RESUMO
At present, stroke remains the second highest cause of death globally and a leading cause of disability. From 1990 to 2019, the absolute number of strokes worldwide increased by 70.0%, and the prevalence of stroke increased by 85.0%, causing millions of deaths and disability. Ischemic stroke accounts for the majority of strokes, which is caused by arterial occlusion. Effective primary prevention strategies, early diagnosis, and timely interventions such as rapid reperfusion are in urgent implementation to control ischemic stroke. Otherwise, the stroke burden will probably continue to grow across the world as a result of population aging and an ongoing high prevalence of risk factors. To help with the diagnosis and management of ischemic stroke, newer techniques such as artificial intelligence (AI) are highly anticipated and may bring a new revolution. AI is a recent fast-growing research area which aims to mimic cognitive processes through a number of techniques such as machine learning (ML) methods of random forest learning (RFL) and convolutional neural networks (CNNs). With the help of AI, several momentous milestones have already been attained across diverse dimensions of ischemic stroke. In the context of predictive, preventive, and personalized medicine (PPPM/3PM), we aim to transform stroke care from a reactive to a proactive and individualized paradigm. In this way, AI demonstrates strong clinical utility across all three levels of prevention in ischemic stroke. In this paper, we synoptically illustrated the history and current situation of AI and ML. Then, we summarized their clinical applications and efficacy in the management of stroke. We finally provided an outlook on how AI approaches might contribute to enhancing favorable outcomes after stroke and proposed our suggestions on developing AI-based PPPM strategies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00343-3.
RESUMO
Purpose: Systemic inflammation plays an important role in the pathophysiology and progression of aneurysmal subarachnoid hemorrhage (aSAH). In this study, we aimed to investigate the association between a new biomarker, the inflammatory burden index (IBI) and the prognosis as well as in-hospital complications of aSAH patients. Patients and Methods: We analyzed data from patients with aSAH between January 2019 and September 2022 who were included in the LongTEAM (Long-term Prognosis of Emergency Aneurysmal Subarachnoid Hemorrhage) registry study. The IBI was formulated as C-reactive protein × neutrophils/lymphocytes. The unfavorable functional prognosis was assessed by the modified Rankin Scale (mRS). Receiver operating characteristic (ROC) curve analysis was conducted to determine the optimal cut-off values for IBI to distinguish the unfavorable functional prognosis. Multivariate logistic regression was applied to investigate the association between IBI and in-hospital complications. Propensity score matching was adjusted for imbalances in baseline characteristics to assess the effect of IBI on prognosis. Results: A total of 408 consecutive patients with aSAH enrolled in the study, of which 235 (57.6%) were female patients and the mean age was 55.28 years old. An IBI equal to 138.03 was identified as the best cut-off threshold to distinguish the unfavorable prognosis at 3 months (area under the curve [AUC] [95% CI] 0.637 [0.568-0.706]). ln IBI was independently associated with 3-month functional prognosis (OR [95% CI] 1.362 [1.148-1.615]; P<0.001), pneumonia (OR [95% CI] 1.427 [1.227-1.659]; P<0.001) and deep venous thrombosis (DVT). (OR [95% CI] 1.326 [1.124-1.564]; P=0.001). After propensity score matching (57:57), an increased proportion of patients with IBI ≥138.03 had a poor functional prognosis at 3 months and in-hospital complications including developed pneumonia and DVT. Conclusion: In patients with aSAH, high IBI level at admission was associated with unfavorable functional prognosis as well as pneumonia and deep vein thrombosis.