Key result
The XGBoost prediction model outperformed logistic regression in predicting postoperative acute renal failure in both internal (AUC 0.82 vs 0.77, p<0.001) and external validation (AUC 0.81 vs 0.75).
Why the study?
Acute renal failure is the most common major complication following cardiac surgery for acute aortic syndrome and worsens postoperative prognosis, prompting the need for a machine learning prediction model.
Does an XGBoost machine learning model improve the prediction of postoperative acute renal failure compared to traditional logistic regression and scoring systems in patients undergoing surgery for acute aortic syndrome?
Observational (n=1,637)
Yes
Does an XGBoost machine learning model improve the prediction of postoperative acute renal failure compared to traditional logistic regression and scoring systems in patients undergoing surgery for acute aortic syndrome?
p-value: p=<0.001
An XGBoost machine learning model provides superior prediction of acute renal failure following surgery for acute aortic syndrome compared to traditional logistic regression and existing risk scoring systems.
May aid ARF prediction after AAS surgery; leaves open prospective validation before clinical use.
Background Acute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients. Methods We included AAS patient data from nine medical centers ( n = 1,637) and analyzed the incidence of ARF and the risk factors for postoperative ARF. We used data from six medical centers to compare the performance of four machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the performance of the optimal machine learning prediction model with that of traditional prediction models. Data from three medical centers were used for external validation. Results The eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77, p < 0.001) and the traditional scoring systems. Upon external validation, the XGBoost prediction model (AUC =0.81) also performed better than both the LR prediction model (AUC = 0.75, p = 0.03) and the traditional scoring systems. We created an online application based on the XGBoost prediction model. Conclusions We have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures.
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Li et al. (2022) conducted an observational in Acute aortic syndrome (AAS) (n=1,637). eXtreme Gradient Boosting (XGBoost) prediction model vs. Logistic regression (LR) prediction model and traditional scoring systems was evaluated on Area under the curve (AUC) for predicting postoperative acute renal failure (p=<0.001). The XGBoost prediction model outperformed logistic regression in predicting postoperative acute renal failure in both internal (AUC 0.82 vs 0.77, p<0.001) and external validation (AUC 0.81 vs 0.75).
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