Evidence map›Paper›PMID 39613447›Full record

ArticleBMJ open2024

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.

Zhengyao Hou, Yong Yang, Bo Deng, Guangjie Gao, Mengting Li, Xinyu Liu, Huan Chang, Hao Shen, Linke Zou, Jinqi Li and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zhengyao Hou *Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.ORCID http://orcid.org/0009-0004-5239-3552
Yong Yang *Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Bo DengPersonalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Guangjie GaoPersonalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Mengting LiPersonalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Xinyu LiuPersonalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Huan ChangPersonalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Hao ShenPersonalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Linke ZouPersonalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Jinqi LiPersonalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China wuxingwei@med.uestc.edu.cn lijinqi2002@126.com.
Xingwei WuPersonalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China wuxingwei@med.uestc.edu.cn lijinqi2002@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAccurate 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.

Indexed as

AlgorithmsHyperuricemiaMachine LearningChinaFemaleHumansKidney DiseasesMaleRetrospective StudiesRisk AssessmentRisk FactorsFactor Analysis, StatisticalMachine LearningPUBLIC HEALTH

Identifiers

PMID39613447
PMCPMC11605815

What Socratic holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.