Characteristics of Patients with Transthyretin Amyloidosis and Methodological Approaches for Establishing Clinical Subtype in the Netherlands


Jeswani N1, Overbeek J2
1Lumanity, Farnham, UK, 2PHARMO Institute for Drug Outcomes Research, Utrecht, Netherlands

OBJECTIVES: Lack of international consensus on transthyretin amyloidosis (ATTR), classification, coupled with limited specificity of ICD-10 codes, poses a challenge in conducting real world research on this understudied population. We sought to characterize amyloidosis patients and explore ways of determining relevant ATTR subtypes of cardiomyopathy, peripheral neuropathy, hereditary, and/or wildtype.

METHODS: We selected patients from the hospital data of the PHARMO Data Network based on hospital admissions or ambulatory consultations of amyloidosis (ICD-10 E85) between 2012-2022. Patients were characterized in terms of demographics, comorbidities, and high-cost medicine use.

RESULTS: We identified 10,878 patients, of which 8,254 had a diagnosis code non-specific to ATTR. Of the 10,878 patients, 56% were male with a mean age (±SD) of 60 (±23). The most common hospital-based comorbidities were cardiovascular related, including: hypertension (16%), atrial fibrillation (8%), presence of cardiac and vascular implants and grafts (6%), heart failure (6%), chronic ischemic heart disease (6%), lipoprotein metabolism disorder or other lipidaemia (6%). The most frequently prescribed high-cost medication classes were antineoplastic agents (3%), selective immunosuppressants (2%), and alkylating agents (2%).

CONCLUSIONS: Identified comorbidities point to a higher incidence of the cardiomyopathy subtype. To establish and validate subtypes, linked data from the PHARMO Data Network can provide clinical insights into biopsy results and gene sequences (from the Pathology Registry) as well as bloods (e.g., troponin T, NT pro-BNP) and immunochemistry (e.g., immunoglobulins) (from clinical labs). Unstructured free text notes (from general practitioner data) can be used to complement coded diagnoses. For patients without an amyloidosis classification, algorithms could be developed to predict subtype.

Conference/Value in Health Info

2024-05, ISPOR 2024, Atlanta, GA, USA

Value in Health, Volume 27, Issue 6, S1 (June 2024)




Epidemiology & Public Health

Topic Subcategory

Disease Classification & Coding


Cardiovascular Disorders (including MI, Stroke, Circulatory), Diabetes/Endocrine/Metabolic Disorders (including obesity), Neurological Disorders, Rare & Orphan Diseases

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