Assessment and management of disease burden and quality of life in patients with hereditary angioedema: a consensus report
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is a rare disease characterized by unpredictable, potentially life-threatening attacks, resulting in significant physical and emotional burdens for patients and families. To optimize care for patients with HAE, an individualized management plan should be considered in partnership with the physician, requiring comprehensive assessment of the patient's frequency and severity of attacks, disease burden, and therapeutic control. Although several guidelines and consensus papers have been published concerning the diagnosis and treatment of HAE, there has been limited specific clinical guidance on the assessment of disease burden and quality of life (QoL) in this patient population. Practical guidance is critical in supporting effective long-term clinical management of HAE and improving patient outcomes. The objective of this review is to provide evidence-based guidelines for an individualized assessment of disease burden and QoL in patients with HAE. METHODS: A consensus meeting was held on February 29, 2020, consisting of 9 HAE experts from the United States and Europe with extensive clinical experience in the treatment of HAE. Consensus statements were developed based on a preliminary literature review and discussions from the consensus meeting. RESULTS: Final statements reflect the consensus of the expert panel and include the assessment of attack severity, evaluation of disease burden, and long-term clinical management of HAE caused by C1-esterase inhibitor deficiency. Patient-reported outcome measures for assessing HAE attack severity and frequency are available and valuable tools; however, attack frequency and severity are insufficient markers of disease severity unless they are evaluated in the broader context of the effect on an individual patient's QoL. QoL assessments should be individualized for each patient and minimally, they should address the interference of HAE with work, school, social, family, and physical activity, along with access to and burden of HAE treatment. Advances in HAE therapies offer the opportunity for comprehensive, individualized treatment plans, allowing patients to achieve minimal attack burden with reduced disease and treatment burden. CONCLUSION: This consensus report builds on existing guidelines by expanding the assessment of disease burden and QoL measures for patients with HAE.
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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 | 0.037 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".