Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning

 

The mentors and senior researchers need to be fired for allowing this prediction crapola instead of actually getting survivors recovered.

Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning

Xiang Li^1,2†, XiDing Pan^3†, ChunLian Jiang^4†, MingRu Wu¹, YuKai Liu^5*, FuSang Wang^1,2, XiaoHan Zheng^1,2, Jie Yang⁶, Chao Sun^1,2, YuBing Zhu³, JunShan Zhou⁵, ShiHao Wang⁷, Zheng Zhao^3* and JianJun Zou^2*

• ¹School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
• ²Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
• ³Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
• ⁴Department of Pathology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
• ⁵Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
• ⁶Department of Neurology, the First Affiliated Hospital of Chengdu Medical College, Chengdu, China
• ⁷School of Public Health, Bengbu Medical College, Bengbu, China

Background and Purpose: Accurate prediction of functional outcome after stroke would provide evidence for reasonable post-stroke management. This study aimed to develop a machine learning-based prediction model for 6-month unfavorable functional outcome in Chinese acute ischemic stroke (AIS) patient.

Methods: We collected AIS patients at National Advanced Stroke Center of Nanjing First Hospital (China) between September 2016 and March 2019. The unfavorable outcome was defined as modified Rankin Scale score (mRS) 3–6 at 6-month. We developed five machine-learning models (logistic regression, support vector machine, random forest classifier, extreme gradient boosting, and fully-connected deep neural network) and assessed the discriminative performance by the area under the receiver-operating characteristic curve. We also compared them to the Houston Intra-arterial Recanalization Therapy (HIAT) score, the Totaled Health Risks in Vascular Events (THRIVE) score, and the NADE nomogram.

Results: A total of 1,735 patients were included into this study, and 541 (31.2%) of them had unfavorable outcomes. Incorporating age, National Institutes of Health Stroke Scale score at admission, premorbid mRS, fasting blood glucose, and creatinine, there were similar predictive performance between our machine-learning models, while they are significantly better than HIAT score, THRIVE score, and NADE nomogram.

Conclusions: Compared with the HIAT score, the THRIVE score, and the NADE nomogram, the RFC model can improve the prediction of 6-month outcome in Chinese AIS patients.

Introduction

Globally, stroke is a leading cause of mortality and disability (1). In developing countries, the prevalence of stroke is increasing as the population ages. Patients who survive stroke have an increased economic burden due to post-stroke care (2). Therefore, accurate prediction of functional outcome after stroke would provide evidence for reasonable post-stroke management and thus improve the allocation of health care resources.

The prognostic prediction requires the processing of patients' clinical data, such as demographic information, clinical features, and laboratory tests results. Then, the model is developed to predict prognosis base on existing data. Several prognostic models have been developed to predict the clinical outcome after stroke, such as Houston Intra-arterial Recanalization Therapy (HIAT) score, Totaled Health Risks in Vascular Events (THRIVE) score and NADE nomogram (3–5). They are generally based on regression model with the assumption of a linear relationship between variables and the outcomes. The THRIVE score and HIAT score were developed based on Whites or Blacks, not Asians. Compared with White patients, the average age of Asian patients was younger (6, 7). In addition, several studies have observed worse survival in Whites with stroke compare to other race (8, 9). Importantly, the long-term outcomes of stroke were significantly different by race (7). Thus, it is difficult for these models to achieve accurate predictive performances on the Chinese population.

Machine-learning (ML) approaches have been widely used in medical fields (10). Recently, it has shown effective capability in disease prediction, especially in the analysis of large datasets with a multitude of variables (11–13). ML uses computer algorithms to build a model from labeled data and to make data-driven predictions. It enables the computer to process complex non-linear relationships between variables and outcomes, which may be hard to be detected by conventional regression models (14). Such advantages increase the accuracy of prediction model. ML includes multiple algorithms, such as logistic regression (LR), random forest classifier (RFC), support vector machine (SVM), fully-connected deep neural network (DNN), and extreme gradient boosting (XGBoost). The optimal selection of algorithm should be in accordance with the characteristics of the dataset. Meanwhile, the popularity of electronic patient record (EPR) systems and wide availability of structured patient data make sophisticated computer algorithms implemented at the bedside a reality.

In this study, we aim to develop the models using ML method to predict 6-month unfavorable outcomes in Chinese stroke patients, and then compare the performance of ML-based methods with existing clinical prediction scores.