Objectives and Scope:This observational study aims to leverage real-world data from Huashan Hospital to develop an AI-driven intelligent decision-making system for assessing dialysis adequacy in maintenance hemodialysis (MHD) patients, and to analyze early warning factors contributing to inadequate dialysis. Core Research Question:Can an AI-based early warning and diagnostic model, built on multidimensional big data, identify the risk of inadequate hemodialysis at an ultra-early stage and accurately diagnose composite complications such as cardiovascular and cerebrovascular diseases? Methodology:The study will conduct a retrospective analysis of adult MHD patients treated at Huashan Hospital between January 2011 and September 2025. The dataset encompasses multidimensional variables, including sociodemographics, treatment parameters, laboratory indicators, metabolomics, and physical functions. Utilizing Dynamic Network Biomarkers (DNB) technology to screen for early warning markers, combined with artificial intelligence algorithms such as Neural Networks and Support Vector Machines (SVM), the study will construct two primary models: "Ultra-early Warning" and "Disease State Diagnosis." These models are designed to provide clinical decision support for precise interventions.
Age range
18 Years – 90 Years
Sex
ALL
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The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Cardiovascular and Cerebrovascular Diseases (CCVD)
Timeframe: 15 years (From January 2011 to September 2025)