Introduction: Managing patients with multiple risk factors for CVDs can present distinct challenges for healthcare providers, therefore addressing them can be paramount to optimize patient care.
Areas covered: This narrative review explores the burden that CVDs place on healthcare systems as well as how we can best optimize the risk management of these patients. Through a comprehensive review of literature, guidelines and clinical studies, this paper explores various approaches to risk management, lifestyle modifications and pharmacological interventions utilized in the management of CVDs. Furthermore, emerging technologies such as machine learning (ML) are discussed, highlighting potential opportunities for future research. By reviewing existing recommendations and evidence, this paper aims to provide insight into optimizing strategies and improving the outcomes for patients with multiple CVDs.
Expert opinion: Optimizing risk factors can have a significant impact on patient outcomes, as such each patient should have a clear plan on how to manage these risk factors to minimize adverse healthcare results.
Keywords: Cardiovascular disease; health; machine learning; multiple risk factors; risk management.