The role of ascending aortic size in outcomes of patients with uncomplicated acute type B aortic dissection

Ray, Hunter M.; Besho, Joseph M.; Au, Jason; Charlton-Ouw, Kristofer M.; Estrera, Anthony L.; Miller, Charles C.; Safi, Hazim J.; Azizzadeh, Ali

doi:10.1016/j.jvs.2018.07.048

Cited by 6 publications

(1 citation statement)

References 33 publications

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“… 36 37 Besides, Ray et al revealed that patients with maximum ascending aortic diameter >40.8 mm were at high risk for subsequent proximal and arch progression. 38 Some researchers demonstrated that the diameter of ascending aorta was an independent risk factor for the long-term prognosis of acute AD; yet, it could not be used as an indicator for evaluating the occurrence of AD. 39 According to the current guidelines, prophylactic interventions for AD are recommended for patients with an ascending aortic diameter above 5.5 cm.…”

Section: Discussionmentioning

confidence: 99%

Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study

et al. 2023

BMJ Open

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ObjectivesTo conduct a comprehensive analysis of demographic information, medical history, and blood pressure (BP) and heart rate (HR) variability during hospitalisation so as to establish a predictive model for preoperative in-hospital mortality of patients with acute aortic dissection (AD) by using machine learning techniques.DesignRetrospective cohort study.SettingData were collected from the electronic records and the databases of Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the First Affiliated Hospital of Anhui Medical University between 2004 and 2018.Participants380 inpatients diagnosed with acute AD were included in the study.Primary outcomePreoperative in-hospital mortality rate.ResultsA total of 55 patients (14.47%) died in the hospital before surgery. The results of the areas under the receiver operating characteristic curves, decision curve analysis and calibration curves indicated that the eXtreme Gradient Boosting (XGBoost) model had the highest accuracy and robustness. According to the SHapley Additive exPlanations analysis of the XGBoost model, Stanford type A, maximum aortic diameter >5.5 cm, high variability in HR, high variability in diastolic BP and involvement of the aortic arch had the greatest impact on the occurrence of in-hospital deaths before surgery. Moreover, the predictive model can accurately predict the preoperative in-hospital mortality rate at the individual level.ConclusionIn the current study, we successfully constructed machine learning models to predict the preoperative in-hospital mortality of patients with acute AD, which can help identify high-risk patients and optimise the clinical decision-making. Further applications in clinical practice require the validation of these models using a large-sample, prospective database.Trial registration numberChiCTR1900025818.

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Section: Discussionmentioning

confidence: 99%