Evidence map›Paper›PMID 42421786›Full record

ArticleBJA open2026

In-hospital mortality among patients with congenital heart disease undergoing noncardiac procedures: machine learning analysis of a multicentre study.

Michael T Kuntz, Mikaela J Mari, Steven J Staffa, Vannessa Chin, Nina Deutsch, David Faraoni, Wanda C Miller-Hance, Susan C Nicolson, Martina Richtsfeld, Viviane G Nasr

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Article in BJA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Michael T KuntzDivision of Pediatric Cardiac Anesthesia, Department of Anesthesiology, Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, USA.
Mikaela J MariDepartment of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Steven J StaffaDepartment of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Vannessa ChinThe Hospital for Sick Children, Department of Anesthesiology and Pain Medicine, Toronto, ON, Canada.
Nina DeutschDepartment of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
David FaraoniDepartment of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Wanda C Miller-HanceDepartment of Anesthesiology, Perioperative and Pain Medicine, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.
Susan C NicolsonDivision of Cardiothoracic Anesthesia, Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Martina RichtsfeldDivision of Pediatric Cardiac Anesthesia, Department of Anesthesiology, University of Minnesota Masonic Children's Hospital, Minneapolis, MN, USA.
Viviane G NasrDepartment of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Improved survival for children with congenital heart disease (CHD) has resulted in an increasing number presenting for noncardiac procedures. Several factors are associated with mortality in these patients, including severity of heart disease, preoperative inotropic support, preoperative ventilatory support, emergency procedure, and comorbidities. Prior literature has relied on multivariable regression analyses. The aim of this analysis was to utilise machine learning (ML) methodology to determine risk factors for in-hospital mortality after noncardiac procedures in paediatric patients with CHD. Methods: Seven paediatric centres collected data between January 2021 and December 2022 involving patients <21 yr of age with CHD undergoing noncardiac procedures. Both the random forest and eXtreme Gradient boosting machine models were tested, with the optimal model chosen based on the area under the precision recall curve in the validation set. Results: Of 5977 patients, 1.74% ( Conclusions: The risk factors shown in our dataset using ML methods are similar to those demonstrated in our prior work utilising logistic regression. Ongoing awareness of these mortality predictors is essential to appropriate care planning.

Indexed as

congenital heart diseasemortalitynoncardiac surgerypaediatricrisk factors

Identifiers

PMID42421786
PMCPMC13342874

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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.