Advancing care for acute gastrointestinal bleeding using artificial intelligence

Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding

BACKGROUND

Acute upper gastrointestinal bleeding (UGIB) is common in clinical practice and has a wide range of severity. Along with medical therapy, endoscopic intervention is the mainstay treatment for hemostasis in high-risk rebleeding lesions. Predicting the need for endoscopic intervention would be beneficial in resource-limited areas for selective referral to an endoscopic center. The proposed risk stratification scores had limited accuracy. We developed a machine learning model to predict the need for endoscopic intervention in patients with acute UGIB.

METHODS

A prospectively collected database of UGIB patients from 2011 to 2020 was retrospectively reviewed. Patients older than 18 years diagnosed with UGIB who underwent endoscopy were included. Data comprised demographic characteristics, clinical presentation, and laboratory parameters. The cleaned data was used for model development and validation in Python. We conducted 80%-20% split sample training and test sets. The training set was used for supervised learning of 15 models using a stratified 5-fold cross-validation process. The model with the highest AUROC was then internally validated with the test set to evaluate performance.

RESULTS

Of 1389 patients, 615 (44.3%) of the cohorts received the endoscopic intervention (293 variceal- and 336 nonvariceal-bleeding interventions). Eighteen features, including demographic characteristics, clinical presentation, and laboratory parameters, were selected as input for 15 machine learning models. The result revealed that the linear discriminant analysis model could achieve the highest AUROC of 0.74 to predict endoscopic intervention. The model was validated with the test set, in which the AUROC was increased from 0.74 to 0.81. Finally, the model was deployed as a web application by Streamlit.

CONCLUSIONS

Our machine learning model can identify patients with acute UGIB who need endoscopic intervention with good performance. This may help primary care physicians prioritize patients who need referrals and optimize resource allocation in resource-limited areas. Further development and identification of more specific features might improve prediction performance.

TRIAL REGISTRATION

None (Retrospective cohort study) PATIENT & PUBLIC INVOLVEMENT: None.

Management of gastrointestinal bleed in the intensive care setting, an updated literature review

Gastrointestinal (GI) bleeding is a critical and potentially life-threatening condition frequently observed in the intensive care unit (ICU). This literature review consolidates current insights on the epidemiology, etiology, management, and outcomes of GI bleeding in critically ill patients. GI bleeding remains a significant concern, especially among patients with underlying risk factors such as coagulopathy, mechanical ventilation, and renal failure. Managing GI bleeding in the ICU requires a multidisciplinary approach, including resuscitation, endoscopic intervention, pharmacologic therapy, and sometimes surgical procedures. Even with enhanced management strategies, GI bleeding in the ICU is associated with considerable morbidity and mortality, particularly when complicated by multi-organ failure. This review reiterates the need for adequate resuscitation and interventions in managing GI bleeding in critically ill patients, aiming to enhance survival rates and improve the quality of care within the ICU setting.

The Prediction and Treatment of Bleeding Esophageal Varices in the Artificial Intelligence Era: A Review

Esophageal varices (EVs), a significant complication of cirrhosis, present a considerable challenge in clinical practice due to their high risk of bleeding and associated morbidity and mortality. This manuscript explores the transformative role of artificial intelligence (AI) in the management of EV, particularly in enhancing diagnostic accuracy and predicting bleeding risks. It underscores the potential of AI in offering noninvasive, efficient alternatives to traditional diagnostic methods such as esophagogastroduodenoscopy (EGD). The complexity of EV management is highlighted, necessitating a multidisciplinary approach that includes pharmacological therapy, endoscopic interventions, and, in some cases, surgical options tailored to individual patient profiles. Additionally, the paper emphasizes the importance of integrating AI into medical education and practice, preparing healthcare professionals for the evolving landscape of medical technology. It projects a future where AI significantly influences the management of gastrointestinal bleeding, improving clinical decision-making, patient outcomes, and overall healthcare efficiency. The study advocates for a patient-centered approach in healthcare, balancing the incorporation of innovative technologies with ethical principles and the diverse needs of patients to optimize treatment efficacy and enhance healthcare accessibility.

Scoring systems for risk stratification in upper and lower gastrointestinal bleeding

Several scoring systems have been developed for both upper and lower GI bleeding to predict the bleeding severity and discriminate between low-risk patients, who may be suitable for outpatient management, and those who would likely need hospital-based interventions and are at high risk for adverse outcomes. Risk scores created to identify low-risk patients (namely the Glasgow Blatchford Score and the Oakland score) showed very good discriminative performances and their implementation has proven to be effective in reducing hospital admissions and healthcare burden. Conversely, the performances of risk scores in identifying specific adverse events to define high-risk patients are less accurate, and whether their integration into routine clinical practice has a tangible impact on patient management remains unproven. This review describes the existing risk score systems for GI bleeding, emphasizes key research findings, elucidates the circumstances in which their utilization can be beneficial, examines their constraints when considering routine clinical application, and discuss future development.

Short Peptide Nanofiber Biomaterials Ameliorate Local Hemostatic Capacity of Surgical Materials and Intraoperative Hemostatic Applications in Clinics

Short designer self-assembling peptide (dSAP) biomaterials are a new addition to the hemostat group. It may provide a diverse and robust toolbox for surgeons to integrate wound microenvironment with much safer and stronger hemostatic capacity than conventional materials and hemostatic agents. Especially in noncompressible torso hemorrhage (NCTH), diffuse mucosal surface bleeding, and internal medical bleeding (IMB), with respect to the optimal hemostatic formulation, dSAP biomaterials are the ingenious nanofiber alternatives to make bioactive neural scaffold, nasal packing, large mucosal surface coverage in gastrointestinal surgery (esophagus, gastric lesion, duodenum, and lower digestive tract), epicardiac cell-delivery carrier, transparent matrix barrier, and so on. Herein, in multiple surgical specialties, dSAP-biomaterial-based nano-hemostats achieve safe, effective, and immediate hemostasis, facile wound healing, and potentially reduce the risks in delayed bleeding, rebleeding, post-operative bleeding, or related complications. The biosafety in vivo, bleeding indications, tissue-sealing quality, surgical feasibility, and local usability are addressed comprehensively and sequentially and pursued to develop useful surgical techniques with better hemostatic performance. Here, the state of the art and all-round advancements of nano-hemostatic approaches in surgery are provided. Relevant critical insights will inspire exciting investigations on peptide nanotechnology, next-generation biomaterials, and better promising prospects in clinics.

Management of Patients With Acute Lower Gastrointestinal Bleeding: An Updated ACG Guideline

Acute lower gastrointestinal bleeding (LGIB) is a common reason for hospitalization in the United States and is associated with significant utilization of hospital resources, as well as considerable morbidity and mortality. These revised guidelines implement the Grading of Recommendations, Assessment, Development, and Evaluation methodology to propose recommendations for the use of risk stratification tools, thresholds for red blood cell transfusion, reversal agents for patients on anticoagulants, diagnostic testing including colonoscopy and computed tomography angiography (CTA), endoscopic therapeutic options, and management of antithrombotic medications after hospital discharge. Important changes since the previous iteration of this guideline include recommendations for the use of risk stratification tools to identify patients with LGIB at low risk of a hospital-based intervention, the role for reversal agents in patients with life-threatening LGIB on vitamin K antagonists and direct oral anticoagulants, the increasing role for CTA in patients with severe LGIB, and the management of patients who have a positive CTA. We recommend that most patients requiring inpatient colonoscopy undergo a nonurgent colonoscopy because performing an urgent colonoscopy within 24 hours of presentation has not been shown to improve important clinical outcomes such as rebleeding. Finally, we provide updated recommendations regarding resumption of antiplatelet and anticoagulant medications after cessation of LGIB.

A Cohort Study to Compare Effects between Ulcer- and Nonulcer-Related Nonvariceal Upper Gastrointestinal Bleeding

Objective

The aim of this study was to better understand the characteristics and etiology of acute nonvariceal upper gastrointestinal bleeding (ANVUGIB) in recent years in this region and to provide evidence-based medical evidence.

Methods

100 patients with acute nonvariceal upper gastrointestinal bleeding (ANVUGIB) who met the clinical diagnostic criteria of ANVUGIB admitted to Suzhou First People's Hospital from January 2017 to December 2021 were analyzed, as well as the age difference and change rule. According to age, 100 patients were divided into young (18-39 years), middle-aged (40-59 years), and elderly (60 years and above), and the differences in the three groups were compared. The etiology was confirmed by endoscopic examination and was recorded one by one in a well-designed ANVUGIB case data registration form. Statistical software SPSS 23.0 was used for analysis.

Results

Gastric ulcer was the main cause in the elderly group (50.0%), duodenal ulcer was the main cause in the middle and young groups, and gastrointestinal cancer (7.1%) and marginal ulcer (2.3%) in the elderly group were higher than those in the young group. Nonsteroidal anti-inflammatory drugs (52.3%) were the main inducement in the elderly group, which was significantly higher than in the middle-aged group (13.1%) and the young group (5%) (P < 0.01). Drinking, fatigue, and emotional excitement led to a higher proportion in the middle-aged group and the young group, in comparison to the elderly group (P < 0.01).

Conclusion

Peptic ulcer is the most common cause of acute nonvariceal upper gastrointestinal bleeding, followed by acute gastric mucosal lesions and upper digestive system tumors, compared with nonulcer.

Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm

Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good-excellent prognostic efficacy, and it outperformed other traditional models.