Incremental Dialysis Decision Model Based on Expert-Guided Machine Learning

This observational prospective study combined clinical expert knowledge with machine learning to develop and validate a predictive model for incremental hemodialysis decision-making. The aim of the predictive model is to assist clinicians in developing individualized incremental dialysis treatment plans to optimize patient outcomes.

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Key information

Conditions

End-stage Renal Disease Chronic Disease Disease Attributes Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Kidney Diseases Kidney Failure, Chronic Male Urogenital Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Renal Insufficiency Renal Insufficiency, Chronic Urogenital Diseases Urologic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Huashan hospital, Fudan university

Shanghai, Shanghai Municipality, 200040, China

About this study

By collecting patients' clinical and biochemical parameters and combining them with experts' judgments of dialysis timing and frequency, the model can dynamically assess patients' risk of needing to increase the frequency of dialysis, thus assisting physicians in formulating individualized incremental dialysis regimens to optimize dialysis outcomes and improve patients' prognosis.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

New hemodialysis patients (Apr 2010-Jun 2024), started within 3 months, including transfers.
Age ≥18, stable hemodialysis >6 months.

Exclusion criteria

Incomplete/unreliable data.
Twice-weekly palliative dialysis.
No baseline urine output or ≤200 mL/24h.
Liver disease, heart failure, or severe comorbidities.

Treatment and study plan

Primary outcomes

Number (Proportion) of Participants Who Experience an Incremental Dialysis Event, Assessed Monthly

Time frame: Baseline and monthly visits from enrollment until incremental dialysis event, death, transfer, or up to 5 years (whichever occurs first)

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.

Sponsors and collaborators

Lead sponsor

Huashan Hospital

Other

Registry information

Official study title

Machine Learning Based on Expert Knowledge to Build and Validate a Decision Model for Incremental Dialysis

Important dates

Study start: 2010
Primary completion: 2024
Study completion: 2024
First posted: Jan 14, 2025
Registry last updated: Jan 14, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed