AIMC Topic: Cardiovascular Diseases

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Development and external validation of machine learning approaches for risk prediction of cardiovascular disease in individuals with schizophrenia: a nationwide Swedish and Danish study.

BMJ mental health
BACKGROUND: Currently available cardiovascular disease (CVD) risk prediction tools may underestimate the risk in individuals with schizophrenia. OBJECTIVE: To develop and externally validate 5-year CVD risk prediction models for people with schizophr...

Non-invasive blood pressure monitoring using wearables for cardiovascular risk assessment: a systematic review.

Archives of gynecology and obstetrics
PURPOSE: Cardiovascular diseases are the leading causes of mortality in women worldwide, with hypertension being a major risk factor. While traditional blood pressure monitoring techniques rely on cuff-based measurements, wearable devices offer a pro...

PODiaCarD: a prototype of a digital twin platform for the management of pediatric obesity and related cardiometabolic complications.

European journal of pediatrics
UNLABELLED: Childhood obesity is the main driver of early metabolic risk, predisposing to cardiovascular disease (CVD) and type 2 diabetes (T2D), which cause millions of deaths worldwide. Their progression is influenced by biological, behavioral, and...

Bridging Gaps in Women's Heart Health: User-Centered Needs Assessment Informed by Patient and Clinician Interviews.

JMIR human factors
BACKGROUND: Women with cardiovascular disease (CVD) remain underserved due to gaps in recognition, diagnosis, and care tailored to sex-specific risks. Digital health tools have the potential to address these inequities, but many fail to reflect the d...

Environment and CVD: moving from Risk Prediction to Risk Management.

Current atherosclerosis reports
PURPOSE OF REVIEW: We attempt to provide a framework for cardiovascular risk assessment related to environmental pollutants to enhance awareness of risk posed by environmental risk factors and highlight approaches for risk intervention.

Technologies, Clinical Applications, and Implementation Barriers of Digital Twins in Precision Cardiology: Systematic Review.

JMIR cardio
BACKGROUND: Digital twin systems are emerging as promising tools in precision cardiology, enabling dynamic, patient-specific simulations to support diagnosis, risk assessment, and treatment planning. However, the current landscape of cardiovascular d...

Machine learning-based cardiovascular risk calculator for non-cardiac surgery.

Open heart
BACKGROUND: Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least one cardiovascular risk factor. It is estimated that the 30-day mortality is between 0.5% and 2%.The main objective of this st...

Unbiased inference for echocardiogram urgency prediction using double machine learning.

PloS one
The increased utilization of echocardiography in clinical practice has witnessed a substantial rise, underscoring its pivotal role as a diagnostic tool for various cardiovascular conditions. However, due to the relative scarcity of echocardiography t...

Research Priorities and Future Directions in Cardio-Oncology.

Current treatment options in oncology
The subspecialty of cardio-oncology has undergone significant growth in recent years, alongside major advances in the management of both cardiovascular disease and cancer, the leading causes of morbidity and mortality in the United States and many co...