Current Strategies for Managing Hypertension in Paraganglioma: A Systematic Review
Bibliographic record
Abstract
Background: Paragangliomas are rare neuroendocrine tumors that may secrete catecholamines, leading to secondary hypertension. Hypertension in paraganglioma can be persistent, paroxysmal, or refractory, posing challenges in diagnosis and management. Effective blood pressure (BP) control is crucial to minimize cardiovascular complications and surgical risks. Objective: This systematic review aims to evaluate the current strategies for managing hypertension in patients with paraganglioma, focusing on pharmacological therapies and preoperative optimization. Method: A comprehensive literature search was performed using PubMed, EMBASE, and Scopus databases up to 8 January 2025. Eligible studies included randomized controlled trials, observational studies, and case series discussing antihypertensive treatments, preoperative management, and outcomes in patients with paraganglioma. Data were synthesized narratively and quantitatively where appropriate. Risk of bias of included studies were assessed using the Newcastle Ottawa Scale (NOS). Result: Sixteen studies (n = 3,647 patients) highlighted alpha-blockers as the primary BP control strategy, effective in 87% of cases. Beta-blockers were frequently employed as supplementary agents following alpha blockade to address tachycardia. Calcium channel blockers and angiotensin receptor blockers provided additional control. Preoperative BP optimization reduced hypertensive crises by 15%. Minimally invasive surgery shortened recovery and stabilized BP. Following NOS assessment, eligible studies had low bias. Conclusion: Hypertension management in paraganglioma relies on alpha-adrenergic blockade as first-line therapy, supplemented by beta-blockers and other antihypertensives as needed. Preoperative optimization is critical for reducing perioperative risks. Further research is needed to refine pharmacological regimens and improve long-term outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".