数据驱动的聚类分析识别出血液透析患者的三种临床表型

数据集获取（Figshare）

• 源站标题：Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients
• DOI：10.6084/m9.figshare.30671632.v1
• 发布日期：2025-11-21
• 文件数：38 个
• 文件总体积：12.60 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Data-driven_cluster_analysis_identifies_three_clinical_phenotypes_in_hemodialysis_patients/30671632

命令行下载

curl -L -o "irnf_a_2588961_sm0967.jpg" "https://ndownloader.figshare.com/files/59743302"
curl -L -o "irnf_a_2588961_sm0966.jpg" "https://ndownloader.figshare.com/files/59743305"
curl -L -o "irnf_a_2588961_sm0965.jpg" "https://ndownloader.figshare.com/files/59743308"
curl -L -o "irnf_a_2588961_sm0964.jpg" "https://ndownloader.figshare.com/files/59743311"
curl -L -o "irnf_a_2588961_sm0963.jpg" "https://ndownloader.figshare.com/files/59743314"
curl -L -o "irnf_a_2588961_sm0962.jpg" "https://ndownloader.figshare.com/files/59743317"
curl -L -o "irnf_a_2588961_sm0961.jpg" "https://ndownloader.figshare.com/files/59743320"
curl -L -o "irnf_a_2588961_sm0960.jpg" "https://ndownloader.figshare.com/files/59743323"

文件清单

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文件总体积 12.60 MB（源站 API 实测）。

数据说明

Clinical heterogeneity among hemodialysis patients necessitates precision medicine approaches transcending conventional single-parameter management. Through machine learning analysis of 1,207 maintenance hemodialysis patients, we developed a novel two-tier phenotyping framework integrating unsupervised K-means clustering across 22 clinical indicators with supervised classification using six universally available biomarkers. Five mechanistically informed composite indicators were constructed, including the Middle-Small Molecule Clearance Index (β2-microglobulin reduction ratio × Kt/V) and ferritin–hemoglobin ratio, achieving superior discriminatory capacity over traditional approaches. Three distinct metabolic phenotypes emerged with exceptional stability (Adjusted Rand Index = 0.9181): high retention-inflammatory (19.5%) characterized by dialysis inadequacy and functional iron deficiency, optimal clearance (24.3%) demonstrating superior toxin removal, and intermediate-stable (56.0%) maintaining balanced parameters. The simplified six-parameter model achieved clinically acceptable performance (AUC: 0.893–0.919, accuracy >88%) enabling automated EMR integration. This cross-sectional phenotype discovery represents the foundational step toward precision nephrology, establishing classification frameworks essential for subsequent longitudinal validation studies. The methodology facilitates phenotype-guided interventions: intensified dialysis for high retention-inflammatory patients, clearance optimization for optimal clearance patients, and proactive monitoring for intermediate-stable patients, advancing hemodialysis toward algorithm-driven individualized care with potential to optimize clinical outcomes and resource utilization, pending prospective validation of phenotype-outcome associations.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.30671632.v1（source: https://tandf.figshare.com/articles/dataset/Data-driven_cluster_analysis_identifies_three_clinical_phenotypes_in_hemodialysis_patients/30671632）。