Abstract

One of the leading causes of death and disability in the world today is still stroke. Age and stroke severity are two examples of baseline, non-modifiable characteristics that are traditionally viewed as the most relevant when assessing the difficulty of forecasting stroke prognosis. But certain problems, like haemorrhagic changes or post-stroke infections, might arise after a stroke and have a negative impact on the patient’s prognosis. For doctors to better individualize and enhance stroke therapy, an early prediction or detection of these conditions based on predictive models with clinical data may be helpful. Moreover, these predictive models may be more predictive if biological data—such as genetic polymorphisms or blood biomarkers—are added. In this study, we primarily discuss the various post-stroke consequences that affect the patient’s short- and long-term outcomes at various times in the disease’s natural history, as well as the clinical and biological data that may be helpful in predicting these difficulties.

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Data Sharing Statement

Funding