Machine learning-based risk profile classification of patients undergoing elective heart valve surgery

Ulrich Bodenhofer, Bettina Haslinger-Eisterer, Alexander Minichmayer, Georg Hermanutz, Jens Meier

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

OBJECTIVES: Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this risk allows for the improved counselling of patients and avoidance of possible complications. We therefore investigated the benefit of modern machine learning methods in personalized risk prediction for patients undergoing elective heart valve surgery. METHODS: We performed a monocentric retrospective study in patients who underwent elective heart valve surgery between 1 January 2008 and 31 December 2014 at our centre. We used random forests, artificial neural networks and support vector machines to predict the 30-day mortality from a subset of 129 available demographic and preoperative parameters. Exclusion criteria were reoperation of the same patient, patients who needed anterograde cerebral perfusion due to aortic arch surgery and patients with grown-up congenital heart disease. Finally, the cohort consisted of 2229 patients with a 30-day mortality of 3.86% (86 of 2229 cases). This trial has been registered at clinicaltrials.gov (NCT03724123). RESULTS: The final random forest model trained on the entire data set provided an out-of-bag area under the receiver operator characteristics curve (AUC) of 0.839, which significantly outperformed the European System for Cardiac Operative Risk Evaluation (EuroSCORE) (AUC = 0.704) and a model trained only on the subset of features EuroSCORE uses (AUC = 0.745). CONCLUSIONS: Advanced machine learning methods can predict outcomes of valve surgery procedures with higher accuracy than established risk scores based on logistic regression on pre-selected parameters. This approach is generalizable to other elective high-risk interventions and allows for training models to the cohorts of specific institutions

Original languageEnglish
Pages (from-to)1378-1385
Number of pages8
JournalEuropean Journal of Cardio-thoracic Surgery
Volume60
Issue number6
Early online date29 May 2021
DOIs
Publication statusPublished - 29 May 2021

Keywords

  • heart valve surgery
  • machine learning
  • random forest
  • support vector machine

Fingerprint

Dive into the research topics of 'Machine learning-based risk profile classification of patients undergoing elective heart valve surgery'. Together they form a unique fingerprint.

Cite this