Prediction of 2-Year Major Adverse Limb Event-Free Survival After Percutaneous Transluminal Angioplasty and Stenting for Lower Limb Atherosclerosis Obliterans: A Machine Learning-Based Study

Artificial Intelligence in Vascular Diseases: From Clinical Practice to Medical Research and Education

Artificial Intelligence (AI) has brought new opportunities in medicine, with a great potential to improve care provided to patients. Given the technical complexity and continuously evolving field, it can be challenging for vascular specialists to anticipate and foresee how AI will shape their practice. The aim of this review is to provide an overview of the current landscape of applications of AI in clinical practice for the management of non-cardiac vascular diseases including aortic aneurysm, peripheral artery disease, carotid stenosis, and venous diseases. The review describes and highlights how AI has the potential to shape the three pillars in the management of vascular diseases including clinical practice, medical research and education. In the limelight of these results, we show how AI should be considered and developed within a responsible ecosystem favoring transdisciplinary collaboration, where multiple stake holders can work together to face current challenges and move forward future directions.

Artificial Intelligence Techniques for Prognostic and Diagnostic Assessments in Peripheral Artery Disease: A Scoping Review

Peripheral artery disease (PAD) is a major public health concern worldwide, associated with high risk of mortality and morbidity related to cardiovascular and adverse limb events. Despite significant advances in both medical and interventional therapies, PAD often remains under-diagnosed, and the prognosis of patients can be difficult to predict. Artificial intelligence (AI) has brought a wide range of opportunities to improve the management of cardiovascular diseases, from advanced imaging analysis to machine-learning (ML)-based predictive models, and medical data management using natural language processing (NLP). The aim of this review is to summarize and discuss current techniques based on AI that have been proposed for the diagnosis and the evaluation of the prognosis in patients with PAD. The review focused on clinical studies that proposed AI-methods for the detection and the classification of PAD as well as studies that used AI-models to predict outcomes of patients. Through evaluation of study design, we discuss model choices including variability in dataset inputs, model complexity, interpretability, and challenges linked to performance metrics used. In the light of the results, we discuss potential interest for clinical decision support and highlight future directions for research and clinical practice.

Factors Associated With Adverse Outcomes Among Patients Undergoing Endovascular Revascularization for Iliac Artery Lesions TASC II A and B: A Single-Center Study

PURPOSE

This prospective study from October 2016 to September 2020 aimed to identify the factors associated with non-revascularization and mortality rates in patients who underwent endovascular interventions for Trans-Atlantic Inter-Society Consensus (TASC) II A/B iliac artery occlusions at the Department of Vascular Surgery.

METHODS

Patients with TASC II A/B iliac artery occlusions who underwent endovascular intervention, including balloon angioplasty and stent placement, were included. The primary outcomes were factors associated with non-revascularization and mortality rate.

RESULTS

A total of 133 patients were enrolled in this study. Univariable analysis revealed significant associations between non-revascularization and diabetes (hazard ratio [HR]=2.61, 95% confidence interval [CI], p=0.03), chronic kidney disease (HR=16.2, 95% CI, p=0.01), and severe calcifications (HR=8.56, 95% CI, p<0.001). Subsequent multivariable analysis confirmed the significance of these factors, showing HRs of 3.04 (95% CI, p=0.02), 13.12 (95% CI, p=0.03), and 8.62 (95% CI, p<0.001), respectively. The overall mortality rate observed was 20.3%. Severe calcifications emerged as a significant risk factor for mortality in both univariable (HR=2.47, 95% CI, p=0.02) and multivariable (HR=3.01, 95% CI, p<0.001) analyses.

CONCLUSION

Severe calcifications correlate with non-revascularization and mortality, while comorbidities like diabetes mellitus and chronic kidney disease are also associated with non-revascularization. Recognizing these identified factors holds substantial promise in enhancing patient selection and procedural approaches, potentially bolstering the success rates of endovascular interventions. However, further research aimed at comprehending the underlying mechanisms and devising strategies to mitigate these risks is imperative for continued improvement in patient outcomes.

CLINICAL IMPACT

The study provides valuable insights into patient selection and procedural planning for endovascular interventions in TASC II A/B iliac artery occlusions. Identifying severe calcifications, diabetes, and chronic kidney disease as key risk factors for non-revascularization and mortality equips clinicians with essential predictive tools, potentially improving outcomes by tailoring treatment approaches. The innovation lies in highlighting the impact of comorbidities and calcification severity, offering a pathway to refine patient eligibility criteria and optimize procedural decisions. This underscores the importance of further research to develop strategies that mitigate these risk factors and enhance intervention success rates.

Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease

BACKGROUND

Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all-cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer-assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events.

METHODS AND RESULTS

Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle-brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all-cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow-up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78-3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49-2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43-22.39]) at 5 years.

CONCLUSIONS

An artificial intelligence-enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Artificial intelligence-based predictive models in vascular diseases

Cardiovascular disease represents a source of major health problems worldwide, and although medical and technical advances have been achieved, they are still associated with high morbidity and mortality rates. Personalized medicine would benefit from novel tools to better predict individual prognosis and outcomes after intervention. Artificial intelligence (AI) has brought new insights to cardiovascular medicine, especially with the use of machine learning techniques that allow the identification of hidden patterns and complex associations in health data without any a priori assumptions. This review provides an overview on the use of artificial intelligence-based prediction models in vascular diseases, specifically focusing on aortic aneurysm, lower extremity arterial disease, and carotid stenosis. Potential benefits include the development of precision medicine in patients with vascular diseases. In addition, the main challenges that remain to be overcome to integrate artificial intelligence-based predictive models in clinical practice are discussed.