Evidence map›Paper›PMID 36971983›Full record

ArticleHepatology international2023

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

Eric Kalo, Jacob George, Scott Read, Avik Majumdar, Golo Ahlenstiel

Abstract read
In one paragraph

Article in Hepatology international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Eric KaloBlacktown Clinical School, School of Medicine, Western Sydney University, Blacktown, NSW, 2148, Australia.
Jacob GeorgeStorr Liver Centre, The Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Westmead, NSW, 2145, Australia.
Scott ReadBlacktown Clinical School, School of Medicine, Western Sydney University, Blacktown, NSW, 2148, Australia.
Avik MajumdarVictorian Liver Transplant Unit, Austin Health, Heidelberg, VIC, 3181, Australia.
Golo AhlenstielBlacktown Clinical School, School of Medicine, Western Sydney University, Blacktown, NSW, 2148, Australia. g.ahlenstiel@westernsydney.edu.au.ORCID http://orcid.org/0000-0003-0026-1457

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

End Stage Liver DiseaseFibrosisHumansLiver CirrhosisPrognosisRetrospective StudiesRisk FactorsSeverity of Illness IndexCirrhosisModelsMortalityPost-operativePredictionSurgical risk

Identifiers

PMID36971983
PMCPMC10224863

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.