Development and Validation of a Diagnostic Model to Predict the Risk of Ischemic Liver Injury After Stanford A Aortic Dissection Surgery.

Maomao Liu,Xuran Lu,Tengke Wang,Wen Yuan,Nan Liu,Wen Tan

doi:10.3389/fcvm.2021.701537

Maomao Liu, Xuran Lu + Show 4 more

Open Access

https://doi.org/10.3389/fcvm.2021.701537

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Abstract

Objective: To define the risk factors of ischemic liver injury (ILI) following Stanford A aortic dissection surgery and to propose a diagnostic model for individual risk prediction.Methods: We reviewed the clinical parameters of ILI patients who underwent cardiac surgery from Beijing Anzhen Hospital, Capital Medical University between January 1, 2015 and October 30, 2020. The data was analyzed by the use of univariable and multivariable logistic regression analysis. A risk prediction model was established and validated, which showed a favorable discriminating ability and might contribute to clinical decision-making for ILI after Stanford A aortic dissection (AAD) surgery. The discriminative ability and calibration of the diagnostic model to predict ILI were tested using C statistics, calibration plots, and clinical usefulness.Results: In total, 1,343 patients who underwent AAD surgery were included in the study. After univariable and multivariable logistic regression analysis, the following variables were incorporated in the prediction of ILI: pre-operative serum creatinine, pre-operative RBC count <3.31 T/L, aortic cross-clamp time >140 min, intraoperative lactic acid level, the transfusion of WRBC, atrial fibrillation within post-operative 24 h. The risk model was validated by internal sets. The model showed a robust discrimination, with an area under the receiver operating characteristic (ROC) curve of 0.718. The calibration plots for the probability of perioperative ischemic liver injury showed coherence between the predictive probability and the actual probability (Hosmer-Lemeshow test, P = 0.637). In the validation cohort, the nomogram still revealed good discrimination (C statistic = 0.727) and good calibration (Hosmer-Lemeshow test, P = 0.872). The 10-fold cross-validation of the nomogram showed that the average misdiagnosis rate was 9.95% and the lowest misdiagnosis rate was 9.81%.Conclusion: Our risk model can be used to predict the probability of ILI after AAD surgery and have the potential to assist clinicians in making treatment recommendations.

Highlights

Ischemic liver injury (ILI) is a clinical syndrome characterized by acute and dramatic increases in serum aminotransferase to a level of more than 10 times the upper limit of normal, which is caused by insufficient oxygen and blood delivery to the hepatocytes [1,2,3]
A risk prediction model was established and validated, which showed a favorable discriminating ability and might contribute to clinical decision-making for ILI after Stanford A aortic dissection (AAD) surgery
In total, 1,343 patients who underwent AAD surgery were included in the study

Summary

Introduction

Ischemic liver injury (ILI) is a clinical syndrome characterized by acute and dramatic increases in serum aminotransferase to a level of more than 10 times the upper limit of normal, which is caused by insufficient oxygen and blood delivery to the hepatocytes [1,2,3]. The all-cause mortality rate is 25 ∼ 73% [1, 2, 5,6,7], of which more than 50% occurred during ICU stay [2, 3, 7, 11]. Despite improvement in perioperative management and surgical techniques, the malperfusion syndromes (i.e., ischemic liver injury) are often present as sequelae of general ischemia [12, 13]

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