ArticleJournal of cardiovascular development and disease2026
Pathways for Patients with Transthyretin Amyloid Cardiomyopathy from a District General Hospital Perspective.
Article in Journal of cardiovascular development and disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe care of patients with transthyretin amyloid cardiomyopathy (ATTR-CM) is often fragmented and routine datasets rarely capture real-world clinical trajectories and reasons for diagnosis. We introduce a novel approach, called forensic data acquisition and pathway analysis, to examine the real-world experiences of patients with ATTR-CM in our district general hospital.
methodsWe retrospectively evaluated inpatient and outpatient healthcare records for our hospital between 2019 to 2025 as a part of a quality improvement project.
resultsWe identified 26 cases of confirmed or likely wild-type ATTR-CM and four hereditary cases from two families carrying the S77Y variant and estimate the prevalence of transthyretin cardiac amyloidosis to be 1 per 10,000 patients. Many red flags were present in patients, including carpal tunnel syndrome (63.3%) and lumbar spinal stenosis (26.7%), as well as echocardiographic features of left ventricular hypertrophy (86.7%), left atrial dilatation (76.7%), right ventricular hypertrophy (43.3%), and a dense or speckled myocardial appearance (43.3%). Among patients with wild-type disease, the most frequent trigger for further investigation was the presence of suspicious features on transthoracic echocardiography, accounting for 13 cases. Incidental abnormalities detected on cardiac MRI contributed to another six diagnoses. In two patients, non-invasive imaging did not provide sufficient diagnostic certainty, and myocardial biopsy was required to confirm ATTR-CM.
conclusionsForensic data acquisition and pathway analysis provides a powerful approach for revealing real-world clinical activity in ATTR-CM, exposing diagnostic patterns and missed opportunities that remain hidden in routine datasets.
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