Developing Trustworthy Artificial Intelligence (AI)-driven Tools to Predict Vascular Disease Risk and Progression

Developing Trustworthy Artificial Intelligence (AI)-driven Tools to Predict Vascular Disease Risk and Progression - VASCUL-AID

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON57799

Enrollment

250

Registered

2024-11-21

Start date

2025-09-29

Completion date

Unknown

Last updated

2026-04-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Abdominal Aortic Aneurysms & Peripheral Arterial Disease Dilated aorta & calcified arteries in the limbs

Interventions

None listed

Eligibility

Age

18 Years to 99 Years

Inclusion criteria

Inclusion criteria: Inclusion criteria AAA: • Males/females between 30 and 90 years of age.  • Males and females with AAA at inclusion, any diameter >3 cm (or 1.5x larger  than their non-dilated part of the aorta). • Include: infrarenal, juxtarenal, pararenal, suprarenal abdominal aortic  aneurysms.  Inclusion criteria PAD: • Males/females between 25 and 90 years of age.  • Patients with peripheral arterial disease stage Fontaine 2a and 2b at  inclusion. Medical history (eg. CLTI) does not matter, current disease stage is  leading at inclusion.

Exclusion criteria

Exclusion criteria: Exclusion criteria AAA: Patients with: • Insufficient schooling or sensorial deficits that interfere understanding informed consent. • Not able to use the VASCUL-AID mobile health app (that will be provided in Dutch, English, German, Portuguese, Serbian, Finish, Swedish languages). • Proven or highly suspected for infected, mycotic AAA • Previous AAA surgery or planned for an AAA surgery within 6 months • Ruptured AAA Exclusion criteria PAD: Patients with: • Insufficient schooling or sensorial deficits that interfere understanding informed consent. • Not able to use the VASCUL-AID mobile health app (on their smart-device, e.g. Ipad or smartphone or similar) (provided in Dutch, English, German, Portuguese, Serbian, Finnish, Swedish languages) or mobile devices in general.

Design outcomes

Primary

Measure	Time frame
Primary endpoints phase 1) VASCUL-AID-PRO development cohort: AAA cohort: Developed VASCUL-AID AI-driven tools to predict high or low risk of AAA progression (AAA rupture, mortality, cerebrovascular accident), MACE and MALE. Also, the data quality and feasibility of data collection for prediction model development is an important study parameter for further app optimization in the VASCUL-AID studies. This is an important outcome to ensure data quality and reduce the burden on the patients. PAD cohort: Developed VASCUL-AID AI-driven tools to predict high or low risk of PAD progression, (mortality, myocardial infarction, major amputation), MACE and MALE. Also, the data quality and feasibility of data collection for prediction model development is an important study parameter for further app optimization in the VASCUL-AID studies. This is an important outcome to ensure data quality and reduce the burden on the patients. Primary endpoints phase 2) VASCUL-AID-PRO internal validation cohort: AAA cohort: Internal validation of VASCUL-AID AI-algorithms that predict a high or low risk of AAA progression, MACE, MALE, and other predefined standard outcomes (core outcomes mentioned above) for AAA patients. We will measure this outcome (accuracy of the risk-classification) by comparing the predicted AAA progression, MACE and mortality rates with the actual disease progression (AAA growth and rupture) of this cohort. PAD cohort: Internal validation of VASCUL-AID AI-driven platform that predicts a high or low risk of PAD progression, MACE, MALE, and other predefined standard outcomes (core outcomes mentioned above) for PAD patients. We will measure this outcome (accuracy of the risk-classification) by comparing the PAD progression (into Fontaine 3 or 4), MALE and MACE and mortality rates with the actual disease progression of this cohort.	—

Measure

Time frame

Primary endpoints phase 1) VASCUL-AID-PRO development cohort: AAA cohort: Developed VASCUL-AID AI-driven tools to predict high or low risk of AAA progression (AAA rupture, mortality, cerebrovascular accident), MACE and MALE. Also, the data quality and feasibility of data collection for prediction model development is an important study parameter for further app optimization in the VASCUL-AID studies. This is an important outcome to ensure data quality and reduce the burden on the patients. PAD cohort: Developed VASCUL-AID AI-driven tools to predict high or low risk of PAD progression, (mortality, myocardial infarction, major amputation), MACE and MALE. Also, the data quality and feasibility of data collection for prediction model development is an important study parameter for further app optimization in the VASCUL-AID studies. This is an important outcome to ensure data quality and reduce the burden on the patients. Primary endpoints phase 2) VASCUL-AID-PRO internal validation cohort: AAA cohort: Internal validation of VASCUL-AID AI-algorithms that predict a high or low risk of AAA progression, MACE, MALE, and other predefined standard outcomes (core outcomes mentioned above) for AAA patients. We will measure this outcome (accuracy of the risk-classification) by comparing the predicted AAA progression, MACE and mortality rates with the actual disease progression (AAA growth and rupture) of this cohort. PAD cohort: Internal validation of VASCUL-AID AI-driven platform that predicts a high or low risk of PAD progression, MACE, MALE, and other predefined standard outcomes (core outcomes mentioned above) for PAD patients. We will measure this outcome (accuracy of the risk-classification) by comparing the PAD progression (into Fontaine 3 or 4), MALE and MACE and mortality rates with the actual disease progression of this cohort.

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Secondary

Measure	Time frame
Secondary endpoints phase 1) VASCUL-AID-PRO development cohort AAA cohort: Accuracy of developed VASCUL-AID AI-driven algorithms that classify AAA patients according to high or low risk disease progression and MACE. In addition, models will be developed on the other predefined outcomes from the core outcome set, these include: clinical success after an operation, graft infection after an operation, reintervention, the AAA can be treated through endovascular treatment, adherence to prescribed drugs, good communication by healthcare worker about diagnosis, health-related quality of life, secondary prevention, treatment options and complications. The measurement tools shall be submitted as an addendum. PAD cohort: Accuracy of developed VASCUL-AID AI-driven algorithms that classify PAD patients according to high or low risk disease progression, MALE and MACE. In addition, models will be developed on the other predefined outcomes from the core outcome set, these include: acute limb ischemia, thromboembolic complications, disease progression, pain-free walking distance, physical activity, health-related quality of life, smoking cessation, good communication by healthcare worker about diagnosis, secondary prevention, treatment options and complications. The measurement tools shall be submitted as an addendum. Secondary endpoints phase 2) VASCUL-AID-PRO internal validation cohort AAA cohort: Evaluate VASCUL-AIDs cost-effectiveness and healthcare quality and sustainability improvements to develop SOPs that enable seamless platform implementation at clinical sites PAD cohort: Evaluate VASCUL-AIDs cost-effectiveness and healthcare quality and sustainability improvements to develop SOPs that enable seamless platform implementation at clinical sites.	—

Measure

Time frame

Secondary endpoints phase 1) VASCUL-AID-PRO development cohort AAA cohort: Accuracy of developed VASCUL-AID AI-driven algorithms that classify AAA patients according to high or low risk disease progression and MACE. In addition, models will be developed on the other predefined outcomes from the core outcome set, these include: clinical success after an operation, graft infection after an operation, reintervention, the AAA can be treated through endovascular treatment, adherence to prescribed drugs, good communication by healthcare worker about diagnosis, health-related quality of life, secondary prevention, treatment options and complications. The measurement tools shall be submitted as an addendum. PAD cohort: Accuracy of developed VASCUL-AID AI-driven algorithms that classify PAD patients according to high or low risk disease progression, MALE and MACE. In addition, models will be developed on the other predefined outcomes from the core outcome set, these include: acute limb ischemia, thromboembolic complications, disease progression, pain-free walking distance, physical activity, health-related quality of life, smoking cessation, good communication by healthcare worker about diagnosis, secondary prevention, treatment options and complications. The measurement tools shall be submitted as an addendum. Secondary endpoints phase 2) VASCUL-AID-PRO internal validation cohort AAA cohort: Evaluate VASCUL-AID*s cost-effectiveness and healthcare quality and sustainability improvements to develop SOPs that enable seamless platform implementation at clinical sites PAD cohort: Evaluate VASCUL-AID*s cost-effectiveness and healthcare quality and sustainability improvements to develop SOPs that enable seamless platform implementation at clinical sites.

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Countries

Finland, Germany, Italy, Netherlands, Portugal, Serbia, United Kingdom

Contacts

Public ContactL.D. Busé

Amsterdam UMC

vascul-aid@amsterdamumc.nl+31631034283

Outcome results