The Construction of a Risk Prediction Model Based on Neural Network for Pre-operative Acute Ischemic Stroke in Acute Type A Aortic Dissection Patients

Abstract

Objective: To establish a pre-operative acute ischemic stroke risk (AIS) prediction model using the deep neural network in patients with acute type A aortic dissection (ATAAD).Methods: Between January 2015 and February 2019, 300 ATAAD patients diagnosed by aorta CTA were analyzed retrospectively. Patients were divided into two groups according to the presence or absence of pre-operative AIS. Pre-operative AIS risk prediction models based on different machine learning algorithm was established with clinical, transthoracic echocardiography (TTE) and CTA imaging characteristics as input. The performance of the difference models was evaluated using the receiver operating characteristic (ROC), precision-recall curve (PRC) and decision curve analysis (DCA).Results: Pre-operative AIS was detected in 86 of 300 patients with ATAAD (28.7%). The cohort was split into a training (211, 70% patients) and validation cohort (89, 30% patients) according to stratified sampling strategy. The constructed deep neural network model had the best performance on the discrimination of AIS group compare with other machine learning model, with an accuracy of 0.934 (95% CI: 0.891–0.963), 0.921 (95% CI: 0.845–0.968), sensitivity of 0.934, 0.960, specificity of 0.933, 0.906, and AUC of 0.982 (95% CI: 0.967–0.997), 0.964 (95% CI: 0.932–0.997) in the training and validation cohort, respectively.Conclusion: The established risk prediction model based on the deep neural network method may have the big potential to evaluate the risk of pre-operative AIS in patients with ATAAD.

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

“…In recent years, machine learning methods have become extremely popular in medical prognosis prediction due to their data-driven nature and minimal assumptions regarding the input variables and their relationship to the outcome. 12 13 Several research teams applied machine learning to predict preoperative acute ischaemic stroke and mortality of patients with AD; yet, their prediction models did not include the variance of blood pressure (BP) and heart rate (HR), [14][15][16] which are crucial indicators for the management of AD. 5 17 18 In this paper, we adopted the machine learning techniques to comprehensively analyse the demographic information, medical history and variation of BP and HR during hospitalisation and establish a prediction model for preoperative in-hospital mortality of patients with acute AD.…”

Machine Learning Approaches for Segmentation of Cardiovascular Neurocristopathy Related Images

“…Only one out of 24 studies (Guo et al [58]) has provided the data set on which they worked. Furthermore, nine other studies ( [59]- [67]) have mentioned that the data set could be provided to researchers on a reasonable request. All other remaining 18 studies (66.7%) have not noted the availability of their dataset.…”

An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data

“…Zhao et al proposed a DNN approach to predict the risk of pre-operative acute ischemic stroke. Using a combination of clinical data, transthoracic echocardiography, and CTA imaging, they achieved a 96.4% AUC score [ 19 ]. Furthermore, Alanazi et al worked on the task of predicting the risk of stroke on an imbalanced clinical dataset (biomarkers) from the National Health and Nutrition Examination Survey (NHANES).…”