Scientific Reports (Nov 2023)

Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms

  • Chie Nagata,
  • Masahiro Hata,
  • Yuki Miyazaki,
  • Hirotada Masuda,
  • Tamiki Wada,
  • Tasuku Kimura,
  • Makoto Fujii,
  • Yasushi Sakurai,
  • Yasuko Matsubara,
  • Kiyoshi Yoshida,
  • Shigeru Miyagawa,
  • Manabu Ikeda,
  • Takayoshi Ueno

DOI
https://doi.org/10.1038/s41598-023-48418-5
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 8

Abstract

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Abstract Associations between delirium and postoperative adverse events in cardiovascular surgery have been reported and the preoperative identification of high-risk patients of delirium is needed to implement focused interventions. We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium. Patients aged ≥ 40 years who underwent cardiovascular surgery at a single hospital were prospectively enrolled. Preoperative and intraoperative factors were assessed. Each patient was evaluated for postoperative delirium 7 days after surgery. We developed machine learning models using the Bernoulli naive Bayes, Support vector machine, Random forest, Extra-trees, and XGBoost algorithms. Stratified fivefold cross-validation was performed for each developed model. Of the 87 patients, 24 (27.6%) developed postoperative delirium. Age, use of psychotropic drugs, cognitive function (Mini-Cog < 4), index of activities of daily living (Barthel Index < 100), history of stroke or cerebral hemorrhage, and eGFR (estimated glomerular filtration rate) < 60 were selected to develop delirium prediction models. The Extra-trees model had the best area under the receiver operating characteristic curve (0.76 [standard deviation 0.11]; sensitivity: 0.63; specificity: 0.78). XGBoost showed the highest sensitivity (AUROC, 0.75 [0.07]; sensitivity: 0.67; specificity: 0.79). Machine learning algorithms could predict post-cardiovascular delirium using preoperative data. Trial registration: UMIN-CTR (ID; UMIN000049390).