Usage Information
Citation Information: JCI Insight. 2019;4(12):e126526. https://doi.org/10.1172/jci.insight.126526.
Abstract
BACKGROUND The hereditary transthyretin (TTR) amyloidoses are a group of diseases for which several disease-modifying treatments are now available. Long-term effectiveness of these therapies is not yet fully known. Moreover, the existence of alternative therapies has resulted in an urgent need to identify patient characteristics that predict response to each therapy.METHODS We carried out a retrospective cohort study of 210 patients with hereditary TTR amyloidosis treated with the kinetic stabilizer tafamidis (20 mg qd). These patients were followed for a period of 18–66 months, after which they were classified by an expert as responders, partial responders, or nonresponders. Correlations between baseline demographic and clinical characteristics, as well as plasma biomarkers and response to therapy, were investigated.RESULTS 34% of patients exhibited an almost complete arrest of disease progression (classified by an expert as responders); 36% had a partial to complete arrest in progression of some but not all disease components (partial responders); whereas the remaining 30% continued progressing despite therapy (nonresponders). We determined that disease severity, sex, and native TTR concentration at the outset of treatment were the most relevant predictors of response to tafamidis. Plasma tafamidis concentration after 12 months of therapy was also a predictor of response for male patients. Using these variables, we built a model to predict responsiveness to tafamidis.CONCLUSION Our study indicates long-term effectiveness for tafamidis, a kinetic stabilizer approved for the treatment of hereditary TTR amyloidosis. Moreover, we created a predictive model that can be potentially used in the clinical setting to inform patients and clinicians in their therapeutic decisions.
Authors
Cecília Monteiro, Jaleh S. Mesgazardeh, João Anselmo, Joana Fernandes, Marta Novais, Carla Rodrigues, Gabriel J. Brighty, David L. Powers, Evan T. Powers, Teresa Coelho, Jeffery W. Kelly
Usage data is cumulative from November 2023 through November 2024.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 610 | 362 |
| 117 | 166 | |
| Figure | 153 | 4 |
| Table | 58 | 0 |
| Supplemental data | 29 | 5 |
| Citation downloads | 43 | 0 |
| Totals | 1,010 | 537 |
| Total Views | 1,547 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
