Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein

Constructing a prediction model for acute pancreatitis severity based on liquid neural network

Acute pancreatitis (AP) is a common disease, and severe acute pancreatitis (SAP) has a high morbidity and mortality rate. Early recognition of SAP is crucial for prognosis. This study aimed to develop a novel liquid neural network (LNN) model for predicting SAP. This study retrospectively analyzed the data of AP patients admitted to the Second Affiliated Hospital of Guilin Medical University between January 2020 and June 2024. Data imbalance was dealt with by data preprocessing and using the synthetic minority oversampling technique (SMOTE). A new feature selection method was designed to optimize model performance. Logistic regression (LR), decision tree (DCT), random forest (RF), Extreme Gradient Boosting (XGBoost), and LNN models were built. The model's performance was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC) and other statistical metrics. In addition, SHapley Additive exPlanations (SHAP) analysis was used to interpret the prediction results of the LNN model. The LNN model performed best in predicting AP severity, with an AUC value of 0.9659 and accuracy, precision, recall, F1 score, and specificity higher than 0.90. SHAP analysis revealed key predictors, such as calcium level, amylase activity, and percentage of basophils, which were strongly associated with AP severity. As an emerging machine learning tool, the LNN model has demonstrated excellent performance and potential in AP severity prediction. The results of this study support the idea that LNN models can be applied to early severity assessment of AP patients in a clinical setting, which can help optimize treatment plans and improve patient prognosis.

Past, Present, and Future: A History Lesson in Artificial Intelligence

Over the past 5 decades, artificial intelligence (AI) has evolved rapidly. Moving from basic models to advanced machine learning and deep learning systems, the impact of AI on various fields, including medicine, has been profound. In gastroenterology, AI-driven computer-aided detection and computer-aided diagnosis systems have revolutionized endoscopy, imaging, and pathology detection. The future promises further advancements in diagnostic precision, personalized treatment, and clinical research. However, challenges such as transparency, liability, and ethical concerns must be addressed. By fostering collaboration, robust governance and development of quality metrics, AI can be leveraged to enhance patient care and advance scientific knowledge.

Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit

BACKGROUND

Current prediction models are suboptimal for determining mortality risk in patients with acute pancreatitis (AP); this might be improved by using a machine learning (ML) model. In this study, we aimed to construct an explainable ML model to calculate the risk of mortality in patients with AP admitted in intensive care unit (ICU) and compared it with existing scoring systems.

METHODS

A gradient-boosting ML (XGBoost) model was developed and externally validated based on two public databases: Medical Information Mart for Intensive Care (MIMIC, training cohort) and the eICU Collaborative Research Database (eICU-CRD, validation cohort). We compared the performance of the XGBoost model with validated clinical risk scoring systems (the APACHE IV, SOFA, and Bedside Index for Severity in Acute Pancreatitis [BISAP]) by area under receiver operating characteristic curve (AUC) analysis. SHAP (SHapley Additive exPlanations) method was applied to provide the explanation behind the prediction outcome.

RESULTS

The XGBoost model performed better than the clinical scoring systems in correctly predicting mortality risk of AP patients, achieving an AUC of 0.89 (95% CI: 0.84-0.94). When set the sensitivity at 100% for death prediction, the model had a specificity of 38%, much higher than the APACHE IV, SOFA and BISAP score, which had a specificity of 1%, 16% and 1% respectively.

CONCLUSIONS

This model might increase identification of very low-risk patients who can be safely monitored in a general ward for management. By making the model explainable, physicians would be able to better understand the reasoning behind the prediction.

Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP

BACKGROUND

Severe acute pancreatitis (SAP) can be fatal if left unrecognized and untreated. The purpose was to develop a machine learning (ML) model for predicting the 30-day all-cause mortality risk in SAP patients and to explain the most important predictors.

METHODS

This research utilized six ML methods, including logistic regression (LR), k-nearest neighbors(KNN), support vector machines (SVM), naive Bayes (NB), random forests(RF), and extreme gradient boosting(XGBoost), to construct six predictive models for SAP. An extensive evaluation was conducted to determine the most effective model and then the Shapley Additive exPlanations (SHAP) method was applied to visualize key variables. Utilizing the optimized model, stratified predictions were made for patients with SAP. Further, the study employed multivariable Cox regression analysis and Kaplan-Meier survival curves, along with subgroup analysis, to explore the relationship between the machine learning-based score and 30-day mortality.

RESULTS

Through LASSO regression and recursive feature elimination (RFE), 25 optimal feature variables are selected. The XGBoost model performed best, with an area under the curve (AUC) of 0.881, a sensitivity of 0.5714, a specificity of 0.9651 and an F1 score of 0.64. The first six most important feature variables were the use of vasopressor, high Charlson comorbidity index, low blood oxygen saturation, history of malignant tumor, hyperglycemia and high APSIII score. Based on the optimal threshold of 0.62, patients were divided into high and low-risk groups, and the 30-day survival rate in the high-risk group decreased significantly. COX regression analysis further confirmed the positive correlation between high-risk scores and 30-day mortality. In the subgroup analysis, the model showed good risk stratification ability in patients with different gender, renal replacement therapy and with or without a history of malignant tumor, but it was not effective in predicting peripheral vascular disease.

CONCLUSIONS

the XGBoost model effectively predicts the severity of SAP, serving as a valuable tool for clinicians to identify SAP early.

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.

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.

Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule

OBJECTIVE

Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect.

METHODS

Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP).

RESULTS

The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817).

CONCLUSIONS

The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

Metabolic-associated fatty liver disease is associated with acute pancreatitis with more severe course: Post hoc analysis of a prospectively collected international registry

INTRODUCTION

Non-alcoholic fatty liver disease (NAFLD) is a proven risk factor for acute pancreatitis (AP). However, NAFLD has recently been redefined as metabolic-associated fatty liver disease (MAFLD). In this post hoc analysis, we quantified the effect of MAFLD on the outcomes of AP.

METHODS

We identified our patients from the multicentric, prospective International Acute Pancreatitis Registry of the Hungarian Pancreatic Study Group. Next, we compared AP patients with and without MAFLD and the individual components of MAFLD regarding in-hospital mortality and AP severity based on the revised Atlanta classification. Lastly, we calculated odds ratios (ORs) with 95% confidence intervals (CIs) using multivariate logistic regression analysis.

RESULTS

MAFLD had a high prevalence in AP, 39% (801/2053). MAFLD increased the odds of moderate-to-severe AP (OR = 1.43, CI: 1.09-1.89). However, the odds of in-hospital mortality (OR = 0.89, CI: 0.42-1.89) and severe AP (OR = 1.70, CI: 0.97-3.01) were not higher in the MAFLD group. Out of the three diagnostic criteria of MAFLD, the highest odds of severe AP was in the group based on metabolic risk abnormalities (OR = 2.68, CI: 1.39-5.09). In addition, the presence of one, two, and three diagnostic criteria dose-dependently increased the odds of moderate-to-severe AP (OR = 1.23, CI: 0.88-1.70, OR = 1.38, CI: 0.93-2.04, and OR = 3.04, CI: 1.63-5.70, respectively) and severe AP (OR = 1.13, CI: 0.54-2.27, OR = 2.08, CI: 0.97-4.35, and OR = 4.76, CI: 1.50-15.4, respectively). Furthermore, in patients with alcohol abuse and aged ≥60 years, the effect of MAFLD became insignificant.

CONCLUSIONS

MAFLD is associated with AP severity, which varies based on the components of its diagnostic criteria. Furthermore, MAFLD shows a dose-dependent effect on the outcomes of AP.

Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence

OBJECTIVE

To evaluate the success of artificial intelligence for early prediction of severe course, survival, and intensive care unit(ICU) requirement in patients with acute pancreatitis(AP).

METHODS

Retrospectively, 1334 patients were included the study. Severity is determined according to the Revised Atlanta Classification(RAC). The success of machine learning(ML) method was evaluated by 13 simple demographic, clinical, etiologic, and laboratory features obtained on ER admission. Additionally, it was evaluated whether Balthazar-computerized tomography severity index(CTSI) at 48-h contributed to success. The dataset was split into two parts, 90% for ML(of which 70% for learning and 30% for testing) and 10% for validation and 5-fold stratified sampling has been utilized. Variable Importance was used in the selection of features during training phase of machine. The Gradient Boost Algorithm trained the machine by KNIME analytics platform. SMOTE has been applied to increase the minority classes for training. The combined effects of the measured features were examined by multivariate logistic regression analysis and reciever operating curve curves of the prediction and confidence of the target variables were obtained.

RESULTS

Accuracy values for the early estimation of Atlanta severity score, ICU requirement, and survival were found as 88.20%, 98.25%, and 92.77% respectively. When Balthazar-CTSI score is used, results were found as 91.02%, 92.25%, and 98% respectively.

CONCLUSIONS

The ML method we used successfully predicted the severe course, ICU requirement and survival, with promising accuracy values of over 88%. If 48-h Balthazar-CTSI is included in the calculation, the severity score and survival rates increase even more.

Deep Learning Models for Severity Prediction of Acute Pancreatitis in the Early Phase From Abdominal Nonenhanced Computed Tomography Images

OBJECTIVES

To develop and validate deep learning (DL) models for predicting the severity of acute pancreatitis (AP) by using abdominal nonenhanced computed tomography (CT) images.

METHODS

The study included 978 AP patients admitted within 72 hours after onset and performed abdominal CT on admission. The image DL model was built by the convolutional neural networks. The combined model was developed by integrating CT images and clinical markers. The performance of the models was evaluated by using the area under the receiver operating characteristic curve.

RESULTS

The clinical, Image DL, and the combined DL models were developed in 783 AP patients and validated in 195 AP patients. The combined models possessed the predictive accuracy of 90.0%, 32.4%, and 74.2% for mild, moderately severe, and severe AP. The combined DL model outperformed clinical and image DL models with 0.820 (95% confidence interval, 0.759-0.871), the sensitivity of 84.76% and the specificity of 66.67% for predicting mild AP and the area under the receiver operating characteristic curve of 0.920 (95% confidence interval, 0.873-0.954), the sensitivity of 90.32%, and the specificity of 82.93% for predicting severe AP.

CONCLUSIONS

The DL technology allows nonenhanced CT images as a novel tool for predicting the severity of AP.

The Potential Role of Model for End-Stage Liver Disease (MELD)-Sodium Score in Predicting the Severity of Acute Pancreatitis

Background and aim Acute pancreatitis is a common inflammation of the pancreas which can be severe and even potentially mortal. High rates of mortality showed the importance of immediate identification of patients at high risk and led the clinicians to refer to various scoring systems. Our aim was to investigate a clinical predictive model using the Model for End-Stage Liver Disease-Sodium (MELD-sodium) scoring system, adapting it to acute pancreatitis patients referring to the systemic inflammatory nature of the disease and potential multi-organ failures in severe form. Methods Our multicenter study was designed retrospectively. The medical records were reviewed for the period of two years. Demographics, biochemical results, MELD-sodium scores and mortality rates were analysed. Results MELD-sodium score was found to be statistically correlated with both mortality and the severity of pancreatitis (p<0.001) and significant difference between both mild and severe (p<0.001), moderate and severe groups (p<0.001). Mortality was found to be significantly higher in patients with MELD-Na score when the cut-off value was accepted as '≥11'. Conclusion We found that MELD-sodium score was significantly associated with both severity of disease and mortality rates and also significantly effective between both mild/severe and moderate/severe groups which may be a guide for future multi-center reviews with larger patient and control groups, which can define the potential role of this non-invasive and easy-to-use predictive model in acute pancreatitis patients.

Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study

BACKGROUND

Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations.

OBJECTIVE

To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables.

METHODS

5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit.

RESULTS

Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08-8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67-7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28-5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14-9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively.

CONCLUSION

Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.

Predicting Severity of Acute Pancreatitis

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.

EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis

BACKGROUND

Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed.

METHODS

The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP).

RESULTS

The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/).

CONCLUSIONS

The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.

Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases

Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complicated or require data that are unavailable on admission or lack sufficient predictive value. We therefore aimed to develop a tool to aid in necrosis prediction. The XGBoost machine learning algorithm processed data from 2387 patients with AP. The confidence of the model was estimated by a bootstrapping method and interpreted via the 10th and the 90th percentiles of the prediction scores. Shapley Additive exPlanations (SHAP) values were calculated to quantify the contribution of each variable provided. Finally, the model was implemented as an online application using the Streamlit Python-based framework. The XGBoost classifier provided an AUC value of 0.757. Glucose, C-reactive protein, alkaline phosphatase, gender and total white blood cell count have the most impact on prediction based on the SHAP values. The relationship between the size of the training dataset and model performance shows that prediction performance can be improved. This study combines necrosis prediction and artificial intelligence. The predictive potential of this model is comparable to the current clinical scoring systems and has several advantages over them.

Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches

Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.

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.

Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction

Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.

Machine learning predictive models for acute pancreatitis: A systematic review

INTRODUCTION

Acute pancreatitis (AP) is a common clinical pancreatic disease. Patients with different severity levels have different clinical outcomes. With the advantages of algorithms, machine learning (ML) has gradually emerged in the field of disease prediction, assisting doctors in decision-making.

METHODS

A systematic review was conducted using the PubMed, Web of Science, Scopus, and Embase databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Publication time was limited from inception to 29 May 2021. Studies that have used ML to establish predictive tools for AP were eligible for inclusion. Quality assessment of the included studies was conducted in accordance with the IJMEDI checklist.

RESULTS

In this systematic review, 24 of 2,913 articles, with a total of 8,327 patients and 47 models, were included. The studies could be divided into five categories: 10 studies (42%) reported severity prediction; 10 studies (42%), complication prediction; 3 studies (13%), mortality prediction; 2 studies (8%), recurrence prediction; and 2 studies (8%), surgery timing prediction. ML showed great accuracy in several prediction tasks. However, most of the included studies were retrospective in nature, conducted at a single centre, based on database data, and lacked external validation. According to the IJMEDI checklist and our scoring criteria, two studies were considered to be of high quality. Most studies had an obvious bias in the quality of data preparation, validation, and deployment dimensions.

CONCLUSION

In the prediction tasks for AP, ML has shown great potential in assisting decision-making. However, the existing studies still have some deficiencies in the process of model construction. Future studies need to optimize the deficiencies and further evaluate the comparability of the ML systems and model performance, so as to consequently develop high-quality ML-based models that can be used in clinical practice.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach

Background

A feasible and accurate risk prediction systems for emergency department (ED) patients is urgently required. The Modified Early Warning Score (MEWS) is a wide-used tool to predict clinical outcomes in ED. Literatures showed that machine learning (ML) had better predictability in specific patient population than traditional scoring system. By analyzing a large multicenter dataset, we aim to develop a ML model to predict in-hospital morality of the adult non traumatic ED patients for different time stages, and comparing performance with other ML models and MEWS.

Methods

A retrospective observational cohort study was conducted in five Taiwan EDs including two tertiary medical centers and three regional hospitals. All consecutively adult (>17 years old) non-traumatic patients admit to ED during a 9-year period (January first, 2008 to December 31th, 2016) were included. Exclusion criteria including patients with (1) out-of-hospital cardiac arrest and (2) discharge against medical advice and transferred to other hospital (3) missing collect variables. The primary outcome was in-hospital mortality and were categorized into 6, 24, 72, 168 hours mortality. MEWS was calculated by systolic blood pressure, pulse rate, respiratory rate, body temperature, and level of consciousness. An ensemble supervised stacking ML model was developed and compared to sensitive and unsensitive Xgboost, Random Forest, and Adaboost. We conducted a performance test and examine both the area under the receiver operating characteristic (AUROC) and the area under the precision and recall curve (AUPRC) as the comparative measures.

Result

After excluding 182,001 visits (7.46%), study group was consisted of 24,37,326 ED visits. The dataset was split into 67% training data and 33% test data for ML model development. There was no statistically difference found in the characteristics between two groups. For the prediction of 6, 24, 72, 168 hours in-hospital mortality, the AUROC of MEW and ML mode was 0.897, 0.865, 0.841, 0.816 and 0.939, 0.928, 0.913, 0.902 respectively. The stacking ML model outperform other ML model as well. For the prediction of in-hospital mortality over 48-hours, AUPRC performance of MEWS drop below 0.1, while the AUPRC of ML mode was 0.317 in 6 hours and 0.2150 in 168 hours. For each time frame, ML model achieved statistically significant higher AUROC and AUPRC than MEWS (all P < 0.001). Both models showed decreasing prediction ability as time elapse, but there was a trend that the gap of AUROC values between two model increases gradually (P < 0.001). Three MEWS thresholds (score >3, >4, and >5) were determined as baselines for comparison, ML mode consistently showed improved or equally performance in sensitivity, PPV, NPV, but not in specific.

Conclusion

Stacking ML methods improve predicted in-hospital mortality than MEWS in adult non-traumatic ED patients, especially in the prediction of delayed mortality.

Ranson score to stratify severity in Acute Pancreatitis remains valid - Old is gold

Introduction: Acute pancreatitis (AP) is a common gastrointestinal disease with a wide spectrum of severity and morbidity. Developed in 1974, the Ranson score was the first scoring system to prognosticate AP. Over the past decades, while the Ranson score remains widely used, it was identified to have certain limitations, such as having low predictive power. It has also been criticized for its 48-hour requirement for computation of the final score, which has been argued to potentially delay management. With advancements in our understanding of AP, is the Ranson score still relevant as an effective prognostication system for AP?Areas covered: This review summarizes the available evidence comparing Ranson score with other conventional and novel scoring systems, in terms of prognostic accuracy, benefits, limitations and clinical applicability. It also evaluates the effectiveness of Ranson score with regard to the Revised Atlanta Classification.Expert opinion: The Ranson score consistently exhibits comparable prognostic accuracy to other newer scoring systems, and the 48-hour timeframe for computing the full Ranson score is an inherent strength, not a weakness. These aspects, coupled with relative ease of use, practicality and universality of the score, advocate for the continued relevance of the Ranson score in modern clinical practice.

Prediction of Multiple Organ Failure Complicated by Moderately Severe or Severe Acute Pancreatitis Based on Machine Learning: A Multicenter Cohort Study

Background

Multiple organ failure (MOF) may lead to an increased mortality rate of moderately severe (MSAP) or severe acute pancreatitis (SAP). This study is aimed to use machine learning to predict the risk of MOF in the course of disease.

Methods

Clinical and laboratory features with significant differences between patients with and without MOF were screened out by univariate analysis. Prediction models were developed for selected features through six machine learning methods. The models were internally validated with a five-fold cross-validation, and a series of optimal feature subsets were generated in corresponding models. A test set was used to evaluate the predictive performance of the six models.

Results

305 (68%) of 455 patients with MSAP or SAP developed MOF. Eighteen features with significant differences between the group with MOF and without it in the training and validation set were used for modeling. Interleukin-6 levels, creatinine levels, and the kinetic time were the three most important features in the optimal feature subsets selected by K-fold cross-validation. The adaptive boosting algorithm (AdaBoost) showed the best predictive performance with the highest AUC value (0.826; 95% confidence interval: 0.740 to 0.888). The sensitivity of AdaBoost (80.49%) and specificity of logistic regression analysis (93.33%) were the best scores among the six models in the test set.

Conclusions

A predictive model of MOF complicated by MSAP or SAP was successfully developed based on machine learning. The predictive performance was evaluated by a test set, for which AdaBoost showed a satisfactory predictive performance. The study is registered with the China Clinical Trial Registry (Identifier: ChiCTR1800016079).

Artificial intelligence in pancreaticobiliary endoscopy

Artificial intelligence (AI) applications in health care have exponentially increased in recent years, and a few of these are related to pancreatobiliary disorders. AI-based methods were applied to extract information, in prognostication, to guide clinical treatment decisions and in pancreatobiliary endoscopy to characterize lesions. AI applications in endoscopy are expected to reduce inter-operator variability, improve the accuracy of diagnosis, and assist in therapeutic decision-making in real time. AI-based literature must however be interpreted with caution given the limited external validation. A multidisciplinary approach combining clinical and imaging or endoscopy data will better utilize AI-based technologies to further improve patient care.

Artificial intelligence for the management of pancreatic diseases

Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.

Application of Artificial Intelligence to Gastroenterology and Hepatology

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

Predicting factors for survival of breast cancer patients using machine learning techniques

BACKGROUND

Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate.

METHODS

A large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis.

RESULTS

In terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis.

CONCLUSION

Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.

Recent advances in understanding and managing acute pancreatitis

This review highlights advances made in recent years in the diagnosis and management of acute pancreatitis (AP). We focus on epidemiological, clinical, and management aspects of AP. Additionally, we discuss the role of using risk stratification tools to guide clinical decision making. The majority of patients suffer from mild AP, and only a subset develop moderately severe AP, defined as a pancreatic local complication, or severe AP, defined as persistent organ failure. In mild AP, management typically involves diagnostic evaluation and supportive care resulting usually in a short hospital length of stay (LOS). In severe AP, a multidisciplinary approach is warranted to minimize morbidity and mortality over the course of a protracted hospital LOS. Based on evidence from guideline recommendations, we discuss five treatment interventions, including intravenous fluid resuscitation, feeding, prophylactic antibiotics, probiotics, and timing of endoscopic retrograde cholangiopancreatography (ERCP) in acute biliary pancreatitis. This review also highlights the importance of preventive interventions to reduce hospital readmission or prevent pancreatitis, including alcohol and smoking cessation, same-admission cholecystectomy for acute biliary pancreatitis, and chemoprevention and fluid administration for post-ERCP pancreatitis. Our review aims to consolidate guideline recommendations and high-quality studies published in recent years to guide the management of AP and highlight areas in need of research.

Recent advances in understanding and managing acute pancreatitis

This review highlights advances made in recent years in the diagnosis and management of acute pancreatitis (AP). We focus on epidemiological, clinical, and management aspects of AP. Additionally, we discuss the role of using risk stratification tools to guide clinical decision making. The majority of patients suffer from mild AP, and only a subset develop moderately severe AP, defined as a pancreatic local complication, or severe AP, defined as persistent organ failure. In mild AP, management typically involves diagnostic evaluation and supportive care resulting usually in a short hospital length of stay (LOS). In severe AP, a multidisciplinary approach is warranted to minimize morbidity and mortality over the course of a protracted hospital LOS. Based on evidence from guideline recommendations, we discuss five treatment interventions, including intravenous fluid resuscitation, feeding, prophylactic antibiotics, probiotics, and timing of endoscopic retrograde cholangiopancreatography (ERCP) in acute biliary pancreatitis. This review also highlights the importance of preventive interventions to reduce hospital readmission or prevent pancreatitis, including alcohol and smoking cessation, same-admission cholecystectomy for acute biliary pancreatitis, and chemoprevention and fluid administration for post-ERCP pancreatitis. Our review aims to consolidate guideline recommendations and high-quality studies published in recent years to guide the management of AP and highlight areas in need of research.

Prediction of Future Chronic Opioid Use Among Hospitalized Patients

BACKGROUND

Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ≥ 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of ≥ 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge.

OBJECTIVE

The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization.

DESIGN

Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression.

PATIENTS

Hospitalized patients at an urban, safety net hospital.

MAIN MEASUREMENTS

Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others.

KEY RESULTS

Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden's index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients.

CONCLUSIONS

Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.

An Empirically Derived Pediatric Cardiac Inotrope Score Associated With Pediatric Heart Surgery

We aimed to empirically derive an inotrope score to predict real-time outcomes using the doses of inotropes after pediatric cardiac surgery. The outcomes evaluated included in-hospital mortality, prolonged hospital length of stay, and composite poor outcome (mortality or prolonged hospital length of stay). The study population included patients <18 years of age undergoing heart operations (with or without cardiopulmonary bypass) of varying complexity. To create this novel pediatric cardiac inotrope score (PCIS), we collected the data on the highest doses of 4 commonly used inotropes (epinephrine, norepinephrine, dopamine, and milrinone) in the first 24 hours after heart operation. We employed a hierarchical framework by representing discrete probability models with continuous latent variables that depended on the dosage of drugs for a particular patient. We used Bayesian conditional probit regression to model the effects of the inotropes on the mean of the latent variables. We then used Markov chain Monte Carlo simulations for simulating posterior samples to create a score function for each of the study outcomes. The training dataset utilized 1030 patients to make the scientific model. An online calculator for the tool can be accessed at https://soipredictiontool.shinyapps.io/InotropeScoreApp. The newly proposed empiric PCIS demonstrated a high degree of discrimination for predicting study outcomes in children undergoing heart operations. The newly proposed empiric PCIS provides a novel measure to predict real-time outcomes using the doses of inotropes among children undergoing heart operations of varying complexity.

Elevated red cell distribution width at initiation of critical care is associated with mortality in surgical intensive care unit patients

PURPOSE

Recent evidence suggests that red cell distribution width (RDW) is associated with mortality in mixed cohorts of critically ill patients. Our goal was to investigate whether elevated RDW at initiation of critical care in the intensive care unit (ICU) is associated with 90-day mortality in surgical patients.

METHODS

We performed a retrospective, single-center cohort study. Normal RDW was defined as 11.5%-14.5%. To investigate the association of admission RDW with 90-day mortality, we performed a logistic regression analysis, controlling for age, sex, race, body mass index, Nutrition Risk Screening 2002 score, Acute Physiology and Chronic Health Evaluation II score, hospital length of stay, as well as levels of creatinine, albumin, and mean corpuscular volume.

RESULTS

500 patients comprised the analytic cohort; 47% patients had elevated RDW and overall 90-day mortality was 28%. Logistic regression analysis demonstrated that patients with elevated RDW had a greater than two-fold increased odds of mortality (OR 2.28: 95%CI 1.20-4.33) compared to patients with normal RDW.

CONCLUSIONS

Elevated RDW at initiation of care is associated with increased odds of 90-day mortality in surgical ICU patients. These data support the need for prospective studies to determine whether RDW can improve risk stratification in surgical ICU patients.

Pediatric Index of Cardiac Surgical Intensive Care Mortality Risk Score for Pediatric Cardiac Critical Care

OBJECTIVE

Comparison of clinical outcomes is imperative in the evaluation of healthcare quality. Risk adjustment for children undergoing cardiac surgery poses unique challenges, due to its distinct nature. We developed a risk-adjustment tool specifically focused on critical care mortality for the pediatric cardiac surgical population: the Pediatric Index of Cardiac Surgical Intensive care Mortality score.

DESIGN

Retrospective analysis of prospectively collected pediatric critical care data.

SETTING

Pediatric critical care units in the United States.

PATIENTS

Pediatric cardiac intensive care surgical patients.

INTERVENTIONS

Prospectively collected data from consecutive patients admitted to ICUs were obtained from The Virtual PICU System (VPS, LLC, Los Angeles, CA), a national pediatric critical care database. Thirty-two candidate physiologic, demographic, and diagnostic variables were analyzed for inclusion in the development of the Pediatric Index of Cardiac Surgical Intensive care Mortality model. Multivariate logistic regression with stepwise selection was used to develop the model.

MEASUREMENTS AND MAIN RESULTS

A total of 16,574 cardiac surgical patients from the 55 PICUs across the United States were included in the analysis. Thirteen variables remained in the final model, including the validated Society of Thoracic Surgeons-European Association of Cardio-Thoracic Surgery Congenital Heart Surgery Mortality (STAT) score and admission time with respect to cardiac surgery, which identifies whether the patient underwent the index surgical procedure before or after admission to the ICU. Pediatric Index of Cardiac Surgical Intensive Care Mortality (PICSIM) performance was compared with the performance of Pediatric Risk of Mortality-3 and Pediatric Index of Mortality-2 risk of mortality scores, as well as the STAT score and STAT categories by calculating the area under the curve of the receiver operating characteristic from a validation dataset: PICSIM (area under the curve = 0.87) performed better than Pediatric Index of Mortality-2 (area under the curve = 0.81), Pediatric Risk of Mortality-3 (area under the curve = 0.82), STAT score (area under the curve = 0.77), STAT category (area under the curve = 0.75), and Risk Adjustment for Congenital Heart Surgery-1 (area under the curve = 0.74).

CONCLUSIONS

This newly developed mortality score, PICSIM, consisting of 13 risk variables encompassing physiology, cardiovascular condition, and time of admission to the ICU showed better discrimination than Pediatric Index of Mortality-2, Pediatric Risk of Mortality-3, and STAT score and category for mortality in a multisite cohort of pediatric cardiac surgical patients. The introduction of the variable "admission time with respect to cardiac surgery" allowed prediction of mortality when patients are admitted to the ICU either before or after the index surgical procedure.

Evidence-Based Pediatric Outcome Predictors to Guide the Allocation of Critical Care Resources in a Mass Casualty Event

OBJECTIVE

ICU resources may be overwhelmed by a mass casualty event, triggering a conversion to Crisis Standards of Care in which critical care support is diverted away from patients least likely to benefit, with the goal of improving population survival. We aimed to devise a Crisis Standards of Care triage allocation scheme specifically for children.

DESIGN

A triage scheme is proposed in which patients would be divided into those requiring mechanical ventilation at PICU presentation and those not, and then each group would be evaluated for probability of death and for predicted duration of resource consumption, specifically, duration of PICU length of stay and mechanical ventilation. Children will be excluded from PICU admission if their mortality or resource utilization is predicted to exceed predetermined levels ("high risk"), or if they have a low likelihood of requiring ICU support ("low risk"). Children entered into the Virtual PICU Performance Systems database were employed to develop prediction equations to assign children to the exclusion categories using logistic and linear regression. Machine Learning provided an alternative strategy to develop a triage scheme independent from this process.

SETTING

One hundred ten American PICUs

SUBJECTS

: One hundred fifty thousand records from the Virtual PICU database.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The prediction equations for probability of death had an area under the receiver operating characteristic curve more than 0.87. The prediction equation for belonging to the low-risk category had lower discrimination. R for the prediction equations for PICU length of stay and days of mechanical ventilation ranged from 0.10 to 0.18. Machine learning recommended initially dividing children into those mechanically ventilated versus those not and had strong predictive power for mortality, thus independently verifying the triage sequence and broadly verifying the algorithm.

CONCLUSION

An evidence-based predictive tool for children is presented to guide resource allocation during Crisis Standards of Care, potentially improving population outcomes by selecting patients likely to benefit from short-duration ICU interventions.

Predictive modeling in pediatric traumatic brain injury using machine learning

BACKGROUND

Pediatric traumatic brain injury (TBI) constitutes a significant burden and diagnostic challenge in the emergency department (ED). While large North American research networks have derived clinical prediction rules for the head injured child, these may not be generalizable to practices in countries with traditionally low rates of computed tomography (CT). We aim to study predictors for moderate to severe TBI in our ED population aged < 16 years.

METHODS

This was a retrospective case-control study based on data from a prospective surveillance head injury database. Cases were included if patients presented from 2006 to 2014, with moderate to severe TBI. Controls were age-matched head injured children from the registry, obtained in a 4 control: 1 case ratio. These children remained well on diagnosis and follow up. Demographics, history, and physical examination findings were analyzed and patients followed up for the clinical course and outcome measures of death and neurosurgical intervention. To predict moderate to severe TBI, we built a machine learning (ML) model and a multivariable logistic regression model and compared their performances by means of Receiver Operating Characteristic (ROC) analysis.

RESULTS

There were 39 cases and 156 age-matched controls. The following 4 predictors remained statistically significant after multivariable analysis: Involvement in road traffic accident, a history of loss of consciousness, vomiting and signs of base of skull fracture. The logistic regression model was created with these 4 variables while the ML model was built with 3 extra variables, namely the presence of seizure, confusion and clinical signs of skull fracture. At the optimal cutoff scores, the ML method improved upon the logistic regression method with respect to the area under the ROC curve (0.98 vs 0.93), sensitivity (94.9% vs 82.1%), specificity (97.4% vs 92.3%), PPV (90.2% vs 72.7%), and NPV (98.7% vs 95.4%).

CONCLUSIONS

In this study, we demonstrated the feasibility of using machine learning as a tool to predict moderate to severe TBI. If validated on a large scale, the ML method has the potential not only to guide discretionary use of CT, but also a more careful selection of head injured children who warrant closer monitoring in the hospital.

Relationship between the degree of severe acute pancreatitis and patient immunity

PURPOSE

To investigate the relationship between the APACHE II score and the immunity of patients with severe acute pancreatitis.

METHODS

Clinical data were collected from 88 patients with acute pancreatitis, divided into four groups according to the severity of the disease. C-reactive protein (CRP), tumor necrosis factor-α, interleukin-6, interleukin-10, interleukin-4 and endotoxin (ET) in serum were measured on admission and then on days 3, 5, and 7.

RESULTS

The incidence of local complications and multiple organ dysfunction syndrome increased with a higher APACHE II score. The CRP levels were increased significantly on day 3 in all four groups, but remained high only in the extremely severe group. In the mild and moderate groups, the pro-/anti-inflammatory cytokines peaked on day 3 and then decreased slowly. In the severe and extremely severe groups, the proinflammatory cytokines levels peaked on days 3 and 5, and then decreased rapidly. The antiinflammatory cytokines increased progressively on days 3, 5 and 7. The ET levels peaked significantly and then decreased slowly in the mild, moderate and severe groups, but remained high in the extremely severe group.

CONCLUSIONS

An APACHE II score of 16 or higher is predictive of more local and systemic complications, excessive immune response, and premature immunosuppression.

Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection

BACKGROUND

The key aim of triage in chest pain patients is to identify those with high risk of adverse cardiac events as they require intensive monitoring and early intervention. In this study, we aim to discover the most relevant variables for risk prediction of major adverse cardiac events (MACE) using clinical signs and heart rate variability.

METHODS

A total of 702 chest pain patients at the Emergency Department (ED) of a tertiary hospital in Singapore were included in this study. The recruited patients were at least 30 years of age and who presented to the ED with a primary complaint of non-traumatic chest pain. The primary outcome was a composite of MACE such as death and cardiac arrest within 72 h of arrival at the ED. For each patient, eight clinical signs such as blood pressure and temperature were measured, and a 5-min ECG was recorded to derive heart rate variability parameters. A random forest-based novel method was developed to select the most relevant variables. A geometric distance-based machine learning scoring system was then implemented to derive a risk score from 0 to 100.

RESULTS

Out of 702 patients, 29 (4.1%) met the primary outcome. We selected the 3 most relevant variables for predicting MACE, which were systolic blood pressure, the mean RR interval and the mean instantaneous heart rate. The scoring system with these 3 variables produced an area under the curve (AUC) of 0.812, and a cutoff score of 43 gave a sensitivity of 82.8% and specificity of 63.4%, while the scoring system with all the 23 variables had an AUC of 0.736, and a cutoff score of 49 gave a sensitivity of 72.4% and specificity of 63.0%. Conventional thrombolysis in myocardial infarction score and the modified early warning score achieved AUC values of 0.637 and 0.622, respectively.

CONCLUSIONS

It is observed that a few predictors outperformed the whole set of variables in predicting MACE within 72 h. We conclude that more predictors do not necessarily guarantee better prediction results. Furthermore, machine learning-based variable selection seems promising in discovering a few relevant and significant measures as predictors.

Prognostic markers in acute pancreatitis

Acute pancreatitis has a mortality rate of 5-10%. Early deaths are mainly due to multiorgan failure and late deaths are due to septic complications from pancreatic necrosis. The recently described 2012 Revised Atlanta Classification and the Determinant Classification both provide a more accurate description of edematous and necrotizing pancreatitis and local complications. The 2012 Revised Atlanta Classification uses the modified Marshall scoring system for assessing organ dysfunction. The Determinant Classification uses the sepsis-related organ failure assessment scoring system for organ dysfunction and, unlike the 2012 Revised Atlanta Classification, includes infected necrosis as a criterion of severity. These scoring systems are used to assess systemic complications requiring intensive therapy unit support and intra-abdominal complications requiring minimally invasive interventions. Numerous prognostic systems and markers have been evaluated but only the Glasgow system and serum CRP levels provide pragmatic prognostic accuracy early on. Novel concepts using genetic, transcriptomic and proteomic profiling and also functional imaging for the identification of specific disease patterns are now required.

The use of intelligent database systems in acute pancreatitis--a systematic review

INTRODUCTION

Acute pancreatitis (AP) is a complex disease with multiple aetiological factors, wide ranging severity, and multiple challenges to effective triage and management. Databases, data mining and machine learning algorithms (MLAs), including artificial neural networks (ANNs), may assist by storing and interpreting data from multiple sources, potentially improving clinical decision-making.

AIMS

1) Identify database technologies used to store AP data, 2) collate and categorise variables stored in AP databases, 3) identify the MLA technologies, including ANNs, used to analyse AP data, and 4) identify clinical and non-clinical benefits and obstacles in establishing a national or international AP database.

METHODS

Comprehensive systematic search of online reference databases. The predetermined inclusion criteria were all papers discussing 1) databases, 2) data mining or 3) MLAs, pertaining to AP, independently assessed by two reviewers with conflicts resolved by a third author.

RESULTS

Forty-three papers were included. Three data mining technologies and five ANN methodologies were reported in the literature. There were 187 collected variables identified. ANNs increase accuracy of severity prediction, one study showed ANNs had a sensitivity of 0.89 and specificity of 0.96 six hours after admission--compare APACHE II (cutoff score ≥8) with 0.80 and 0.85 respectively. Problems with databases were incomplete data, lack of clinical data, diagnostic reliability and missing clinical data.

CONCLUSION

This is the first systematic review examining the use of databases, MLAs and ANNs in the management of AP. The clinical benefits these technologies have over current systems and other advantages to adopting them are identified.

Validation of an ontological medical decision support system for patient treatment using a repository of patient data

In this article, we begin by presenting OMeD, a medical decision support system, and argue for its value over purely probabilistic approaches that reason about patients for time-critical decision scenarios. We then progress to present Holmes, a Hybrid Ontological and Learning MEdical System which supports decision making about patient treatment. This system is introduced in order to cope with the case of missing data. We demonstrate its effectiveness by operating on an extensive set of real-world patient health data from the CDC, applied to the decision-making scenario of administering sleeping pills. In particular, we clarify how the combination of semantic, ontological representations, and probabilistic reasoning together enable the proposal of effective patient treatments. Our focus is thus on presenting an approach for interpreting medical data in the context of real-time decision making. This constitutes a comprehensive framework for the design of medical recommendation systems for potential use by medical professionals and patients both, with the end result being personalized patient treatment. We conclude with a discussion of the value of our particular approach for such diverse considerations as coping with misinformation provided by patients, performing effectively in time-critical environments where real-time decisions are necessary, and potential applications facilitating patient information gathering.

Scoring of human acute pancreatitis: state of the art

BACKGROUND

Acute pancreatitis remains as one of the most difficult and challenging digestive disorder to predict in terms of clinical course and outcome. Every case has an individual course and therefore acute pancreatitis remains challenging and fascinating. Due to this variability, many different scoring systems have evolved during the last decades. Every scoring system has advantages and disadvantages. Not every scoring system is capable of assessing the clinical time course of the disease, some are only suitable for the time of initial presentation.

AIM

This paper will give an overview on the development of different widely used scoring systems and their performance in assessing severity and prognosis of acute pancreatitis.

CONCLUSION

Severity assessment means objective quantification of overall severity of illness. Early and reliable stratification of severity is required to decide best treatment of the individual patient, preparation for possible evolving complications or for referral to specialist centers. No single scoring system is able to cover the entire range of problems associated with treatment and assessment of acute pancreatitis. In our clinical experience, we recommend hematocrit upon admission, daily sequential organ failure assessment score and procalcitonin, C-reactive protein on day 3 and CT severity index beyond the first week. These scoring tools together with close clinical follow-up of the patient ultimately lead to an optimized treatment of this challenging disease.

Ultrasound-guided percutaneous drainage may decrease the mortality of severe acute pancreatitis

OBJECTIVES

To evaluate the efficacy and safety of ultrasound-guided percutaneous catheter drainage (PCD) treatment for severe acute pancreatitis compared to conservative and conventional surgical treatments.

METHODS

Eighty-one patients with severe acute pancreatitis (SAP) were admitted and divided into three groups: forty-nine cases in the conservative therapy group; nineteen cases in the surgery group; and thirteen cases in the PCD therapy group. Forty-five patients with a CT severity index (CTSI) < or = 8.0 received conservative treatment. One patient with CTSI = 7.0 underwent surgery. Thirty-five patients with a CTSI > 8.0 were randomly selected for surgery or PCD treatment. After randomization, six patients (four patients in the surgery group and two patients in the PCD group) were dropped from the study. The total number of patients included in the surgery and PCD groups was sixteen and thirteen, respectively.

RESULTS

Four patients (8.2%) in the conservative therapy group died, five patients (31.3%) in surgery group with a CTSI > 8.0 died, and all patients in the PCD group survived. The mortality rate was lower in the PCD group than in the surgery group (P = 0.048). The serum C-reactive protein (CRP) level recovered more quickly in patients in the PCD group compared to those in the surgery group (P < 0.001).

CONCLUSIONS

Patients with SAP and a CTSI < or = 8.0 could be treated with conservative therapy, while patients with a CTSI > 8.0 should be treated with surgery or PCD therapy if the life-threatening complications of extensive fluid collection or necrosis are a factor. However, PCD therapy used in a timely manner for drainage may decrease mortality in patients with SAP, decrease inflammatory mediator release, and avoid incidence of severe sepsis or acute respiratory distress syndrome (ARDS) and emergency surgery.

Prediction of severe sepsis using SVM model

Sepsis is an infectious condition that results in damage to organs. This paper proposes a severe sepsis model based on Support Vector Machine (SVM) for predicting whether a septic patient will become severe sepsis. We chose several clinical physiology of sepsis for identifying the features used for SVM. Based on the model, a medical decision support system is proposed for clinical diagnosis. The results show that the prognosis of a septic patient can be more precisely predicted than ever. We conduct several experiments, whose results demonstrate that the proposed model provides high accuracy and high sensitivity and can be used as a reminding system to provide in-time treatment in ICU.