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Evolution of risk prediction models for post-operative mortality in patients with cirrhosis. | LitMetric

Evolution of risk prediction models for post-operative mortality in patients with cirrhosis.

Hepatol Int

Blacktown Clinical School, School of Medicine, Western Sydney University, Blacktown, NSW, 2148, Australia.

Published: June 2023

AI Article Synopsis

  • Patients with cirrhosis are often considered high surgical risk, leading to hesitance in performing operations on them.
  • Traditional risk assessment tools like Child-Turcotte-Pugh (CTP) and MELD provide some insight but typically overestimate the actual surgical risk.
  • Newer prediction models such as the Mayo Risk Score and VOCAL-Penn score improve risk assessment and highlight the importance of developing future tools that are both effective and easy for healthcare professionals to use.

Article Abstract

The perception of high surgical risk among patients with cirrhosis has resulted in a long-standing reluctance to operate. Risk stratification tools, first implemented over 60 years ago, have attempted to assess mortality risk among cirrhotic patients and ensure the best possible outcomes for this difficult to treat cohort. Existing postoperative risk prediction tools including the Child-Turcotte-Pugh (CTP) and Model for End-stage Liver Disease (MELD) provide some prediction of risk in counselling patients and their families but tend to overestimate surgical risk. More personalised prediction algorithms such as the Mayo Risk Score and VOCAL-Penn score that incorporate surgery-specific risks have demonstrated a significant improvement in prognostication and can ultimately aid multidisciplinary team determination of potential risks. The development of future risk scores will need to incorporate, first and foremost, predictive efficacy, but perhaps just as important is the feasibility and usability by front-line healthcare professionals to ensure timely and efficient prediction of risk for cirrhotic patients.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224863PMC
http://dx.doi.org/10.1007/s12072-023-10494-0DOI Listing

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