The predictors of death within 1 year in acute ischemic stroke patients based on machine learning

 WOW! Predicting death! The whole idea of stroke research is to get survivors recovered. WHAT THE FUCK WAS THIS RESEARCH DONE FOR?

The predictors of death within 1 year in acute ischemic stroke patients based on machine learning

Kai Wang^1,2†, Longyuan Gu^3†, Wencai Liu^4†, Chan Xu⁵, Chengliang Yin⁶, Haiyan Liu^1,2, Liangqun Rong^1,2*, Wenle Li^2,7*‡ and Xiu'e Wei^1,2*

• ¹Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
• ²Key Laboratory of Neurological Diseases, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
• ³Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
• ⁴Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
• ⁵Department of Dermatology, Xianyang Central Hospital, Xianyang, China
• ⁶Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao SAR, China
• ⁷The State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China

Objective: To explore the predictors of death in acute ischemic stroke (AIS) patients within 1 year based on machine learning (ML) algorithms.

Methods: This study retrospectively analyzed the clinical data of patients hospitalized and diagnosed with AIS in the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. The patients were randomly divided into training and validation sets at a ratio of 7:3, and the clinical characteristic variables of the patients were screened using univariate and multivariate logistics regression. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGB), random forest (RF), decision tree (DT), and naive Bayes classifier (NBC), were applied to develop models to predict death in AIS patients within 1 year. During training, a 10-fold cross-validation approach was used to validate the training set internally, and the models were interpreted using important ranking and the SHapley Additive exPlanations (SHAP) principle. The validation set was used to externally validate the models. Ultimately, the highest-performing model was selected to build a web-based calculator.

Results: Multivariate logistic regression analysis revealed that C-reactive protein (CRP), homocysteine (HCY) levels, stroke severity (SS), and the number of stroke lesions (NOS) were independent risk factors for death within 1 year in patients with AIS. The area under the curve value of the XGB model was 0.846, which was the highest among the six ML algorithms. Therefore, we built an ML network calculator (https://mlmedicine-de-stroke-de-stroke-m5pijk.streamlitapp.com/) based on XGB to predict death in AIS patients within 1 year.

Conclusions: The network calculator based on the XGB model developed in this study can help clinicians make more personalized and rational clinical decisions.

1. Introduction

Acute ischemic stroke (AIS) is a disease caused by the occlusion of cerebral arteries, accompanied by brain tissue infarction and neuronal cell damage, causing severe trauma to the body. AIS is the leading cause of disability in adults and the primary cause of human death worldwide (1, 2). In 2019, there were 7,630,800 cases of AIS globally, an 87.55% increase compared to the previous 30 years. The high morbidity, mortality, and disability rates associated with AIS impose a severe economic burden on society and families (3). Several factors may have a significant impact on the pathogenesis and prognosis of patients with AIS, including the immune inflammatory response during AIS development, with the involvement of different pathways and sources of activated inflammatory factors, and is an important regulator of stroke progression, post-stroke damage, cerebral function repair and death (4–6). Approximately 10% of AIS patients, representing a type of morbidity, experience a fatal event within 1 year (7). There is an urgent need to identify the early and effective predictors of death 1 year after the onset of AIS. The construction of a model of death prediction in stroke patients within 1 year could provide clinicians with a reliable tool to assess the condition of their patients. However, there are few reports in this area.

ML-assisted clinical decision-making and analysis have been widely used in clinical settings (8–11), especially in the screening phase of big data feature variables (12, 13). The superior performance demonstrated by ML algorithms in medical big data makes it possible to obtain better predictive tools than traditional statistical models under certain conditions. However, few studies have been conducted to screen the risk factors of death in AIS patients within 1 year using ML algorithms.

Therefore, this study aimed to develop and validate an interpretable ML model that used clinically relevant variables to predict death within 1 year in AIS patients and construct an easy-to-use web calculator as a convenient and practical protective tool for clinical practitioners to provide valid information for AIS patients.(REALLY, YOU'RE GOING TO TELL YOUR PATIENTS THEY'RE GOING TO DIE?)

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