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 crossvalidation.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.

Machine Learning for the Prediction of Complications in Patients After Mitral Valve Surgery

“…Based on previous studies, there is a general convergence of the incidence of postoperative complications among different hospitals. Some high-turnover institutions have low mortality rates and may be associated with higher complication rates ( 17 ). In other words, surgical patients in high-turnover facilities may experience one or more postoperative complications, but they have a low mortality rate, because these facilities have a higher rate of rescue success ( 18 ).…”

Artificial Intelligence for the Prediction of Acute Kidney Injury During the Perioperative Period: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

“…Seventeen studies described the demographic characteristics of their study population, of whom the mean age was 37 to 71 years old and the percentage of males was 16% to 88% 17,[19][20][21][22][23][24][25][26][27][28][29][30][31][33][34][35] .…”

“…The total number of subjects tested in the included studies was 304,076, with the sample size ranged from 109 to 96,653 [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] .…”

Artificial Intelligence for the prediction of Acute Kidney Injury during the perioperative period: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

“…The included studies were categorized based on the type of the surgery participants received, including cardiothoracic surgery, any inpatient operative procedure, liver transplantation, total knee arthroplasty [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] .…”

“…Enrolled studies presented the performance of the AI algorithms with test dataset (internal validation), and there were only four studies 21,26,27,34 that presented the performance of external validation. Nine studies [21][22][23][24][25]28,[32][33][34] established the AI algorithm based on the gradient boosting machine (GBM), three studies 17,19,35 established random forest (RF)-based algorithms, three studies 20,27,29 established two types of arti cial neural network (ANN)based algorithms, one study 26 established Bayesian network (BN)-based algorithm, one study 31 established decision-tree (DT)-based algorithm, one study 30 established an ensemble algorithm, and another study even conducted a novel machine learning risk algorithm 18 called: MySurgeryRisk .…”