scholarly journals A clinical predictive model for risk stratification of patients with severe acute lower gastrointestinal bleeding

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Manraj Singh ◽  
Jayne Chiang ◽  
Andre Seah ◽  
Nan Liu ◽  
Ronnie Mathew ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
Predictive Model ◽  
Lower Gastrointestinal Bleeding ◽  
Initial Assessment ◽  
Clinical Parameters ◽  
Predictive Values ◽  
Management Algorithm ◽  
Patients At Risk ◽  
Clinical Predictive Model

Abstract Background Lower gastrointestinal bleeding (LGIB) is a common presentation of surgical admissions, imposing a significant burden on healthcare costs and resources. There is a paucity of standardised clinical predictive tools available for the initial assessment and risk stratification of patients with LGIB. We propose a simple clinical scoring model to prognosticate patients at risk of severe LGIB and an algorithm to guide management of such patients. Methods A retrospective cohort study was conducted, identifying consecutive patients admitted to our institution for LGIB over a 1-year period. Baseline demographics, clinical parameters at initial presentation and treatment interventions were recorded. Multivariate logistic regression was performed to identify factors predictive of severe LGIB. A clinical management algorithm was developed to discriminate between patients requiring admission, and to guide endoscopic, angiographic and/or surgical intervention. Results 226/649 (34.8%) patients had severe LGIB. Six variables were entered into a clinical predictive model for risk stratification of LGIB: Tachycardia (HR ≥ 100), hypotension (SBP < 90 mmHg), anaemia (Hb < 9 g/dL), metabolic acidosis, use of antiplatelet/anticoagulants, and active per-rectal bleeding. The optimum cut-off score of ≥ 1 had a sensitivity of 91.9%, specificity of 39.8%, and positive and negative predictive Values of 45% and 90.2%, respectively, for predicting severe LGIB. The area under curve (AUC) was 0.77. Conclusion Early diagnosis and management of severe LGIB remains a challenge for the acute care surgeon. The predictive model described comprises objective clinical parameters routinely obtained at initial triage to guide risk stratification, disposition and inpatient management of patients.

A Clinical Predictive Model for Risk Stratification of Patients with Severe Acute Lower Gastrointestinal Bleeding

2021 ◽  
Author(s):  
Manraj Singh ◽  
Jayne Chiang ◽  
Andre Seah ◽  
Nan Liu ◽  
Ronnie Mathew ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Predictive Model ◽  
Primary Outcome Measure ◽  
Initial Assessment ◽  
Intestinal Bleeding ◽  
Clinical Parameters ◽  
Predictive Values ◽  
Management Algorithm ◽  
Patients At Risk ◽  
Clinical Predictive Model

Abstract Background: Lower Gastro-Intestinal Bleeding (LGIB) is a common presentation of surgical admissions, imposing a significant burden on healthcare costs and resources. There is a paucity of standardised clinical predictive tools available for the initial assessment and risk stratification of patients with LGIB. We propose a simple clinical scoring model to prognosticate patients at risk of severe LGIB and an algorithm to guide management of such patients.Methods: A retrospective cohort study was conducted, identifying consecutive patients admitted to our institution for LGIB over a 1-year period. Baseline demographics, clinical parameters at initial presentation and treatment interventions were recorded. Severe LGIB was the primary outcome measure. Multivariate logistic regression was performed to identify factors predictive of severe LGIB. A clinical management algorithm was developed to discriminate between patients requiring admission, and to guide endoscopic, angiographic and/or surgical intervention.Results: 226/649 (34.8%) patients had severe LGIB. Six variables were entered into a clinical predictive model for risk stratification of LGIB: Tachycardia (HR>100), hypotension (SBP<90mmHg), anemia (Hb<9g/dL), metabolic acidosis, use of antiplatelet/anticoagulants, and active per-rectal bleeding. The optimum cut-off score of >1 had a sensitivity of 91.9%, specificity of 39.8%, and Positive and Negative Predictive Values of 45% and 90.2% respectively for predicting severe LGIB. The Area Under Curve (AUC) was 0.77.BConclusion: Early diagnosis and management of severe LGIB remains a challenge for the acute care surgeon. The predictive model described comprises objective clinical parameters routinely obtained at initial triage to guide risk stratification, disposition and inpatient management of patients.

888 Challenges in The Management of Lower Gastrointestinal Bleeding in General Surgery: Developing Consistent Practice and Application of BSG Guidelines

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
A Bhojwani ◽  
M Ahmed ◽  
F Mahmood ◽  
C Sellahewa ◽  
C Desai
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
District Hospital ◽  
Hospital Setting ◽  
Lower Gastrointestinal Bleeding ◽  
Shock Index ◽  
Retrospective Audit ◽  
Ct Angiogram ◽  
Sensitive Tool ◽  
The Uk

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.

scholarly journals Using Predictive Modeling and Supervised Machine Learning to Identify Patients at Risk for Venous Thromboembolism Following Posterior Lumbar Fusion

2021 ◽  
pp. 219256822110193
Author(s):  
Kevin Y. Wang ◽  
Ijezie Ikwuezunma ◽  
Varun Puvanesarajah ◽  
Jacob Babu ◽  
Adam Margalit ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Venous Thromboembolism ◽  
Risk Stratification ◽  
Predictive Model ◽  
Predictive Modeling ◽  
Lumbar Fusion ◽  
Posterior Lumbar Fusion ◽  
Single Level ◽  
Patients At Risk

Study Design: Retrospective review. Objective: To use predictive modeling and machine learning to identify patients at risk for venous thromboembolism (VTE) following posterior lumbar fusion (PLF) for degenerative spinal pathology. Methods: Patients undergoing single-level PLF in the inpatient setting were identified in the National Surgical Quality Improvement Program database. Our outcome measure of VTE included all patients who experienced a pulmonary embolism and/or deep venous thrombosis within 30-days of surgery. Two different methodologies were used to identify VTE risk: 1) a novel predictive model derived from multivariable logistic regression of significant risk factors, and 2) a tree-based extreme gradient boosting (XGBoost) algorithm using preoperative variables. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area-under-the-curve (AUC) statistic. Results: 13, 500 patients who underwent single-level PLF met the study criteria. Of these, 0.95% had a VTE within 30-days of surgery. The 5 clinical variables found to be significant in the multivariable predictive model were: age > 65, obesity grade II or above, coronary artery disease, functional status, and prolonged operative time. The predictive model exhibited an AUC of 0.716, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.001), and comparable to that of the XGBoost algorithm ( P > 0.05). Conclusion: Predictive analytics and machine learning can be leveraged to aid in identification of patients at risk of VTE following PLF. Surgeons and perioperative teams may find these tools useful to augment clinical decision making risk stratification tool.

scholarly journals Management of Diverticular Bleeding: Evaluation, Stabilization, Intervention, and Recurrence of Bleeding and Indications for Resection after Control of Bleeding

2018 ◽  
Vol 31 (04) ◽  
pp. 243-250 ◽  
Author(s):  
Mohammed Iyoob Mohammed Ilyas ◽  
Eric Szilagy
Keyword(s):  
Gastrointestinal Bleeding ◽  
Hemodynamic Instability ◽  
The United States ◽  
Lower Gastrointestinal Bleeding ◽  
Therapeutic Interventions ◽  
Endoscopic Management ◽  
Diverticular Bleeding ◽  
Bleeding Management ◽  
Management Algorithm ◽  
Partial Colectomy

AbstractDiverticular bleeding is the most common cause of lower gastrointestinal bleeding with nearly 200,000 admissions in the United States annually. Less than 5% of patients with diverticulosis present with diverticular bleeding and present usually as painless, intermittent, and large volume of lower gastrointestinal bleeding. Management algorithm for patients presenting with diverticular bleeding includes resuscitation followed by diagnostic evaluation. Colonoscopy is the recommended first-line investigation and helps in identifying the stigmata of recent hemorrhage and endoscopic management of the bleeding. Radionuclide scanning is the most sensitive but least accurate test due to low spatial resolution. Angiography is helpful when patients are actively bleeding and therapeutic interventions are performed with angioembolization. Surgery for diverticular bleeding is necessary when associated with hemodynamic instability and after failed endoscopic or angiographic interventions. When the bleeding site is localized preoperatively, partial colectomy is sufficient, but subtotal colectomy is necessary when localization is not possible preoperatively.

scholarly journals CROSSING POINT IN THE MANAGEMENT ALGORITHM OF ACUTE LOWER GASTROINTESTINAL BLEEDING – CASE REPORTS AND LITERATURE REVIEW

2019 ◽  
Vol 6 (2) ◽  
Author(s):  
G. N. Andrei ◽  
B. Dumitriu ◽  
L. Gulie ◽  
F. Bulca ◽  
M. Beuran
Keyword(s):  
Small Intestine ◽  
Gastrointestinal Bleeding ◽  
Case Reports ◽  
Hemodynamic Instability ◽  
Massive Bleeding ◽  
Lower Gastrointestinal Bleeding ◽  
Management Algorithm ◽  
Obscure Bleeding ◽  
Threatening Situation ◽  
Crossing Point

Acute  lower  gastrointestinal  bleeding  is  a significant  problem, a  rare  and  life  threatening  situation,  that has  a  mortality  rate  situated  between  2  and  4% (1).  According to recent studies up to 15%  of them present as  massive  bleeding  and  5%  require  surgery (2). Acute lower  gastrointestinal  bleeding  can  be  divided  according  to  their  location  in  the  small  or  large  bowel. The  small  intestine  is  less interested  and is known to be  the  commonest  cause  of  obscure  bleeding. About 5%  of  total  lower  gastrointestinal  bleeding  comes from the small intestine (3). When conservative medical treatment associated with  interventional endoscopy can not handle the bleeding, endovascular therapy  can be salutary. Also  in  some  rare  cases  of  acute  lower  gastrointestinal   bleeding  with  hemodynamic  instability  the  last  therapeutic  resource  remains  surgery.  Thus, we will present  two  cases  of  acute  lower  gastrointestinal  bleeding  which  were  managed  different, we will then summarize the  different  variants  of  available  treatment  and  finally, in  the  conclusions, we  will underscore  the  most  important  steps  of  the  management algorithm and highlight the crossing point   in  acute  lower  gastrointestinal  bleeding.

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