Yuling Pan

Hubei University of Chinese Medicine

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Liang Yu

University of Electronic Science and Technology of China

Lian Liu

Hebei University of Technology

Delin Lai

South China University of Technology

Yanbin Zhu

Hebei Medical University

Yunfan Liu

South China Agricultural University

Shuanghong Zhang

Guangzhou Special Pressure Equipment Testing and Research Institute

Gang Kong

Changshu No.1 People's Hospital

Haihang You

Chinese Academy of Sciences

Qingkun Fan

Northwestern Polytechnical University

Chunzi Liang

Hubei University of Chinese Medicine

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South China University of Technology

Wuhan University

Chinese Academy of Sciences

University of Chinese Academy of Sciences

Nanjing University

Hubei University of Chinese Medicine

Huazhong University of Science and Technology

Wuhan University of Science and Technology

University of Tennessee at Knoxville

University of Electronic Science and Technology of China

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

Scientific Reports (2024)

Yuling Pan Qingkun Fan Liang Yu Yunfan Liu Haihang You

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.

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10.1038/s41598-024-77466-8

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An improved Hummers method to synthesize graphene oxide using much less concentrated sulfuric acid

Chinese Chemical Letters (2022)

Yanbin Zhu Gang Kong Yuling Pan Lian Liu Bo Yang

Graphite oxide

10.1016/j.cclet.2022.01.060

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Citations (50)