Evidence map›Paper›PMID 40695470›Full record

ReviewExperimental physiology2026

EuroSCORE II: Current limitations and physiological gaps in risk stratification.

Jing Yong Ng, Eu Fon Tan, Marsioleda Kemberi, Eduardo Urgesi, Matti Jubouri, Damian M Bailey, Mohamad Bashir, Wael I Awad

Abstract readReview
In one paragraph

Review in Experimental physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Predictive Value of PreoperativeInternational journal of molecular sciences · 2026
    Article
  6. Article
  7. Review
  8. Review
  9. Observational
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

8 authors.

Jing Yong NgBarts Heart Centre, St Bartholomew's Hospital, London, UK.ORCID https://orcid.org/0000-0003-1172-6627
Eu Fon TanBarts Health NHS Trust, Milton Keynes University Hospital, Eaglestone, Milton Keynes, UK.
Marsioleda KemberiBarts Heart Centre, St Bartholomew's Hospital, London, UK.ORCID https://orcid.org/0009-0008-5466-1118
Eduardo UrgesiBarts Heart Centre, St Bartholomew's Hospital, London, UK.
Matti JubouriHull York Medical School, University of York, York, UK.ORCID https://orcid.org/0000-0001-6725-6164
Damian M BaileyNeurovascular Research Laboratory, Faculty of Life Sciences and Education, University of South Wales, Cardiff, UK.ORCID https://orcid.org/0000-0003-0498-7095
Mohamad BashirNeurovascular Research Laboratory, Faculty of Life Sciences and Education, University of South Wales, Cardiff, UK.ORCID https://orcid.org/0000-0002-8040-5811
Wael I AwadBarts Heart Centre, St Bartholomew's Hospital, London, UK.

Funding

Royal Society Wolfson Research Fellowship WM170007
6 · The paper itself

Abstract

Risk stratification remains critical in cardiac surgery, enabling clinicians to predict adverse outcomes and guide perioperative management. The European System for Cardiac Operative Risk Evaluation (EuroSCORE) II, introduced in 2011, incorporates 18 key variables to provide an evidence-based approach to risk assessment. However, evolving surgical techniques, changing patient demographics, and emerging evidence reveal limitations in the model's predictive capabilities. Important factors such as frailty, race, liver dysfunction, left ventricular dimensions, and advanced cardiac function metrics are not incorporated, reducing its accuracy in diverse and high-risk populations. Additionally, the model does not fully account for key conditions, such as infective endocarditis, where high-risk features like embolic events and abscesses significantly impact surgical outcomes. Simplified categorisation of procedures and the binary assessment of coronary artery disease overlook critical complexities, such as lesion severity and procedural variability. Advanced parameters like global longitudinal strain (GLS), SYNTAX, and Model for End-Stage Liver Disease (MELD) scores could enhance the model's granularity and predictive power. Furthermore, integrating machine learning into future iterations of EuroSCORE offers the potential to capture non-linear interactions and continuously refine predictions. These updates could pave the way for a 'EuroSCORE III' better aligned with modern surgical practices, offering improved precision in risk stratification, more personalised clinical decision-making and optimised patient outcomes.

Indexed as

Cardiac Surgical ProceduresHumansRisk AssessmentRisk Factorscardiac surgeryEuroScorerisk evaluationrisk modellingrisk stratification

Identifiers

PMID40695470
PMCPMC12949086

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.