Conditions

End-stage Renal Disease

Keywords

Machine LearningExpert KnowledgeInterpretabilityIncremental HemodialysisDecision Model

Outcome Measures

Primary Outcomes (1)

Number (Proportion) of Participants Who Experience an Incremental Dialysis Event, Assessed Monthly
An incremental dialysis event is defined as an increase in a patient's dialysis frequency (e.g., from 1 session per week to 2 sessions per week, or from 2 to 3 sessions per week, etc.) due to clinical considerations such as decreased residual renal function, fluid overload, or other physician-determined criteria. At each monthly visit (up to 5 years from enrollment), investigators will record whether each participant experiences an incremental event. We will quantify the primary outcome as the number and proportion of participants who transition to a higher dialysis frequency per month, as well as the cumulative incidence over time.
Baseline and monthly visits from enrollment until incremental dialysis event, death, transfer, or up to 5 years (whichever occurs first)

Study Arms (1)

Huashan Hospital Hemodialysis Cohort

This is a single-center prospective cohort study that included 175 patients with end-stage renal disease (ESKD) who received maintenance hemodialysis at the hemodialysis center of Huashan Hospital from April 2010 to June 2024. The ESKD patient population was comprised of 175 cases in total. All patients retained some residual kidney function (RKF), and their dialysis records and regular laboratory test results were integrated as input features for the machine learning model. The primary objective of the model was twofold: first, to integrate expert knowledge with machine learning to predict when a switch from lower frequency incremental dialysis (I-HD) to higher frequency dialysis should be made; and second, to identify key variables affecting the risk of adverse outcomes over a two-year period.

Eligibility Criteria

Age18 Years+

Sexall

Healthy VolunteersNo

Age GroupsAdult (18-64), Older Adult (65+)

Sampling MethodNon-Probability Sample

Study Population

Hemodialysis patients with residual renal function admitted to dialysis centers

You may qualify if:

New hemodialysis patients (Apr 2010-Jun 2024), started within 3 months, including transfers.

Sponsors & Collaborators

Huashan Hospital
Otherlead

Study Sites (1)

Huashan hospital, Fudan university

Shanghai, Shanghai Municipality, 200040, China

MeSH Terms

Conditions

Kidney Failure, Chronic

Condition Hierarchy (Ancestors)

Renal Insufficiency, ChronicRenal InsufficiencyKidney DiseasesUrologic DiseasesFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesMale Urogenital DiseasesChronic DiseaseDisease AttributesPathologic ProcessesPathological Conditions, Signs and Symptoms

Study Officials

Jing Chen
Huashan Hospital
PRINCIPAL INVESTIGATOR

Incremental Dialysis Decision Model Based on Expert-Guided Machine Learning

Brief Summary

Registry Overview

Trial Relationships

Study Timeline

Study Start

Primary Completion

Study Completion

First Submitted

First Posted

Conditions

Keywords

Outcome Measures

Primary Outcomes (1)

Study Arms (1)

Huashan Hospital Hemodialysis Cohort

Eligibility Criteria

You may qualify if:

Sponsors & Collaborators

Study Sites (1)

MeSH Terms

Conditions

Condition Hierarchy (Ancestors)

Study Officials

Enrollment

Study Design

Study Record Dates

You may not qualify if:

Source record

Locations

Brief Summary

Registry Overview

Trial Relationships

Related Scientific Literature

Study Timeline

Study Start

Primary Completion

Study Completion

First Submitted

First Posted

Conditions

Keywords

Outcome Measures

Primary Outcomes (1)

Study Arms (1)

Huashan Hospital Hemodialysis Cohort

Eligibility Criteria

You may qualify if:

Sponsors & Collaborators

Study Sites (1)

MeSH Terms

Conditions

Condition Hierarchy (Ancestors)

Study Officials

Enrollment

Study Design

Study Record Dates

You may not qualify if:

Source record

Locations