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

World Journal of Emergency Surgery

Andre Seah¹  Ronnie Mathew²  Jayne Chiang³  Sachin Mathur³  Manraj Singh³  Nan Liu⁴ 

[1] ;Department of Colorectal Surgery, Singapore General Hospital;Department of General Surgery, Singapore General Hospital;Health Services Research Centre, Singapore Health Services;

关键词: Lower gastrointestinal bleeding;    Predictive model;    Per-rectal bleed;    Haematochezia;    Haemorrhagic;    Shock;   

DOI  :  10.1186/s13017-021-00402-y

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown