Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Singh, Manasvi; Kumar, Ashish; Khanna, Narendra N.; Laird, John R.; Nicolaides, Andrew; Faa, Gavino; Johri, Amer M.; Mantella, Laura E.; Fernandes, Jose Fernandes E.; Teji, Jagjit S.; Singh, Narpinder; Fouda, Mostafa M.; Singh, Rajesh; Sharma, Aditya; Kitas, George; Rathore, Vijay; Singh, Inder M.; Tadepalli, Kalyan; Al-Maini, Mustafa; Isenovic, Esma R.; Chaturvedi, Seemant; Garg, Deepak; Paraskevas, Kosmas I.; Mikhailidis, Dimitri P.; Viswanathan, Vijay; Kalra, Manudeep K.; Ruzsa, Zoltan; Saba, Luca; Laine, Andrew F.; Bhatt, Deepak L.; Suri, Jasjit S.

doi:10.1016/j.eclinm.2024.102660

Cited by 3 publications

(9 citation statements)

References 112 publications

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“…The outcomes assessed were blood pressure, cholesterol levels, medication adherence, cardiovascular events, and quality of life. 6), and big data analytics (5,8). The outcomes measured included diagnostic accuracy, disease prediction, patient management, medication adherence, cardiovascular events, and quality of life.…”

Section: Resultsmentioning

confidence: 99%

“…These technologies not only enhance patient monitoring and adherence but also facilitate remote consultations and real-time health data analysis, potentially reducing the incidence of cardiovascular events [1,2,3,4,5,6,7]. Second, more research is needed to determine the long-term effects of these technologies and to identify the best ways to implement them in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence and machine learning tools include algorithms and software that analyze large datasets to predict cardiovascular events, personalize treatment plans, and improve diagnostic accuracy (1,4,5). Internet of Things (IoT) devices are connected devices that collect and share health data through the internet, enabling integrated cardiovascular disease management (7).…”

Section: Types Of Innovative E-health Technologiesmentioning

confidence: 99%

“…Cholesterol Levels Seven studies evaluated the impact of e-health interventions on cholesterol levels. Mobile health applications and AI-driven feedback systems were particularly effective, resulting in a mean reduction in LDL cholesterol of -10.8 mg/dL (95% CI: -13.9 to -7.7) [1,2,3,4,5,6,7].…”

Section: Primary Outcomesmentioning

confidence: 99%

“…For instance, AI and ML algorithms can analyze vast datasets to predict patientspecific cardiovascular risks and tailor interventions accordingly (1,3,4). Wearable devices and mHealth applications provide real-time feedback and support, improving adherence to therapeutic regimens (2,5,6).…”

Section: Introductionmentioning

confidence: 99%

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Innovative E-Health Technologies for Cardiovascular Disease Treatment: A 2024 Updated Systematic Review and Meta-Analysis

Borges

2024

Preprint

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Background and Objectives:Cardiovascular disease (CVD) remains the leading cause of death globally, with an estimated 18.6 million deaths in 2021. E-health interventions have the potential to improve CVD management by providing remote monitoring, patient education, and support. This updated systematic review and meta-analysis aimed to synthesize the evidence on the effectiveness of innovative e-health technologies for CVD treatment, including studies published up to November 2023.Methods:A comprehensive literature search was conducted in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, CINAHL, and PsycINFO from inception to November 2023. Randomized controlled trials (RCTs) comparing an innovative e-health technology to usual care or another intervention in adults with CVD or at risk of CVD were included. The risk of bias was assessed using the Cochrane Collaboration’s risk of bias tool. A meta-analysis was conducted for outcomes with sufficient studies and similar interventions and outcomes. For other outcomes, a narrative synthesis was performed. The certainty of evidence for each outcome was assessed using the GRADE approach.Findings:Thirty studies met the inclusion criteria, with a total of 10,234 participants. The interventions included artificial intelligence, machine learning, wearable devices, mobile health, telehealth, virtual reality, augmented reality, blockchain technology, Internet of Things (IoT), and big data analytics. The outcomes assessed were blood pressure, cholesterol levels, medication adherence, cardiovascular events, and quality of life. The meta-analysis showed that innovative e-health technologies were effective in improving blood pressure (mean difference: -5.7 mmHg; 95% CI: -7.3 to -4.1), cholesterol levels (mean difference: -10.8 mg/dL; 95% CI: -13.9 to -7.7), and medication adherence (odds ratio: 1.48; 95% CI: 1.31 to 1.67). The certainty of evidence for these outcomes was moderate. The narrative synthesis indicated that innovative e-health technologies were also effective in reducing cardiovascular events and improving quality of life. However, the evidence for these outcomes was limited, and more research is needed.Conclusions:The findings from this systematic review and meta-analysis provide robust evidence that innovative e-health technologies significantly enhance the management of cardiovascular disease (CVD). The included interventions, spanning artificial intelligence (AI), machine learning (ML), wearable devices, mobile health (mHealth), telehealth, virtual reality (VR), augmented reality (AR), blockchain technology, Internet of Things (IoT), and big data analytics, have demonstrated substantial improvements in key clinical outcomes. However, limitations such as variability in study design and the need for more high-quality RCTs highlight areas for further research. In clinical practice, these technologies offer promising avenues for improving patient outcomes and optimizing CVD management strategies.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Types Of Innovative E-health Technologiesmentioning

confidence: 99%

Section: Primary Outcomesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Innovative E-Health Technologies for Cardiovascular Disease Treatment: A 2024 Updated Systematic Review and Meta-Analysis

Borges

2024

Preprint

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Artificial Intelligence in Cardiovascular Diseases and Vascular Surgery

Paraskevas,

Saba,

Papaioannou

et al. 2024

Angiology

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The Genetic Blueprint of Cardiovascular Therapy: Pharmacogenomics for Improved Efficacy and Safety

Andhi,

Darawadi

2024

Journal of Indian College of Cardiology

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Globally, cardio vascular diseases (CVD) remain the primary cause of morbidity and mortality. Pharmacogenomics (PGxs) has profoundly changed how various drug classes are managed in CVDs. For example, genetic polymorphisms in genes such as SLCO1B1 impact how a person responds to statins such as rosuvastatin and atorvastatin, where as the interindividual variability in the reaction to statins (Fluvastatin)used in lipid-lowering therapy can be partly explained by genetic variations in genes encoding drug-metabolizing enzymes such cytochrome P450 and transporters like OATP1B1. Similarly, in antiplatelet therapy, polymorphisms in CYP2C19 affect clopidogrel metabolism, influencing its efficacy in preventing thrombotic events. Genes such as CYP2C9 and VKORC1 are crucial for the metabolism and response to acenocoumarol and warfarin during anticoagulant therapy and monitoring bleeding risk. Genetic variations in CYP2D6 affect the metabolism and effectiveness of propafenone and metoprolol. Understanding the PGx presumptions of these cardiovascular drugs may help develop personalized treatment strategies that lower the possibility of adverse drug reactions, obtain desired therapeutic outcomes, and improve patient compliance and safety with respect to each patient’s unique genetic makeup.

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Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Cited by 3 publications

References 112 publications

Innovative E-Health Technologies for Cardiovascular Disease Treatment: A 2024 Updated Systematic Review and Meta-Analysis

Innovative E-Health Technologies for Cardiovascular Disease Treatment: A 2024 Updated Systematic Review and Meta-Analysis

Artificial Intelligence in Cardiovascular Diseases and Vascular Surgery

The Genetic Blueprint of Cardiovascular Therapy: Pharmacogenomics for Improved Efficacy and Safety

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