Gastrointestinal Bleeding: Resuscitation, ICU Care and Risk Stratification

Abstract Introduction Lower gastrointestinal bleeding (LGIB) accounts for 3% of all surgical referrals in the UK, with an in-hospital mortality of 3.4%. The BSG 2019 guidelines recommend risk stratification as per Oakland scoring, inpatient lower GI endoscopy for admissions and CT-angiography for unstable patients. This study evaluates the delivery of these outcomes in a district hospital setting. Method Retrospective audit assessing all acute LGI bleed admissions from 01-07-2019 to 28-02-2020 at Russells Hall Hospital. Shock Index (SI) and Oakland score used to stratify patients into unstable, stable-major and stable-minor LGIB. Compliance with BSG standards was assessed by review of investigations and emergent patient management. Results 143 patients (Median age = 70years) evaluated, with 64 admissions having no formal risk stratification (OAKLAND-score) documented. Only 12 admissions underwent inpatient LGI endoscopy with sigmoid diverticulosis the most common pathology (39.3%). CT-angiogram was the initial investigation for 75% of patients admitted with unstable LGIB. Conclusions OAKLAND-scoring is a sensitive tool to stratify LGIB patients based on clinical parameters. Application of BSG-2019 guidelines and developing consistency in management is challenged by the lack of routine access to LGI endoscopy and tools to manage bleeding endoscopically.

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Early Identification of Patients with Acute Gastrointestinal Bleeding using Electronic Health Record Phenotyping

10.1101/2020.07.06.20136374 ◽

2020 ◽

Author(s):

Dennis Shung ◽

Cynthia Tsay ◽

Loren Laine ◽

Prem Thomas ◽

Caitlin Partridge ◽

...

Keyword(s):

Risk Stratification ◽

Gastrointestinal Bleeding ◽

Real Time ◽

Decision Rule ◽

Language Processing ◽

Clinical Decision Making ◽

External Validation ◽

Clinical Decision ◽

Internal Validation ◽

Electronic Health

Background and AimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify (“phenotype”) patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.MethodsWe specified criteria using structured data elements to create rules for identifying patients, and also developed a natural-language-processing (NLP)-based algorithm for automated phenotyping of patients, tested them with tenfold cross-validation (n=7144) and external validation (n=2988), and compared them with the standard method for encoding patient conditions in the EHR, Systematized Nomenclature of Medicine (SNOMED). The gold standard for GIB diagnosis was independent dual manual review of medical records. The primary outcome was positive predictive value (PPV).ResultsA decision rule using GIB-specific terms from ED triage and from ED review-of-systems assessment performed better than SNOMED on internal validation (PPV=91% [90%-93%] vs. 74% [71%-76%], P<0.001) and external validation (PPV=85% [84%-87%] vs. 69% [67%-71%], P<0.001). The NLP algorithm (external validation PPV=80% [79-82%]) was not superior to the structured-datafields decision rule.ConclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision-making in real time for patients with acute GIB presenting to the ED.

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Acute gastrointestinal bleeding

InnovAiT Education and inspiration for general practice ◽

10.1177/17557380211046972 ◽

2021 ◽

pp. 175573802110469

Author(s):

Victoria Evans ◽

Helen King

Keyword(s):

Risk Stratification ◽

Gastrointestinal Bleeding ◽

Mortality Rates ◽

Morbidity And Mortality ◽

Significant Morbidity ◽

Acute Gastrointestinal Bleeding ◽

Surgical Emergency

Acute gastrointestinal bleeding is a common medical and/or surgical emergency that can be caused by a range of diverse pathologies. Gastrointestinal Bleeding can be divided into upper and lower in nature, presenting in sometimes subtly different fashions, but with differing requirements for investigation and management. Prompt identification, risk stratification and treatment are required in order to minimise the ongoing significant morbidity and mortality rates associated with severe presentations of gastrointestinal bleeding.

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Comparison of AIMS65, Glasgow-Blatchford, and pre-endoscopy Rockall scoring systems for risk stratification in Egyptian patients with upper gastrointestinal bleeding

Journal of Medicine in Scientific Research ◽

10.4103/jmisr.jmisr_20_20 ◽

2020 ◽

Vol 3 (4) ◽

pp. 270

Author(s):

TarekA Fouad ◽

El SaiedE Shabaan

Keyword(s):

Risk Stratification ◽

Gastrointestinal Bleeding ◽

Upper Gastrointestinal Bleeding ◽

Scoring Systems ◽

Egyptian Patients ◽

Upper Gastrointestinal

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Role of Helicobacter pylori in Upper Gastrointestinal Bleeding Among Ischemic Stroke Hospitalizations: A Nationwide Study of Outcomes

Gastrointestinal Disorders ◽

10.3390/gidisord1030029 ◽

2019 ◽

Vol 1 (3) ◽

pp. 358-371

Author(s):

Urvish K. Patel ◽

Mihir Dave ◽

Anusha Lekshminarayanan ◽

Nidhi Patel ◽

Abhishek Lunagariya ◽

...

Keyword(s):

Helicobacter Pylori ◽

Ischemic Stroke ◽

Risk Stratification ◽

Gastrointestinal Bleeding ◽

Health Care Cost ◽

Upper Gastrointestinal Bleeding ◽

Increased Risk ◽

H Pylori ◽

Upper Gastrointestinal

Introduction: Helicobacter pylori (H. pylori) is a well-recognized risk factor for upper gastrointestinal bleeding (UGIB). The exposure to tissue plasminogen activator (tPA), anti-platelets, and anticoagulants increases the risk of UGIB in acute ischemic stroke (AIS) patients, the risk stratification of H. pylori infection is not known. In this retrospective cross-sectional study, we aimed to evaluate the relationship between H. pylori and GIB in patients hospitalized with AIS. Methods: In the nationwide data, hospitalization for AIS was identified by primary diagnosis using International Classification of Diseases, clinical modification (ICD-9-CM) codes. Subgroup of patients with GIB and H. pylori were identified in AIS cohort. A stepwise multivariable logistic regression model was fitted to evaluate the outcome of upper GIB and role of H. Pylori in UGIB. Results: Overall 4,224,924 AIS hospitalizations were identified, out of which 18,629 (0.44%) had UGIB and 3122 (0.07%) had H. pylori. The prevalence of H. pylori-induced UGIB among UGIB in AIS was 3.05%. The prevalence of UGIB was markedly elevated among the H. pylori infection group (18.23% vs. 0.43%; p < 0.0001) compared to the non-H. pylori group. In multivariable regression analysis, H. pylori was associated with markedly elevated odds of UGIB (aOR:27.75; 95%CI: 21.07–36.55; p < 0.0001). Conclusion: H. pylori infection had increased risk-adjusted occurrence of UGIB amongst the AIS hospitalized patients. H. pylori testing may improve risk stratification for UGIB and lower the health care cost burden in stroke hospitalization.

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