Development of ACRODAT®, a new software medical device to assess disease activity in patients with acromegaly
Summary, in English
Purpose: Despite availability of multimodal treatment options for acromegaly, achievement of long-term disease control is suboptimal in a significant number of patients. Furthermore, disease control as defined by biochemical normalization may not always show concordance with disease-related symptoms or patient’s perceived quality of life. We developed and validated a tool to measure disease activity in acromegaly to support decision-making in clinical practice. Methods: An international expert panel (n = 10) convened to define the most critical indicators of disease activity. Patient scenarios were constructed based on these chosen parameters. Subsequently, a panel of 21 renowned endocrinologists at pituitary centers (Europe and Canada) categorized each scenario as stable, mild, or significant disease activity in an online validation study. Results: From expert opinion, five parameters emerged as the best overall indicators to evaluate disease activity: insulin-like growth factor I (IGF-I) level, tumor status, presence of comorbidities (cardiovascular disease, diabetes, sleep apnea), symptoms, and health-related quality of life. In the validation study, IGF-I and tumor status became the predominant parameters selected for classification of patients with moderate or severe disease activity. If IGF-I level was ≤1.2x upper limit of normal and tumor size not significantly increased, the remaining three parameters contributed to the decision in a compensatory manner. Conclusion: The validation study underlined IGF-I and tumor status for routine clinical decision-making, whereas patient-oriented outcome measures received less medical attention. An Acromegaly Disease Activity Tool (ACRODAT) is in development that might assist clinicians towards a more holistic approach to patient management in acromegaly.
- Endocrinology and Diabetes
- Patient-reported outcomes
- Genomics, Diabetes and Endocrinology
- ISSN: 1386-341X