Patient Journey Toward a Diagnosis of Light Chain Amyloidosis in a National Sample: Cross-Sectional Web-Based Study

Xuelin Dou; Yang Liu; Aijun Liao; Yuping Zhong; Rong Fu; Lihong Liu; Canchan Cui; Xiaohong Wang; Jin Lu

doi:10.2196/44420

Patient Journey Toward a Diagnosis of Light Chain Amyloidosis in a National Sample: Cross-Sectional Web-Based Study

JMIR Form Res. 2023 Nov 2:7:e44420. doi: 10.2196/44420.

Authors

Xuelin Dou^#¹, Yang Liu^#¹, Aijun Liao², Yuping Zhong³, Rong Fu⁴, Lihong Liu⁵, Canchan Cui⁶, Xiaohong Wang⁷, Jin Lu¹

Affiliations

¹ Hematology Department, Peking University People's Hospital, Beijing, China.
² Hematology Department, Shengjing Hospital of China Medical University, Shenyang, China.
³ Hematology Department, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
⁴ Hematology Department, Tianjin Medical University General Hospital, Tianjin, China.
⁵ Hematology Department, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
⁶ Medical Affairs, Xi'an Janssen Pharmaceutical Ltd, Beijing, China.
⁷ Medical Affairs, Xi'an Janssen Pharmaceutical Ltd, Shanghai, China.

^# Contributed equally.

PMID: 37917132
PMCID: PMC10654903
DOI: 10.2196/44420

Abstract

Background: Systemic light chain (AL) amyloidosis is a rare and multisystem disease associated with increased morbidity and a poor prognosis. Delayed diagnoses are common due to the heterogeneity of the symptoms. However, real-world insights from Chinese patients with AL amyloidosis have not been investigated.

Objective: This study aimed to describe the journey to an AL amyloidosis diagnosis and to build an in-depth understanding of the diagnostic process from the perspective of both clinicians and patients to obtain a correct and timely diagnosis.

Methods: Publicly available disease-related content from social media platforms between January 2008 and April 2021 was searched. After performing data collection steps with a machine model, a series of disease-related posts were extracted. Natural language processing was used to identify the relevance of variables, followed by further manual evaluation and analysis.

Results: A total of 2204 valid posts related to AL amyloidosis were included in this study, of which 1968 were posted on haodf.com. Of these posts, 1284 were posted by men (median age 57, IQR 46-67 years); 1459 posts mentioned renal-related symptoms, followed by heart (n=833), liver (n=491), and stomach (n=368) symptoms. Furthermore, 1502 posts mentioned symptoms related to 2 or more organs. Symptoms for AL amyloidosis most frequently mentioned by suspected patients were nonspecific weakness (n=252), edema (n=196), hypertrophy (n=168), and swelling (n=140). Multiple physician visits were common, and nephrologists (n=265) and hematologists (n=214) were the most frequently visited specialists by suspected patients for initial consultation. Additionally, interhospital referrals were also commonly seen, centralizing in tertiary hospitals.

Conclusions: Chinese patients with AL amyloidosis experienced referrals during their journey toward accurate diagnosis. Increasing awareness of the disease and early referral to a specialized center with expertise may reduce delayed diagnosis and improve patient management.

Keywords: AL amyloidosis; big data; machine model; natural language processing; network analysis; rare disease; systemic light chain amyloidosis; web-based.

©Xuelin Dou, Yang Liu, Aijun Liao, Yuping Zhong, Rong Fu, Lihong Liu, Canchan Cui, Xiaohong Wang, Jin Lu. Originally published in JMIR Formative Research (https://formative.jmir.org), 02.11.2023.