Journal Article Epublish
PubMed: 42421786 DOI: 10.1016/j.bjao.2026.100581
In-hospital mortality among patients with congenital heart disease undergoing noncardiac procedures: machine learning analysis of a multicentre study.
BJA Open 19:100581
Authors: 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
Affiliations
- Division of Pediatric Cardiac Anesthesia, Department of Anesthesiology, Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, USA.
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- The Hospital for Sick Children, Department of Anesthesiology and Pain Medicine, Toronto, ON, Canada.
- Department of Anesthesiology, Pain and Perioperative Medicine, Children's National Hospital, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
- Department of Anesthesiology, Perioperative and Pain Medicine, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.
- Division 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.
- Division of Pediatric Cardiac Anesthesia, Department of Anesthesiology, University of Minnesota Masonic Children's Hospital, Minneapolis, MN, USA.
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% (n=104) had in-hospital mortality; among 9837 surgical encounters, 1.48% (n=146) were associated with in-hospital mortality. The top features contributing to mortality prediction in the eXtreme Gradient boost model were similar to defined factors in previous multivariable regression analyses: pre-procedural inotropic support; duration of anaesthesia; severe cardiac disease; prematurity; white race; emergent procedure; gender; inpatient procedure; black race; weekend procedure; and gastrointestinal, neurological, haematological, and respiratory comorbidities. A decision curve analysis shows benefit to understanding patients at increased risk of mortality when the probability of mortality is between 0% and 60%.
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.
MeSH
Heart Defects, Congenital, Regression Analysis, Comorbidity, Mortality, Heart Diseases, Heart, Hospital Mortality, Machine Learning, Risk Factors, Learning