Pleural effusion volume in patients with acute pancreatitis: a retrospective study from three acute pancreatitis centers

Yan, Gaowu; Li, Hongwei; Bhetuwal, Anup; McClure, Morgan A.; Li, Yongmei; Yang, Guoqing; Li, Yong; Zhao, Linwei; Fan, Xiaoping

doi:10.1080/07853890.2021.1998594

Cited by 17 publications

(40 citation statements)

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“…Pleural effusion is reported to be associated with a severe course for initial risk assessment severity in AP and a sign of SAP (Heller et al, 1997;Tenner et al, 2013;Lankisch et al, 2015). Yan et al reported that pleural effusion volume quantified on chest CT was positively associated with the duration of hospitalization (Yan et al, 2021). As a prognostic factor, pleural effusion has been incorporated in SAP severity predictive systems such as the Bedside Index for Severity in Acute Pancreatitis (BISAP) score (Gao et al, 2015), the Panc 3 score (Brown et al, 2007), and the Extra Pancreatic Inflammation on CT (EPIC) score (De Waele et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Takeda et al reported that fluid sequestration is a useful parameter in the early identification of SAP (Takeda et al, 2019). Yan et al described that pleural effusion volume could be a reliable radiologic biomarker in the prediction of severity and clinical outcomes of AP (Yan et al, 2021). Gibor et al reported that circulating cell-free DNA in patients with acute biliary pancreatitis is associated with disease markers and prolonged hospitalization time (Gibor et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis

Hong

Zhou

et al. 2022

Front. Cell. Infect. Microbiol.

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Background and AimsThis study aimed to develop an interpretable random forest model for predicting severe acute pancreatitis (SAP).MethodsClinical and laboratory data of 648 patients with acute pancreatitis were retrospectively reviewed and randomly assigned to the training set and test set in a 3:1 ratio. Univariate analysis was used to select candidate predictors for the SAP. Random forest (RF) and logistic regression (LR) models were developed on the training sample. The prediction models were then applied to the test sample. The performance of the risk models was measured by calculating the area under the receiver operating characteristic (ROC) curves (AUC) and area under precision recall curve. We provide visualized interpretation by using local interpretable model-agnostic explanations (LIME).ResultsThe LR model was developed to predict SAP as the following function: -1.10-0.13×albumin (g/L) + 0.016 × serum creatinine (μmol/L) + 0.14 × glucose (mmol/L) + 1.63 × pleural effusion (0/1)(No/Yes). The coefficients of this formula were utilized to build a nomogram. The RF model consists of 16 variables identified by univariate analysis. It was developed and validated by a tenfold cross-validation on the training sample. Variables importance analysis suggested that blood urea nitrogen, serum creatinine, albumin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, calcium, and glucose were the most important seven predictors of SAP. The AUCs of RF model in tenfold cross-validation of the training set and the test set was 0.89 and 0.96, respectively. Both the area under precision recall curve and the diagnostic accuracy of the RF model were higher than that of both the LR model and the BISAP score. LIME plots were used to explain individualized prediction of the RF model.ConclusionsAn interpretable RF model exhibited the highest discriminatory performance in predicting SAP. Interpretation with LIME plots could be useful for individualized prediction in a clinical setting. A nomogram consisting of albumin, serum creatinine, glucose, and pleural effusion was useful for prediction of SAP.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis

Hong

Zhou

et al. 2022

Front. Cell. Infect. Microbiol.

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“…The SHAP analysis demonstrated that the occurrence of PE at admission was the most important feature for the GBM model. Yan et al (2021) conducted research on pleural effusion volume (PEV) for predicting the severity of acute pancreatitis, with an AUC of 0.8158. Similarly, a study from Peng et al in 2019(Peng et al, 2020 also revealed that PEV holds a high accuracy (AUC = 0.839) for predicting the occurrence of SAP.…”

Section: Discussionmentioning

confidence: 99%

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

Yin

Zhang

Zhou

et al. 2022

Front. Cell. Infect. Microbiol.

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BackgroundMachine 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.MethodsThis 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.ResultsA 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.ConclusionsThe AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability.

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“…Heller et al reported that pleural effusion was observed in 84.2% of patients with severe AP, while it was observed in only 8.6% of those with mild AP [37]. Furthermore, the pleural effusion volume was associated with the duration of admission, and CRP levels showed the possibility of a reliable radiologic predictor of the severity of AP [38]. Subcutaneous or visceral adipose tissue, skeletal muscle mass or density, and mean muscle attenuation were evaluated in the context of CT defined body composition analysis, and reported promising results for the prediction of AP severity; however, a lack of prospective studies of different races is hurdle to overcome [39,40].…”

Section: Computed Tomographymentioning

confidence: 99%

Predicting Severity of Acute Pancreatitis

Lee

Cho

2022

Medicina

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Acute pancreatitis has a diverse etiology and natural history, and some patients have severe complications with a high risk of mortality. The prediction of the severity of acute pancreatitis should be achieved by a careful ongoing clinical assessment coupled with the use of a multiple-factor scoring system and imaging studies. Over the past 40 years, various scoring systems have been suggested to predict the severity of acute pancreatitis. However, there is no definite and ideal scoring system with a high sensitivity and specificity. The interest in new biological markers and predictive models for identifying severe acute pancreatitis testifies to the continued clinical importance of early severity prediction. Although contrast-enhanced computed tomography (CT) is considered the gold standard for diagnosing pancreatic necrosis, early scanning for the prediction of severity is limited because the full extent of pancreatic necrosis may not develop within the first 48 h of presentation. This article provides an overview of the available scoring systems and biochemical markers for predicting severe acute pancreatitis, with a focus on their characteristics and limitations.

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Pleural effusion volume in patients with acute pancreatitis: a retrospective study from three acute pancreatitis centers

Cited by 17 publications

References 50 publications

Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis

Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis

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

Predicting Severity of Acute Pancreatitis

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