Machine learning for the prediction of acute kidney injury and paraplegia after thoracoabdominal aortic aneurysm repair

Abstract

Objective Prediction of acute renal failure (ARF) and paraplegia after thoracoabdominal aortic aneurysm repair (TAAAR) is helpful for decision-making during the postoperative phase. To find a more efficient method for making a prediction, we performed tests on the efficacy of different machine learning predicting models. Methods Perioperative TAAAR data were retrospectively collected from Beijing Anzhen Hospital and Shanghai DeltaHealth Hospital. Operations were conducted under normothermia using a four-branched graft. Four commonly used machine learning classification models (ie, logistic regression, linear and Gaussian kernel support vector machine, and random forest) were chosen to predict ARF and paraplegia separately. The efficacy of the models was validated by five-fold cross-validation. Results From 2009 to 2017, 212 TAAARs were performed. ARF was identified in 27 patients, and paraplegia was found in 18 patients. Five-fold cross-validation showed that among the four classification models, random forest was the most precise model for predicting ARF, with an average area under the curve (AUC) of 0.89 ± 0.08. Linear support vector machine was the most precise model for predicting paraplegia, with an average AUC of 0.89 ± 0.18. The prediction program has been uploaded to GitHub for open access. Conclusion Machine learning models can precisely predict ARF and paraplegia during early stages after surgery. This program allows cardiac surgeons to address complications earlier and may help improve the clinical outcomes of TAAAR.