Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic Dissection

Abstract

Background: Extended aortic arch repair (EAR) is increasingly adopted for treating acute type A aortic dissection (ATAAD). However, existing prediction models may not be suitable for assessing the in-hospital death risk in ATAAD patients undergoing EAR. This study aims to develop a comprehensive risk prediction model for in-hospital death following EAR based on patient's preoperative status and surgical data, which may contribute to identification of high-risk individuals and improve outcomes following EAR.

Methods: We reviewed clinical records of consecutive adult ATAAD patients undergoing EAR at our institute between January 2015 and December 2022. Utilizing data from 925 ATAAD patients undergoing EAR, we employed multivariable logistic regression and machine learning techniques, respectively, to develop nomograms for in-hospital mortality. Employed machine learning techniques included simple decision tree, random forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM).

Results: The nomogram based on SVM outperformed others, achieving a mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.842 on training dataset and a mean AUC of 0.782 on testing dataset, accompanied by a Brier score of 0.058. Key risk factors included cerebral malperfusion, mesenteric malperfusion, preoperative critical station, Marfan syndrome, platelet count, D-dimer, coronary artery bypass grafting, and cardiopulmonary bypass time. A web-based application was developed for clinical use.

Conclusions: We develop a novel nomogram risk prediction model based on SVM algorithm for in-hospital death following extended aortic arch repair for ATAAD with good discrimination and accuracy.

Clinical trial registration: Registration number ChiCTR2200066414, https://www.chictr.org.cn/showproj.html?proj=187074.

Keywords: acute type A aortic dissection; extended aortic arch repair; machine learning; nomogram; prediction model.

Fig. 1.

Flow chart for the selection of study population and study design. ATAAD, acute type A aortic dissection; FET, frozen elephant trunk; LR, logistic regression analysis; RF, random forest; Dtree, decision tree; XGBoost, eXtreme Gradient Boosting; SVM, support vector machine; SHAP, SHapley Additive exPlanations; ROC, receiver operating characteristic; DCA, decision curve analysis.

Fig. 2.

The ROC curves of models. (A) The ROC curves of Fit.LR model; (B) the ROC curves of Fit.RF model; (C) the ROC curves of Fit.Dtree model; (D) the ROC curves of Fit.XGBoost model; (E) the ROC curves of Fit.SVM model. ROC, receiver operating characteristic; LR, logistic regression analysis; RF, random forest; Dtree, decision tree; XGBoost, eXtreme Gradient Boosting; SVM, support vector machine; AUC, the area under the receiver operating characteristic curve.

Fig. 3.

The calibration curves of models. (A) The calibration curves of Fit.LR model; (B) the calibration curves of Fit.RF model; (C) the calibration curves of Fit.Dtree model; (D) the calibration curves of Fit.XGBoost model; (E) the calibration curves of Fit.SVM model. ROC, receiver operating characteristic; R2, coefficient of complex determination; D, discrimination index; U, unreliability index; Q, quality index; LR, logistic regression analysis; RF, random forest; Dtree, decision tree; XGBoost, eXtreme Gradient Boosting; SVM, support vector machine; Dxy, the magnitude of the rank correlation between the predicted probability and the observed value; Emax, the maximum absolute difference between the predicted value and the actual value; E90, the 90th percentile of the difference between the predicted value and the true value; Eavg, the average difference between the predicted value and the actual value; S:z, Z-value of Spiegelhalter Z-test; S:p, p-value of Spiegelhalter Z-test.

Fig. 4.

The DCA curves of models. LR, logistic regression analysis; RF, random forest; Dtree, decision tree; XGBoost, eXtreme Gradient Boosting; SVM, support vector machine; DCA, decision curve analysis.

Fig. 5.

The nomogram of Fit.SVM. IscCerebral, cerebral malperfusion; D2, D-dimer; Plt, platelet count; CABG, coronary artery bypass grafting; IscMesenteric, mesenteric malperfusion; CPStatus, critical preoperative status; Transfusion, intraoperative blood product transfusion; CPBT, cardiopulmonary bypass time; SVM, support vector machine.