Evidence map›Paper›PMID 34041539›Full record

SynthesisInteractive cardiovascular and thoracic surgery2021

Systematic review and meta-analysis of mortality risk prediction models in adult cardiac surgery.

Shubhra Sinha, Arnaldo Dimagli, Lauren Dixon, Mario Gaudino, Massimo Caputo, Hunaid A Vohra, Gianni Angelini, Umberto Benedetto

Open access · hybridAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Interactive cardiovascular and thoracic surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.8field-weighted citation impact, top 8% of its field
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

13 citing papers in PubMed, 25 citations in OpenAlex.

  1. Trial
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  3. Review
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  11. Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2023
    Article
  12. Periprocedural adverse events after revascularisation: the importance of getting off to a good start.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2023
    Article
  13. Article
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 at 2 institutions in 2 countries.

Shubhra SinhaBristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0001-8554-6704
Arnaldo DimagliBristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0003-0055-8656
Lauren DixonBristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0002-5573-7080
Mario GaudinoWeill Cornell Medical College, Cornell University, New York, USA.ORCID 0000-0003-4680-0815
Massimo CaputoBristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0001-7508-0891
Hunaid A VohraBristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0002-6665-8724
Gianni AngeliniBristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol, UK.
Umberto BenedettoBristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0002-7074-7949
University of Bristol · GBCornell University · US

Funding

Bristol Biomedical Research CentreBritish Heart Foundation CH/17/1/32804British Heart Foundation CH/1992027/7163British Heart Foundation PG/15/33/31394
6 · The paper itself

Abstract

objectivesThe most used mortality risk prediction models in cardiac surgery are the European System for Cardiac Operative Risk Evaluation (ES) and Society of Thoracic Surgeons (STS) score. There is no agreement on which score should be considered more accurate nor which score should be utilized in each population subgroup. We sought to provide a thorough quantitative assessment of these 2 models.

methodsWe performed a systematic literature review and captured information on discrimination, as quantified by the area under the receiver operator curve (AUC), and calibration, as quantified by the ratio of observed-to-expected mortality (O:E). We performed random effects meta-analysis of the performance of the individual models as well as pairwise comparisons and subgroup analysis by procedure type, time and continent.

resultsThe ES2 {AUC 0.783 [95% confidence interval (CI) 0.765-0.800]; O:E 1.102 (95% CI 0.943-1.289)} and STS [AUC 0.757 (95% CI 0.727-0.785); O:E 1.111 (95% CI 0.853-1.447)] showed good overall discrimination and calibration. There was no significant difference in the discrimination of the 2 models (difference in AUC -0.016; 95% CI -0.034 to -0.002; P = 0.09). However, the calibration of ES2 showed significant geographical variations (P < 0.001) and a trend towards miscalibration with time (P=0.057). This was not seen with STS.

conclusionsES2 and STS are reliable predictors of short-term mortality following adult cardiac surgery in the populations from which they were derived. STS may have broader applications when comparing outcomes across continents as compared to ES2. REGISTRATION: Prospero (https://www.crd.york.ac.uk/PROSPERO/) CRD42020220983.

Indexed as

Cardiac Surgical ProceduresSurgeonsThoracic SurgeryAdultHumansRisk AssessmentRisk FactorsCardiac surgeryEuropean System for Cardiac Operative Risk EvaluationMortalityPredictionSociety of Thoracic Surgeons

Identifiers

PMID34041539
PMCPMC8557799
OpenAlexW3165491583

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.