Incremental Dialysis Decision Model Based on Expert-Guided Machine Learning

Fudan University

Status: Completed

Conditions: End-stage Renal Disease

Study type: Observational

Funder types: Other

Identifiers

NCT06775067

KY2019-585

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 175 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

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

Trial design

175 participants in 1 patient group

Huashan Hospital Hemodialysis Cohort

Description:

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.