Development, validation and economic evaluation of a machine learning algorithm for predicting the probability of kidney damage in patients with hyperuricaemia: protocol for a retrospective study.

INTRODUCTION

Accurate identification of the risk factors is essential for the effective prevention of hyperuricaemia (HUA)-related kidney damage. Previous studies have established the efficacy of machine learning (ML) methodologies in predicting kidney damage due to other chronic diseases. Nevertheless, a scarcity of precise and clinically applicable prediction models exists for assessing the risk of HUA-related kidney damage. This study aims to accurately predict the risk of developing HUA-related kidney damage using a ML algorithm, which is based on a retrospective database.

METHODS AND ANALYSIS

This retrospective study aims to collect clinical data on outpatients and inpatients from the Sichuan Provincial People's Hospital, China, covering the period from 1 January 2018 to 31 December 2021 with a focus on patients diagnosed with 'hyperuricaemia' or 'gout'. Predictive models will be constructed using techniques such as data imputation, sampling, feature selection and ML algorithms. This research will evaluate the predictive accuracy, interpretability and fairness of the developed models to determine their clinical applicability. The net benefit and net saving will be calculated to gauge the economic value of the model. The most effective model will then undergo external validation and be made available as an online predictive tool to facilitate user access.

ETHICS AND DISSEMINATION

The Ethics Review Committee at Sichuan Provincial People's Hospital granted approval for the ethical review of this study without requiring informed consent. The findings of the study will be disseminated in a peer-reviewed journal.