ABSTRACT
Objectives (Importance) Cerebrovascular accident (Stroke) is a term used in medicine to describe cutting off blood supply to a portion of the brain, which causes tissue damage in the brain. Clots of blood that form in the brain’s blood vessels and ruptures in the brain’s blood vessels are the root causes of cerebrovascular accidents. Dizziness, numbness, weakness on one side of the body, and difficulties communicating verbally, writing, or comprehending language are the symptoms of this condition. Smoking, being older and having high blood pressure, diabetes, high cholesterol, heart disease, a history of cerebrovascular accident in the family, atherosclerosis (which is the buildup of fatty material and plaque inside the coronary arteries), or high cholesterol all contribute to an increased risk of having a cerebrovascular accident. (Objective) This paper analyzes available studies on Cerebrovascular accident medication combinations.
Evidence acquisition (Data sources) This systematic review and network meta-analysis analyzed the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI), and Google Scholar databases without a lower time limit and up to July 2022. A network meta-analysis examines the efficacy of this drug combination on genes/proteins that serve as progression targets for cerebrovascular accidents.
Results and Conclusion In scenarios 1 through 3, the p-values for the suggested medication combination and Cerebrovascular accident were 0.036633, 0.007763, and 0.003638, respectively. Scenario I is the combination of medications initially indicated for treating a cerebrovascular accident. The recommended combination of medications for cerebrovascular accidents is ten times more effective. This systematic review and network meta-analysis demonstrate that the recommended medication combination decreases the p-value between cerebrovascular accidents and the genes as potential progression targets, thereby enhancing the treatment for cerebrovascular accidents. The optimal combination of medications improves community health and decreases per-person management costs.
Highlights
Combined drugs that make the p-value between Stroke and target genes close to 1
Using Reinforcement Learning to recommend drug combination
A comprehensive systematic review of recent works
A Network meta-analysis to measure the comparative efficacy
Considered drug interactions
Competing Interest Statement
The authors have declared no competing interest.
Clinical Protocols
https://academic.oup.com/biomethods/article/8/1/bpac038/6989630
Funding Statement
Not applicable
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Footnotes
This version of the manuscript has been revised to update citations and adding supplemental materials.
Data Availability
Not applicable