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Background & Aims: Early identification and accurate characterization of overt gastrointestinal bleeding (GIB) enables opportunities to optimize patient management and ensures appropriately risk-adjusted coding for claims-based quality measures and reimbursement. Recent advancements in generative artificial intelligence, particularly large language models (LLMs), create opportunities to support accurate identification of clinical conditions. In this study, we present the first LLM-based pipeline for identification of overt GIB in the electronic health record (EHR). We demonstrate 2 clinically relevant applications: the automated detection of recurrent bleeding and appropriate reimbursement coding for patients with GIB., Methods: Development of the LLM-based pipeline was performed on 17,712 nursing notes from 1108 patients who were hospitalized with acute GIB and underwent endoscopy in the hospital from 2014 to 2023. The pipeline was used to train an EHR-based machine learning model for detection of recurrent bleeding on 546 patients presenting to 2 hospitals and externally validated on 562 patients presenting to 4 different hospitals. The pipeline was used to develop an algorithm for appropriate reimbursement coding on 7956 patients who underwent endoscopy in the hospital from 2019 to 2023., Results: The LLM-based pipeline accurately detected melena (positive predictive value, 0.972; sensitivity, 0.900), hematochezia (positive predictive value, 0.900; sensitivity, 0.908), and hematemesis (positive predictive value, 0.859; sensitivity, 0.932). The EHR-based machine learning model identified recurrent bleeding with area under the curve of 0.986, sensitivity of 98.4%, and specificity of 97.5%. The reimbursement coding algorithm resulted in an average per-patient reimbursement increase of $1299 to $3247 with a total difference of $697,460 to $1,743,649., Conclusions: An LLM-based pipeline can robustly detect overt GIB in the EHR with clinically relevant applications in detection of recurrent bleeding and appropriate reimbursement coding., (Copyright © 2025 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Background: Randomized clinical trials (RCTs) of computer-aided detection (CADe) system-enhanced colonoscopy compared with conventional colonoscopy suggest increased adenoma detection rate (ADR) and decreased adenoma miss rate (AMR), but the effect on detection of advanced colorectal neoplasia (ACN) is unclear., Purpose: To conduct a systematic review to compare performance of CADe-enhanced and conventional colonoscopy., Data Sources: Cochrane Library, Google Scholar, Ovid EMBASE, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection databases were searched through February 2024., Study Selection: Published RCTs comparing CADe-enhanced and conventional colonoscopy., Data Extraction: Average adenoma per colonoscopy (APC) and ACN per colonoscopy were primary outcomes. Adenoma detection rate, AMR, and ACN detection rate (ACN DR) were secondary outcomes. Balancing outcomes included withdrawal time and resection of nonneoplastic polyps (NNPs). Subgroup analyses were done by neural network architecture., Data Synthesis: Forty-four RCTs with 36 201 cases were included. Computer-aided detection-enhanced colonoscopies have higher average APC (12 090 of 12 279 [0.98] vs. 9690 of 12 292 [0.78], incidence rate difference [IRD] = 0.22 [95% CI, 0.16 to 0.28]) and higher ADR (7098 of 16 253 [44.7%] vs. 5825 of 15 855 [36.7%], rate ratio [RR] = 1.21 [CI, 1.15 to 1.28]). Average ACN per colonoscopy was similar (1512 of 9296 [0.16] vs. 1392 of 9121 [0.15], IRD = 0.01 [CI, -0.01 to 0.02]), but ACN DR was higher with CADe system use (1260 of 9899 [12.7%] vs. 1119 of 9746 [11.5%], RR = 1.16 [CI, 1.02 to 1.32]). Using CADe systems resulted in resection of almost 2 extra NNPs per 10 colonoscopies and longer total withdrawal time (0.53 minutes [CI, 0.30 to 0.77])., Limitation: Statistically significant heterogeneity in quality and sample size and inability to blind endoscopists to the intervention in included studies may affect the performance estimates., Conclusion: Computer-aided detection-enhanced colonoscopies have increased APC and detection rate but no difference in ACN per colonoscopy and a small increase in ACN DR. There is minimal increase in procedure time and no difference in performance across neural network architectures., Primary Funding Source: None. (PROSPERO: CRD42023422835)., Competing Interests: Disclosures: Disclosure forms are available with the article online.

Background & Aims: Guidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify very-low-risk patients eligible for discharge from emergency departments. Machine learning models may outperform existing scores and can be integrated within the electronic health record (EHR) to provide real-time risk assessment without manual data entry. We present the first EHR-based machine learning model for GIB., Methods: The training cohort comprised 2546 patients and internal validation of 850 patients presenting with overt GIB (ie, hematemesis, melena, and hematochezia) to emergency departments of 2 hospitals from 2014 to 2019. External validation was performed on 926 patients presenting to a different hospital with the same EHR from 2014 to 2019. The primary outcome was a composite of red blood cell transfusion, hemostatic intervention (ie, endoscopic, interventional radiologic, or surgical), and 30-day all-cause mortality. We used structured data fields in the EHR, available within 4 hours of presentation, and compared the performance of machine learning models with current guideline-recommended risk scores, Glasgow-Blatchford Score, and Oakland Score. Primary analysis was area under the receiver operating characteristic curve. Secondary analysis was specificity at 99% sensitivity to assess the proportion of patients correctly identified as very low risk., Results: The machine learning model outperformed the Glasgow-Blatchford Score (area under the receiver operating characteristic curve, 0.92 vs 0.89; P < .001) and Oakland Score (area under the receiver operating characteristic curve, 0.92 vs 0.89; P < .001). At the very-low-risk threshold of 99% sensitivity, the machine learning model identified more very-low-risk patients: 37.9% vs 18.5% for Glasgow-Blatchford Score and 11.7% for Oakland Score (P < .001 for both comparisons)., Conclusions: An EHR-based machine learning model performs better than currently recommended clinical risk scores and identifies more very-low-risk patients eligible for discharge from the emergency department., (Copyright © 2024 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Large Language Models (LLMs) are transformer-based neural networks with billions of parameters trained on very large text corpora from diverse sources. LLMs have the potential to improve healthcare due to their capability to parse complex concepts and generate context-based responses. The interest in LLMs has not spared digestive disease academics, who have mainly investigated foundational LLM accuracy, which ranges from 25% to 90% and is influenced by the lack of standardized rules to report methodologies and results for LLM-oriented research. In addition, a critical issue is the absence of a universally accepted definition of accuracy, varying from binary to scalar interpretations, often tied to grader expertise without reference to clinical guidelines. We address strategies and challenges to increase accuracy. In particular, LLMs can be infused with domain knowledge using Retrieval Augmented Generation (RAG) or Supervised Fine-Tuning (SFT) with reinforcement learning from human feedback (RLHF). RAG faces challenges with in-context window limits and accurate information retrieval from the provided context. SFT, a deeper adaptation method, is computationally demanding and requires specialized knowledge. LLMs may increase patient quality of care across the field of digestive diseases, where physicians are often engaged in screening, treatment and surveillance for a broad range of pathologies for which in-context learning or SFT with RLHF could improve clinical decision-making and patient outcomes. However, despite their potential, the safe deployment of LLMs in healthcare still needs to overcome hurdles in accuracy, suggesting a need for strategies that integrate human feedback with advanced model training., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Interest in large language models (LLMs), such as OpenAI's ChatGPT, across multiple specialties has grown as a source of patient-facing medical advice and provider-facing clinical decision support. The accuracy of LLM responses for gastroenterology and hepatology-related questions is unknown., Aims: To evaluate the accuracy and potential safety implications for LLMs for the diagnosis, management and treatment of questions related to gastroenterology and hepatology., Methods: We conducted a systematic literature search including Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus and the Web of Science Core Collection to identify relevant articles published from inception until January 28, 2024, using a combination of keywords and controlled vocabulary for LLMs and gastroenterology or hepatology. Accuracy was defined as the percentage of entirely correct answers., Results: Among the 1671 reports screened, we identified 33 full-text articles on using LLMs in gastroenterology and hepatology and included 18 in the final analysis. The accuracy of question-responding varied across different model versions. For example, accuracy ranged from 6.4% to 45.5% with ChatGPT-3.5 and was between 40% and 91.4% with ChatGPT-4. In addition, the absence of standardised methodology and reporting metrics for studies involving LLMs places all the studies at a high risk of bias and does not allow for the generalisation of single-study results., Conclusions: Current general-purpose LLMs have unacceptably low accuracy on clinical gastroenterology and hepatology tasks, which may lead to adverse patient safety events through incorrect information or triage recommendations, which might overburden healthcare systems or delay necessary care., (© 2024 John Wiley & Sons Ltd.)

Background: Acute upper gastrointestinal bleeding (UGIB) is a common emergency requiring hospital-based care. Advances in care across pre-endoscopic, endoscopic and post-endoscopic phases have led to improvements in clinical outcomes., Aims: To provide a detailed, evidence-based update on major aspects of care across pre-endoscopic, endoscopic and post-endoscopic phases., Methods: We performed a structured bibliographic database search for each topic. If a recent high-quality meta-analysis was not available, we performed a meta-analysis with random effects methods and odds ratios with 95% confidence intervals., Results: Pre-endoscopic management of UGIB includes risk stratification, a restrictive red blood cell transfusion policy unless the patient has cardiovascular disease, and pharmacologic therapy with erythromycin and a proton pump inhibitor. Patients with cirrhosis should be treated with prophylactic antibiotics and vasoactive medications. Tranexamic acid should not be used. Endoscopic management of UGIB depends on the aetiology. For peptic ulcer disease (PUD) with high-risk stigmata, endoscopic therapy, including over-the-scope clips (OTSCs) and TC-325 powder spray, should be performed. For variceal bleeding, treatment should be customised by severity and anatomic location. Post-endoscopic management includes early enteral feeding for all UGIB patients. For high-risk PUD, PPI should be continued for 72 h, and rebleeding should initially be evaluated with a repeat endoscopy. For variceal bleeding, high-risk patients or those with further bleeding, a transjugular intrahepatic portosystemic shunt can be considered., Conclusions: Management of acute UGIB should include treatment plans for pre-endoscopic, endoscopic and post-endoscopic phases of care, and customise treatment decisions based on aetiology and severity of bleeding., (© 2024 John Wiley & Sons Ltd.)

Introduction: Non-selective beta-blockers (NSBB) are used for primary prophylaxis in patients with liver cirrhosis and high-risk varices (HRVs). Assessing therapeutic response is challenging due to the invasive nature of hepatic venous pressure gradient (HVPG) measurement. This study aims to define a noninvasive machine-learning based approach to determine response to NSBB in patients with liver cirrhosis and HRVs., Methods: We conducted a prospective study on a cohort of cirrhotic patients with documented HRVs receiving NSBB treatment. Patients were followed-up with clinical and elastography appointments at 3, 6, and 12 months after NSBB treatment initiation. NSBB response was defined as stationary or downstaging variceal grading at the 12-month esophagogastroduodenoscopy (EGD). In contrast, non-response was defined as upstaging variceal grading at the 12-month EGD or at least one variceal hemorrhage episode during the 12-month follow-up. We chose cut-off values for univariate and multivariate model with 100% specificity., Results: According to least absolute shrinkage and selection operator (LASSO) regression, spleen stiffness (SS) and liver stiffness (LS) percentual decrease, along with changes in heart rate (HR) at 3 months were the most significant predictors of NSBB response. A decrease > 11.5% in SS, > 16.8% in LS, and > 25.3% in HR was associated with better prediction of clinical response to NSBB. SS percentual decrease showed the highest accuracy (86.4%) with high sensitivity (78.8%) when compared to LS and HR. The multivariate model incorporating SS, LS, and HR showed the highest discrimination and calibration metrics (AUROC = 0.96), with the optimal cut-off of 0.90 (sensitivity 94.2%, specificity 100%, PPV 95.7%, NPV 100%, accuracy 97.5%)., (© 2024. The Author(s).)

Large language models (LLMs) can potentially transform healthcare, particularly in providing the right information to the right provider at the right time in the hospital workflow. This study investigates the integration of LLMs into healthcare, specifically focusing on improving clinical decision support systems (CDSSs) through accurate interpretation of medical guidelines for chronic Hepatitis C Virus infection management. Utilizing OpenAI's GPT-4 Turbo model, we developed a customized LLM framework that incorporates retrieval augmented generation (RAG) and prompt engineering. Our framework involved guideline conversion into the best-structured format that can be efficiently processed by LLMs to provide the most accurate output. An ablation study was conducted to evaluate the impact of different formatting and learning strategies on the LLM's answer generation accuracy. The baseline GPT-4 Turbo model's performance was compared against five experimental setups with increasing levels of complexity: inclusion of in-context guidelines, guideline reformatting, and implementation of few-shot learning. Our primary outcome was the qualitative assessment of accuracy based on expert review, while secondary outcomes included the quantitative measurement of similarity of LLM-generated responses to expert-provided answers using text-similarity scores. The results showed a significant improvement in accuracy from 43 to 99% (p < 0.001), when guidelines were provided as context in a coherent corpus of text and non-text sources were converted into text. In addition, few-shot learning did not seem to improve overall accuracy. The study highlights that structured guideline reformatting and advanced prompt engineering (data quality vs. data quantity) can enhance the efficacy of LLM integrations to CDSSs for guideline delivery., (© 2024. The Author(s).)

Introduction: We estimate the economic impact of applying risk assessment tools to identify very low-risk patients with upper gastrointestinal bleeding who can be safely discharged from the emergency department using a cost minimization analysis., Methods: We compare triage strategies (Glasgow-Blatchford score = 0/0-1 or validated machine learning model) with usual care using a Markov chain model from a US health care payer perspective., Results: Over 5 years, the Glasgow-Blatchford score triage strategy produced national cumulative savings over usual care of more than $2.7 billion and the machine learning strategy of more than $3.4 billion., Discussion: Implementing risk assessment models for upper gastrointestinal bleeding reduces costs, thereby increasing value., (Copyright © 2023 by The American College of Gastroenterology.)

Background: Recent guidelines do not recommend routine use of aspirin for primary cardiovascular prevention (ppASA) and suggest avoidance of ppASA in older individuals due to bleeding risk. However, ppASA is frequently taken without an appropriate indication. Estimates of the incidence of upper gastrointestinal bleeding due to ppASA in the United States are lacking. In this study, we provide national estimates of upper gastrointestinal bleeding incidence, characteristics, and costs in ppASA users from 2016-2020., Methods: Primary cardiovascular prevention users (patients on long-term aspirin therapy without cardiovascular disease) presenting with upper gastrointestinal bleeding were identified in the Nationwide Emergency Department Sample using International Statistical Classification of Diseases and Related Health Problems, 10th revision codes. Trends in upper gastrointestinal bleeding incidence, etiology, severity, associated Medicare reimbursements, and the impact of ppASA on bleeding outcomes were assessed with regression models., Results: From 2016-2020, adjusted incidence of upper gastrointestinal bleeding increased 29.2% among ppASA users, with larger increases for older patients (increase of 41.6% for age 65-74 years and 36.0% for age ≥75 years). The most common etiology among ppASA users was ulcer disease but increases in bleeding incidence due to angiodysplasias were observed. The proportion of hospitalizations with major complications or comorbidities increased 41.5%, and Medicare reimbursements increased 67.6%. Among patients without cardiovascular disease, ppASA was associated with increased odds of hospital admission, red blood cell transfusion, and endoscopic intervention as compared to no ppASA use., Conclusions: Considering recent guideline recommendations, the rising incidence, severity, and costs associated with upper gastrointestinal bleeding among patients on ppASA highlights the importance of careful assessment for appropriate ppASA use., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use., (© 2023. Springer Nature Limited.)

Competing Interests: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M23-2022.

Competing Interests: Conflicts of interest None disclosed.

Background: Recent epidemiologic studies of trends in gastrointestinal bleeding (GIB) provided results through 2014 or earlier and assessed only hospitalised patients, excluding patients presenting to emergency departments (EDs) who are not hospitalised., Aims: To provide the first U.S. nationwide epidemiological evaluation of all patients presenting to EDs with GIB METHODS: We used the Nationwide Emergency Department Sample for 2006-2019 to calculate yearly projected incidence of patients presenting to EDs with primary diagnoses of GIB, categorised by location and aetiology. Outcomes were assessed with multivariable analyses., Results: The age/sex-adjusted incidence for GIB increased from 378.4 to 397.5/100,000 population from 2006 to 2019. Upper gastrointestinal bleeding (UGIB) incidence decreased from 2006 to 2014 (112.3-94.4/100,000) before increasing to 116.2/100,000 by 2019. Lower gastrointestinal bleeding (LGIB) incidence increased from 2006 to 2015 (146.0 to 161.0/100,000) before declining to 150.2/100,000 by 2019. The proportion of cases with one or more comorbidities increased from 27.4% to 35.9% from 2006 to 2019. Multivariable analyses comparing 2019 to 2006 showed increases in ED discharges (odds ratio [OR] = 1.45; 95% confidence interval [CI] = 1.43-1.48) and decreases in red blood cell (RBC) transfusions (OR = 0.62; 0.61-0.63), endoscopies (OR = 0.60; 0.59-0.61), death (OR = 0.51; 0.48-0.54) and length of stay (relative ratio [RR] = 0.81; 0.80-0.82). Inpatient cost decreased from 2012 to 2019 (RR = 0.92; 0.91-0.93)., Conclusions: The incidence of GIB in the U.S. is increasing. UGIB incidence has been increasing since 2014 while LGIB incidence has been decreasing since 2015. Despite a more comorbid population in 2019, case fatality rate, length of stay and costs have decreased. More patients are discharged from the ED and the rate of RBC transfusions has decreased, possibly reflecting changing clinical practice in response to updated guidelines., (© 2022 John Wiley & Sons Ltd.)

Importance: Despite the potential of machine learning to improve multiple aspects of patient care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a prerequisite to large-scale clinical adoption of an intervention, and important questions remain regarding how machine learning interventions are being incorporated into clinical trials in health care., Objective: To systematically examine the design, reporting standards, risk of bias, and inclusivity of RCTs for medical machine learning interventions., Evidence Review: In this systematic review, the Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection online databases were searched and citation chasing was done to find relevant articles published from the inception of each database to October 15, 2021. Search terms for machine learning, clinical decision-making, and RCTs were used. Exclusion criteria included implementation of a non-RCT design, absence of original data, and evaluation of nonclinical interventions. Data were extracted from published articles. Trial characteristics, including primary intervention, demographics, adherence to the CONSORT-AI reporting guideline, and Cochrane risk of bias were analyzed., Findings: Literature search yielded 19 737 articles, of which 41 RCTs involved a median of 294 participants (range, 17-2488 participants). A total of 16 RCTS (39%) were published in 2021, 21 (51%) were conducted at single sites, and 15 (37%) involved endoscopy. No trials adhered to all CONSORT-AI standards. Common reasons for nonadherence were not assessing poor-quality or unavailable input data (38 trials [93%]), not analyzing performance errors (38 [93%]), and not including a statement regarding code or algorithm availability (37 [90%]). Overall risk of bias was high in 7 trials (17%). Of 11 trials (27%) that reported race and ethnicity data, the median proportion of participants from underrepresented minority groups was 21% (range, 0%-51%)., Conclusions and Relevance: This systematic review found that despite the large number of medical machine learning-based algorithms in development, few RCTs for these technologies have been conducted. Among published RCTs, there was high variability in adherence to reporting standards and risk of bias and a lack of participants from underrepresented minority groups. These findings merit attention and should be considered in future RCT design and reporting.

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The future of gastrointestinal bleeding will include the integration of machine learning algorithms to enhance clinician risk assessment and decision making. Machine learning algorithms have shown promise in outperforming existing clinical risk scores for both upper and lower gastrointestinal bleeding but have not been validated in any prospective clinical trials. The adoption of electronic health records provides an exciting opportunity to deploy risk prediction tools in real time and also to expand the data available to train predictive models. Machine learning algorithms can be used to identify patients with acute gastrointestinal bleeding using data extracted from the electronic health record. This can lead to an automated process to find patients with symptoms of acute gastrointestinal bleeding so that risk prediction tools can be then triggered to consistently provide decision support to the physician. Neural network models can be used to provide continuous risk predictions for patients who are at higher risk, which can be used to guide triage of patients to appropriate levels of care. Finally, the future will likely include neural network-based analysis of endoscopic stigmata of bleeding to help guide best practices for hemostasis during the endoscopic procedure. Machine learning will enhance the delivery of care at every level for patients with acute gastrointestinal bleeding through identifying very low risk patients for outpatient management, triaging high risk patients for higher levels of care, and guiding optimal intervention during endoscopy., (© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

Machine learning, a subset of artificial intelligence (AI), is a set of computational tools that can be used to enhance provision of clinical care in all areas of medicine. Gastroenterology and hepatology utilize multiple sources of information, including visual findings on endoscopy, radiologic imaging, manometric testing, genomes, proteomes, and metabolomes. However, clinical care is complex and requires a thoughtful approach to best deploy AI tools to improve quality of care and bring value to patients and providers. On the operational level, AI-assisted clinical management should consider logistic challenges in care delivery, data management, and algorithmic stewardship. There is still much work to be done on a broader societal level in developing ethical, regulatory, and reimbursement frameworks. A multidisciplinary approach and awareness of AI tools will create a vibrant ecosystem for using AI-assisted tools to guide and enhance clinical practice. From optically enhanced endoscopy to clinical decision support for risk stratification, AI tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time., (© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract., Competing Interests: Disclosure D.L. Shung: Grant Support through the National Institutes of Health T32 DK007017; M.F. Byrne: CEO and shareholder, Satisfai Health; founder of AI4GI joint venture. Co-development agreement between Olympus America and AI4GI in artificial intelligence and colorectal polyps., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background & Aims: Scoring systems are suboptimal for determining risk in patients with upper gastrointestinal bleeding (UGIB); these might be improved by a machine learning model. We used machine learning to develop a model to calculate the risk of hospital-based intervention or death in patients with UGIB and compared its performance with other scoring systems., Methods: We analyzed data collected from consecutive unselected patients with UGIB from medical centers in 4 countries (the United States, Scotland, England, and Denmark; n = 1958) from March 2014 through March 2015. We used the data to derive and internally validate a gradient-boosting machine learning model to identify patients who met a composite endpoint of hospital-based intervention (transfusion or hemostatic intervention) or death within 30 days. We compared the performance of the machine learning prediction model with validated pre-endoscopic clinical risk scoring systems (the Glasgow-Blatchford score [GBS], admission Rockall score, and AIMS65). We externally validated the machine learning model using data from 2 Asia-Pacific sites (Singapore and New Zealand; n = 399). Performance was measured by area under receiver operating characteristic curve (AUC) analysis., Results: The machine learning model identified patients who met the composite endpoint with an AUC of 0.91 in the internal validation set; the clinical scoring systems identified patients who met the composite endpoint with AUC values of 0.88 for the GBS (P = .001), 0.73 for Rockall score (P < .001), and 0.78 for AIMS65 score (P < .001). In the external validation cohort, the machine learning model identified patients who met the composite endpoint with an AUC of 0.90, the GBS with an AUC of 0.87 (P = .004), the Rockall score with an AUC of 0.66 (P < .001), and the AIMS65 with an AUC of 0.64 (P < .001). At cutoff scores at which the machine learning model and GBS identified patients who met the composite endpoint with 100% sensitivity, the specificity values were 26% with the machine learning model versus 12% with GBS (P < .001)., Conclusions: We developed a machine learning model that identifies patients with UGIB who met a composite endpoint of hospital-based intervention or death within 30 days with a greater AUC and higher levels of specificity, at 100% sensitivity, than validated clinical risk scoring systems. This model could increase identification of low-risk patients who can be safely discharged from the emergency department for outpatient management., (Copyright © 2020 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Background/aims: The complications of therapy, hospitalization, and surgery related to inflammatory bowel disease (IBD) in the elderly are not well described. While multiple reviews have described the management and complications of elderly patients with IBD, none have been performed in a systematic fashion., Methods: We performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to evaluate the association between elderly patients with IBD and complications from therapy, hospitalizations, and surgery. Eligible studies were identified via structured keyword searches in PubMed and manual literature searches., Results: A total of 5,644 publications were identified. Of these, fourteen studies met inclusion criteria, encompassing 963 elderly IBD patients (113 Crohn's disease and 850 ulcerative colitis patients), over 37,000 hospitalizations of elderly IBD patients and over 4,500 controls. Consistent associations were observed between increased age and higher nocturnal stool frequency post-ileal pouch anal anastomosis. Only two studies met inclusion criteria for medication-related complications, one observed an increased mortality and infection risk among elderly patients treated with tumor necrosis factor antagonists and the other observed increased hospital-related complications among elderly patients treated with steroids., Conclusions: Elderly patients with IBD are at an increased risk of hospital- and therapy-related complications. We found a paucity of high-quality studies evaluating outcomes in elderly patients with IBD. Further studies of elderly patients with IBD are needed to further evaluate the effect of age on medical and surgical complications.