Development and validation of a nomogram for early assessment the severity of acute pancreatitis

Clinical effects of phospholipase D2 in attenuating acute pancreatitis

BACKGROUND

The objective of the current study was to elucidate the clinical mechanism through which phospholipase D2 (PLD2) exerted a regulatory effect on neutrophil migration, thereby alleviating the progression of acute pancreatitis.

AIM

To elucidate the clinical mechanism through which PLD2 exerted a regulatory effect on neutrophil migration, thereby alleviating the progression of acute pancreatitis.

METHODS

The study involved 90 patients diagnosed with acute pancreatitis, admitted to our hospital between March 2020 and November 2022. A retrospective analysis was conducted, categorizing patients based on Ranson score severity into mild (n = 25), moderate (n = 30), and severe (n = 35) groups. Relevant data was collected for each group. Western blot analysis assessed PLD2 protein expression in patient serum. Real-time reverse transcription polymerase chain reaction was used to evaluate the mRNA expression of chemokine receptors associated with neutrophil migration. Serum levels of inflammatory factors in patients were detected using enzyme-linked immunosorbent assay. Transwell migration tests were conducted to compare migration of neutrophils across groups and analyze the influence of PLD2 on neutrophil migration.

RESULTS

Overall data analysis did not find significant differences between patient groups (P > 0.05). The expression of PLD2 protein in the severe group was lower than that in the moderate and mild groups (P < 0.05). The expression level of PLD2 in the moderate group was also lower than that in the mild group (P < 0.05). The severity of acute pancreatitis is negatively correlated with PLD2 expression (r = -0.75, P = 0.002). The mRNA levels of C-X-C chemokine receptor type 1, C-X-C chemokine receptor type 2, C-C chemokine receptor type 2, and C-C chemokine receptor type 5 in the severe group are significantly higher than those in the moderate and mild groups (P < 0.05), and the expression levels in the moderate group are also higher than those in the mild group (P < 0.05). The levels of C-reactive protein, tumor necrosis factor-α, interleukin-1β, and interleukin-6 in the severe group were higher than those in the moderate and mild groups (P < 0.05), and the levels in the moderate group were also higher than those in the mild group (P < 0.05). The number of migrating neutrophils in the severe group was higher than that in the moderate and mild groups (P < 0.05), and the moderate group was also higher than the mild group (P < 0.05). In addition, the number of migrating neutrophils in the mild group combined with PLD2 inhibitor was higher than that in the mild group (P < 0.05), and the number of migrating neutrophils in the moderate group combined with PLD2 inhibitor was higher than that in the moderate group (P < 0.05). The number of migrating neutrophils in the severe group + PLD2 inhibitor group was significantly higher than that in the severe group (P < 0.05), indicating that PLD2 inhibitors significantly stimulated neutrophil migration.

CONCLUSION

PLD2 exerted a crucial regulatory role in the pathological progression of acute pancreatitis. Its protein expression varied among patients based on the severity of the disease, and a negative correlation existed between PLD2 expression and disease severity. Additionally, PLD2 appeared to impede acute pancreatitis progression by limiting neutrophil migration.

Prediction of acute pancreatitis severity based on early CT radiomics

BACKGROUND

This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity.

METHODS

A retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for training and validation groups. CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. Feature selection was carried out using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. A radiomics model was constructed through logistic regression to compute the radiomics score. Concurrently, univariate and multivariate logistic regression were employed to identify independent clinical risk factors for the clinical model. The radiomics score and clinical variables were integrated into a combined model, which was visualized with a nomogram. Model performance and net clinical benefit were evaluated through the area under the receiver operating characteristic curve (AUC), the DeLong test, and decision curve analysis.

RESULTS

A total of 913 radiomics features demonstrated satisfactory consistency. Eight features were selected for the radiomics model. Serum calcium, C-reactive protein, and white blood cell count were identified as independent clinical predictors. The AUC of the radiomics model was 0.871 (95% CI, 0.793-0.949) in the training cohort and 0.859 (95% CI, 0.751-0.967) in the validation cohort. The clinical model achieved AUCs of 0.833 (95% CI, 0.756-0.910) and 0.810 (95% CI, 0.692-0.929) for the training and validation cohorts, respectively. The combined model outperformed both the radiomics and clinical models, with an AUC of 0.905 (95% CI, 0.837-0.973) in the training cohort and 0.908 (95% CI, 0.824-0.992) in the validation cohort. The DeLong test confirmed superior predictive performance of the combined model over both the radiomics and clinical models in the training cohort, and over the clinical model in the validation cohort. Decision curve analysis further demonstrated that the combined model provided greater net clinical benefit than the radiomics or clinical models alone.

CONCLUSION

The clinical-radiomics model offers a novel tool for the early prediction of acute pancreatitis severity, providing valuable support for clinical decision-making.

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Background

Predicting the severity of acute pancreatitis (AP) early poses a challenge in clinical practice. While there are well-established clinical scoring tools, their actual predictive performance remains uncertain. Various studies have explored the application of machine-learning methods for early AP prediction. However, a more comprehensive evidence-based assessment is needed to determine their predictive accuracy. Hence, this systematic review and meta-analysis aimed to evaluate the predictive accuracy of machine learning in assessing the severity of AP.

Methods

PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched until December 5, 2023. The risk of bias in eligible studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses, based on different machine learning types, were performed. Additionally, the predictive accuracy of mainstream scoring tools was summarized.

Results

This systematic review ultimately included 33 original studies. The pooled c-index in both the training and validation sets was 0.87 (95 % CI: 0.84-0.89) and 0.88 (95 % CI: 0.86-0.90), respectively. The sensitivity in the training set was 0.81 (95 % CI: 0.77-0.84), and in the validation set, it was 0.79 (95 % CI: 0.71-0.85). The specificity in the training set was 0.84 (95 % CI: 0.78-0.89), and in the validation set, it was 0.90 (95 % CI: 0.86-0.93). The primary model incorporated was logistic regression; however, its predictive accuracy was found to be inferior to that of neural networks, random forests, and xgboost. The pooled c-index of the APACHE II, BISAP, and Ranson were 0.74 (95 % CI: 0.68-0.80), 0.77 (95 % CI: 0.70-0.85), and 0.74 (95 % CI: 0.68-0.79), respectively.

Conclusions

Machine learning demonstrates excellent accuracy in predicting the severity of AP, providing a reference for updating or developing a straightforward clinical prediction tool.

Quantitative CT features on admission combined with laboratory biomarkers for predicting severe acute pancreatitis

AIM

To assess the association of quantitative computed tomography (CT) features on admission with acute pancreatitis (AP) severity, and to explore the performance of combined CT and laboratory markers for predicting severe AP (SAP).

MATERIALS AND METHODS

Data from 208 AP patients were reviewed retrospectively. Pancreas volume, the area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were calculated based on CT images on admission. Laboratory biomarkers within 24 h of admission were collected. Interobserver agreement for CT measurements was measured by calculating interclass correlation coefficient (ICC). The associations of quantitative CT features with AP severity were evaluated. Predictive models for SAP were constructed based on CT and laboratory markers. Performances of single marker and the models were evaluated using receiver operating characteristic (ROC) curve and area under the ROC curve (AUC).

RESULTS

Pancreas volume, area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were significantly different between severe and non-severe AP groups. In predicting SAP, the AUCs of quantitative CT indicators ranged from 0.72 to 0.79; the AUCs of laboratory biomarkers were between 0.53 and 0.66. The combined model of area of extrapancreatic inflammation, serum calcium, and haematocrit yielded an AUC of 0.84, significantly higher than that of the laboratory model, single CT, or laboratory marker. Interobserver agreements for quantitative CT indicators were excellent, with ICC ranging from 0.91 to 0.98.

CONCLUSION

Quantitative CT features on admission were significantly associated with AP severity; the combination of extrapancreatic inflammation area, serum calcium, and haematocrit could be taken as a new method for predicting SAP.

Development and evaluation of machine learning models and nomogram for the prediction of severe acute pancreatitis

BACKGROUND AND AIM

Severe acute pancreatitis (SAP) in patients progresses rapidly and can cause multiple organ failures associated with high mortality. We aimed to train a machine learning (ML) model and establish a nomogram that could identify SAP, early in the course of acute pancreatitis (AP).

METHODS

In this retrospective study, 631 patients with AP were enrolled in the training cohort. For predicting SAP early, five supervised ML models were employed, such as random forest (RF), K-nearest neighbors (KNN), and naive Bayes (NB), which were evaluated by accuracy (ACC) and the areas under the receiver operating characteristic curve (AUC). The nomogram was established, and the predictive ability was assessed by the calibration curve and AUC. They were externally validated by an independent cohort of 109 patients with AP.

RESULTS

In the training cohort, the AUC of RF, KNN, and NB models were 0.969, 0.954, and 0.951, respectively, while the AUC of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Ranson and Glasgow scores were only 0.796, 0.847, and 0.837, respectively. In the validation cohort, the RF model also showed the highest AUC, which was 0.961. The AUC for the nomogram was 0.888 and 0.955 in the training and validation cohort, respectively.

CONCLUSIONS

Our findings suggested that the RF model exhibited the best predictive performance, and the nomogram provided a visual scoring model for clinical practice. Our models may serve as practical tools for facilitating personalized treatment options and improving clinical outcomes through pre-treatment stratification of patients with AP.

Prediction of the severity of acute pancreatitis using machine learning models

BACKGROUND

Acute pancreatitis (AP) is the most common pancreatic disease. Predicting the severity of AP is critical for making preventive decisions. However, the performance of existing scoring systems in predicting AP severity was not satisfactory. The purpose of this study was to develop predictive models for the severity of AP using machine learning (ML) algorithms and explore the important predictors that affected the prediction results.

METHODS

The data of 441 patients in the Department of Gastroenterology in our hospital were analyzed retrospectively. The demographic data, blood routine and blood biochemical indexes, and the CTSI score were collected to develop five different ML predictive models to predict the severity of AP. The performance of the models was evaluated by the area under the receiver operating characteristic curve (AUC). The important predictors were determined by ranking the feature importance of the predictive factors.

RESULTS

Compared to other ML models, the extreme gradient boosting model (XGBoost) showed better performance in predicting severe AP, with an AUC of 0.906, an accuracy of 0.902, a sensitivity of 0.700, a specificity of 0.961, and a F1socre of 0.764. Further analysis showed that the CTSI score, ALB, LDH, and NEUT were the important predictors of the severity of AP.

CONCLUSION

The results showed that the XGBoost algorithm can accurately predict the severity of AP, which can provide an assistance for the clinicians to identify severe AP at an early stage.