Introduction to VIA
VIA, or Value Iteration Algorithm, is a key concept in the field of Peripheral Artery Disease (PAD) diagnosis and treatment. It is a computational method used to assess the severity of PAD and guide treatment decisions. In this article, we will delve into the details of VIA, its applications in PAD, and its significance in improving patient outcomes.
What is Peripheral Artery Disease (PAD)?
Peripheral Artery Disease (PAD) is a condition characterized by the narrowing of arteries in the legs, arms, and other peripheral regions of the body. This narrowing is typically caused by the buildup of plaque in the arterial walls, a process known as atherosclerosis. PAD can lead to reduced blood flow to the affected limbs, causing symptoms such as pain, numbness, and weakness. If left untreated, PAD can result in severe complications, including critical limb ischemia and amputation.
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” alt=”” class=”wp-image-136″ >
The Role of VIA in PAD Diagnosis
VIA plays a crucial role in the diagnosis of PAD. It is a non-invasive method that utilizes imaging techniques, such as ultrasound or computed tomography (CT) angiography, to assess the blood flow in the peripheral arteries. The algorithm analyzes the imaging data and calculates various parameters, such as the ankle-brachial index (ABI) and the toe-brachial index (TBI), which provide insights into the severity of PAD.
Ankle-Brachial Index (ABI)
The ankle-brachial index (ABI) is a key parameter used in the diagnosis of PAD. It is calculated by dividing the systolic blood pressure measured at the ankle by the systolic blood pressure measured at the arm. A normal ABI value ranges from 0.9 to 1.3. Values below 0.9 indicate the presence of PAD, with lower values suggesting more severe disease.
ABI Value
Interpretation
0.9 – 1.3
Normal
0.7 – 0.9
Mild PAD
0.4 – 0.7
Moderate PAD
< 0.4
Severe PAD
Toe-Brachial Index (TBI)
The toe-brachial index (TBI) is another parameter used in PAD diagnosis, particularly in cases where the ABI may be unreliable, such as in patients with heavily calcified arteries. The TBI is calculated by dividing the systolic blood pressure measured at the toe by the systolic blood pressure measured at the arm. A normal TBI value is typically above 0.7, with lower values indicating the presence of PAD.
VIA in Treatment Planning
In addition to its diagnostic role, VIA also plays a significant part in treatment planning for PAD patients. By providing detailed information about the location and severity of arterial narrowing, VIA helps healthcare professionals determine the most appropriate course of action.
Lifestyle Modifications
For patients with mild to moderate PAD, lifestyle modifications are often the first line of treatment. VIA results can guide the recommendations for lifestyle changes, such as:
– Smoking cessation
– Regular exercise
– Healthy diet
– Weight management
– Stress reduction
Pharmacological Interventions
VIA can also help identify patients who may benefit from pharmacological interventions. Medications commonly used in the treatment of PAD include:
– Antiplatelet agents (e.g., aspirin, clopidogrel)
– Cholesterol-lowering drugs (e.g., statins)
– Blood pressure-lowering medications (e.g., ACE inhibitors, beta-blockers)
– Vasodilators (e.g., cilostazol)
Endovascular Procedures
For patients with more severe PAD, VIA can guide the decision to pursue endovascular procedures. These minimally invasive techniques aim to restore blood flow by widening the narrowed arteries. Common endovascular procedures include:
– Angioplasty: A balloon is inflated inside the narrowed artery to widen it.
– Stenting: A small mesh tube is placed in the artery to keep it open.
– Atherectomy: A specialized device is used to remove the plaque buildup from the arterial walls.
Surgical Interventions
In cases of advanced PAD, where endovascular procedures may not be suitable, VIA can help determine the need for surgical interventions. The two main surgical options for PAD are:
– Bypass surgery: A graft is used to create a new pathway for blood flow around the blocked artery.
– Endarterectomy: The plaque buildup is surgically removed from the arterial walls.
Advantages of VIA in PAD Management
The use of VIA in PAD management offers several key advantages:
Non-invasive: VIA relies on imaging techniques that do not require any invasive procedures, making it a safer and more comfortable option for patients.
Accurate diagnosis: By providing detailed information about the location and severity of arterial narrowing, VIA enables accurate diagnosis of PAD.
Personalized treatment: VIA results allow healthcare professionals to tailor treatment plans to each patient’s specific needs, taking into account the extent and distribution of their disease.
Monitoring disease progression: VIA can be used to monitor the progression of PAD over time, helping to identify any changes in the patient’s condition and adjust treatment accordingly.
Cost-effective: As a non-invasive method, VIA can help reduce the need for more costly and invasive diagnostic procedures, potentially lowering healthcare costs.
Limitations of VIA in PAD
While VIA offers numerous benefits in PAD management, it is essential to acknowledge its limitations:
Operator-dependent: The accuracy of VIA results may be influenced by the skill and experience of the operator performing the imaging studies.
Imaging artifacts: Certain factors, such as heavy arterial calcification or patient movement, can create imaging artifacts that may affect the interpretation of VIA results.
Limited accessibility: Access to the imaging equipment and trained personnel required for VIA may be limited in some healthcare settings, particularly in resource-constrained environments.
Future Directions in VIA and PAD
As research continues to advance our understanding of PAD and its management, several exciting developments are on the horizon for VIA:
Artificial intelligence (AI) integration: The incorporation of AI algorithms into VIA could help improve the accuracy and efficiency of PAD diagnosis and treatment planning.
Portable imaging devices: The development of portable, handheld imaging devices could expand access to VIA in various healthcare settings, including primary care and community-based clinics.
Novel imaging techniques: Emerging imaging techniques, such as photoacoustic imaging and hyperspectral imaging, may provide new insights into PAD and enhance the capabilities of VIA.
Frequently Asked Questions (FAQ)
1. What is the difference between VIA and traditional PAD diagnostic methods?
VIA is a non-invasive method that relies on imaging techniques to assess the severity of PAD, while traditional methods, such as the ankle-brachial index (ABI) and toe-brachial index (TBI), are based on blood pressure measurements. VIA provides more detailed information about the location and extent of arterial narrowing.
2. How often should VIA be performed in PAD patients?
The frequency of VIA in PAD patients depends on the severity of their condition and their individual risk factors. Generally, patients with mild to moderate PAD may undergo VIA every 1-2 years, while those with more severe disease may require more frequent monitoring. Healthcare professionals will determine the appropriate follow-up schedule based on each patient’s specific needs.
3. Is VIA covered by insurance?
In most cases, VIA is covered by insurance when it is deemed medically necessary for the diagnosis and management of PAD. However, coverage may vary depending on the specific insurance plan and the patient’s individual circumstances. It is essential for patients to check with their insurance provider to understand their coverage and any potential out-of-pocket costs.
4. Can VIA be used in patients with other cardiovascular conditions?
Yes, VIA can be used in patients with other cardiovascular conditions, such as coronary artery disease or carotid artery stenosis. In fact, patients with PAD often have a higher risk of developing other cardiovascular diseases, making VIA an important tool for comprehensive cardiovascular risk assessment and management.
5. Are there any risks associated with VIA?
As a non-invasive method, VIA carries minimal risks for patients. The imaging techniques used in VIA, such as ultrasound or CT angiography, do not involve any incisions or injections. However, some patients may experience mild discomfort or anxiety during the imaging process. In rare cases, patients may have an allergic reaction to the contrast dye used in CT angiography. Healthcare professionals will carefully evaluate each patient’s individual risk factors before performing VIA.
Conclusion
VIA is a powerful tool in the diagnosis and management of Peripheral Artery Disease (PAD). By providing detailed, non-invasive assessment of arterial narrowing, VIA enables accurate diagnosis, personalized treatment planning, and ongoing monitoring of disease progression. As research continues to advance, the integration of artificial intelligence, portable imaging devices, and novel imaging techniques promises to further enhance the capabilities of VIA in PAD management. With its numerous advantages and minimal risks, VIA is poised to play an increasingly important role in improving outcomes for patients with PAD.
No responses yet