A new scoring system can be applied to predict the organ failure related events in acute pancreatitis accurately and rapidly

Radiomics models of contrast-enhanced computed tomography for predicting the activity and prognosis of acute pancreatitis

BACKGROUND

The modified pancreatitis activity scoring system (mPASS) was proposed to assess the activity of acute pancreatitis (AP) while it doesn't include indicators that directly reflect pathophysiology processes and imaging characteristics.

OBJECTIVES

To determine the threshold of admission mPASS and investigate radiomics and laboratory parameters to construct a model to predict the activity of AP.

METHODS

AP inpatients at institution 1 were randomly divided into training and validation groups based on a 5:5 ratio. AP inpatients at Institution 2 were served as test group. The cutoff value of admission mPASS scores in predicting severe AP was selected to divide patients into high and low level of disease activity group. LASSO was used in screening features. Multivariable logistic regression was used to develop radiomics model. Meaningful laboratory parameters were used to construct combined model.

RESULTS

There were 234 (48 years ± 10, 155 men) and 101 (48 years ± 11, 69 men) patients in two institutions. The threshold of admission mPASS score was 112.5 in severe AP prediction. The AUC of the radiomics model was 0.79, 0.72, and 0.76 and that of the combined model incorporating rad-score and white blood cell were 0.84, 0.77, and 0.80 in three groups for activity prediction. The AUC of the combined model in predicting disease without remission was 0.74.

CONCLUSIONS

The threshold of admission mPASS was 112.5 in predicting severe AP. The model based on CECT radiomics has the ability to predict AP activity. Its ability to predict disease without remission is comparable to mPASS.

CRITICAL RELEVANCE STATEMENT

This work is the first attempt to assess the activity of acute pancreatitis using contrast-enhanced CT radiomics and laboratory parameters. The model provides a new method to predict the activity and prognosis of AP, which could contribute to further management.

KEY POINTS

Radiomics features and laboratory parameters are associated with the activity of acute pancreatitis. The combined model provides a new method to predict the activity and prognosis of AP. The ability of the combined model is comparable to the modified Pancreatitis Activity Scoring System.

Early Predictive Value of Different Indicators for Persistent Organ Failure in Acute Pancreatitis: A Systematic Review and Network Meta-Analysis

GOALS

In this study, we conducted this network meta-analysis (based on the ANOVA model) to evaluate the predictive efficacy of each early predictor.

BACKGROUND

Persistent organ failure (POF) is one of the determining factors in patients with acute pancreatitis (AP); however, the diagnosis of POF has a long-time lag (>48 h). It is of great clinical significance for the early noninvasive prediction of POF.

STUDY

We conducted a comprehensive and systematic search in PubMed, Cochrane library, Embase, and Web of Science to identify relevant clinical trials, case-control studies, or cohort studies, extracted the early indicators of POF in studies, and summarized the predictive efficacy of each indicator through network meta-analysis. The diagnostic odds ratio (DOR) was used to rank the prediction efficiency of each indicator.

RESULTS

We identified 23 studies in this network meta-analysis, including 10,393 patients with AP, of which 2014 patients had POF. A total of 10 early prediction indicators were extracted. The mean and 95% CI lower limit of each predictive indicator were greater than 1.0. Albumin had the largest diagnostic odds ratio, followed by high-density lipoprotein-cholesterol (HDL-C), Ranson Score, beside index for severity in acute pancreatitis Score, acute physiology and chronic health evaluation II, C-reactive protein (CRP), Interleukin 6 (IL-6), Interleukin 8 (IL-8), Systemic Inflammatory Response Syndrome (SIRS) and blood urea nitrogen.

CONCLUSIONS

Albumin, high-density lipoprotein-cholesterol, Ranson Score, and beside index for severity in acute pancreatitis Score are effective in the early prediction of POF in patients with AP, which can provide evidence for developing effective prediction systems. However, due to the limitations of the extraction method of predictive indicators in this study, some effective indicators may not be included in this meta-analysis.

Automated machine learning for early prediction of acute kidney injury in acute pancreatitis

BACKGROUND

Acute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating both mortality rates and the financial burden of hospitalization. The aim of our study is to construct a predictive model utilizing automated machine learning (AutoML) algorithms for the early prediction of AKI in patients with AP.

METHODS

We retrospectively analyzed patients who were diagnosed with AP in our hospital from January 2017 to December 2021. These patients were randomly allocated into a training set and a validation set at a ratio of 7:3. To develop predictive models for each set, we employed the least absolute shrinkage and selection operator (LASSO) algorithm along with AutoML. A nomogram was developed based on multivariate logistic regression analysis outcomes. The model's efficacy was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Additionally, the performance of the model constructed via AutoML was evaluated using decision curve analysis (DCA), feature importance, SHapley Additive exPlanations (SHAP) plots, and locally interpretable model-agnostic explanations (LIME).

RESULTS

This study incorporated a total of 437 patients who met the inclusion criteria. Out of these, 313 were assigned to the training cohort and 124 to the validation cohort. In the training and validation cohorts, AKI occurred in 68 (21.7%) and 29(23.4%) patients, respectively. Comparative analysis revealed that the AutoML models exhibited enhanced performance over traditional logistic regression (LR). Furthermore, the deep learning (DL) model demonstrated superior predictive accuracy, evidenced by an area under the ROC curve of 0.963 in the training set and 0.830 in the validation set, surpassing other comparative models. The key variables identified as significant in the DL model within the training dataset included creatinine (Cr), urea (Urea), international normalized ratio (INR), etiology, smoking, alanine aminotransferase (ALT), hypertension, prothrombin time (PT), lactate dehydrogenase (LDH), and diabetes.

CONCLUSION

The AutoML model, utilizing DL algorithm, offers considerable clinical significance in the early detection of AKI among patients with AP.

Clinical characteristics and risk factors of organ failure and death in necrotizing pancreatitis

BACKGROUND

Organ failure (OF) and death are considered the most significant adverse outcomes in necrotizing pancreatitis (NP). However, there are few NP-related studies describing the clinical traits of OF and aggravated outcomes.

PURPOSE

An improved insight into the details of OF and death will be helpful to the management of NP. Thus, in our research, we addressed the risk factors of OF and death in NP patients.

METHODS

We performed a study of 432 NP patients from May 2017 to December 2021. All patients with NP were followed up for 36 months. The primary end-points were risk factors of OF and death in NP patients. The risk factors were evaluated by logistic regression analysis.

RESULTS

NP patients with OF or death patients were generally older, had a higher APACHE II score, longer hospital stay, longer ICU stay, as well as a higher incidence of severe acute pancreatitis (SAP), shock and pancreatic necrosis. Independent risk factors related to OF included BMI, APACHE II score and SAP (P < 0.05). Age, shock and APACHE II score (P < 0.05) were the most significant factors correlated with the risk of death in NP patients. Notably, increased mortality was linked to the number of failed organs.

CONCLUSIONS

NP is a potentially fatal disease with a long hospital or ICU stay. Our study indicated that the incidence of OF and death in NP patients was 69.9% and 10.2%, respectively. BMI, SAP, APACHE II score, age and shock are potential risk factors of OF and death in NP patients. Clinicians should focus on these factors for early diagnosis and appropriate therapy.

Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals

Background

Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. This study aims to explore different ML models for early identification of severe acute pancreatitis (SAP) among patients hospitalized for acute pancreatitis.

Methods

This retrospective study enrolled patients with acute pancreatitis (AP) from multiple centers. Data from the First Affiliated Hospital and Changshu No. 1 Hospital of Soochow University were adopted for training and internal validation, and data from the Second Affiliated Hospital of Soochow University were adopted for external validation from January 2017 to December 2021. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of acute pancreatitis. Models were built using traditional logistic regression (LR) and automated machine learning (AutoML) analysis with five types of algorithms. The performance of models was evaluated by the receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) based on LR and feature importance, SHapley Additive exPlanation (SHAP) Plot, and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML.

Results

A total of 1,012 patients were included in this study to develop the AutoML models in the training/validation dataset. An independent dataset of 212 patients was used to test the models. The model developed by the gradient boost machine (GBM) outperformed other models with an area under the ROC curve (AUC) of 0.937 in the validation set and an AUC of 0.945 in the test set. Furthermore, the GBM model achieved the highest sensitivity value of 0.583 among these AutoML models. The model developed by eXtreme Gradient Boosting (XGBoost) achieved the highest specificity value of 0.980 and the highest accuracy of 0.958 in the test set.

Conclusions

The AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability.

Dynamic nomogram for persistent organ failure in acute biliary pancreatitis: Development and validation in a retrospective study

BACKGROUND

Persistent organ failure (POF) increases the risk of death in patients with acute biliary pancreatitis (ABP). Currently, there is no early risk assessment tool for POF in patients with ABP.

AIMS

To establish and validate a dynamic nomogram for predicting the risk of POF in ABP.

METHODS

This was a retrospective study of 792 patients with ABP, with 595 cases in the development group and 197 cases in the validation group. Least absolute shrinkage and selection operator regression screened the predictors of POF, and logistic regression established the model (P < 0.05). A dynamic nomogram showed the model. We evaluated the model's discrimination, calibration, and clinical effectiveness; used the bootstrap method for internal validation; and conducted external validation in the validation group.

RESULTS

Neutrophils, haematocrit, serum calcium, and blood urea nitrogen were predictors of POF in ABP. In the development group and validation group, the areas under the receiver operating characteristic curves (AUROCs) were 0.875 and 0.854, respectively, and the Hosmer-Lemeshow test (P > 0.05) and calibration curve showed good consistency between the actual and prediction probability. Decision curve analysis showed that the dynamic nomogram has excellent clinical value.

CONCLUSION

This dynamic nomogram helps with the early identification and screening of high-risk patients with POF in ABP.

Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning

Background: Current approaches to predicting intervention needs and mortality have reached 65–85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning (ML). Methods: Data were obtained from three databases of patients admitted with AP: one retrospective (Chengdu) and two prospective (Liverpool and Chengdu) databases. Intervention and mortality differences, as well as potential predictors, were investigated. Univariate analysis was conducted, followed by a random forest ML algorithm used in multivariate analysis, to identify predictors. The ML performance matrix was applied to evaluate the model’s performance. Results: Three datasets of 2846 patients included 25 potential clinical predictors in the univariate analysis. The top ten identified predictors were obtained by ML models, for predicting interventions and mortality, from the training dataset. The prediction of interventions includes death in non-intervention patients, validated with high accuracy (96%/98%), the area under the receiver-operating-characteristic curve (0.90/0.98), and positive likelihood ratios (22.3/69.8), respectively. The post-test probabilities in the test set were 55.4% and 71.6%, respectively, which were considerably superior to existing prognostic scores. The ML model, for predicting mortality in intervention patients, performed better or equally with prognostic scores. Conclusions: ML, using admission clinical predictors, can accurately predict therapeutic interventions and mortality in patients with AP.

The association of parameters of body composition and laboratory markers with the severity of hypertriglyceridemia-induced pancreatitis

BACKGROUND

Hypertriglyceridemia has arisen as the third leading cause of acute pancreatitis. This study aimed at exploring the association between the severity of hypertriglyceridemia-induced pancreatitis (HTGP) and computed tomography (CT)-based body composition parameters and laboratory markers.

METHODS

Laboratory and clinical parameters were collected from 242 patients with HTGP between 2017 and 2020. Severity of HTGP was evaluated by original or modified CT severity index. Body composition parameters such as area and radiodensity of muscle, subcutaneous adipose tissue and visceral adipose tissue were calculated by CT at the level of third lumbar vertebra. Parameters were compared between mild and moderately severe to severe HTGP. Uni-variate and multi-variate Logistic regression analyses were employed to assess the risk factors of the severity of HTGP.

RESULTS

Seventy patients (28.9%) presented with mild HTGP. Body mass index, waist circumference and all CT-based body composition parameters differed between male and female patients. None was associated with the severity of HTGP, neither in males nor in females. Receiver operating characteristic curves showed that areas under the curves of apolipoprotein A-I and albumin to predict the severity of HTGP were 0.786 and 0.759, respectively (all P < 0.001). Uni-variate and further multi-variate Logistic regression analysis confirmed that low serum albumin (< 35 g/L, P = 0.004, OR = 3.362, 95%CI = 1.492-8.823) and apolipoprotein A-I (< 1.1 g/L, P < 0.001, OR = 5.126, 95%CI = 2.348-11.195), as well as high C-reactive protein (> 90 mg/L, P = 0.005, OR = 3.061, 95%CI = 1.407-6.659) and lipase (P = 0.033, OR = 2.283, 95%CI = 1.070-4.873) were risk factors of moderately severe to severe HTGP. Levels of albumin, apolipoprotein A-I, C-reactive protein and lipase were also associated with the length of hospital stay (all P < 0.05). Besides, low serum albumin, low-density lipoprotein cholesterol and high radiodensity of subcutaneous adipose tissue were significant risk factors of pancreatic necrosis in patients with HTGP (all P < 0.05).

CONCLUSIONS

Low serum albumin and apolipoprotein A-I, and high C-reactive protein and lipase upon admission were associated with a more severe type of HTGP and longer hospital stay for these patients. Albumin and apolipoprotein A-I may serve as novel biomarkers for the severity of HTGP. However, none of the body composition parameters was associated with the severity of HTGP.