Clinical Scorecard: AI, multiomics help advance endocrine HTN
At a Glance
| Category | Detail |
|---|---|
| Condition | Endocrine Hypertension (EH) |
| Key Mechanisms | Integration of multiomics profiling and AI for diagnosis and management. |
| Target Population | Patients with hormonally mediated hypertension, including primary aldosteronism, pheochromocytoma/paraganglioma, Cushing syndrome, and thyroid-related hypertension. |
| Care Setting | Tertiary referral centers and routine clinical practice. |
Key Highlights
- EH accounts for 5% to 10% of hypertension cases, higher in tertiary centers.
- Primary aldosteronism prevalence may be 3- to 5-fold higher than previously cited.
- AI models show diagnostic accuracy comparable to invasive standards.
- Multiomics technologies enable earlier detection and personalized treatment strategies.
- Integration of AI could transform EH management into a precision-based model.
Guideline-Based Recommendations
Diagnosis
- Utilize multiomics and AI for improved diagnostic accuracy.
- Screen for primary aldosteronism in hypertensive patients.
Management
- Adopt data-driven approaches for personalized treatment strategies.
Monitoring & Follow-up
- Implement AI-based tools for ongoing assessment of treatment efficacy.
Risks
- Underdiagnosis due to complex diagnostic pathways and lack of screening.
Patient & Prescribing Data
Hypertensive patients, particularly those with suspected endocrine causes.
AI-driven models may reduce unnecessary invasive testing and streamline diagnostic pathways.
Clinical Best Practices
- Incorporate multiomics profiling in routine diagnostic evaluations.
- Leverage AI for risk stratification and decision support in EH management.
- Ensure explainability in AI systems to address the 'black box' problem.
References
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
