 31.3%, respectively. The 5-year cumulative incidence of hepatocellular carcinoma of high-risk group was higher than those of medium-risk group and low-risk group (log-rank test=19.27, P<0.001). There were 97 cases (13.7%), 246 cases (34.8%) and 364 cases (51.5%) in the low-risk group (<9), medium-risk group (9 to 12) and high-risk group (>12) as assessed by mPAGE-B. The incidence of hepatocellular carcinoma was 2.1% (2/97), 3.7% (9/246) and 12.6%(46/364), the annual average incidence of hepatocellular carcinoma was 0.6%, 1.1% and 4.7%, respectively, the 5-year cumulative incidence of hepatocellular carcinoma was 2.4%, 5.1% and 26.7%, respectively. The 5-year cumulative incidence of hepatocellular carcinoma of high-risk group was higher than those of medium-risk group and low-risk group (log-rank test value=18.64, P<0.001). Conclusions:Both PAGE-B and mPAGE-B can predict the occurrence of hepatocellular carcinoma within 5 years in patients with HBV-associated liver cirrhosis treated with antiviral therapy, identify liver cirrhotic patients at high risk of development of hepatocellular carcinoma and guide clinicans to use more efficient screening strategies.