Machine learning-based prediction of mortality after heart transplantation in adults with congenital heart disease: A UNOS database analysis

Artificial intelligence in cardiac surgery: A systematic review

BACKGROUND

Artificial intelligence (AI) has emerged as a tool to potentially increase the efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims to provide an overview of applications of AI in cardiac surgery.

METHODS

A systematic literature search on AI applications in cardiac surgery from inception to February 2024 was conducted. Articles were then filtered based on the inclusion and exclusion criteria and the risk of bias was assessed. Key findings were then summarized.

RESULTS

A total of 81 studies were found that reported on AI applications in cardiac surgery. There is a rapid rise in studies since 2020. The most popular machine learning technique was random forest (n = 48), followed by support vector machine (n = 33), logistic regression (n = 32), and eXtreme Gradient Boosting (n = 31). Most of the studies were on adult patients, conducted in China, and involved procedures such as valvular surgery (24.7%), heart transplant (9.4%), coronary revascularization (11.8%), congenital heart disease surgery (3.5%), and aortic dissection repair (2.4%). Regarding evaluation outcomes, 35 studies examined the performance, 26 studies examined clinician outcomes, and 20 studies examined patient outcomes.

CONCLUSION

AI was mainly used to predict complications following cardiac surgeries and improve clinicians' decision-making by providing better preoperative risk assessment, stratification, and prognostication. While the application of AI in cardiac surgery has greatly progressed in the last decade, further studies need to be conducted to verify accuracy and ensure safety before use in clinical practice.

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.

Noninvasive Advanced Cardiovascular Magnetic Resonance-Derived Fontan Hemodynamics Are Associated With Reduced Kidney Function But Not Albuminuria

BACKGROUND

Kidney disease is the most important predictor of death in patients with a Fontan circulation, yet its clinical and hemodynamic correlates have not been well established.

METHODS AND RESULTS

A total of 53 ambulatory patients with a Fontan circulation (median age, 16.2 years, 52.8% male patients) underwent advanced cardiovascular magnetic resonance assessment, including 4-dimensional flow imaging and computational fluid dynamics. Estimated glomerular filtration rate (eGFR) <90 mL/min per 1.73 m2 was observed in 20.8% and albumin-to-creatinine ratio >3 mg/mmol in 39.6%. The average eGFR decline rate was -1.83 mL/min per 1.73 m2 per year (95% CI, -2.67 to -0.99; P<0.001). Lower eGFR was associated with older age, larger body surface area at examination, longer time since Fontan procedure, and lower systemic ventricular ejection fraction. Higher albumin-to-creatinine ratio was associated with absence of fenestration at the Fontan operation, and older age and lower systemic ventricular ejection fraction at the assessment. Lower cross-sectional area of the Fontan conduit indexed to flow (r=0.32, P=0.038), higher inferior vena cava-conduit velocity mismatch factor (r=-0.35, P=0.022), higher kinetic energy indexed to flow in the total cavopulmonary connection (r=-0.59, P=0.005), and higher total cavopulmonary connection resistance (r=-0.42, P=0.005 at rest; r=-0.43, P=0.004 during exercise) were all associated with lower eGFR but not with albuminuria.

CONCLUSIONS

Kidney dysfunction and albuminuria are common among clinically well adolescents and young adults with a Fontan circulation. Advanced cardiovascular magnetic resonance-derived metrics indicative of declining Fontan hemodynamics are associated with eGFR and might serve as targets to improve kidney health. Albuminuria might be driven by other factors that need further investigation.

The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases

This narrative review delves into the potential of artificial intelligence (AI) in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease (CHD). CHD is a complex condition that affects individuals across various age groups. The review highlights the challenges in predicting risks, planning treatments, and prognosticating long-term outcomes due to CHD's multifaceted nature, limited data, ethical concerns, and individual variabilities. AI, with its ability to analyze extensive data sets, presents a promising solution. The review emphasizes the need for larger, diverse datasets, the integration of various data sources, and the analysis of longitudinal data. Prospective validation in real-world clinical settings, interpretability, and the importance of human clinical expertise are also underscored. The ethical considerations surrounding privacy, consent, bias, monitoring, and human oversight are examined. AI's implications include improved patient outcomes, cost-effectiveness, and real-time decision support. The review aims to provide a comprehensive understanding of AI's potential for revolutionizing CHD management and highlights the significance of collaboration and transparency to address challenges and limitations.