Amyloidosis is a rare protein misfolding disease caused by the accumulation of amyloid fibrils in various tissues and organs. There are different subtypes of amyloidosis, with light chain (AL) amyloidosis being the most common. Amyloidosis is notoriously difficult to diagnose because it is clinically heterogeneous, no single test is diagnostic for the disease, and diagnosis typically involves multiple specialists. Here, we propose an integrated, multidisciplinary algorithm for efficiently diagnosing amyloidosis. Drawing on research from several medical disciplines, we have combined clinical decisions and best practices into a comprehensive algorithm to facilitate the early detection of amyloidosis. Currently, many patients are diagnosed more than 6 months after symptom onset, yet early diagnosis is the major predictor of survival. Our algorithm aims to shorten the time to diagnosis with efficient sequencing of tests and minimizing uninformative investigations. We also recommend typing and staging of confirmed amyloidosis to guide treatment. By reducing time to diagnosis, our algorithm could lead to earlier and more targeted treatment, ultimately improving prognosis and survival.
Keywords: AA Amyloidosis; ATTR amyloidosis; Cardiac amyloidosis; Hereditary amyloidosis; Light Chain Amyloidosis.
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