A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy

doi:10.1038/s41598-024-77466-8

	A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy
	Pan, Yuling 1,2; Fan, Qingkun 3; Liang, Yu 1,2; Liu, Yunfan 5; You, Haihang 4; Liang, Chunzi 1,2
	2024-11-19
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
卷号	14 期号:1 页码:11
摘要	Current approaches for cardiac amyloidosis (CA) identification are time-consuming, labor-intensive, and present challenges in sensitivity and accuracy, leading to limited treatment efficacy and poor prognosis for patients. In this retrospective study, we aimed to leverage machine learning (ML) to create a diagnostic model for CA using data from routine blood tests. Our dataset included 6,563 patients with left ventricular hypertrophy, 261 of whom had been diagnosed with CA. We divided the dataset into training and testing cohorts, applying ML algorithms such as logistic regression, random forest, and XGBoost for automated learning and prediction. Our model's diagnostic accuracy was then evaluated against CA biomarkers, specifically serum-free light chains (FLCs). The model's interpretability was elucidated by visualizing the feature importance through the gain map. XGBoost outperformed both random forest and logistic regression in internal validation on the testing cohort, achieving an area under the curve (AUC) of 0.95 (95%CI: 0.92-0.97), sensitivity of 0.92 (95%CI: 0.86-0.98), specificity of 0.95 (95%CI: 0.94-0.97), and an F1 score of 0.89 (95%CI: 0.85-0.92). Its performance was also superior to the serum FLC-kappa and FLC-lambda combination (AUC of 0.88). Furthermore, XGBoost identified unique biomarker signatures indicative of multisystem dysfunction in CA patients, with significant changes in eGFR, FT3, cTnI, ANC, and NT-proBNP. This study develops a highly sensitive and accurate ML model for CA detection using routine clinical laboratory data, effectively streamlining diagnostic procedures, and providing valuable clinical insights and guiding future research into disease mechanisms.
关键词	Cardiac amyloidosis Machine learning Routine blood tests Multi-system dysfunction profile Prediction model
DOI	10.1038/s41598-024-77466-8
收录类别	SCI
语种	英语
资助项目	High-level Talent Research Startup Funding of Hubei University of Chinese Medicine[100501070302] ; Wuhan Clinical Medical Research Center for Cardiovascular Imaging Internal Fund[CMRC202304]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001360400200011
出版者	NATURE PORTFOLIO
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40771
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	You, Haihang; Liang, Chunzi
作者单位	1.Hubei Univ Chinese Med, Sch Lab Med, 16 Huangjia Lake West Rd, Wuhan 430065, Peoples R China 2.Hubei Univ Chinese Med, Hubei Shizhen Lab, 16 Huangjia Lake West Rd, Wuhan 430065, Peoples R China 3.Wuhan Asia Heart Hosp, Dept Med Lab, Wuhan 430022, Hubei, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Univ Toronto, 63 St George St, Toronto, ON M5S 2Z9, Canada
推荐引用方式 GB/T 7714	Pan, Yuling,Fan, Qingkun,Liang, Yu,et al. A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy[J]. SCIENTIFIC REPORTS,2024,14(1):11.
APA	Pan, Yuling,Fan, Qingkun,Liang, Yu,Liu, Yunfan,You, Haihang,&Liang, Chunzi.(2024).A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy.SCIENTIFIC REPORTS,14(1),11.
MLA	Pan, Yuling,et al."A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy".SCIENTIFIC REPORTS 14.1(2024):11.