473 A Review of The Risk Stratification Models Used in The Management of Oncological Hepato-Pancreato-Biliary Surgical Patients. (12th October 2021)

• Record Type: Journal Article
• Authors: Coombs, A; Jordan, C; Hussain, S; Ghandour, O
• Is Part Of: British journal of surgery. Volume 108:Supplement 6(2021)
• Publication Date: 2021-10-12
• Subjects: Surgery -- Periodicals; 617.005
• DOI: 10.1093/bjs/znab259.668
• Languages: English
• ISSNs: 0007-1323

Abstract

Abstract: Introduction: Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) subspecialty is limited as concerns over precision and applicability prevent widespread clinical implementation. The aim of the review is to discuss clinically useful oncological scoring systems for the surgical management of HPB patients. Method: Primary articles of validated novel and established scoring systems were searched over a 25-year period using PubMed, Cochrane and Ovid Medline. Results: This review discusses 9 clinically useful scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong et al.), pancreas (Genc et al., mGPS) and biliary tract (TMHSS, MEGNA). CLIP and BCLC are extensively validated prognostic tools, with BCLC clinically endorsed by guidelines. Conversely, patient and treatment stratification is limited in CLIP and BCLC respectively - ALBI works to improve patient stratification. RETREAT, Fong et al. and Genc et al. scores predict recurrence following surgery, however these scores require further validation in heterogenous patient groups. mGPS and MEGNA are simple prognostic scores, but also require further validation in varied patient cohorts. TMHSS is user-friendly, however is limited at discriminating treatment for the middle patient group. Conclusion: A diverse range of HPB surgical scoring systems may facilitate evidenced-based treatment decisions and improve management. Future scoring systems need to be developed on heterogenous patient cohorts with improved stratification, with current trends towards implementing machine learning and genetics to improve outcome prediction.