Evidence map›Paper›PMID 39752664›Full record

ArticleJournal of medical Internet research2025

Major Adverse Kidney Events in Hospitalized Older Patients With Acute Kidney Injury: Machine Learning-Based Model Development and Validation Study.

Xiao-Qin Luo, Ning-Ya Zhang, Ying-Hao Deng, Hong-Shen Wang, Yi-Xin Kang, Shao-Bin Duan

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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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

6 authors.

Xiao-Qin LuoDepartment of Geriatrics, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0000-0003-1832-2350
Ning-Ya ZhangInformation Center, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0000-0001-9700-4996
Ying-Hao DengHealth Management Center, National Clinical Research Center for Metabolic Diseases, Hunan Provincial Clinical Medicine Research Center for Intelligent Management of Chronic Disease, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0009-0005-1671-6390
Hong-Shen WangDepartment of Critical Care Medicine, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0000-0001-7873-9524
Yi-Xin KangDepartment of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0000-0002-5546-7535
Shao-Bin DuanDepartment of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, China.ORCID 0000-0002-4677-5704

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute kidney injury (AKI) is a common complication in hospitalized older patients, associated with increased morbidity, mortality, and health care costs. Major adverse kidney events within 30 days (MAKE30), a composite of death, new renal replacement therapy, or persistent renal dysfunction, has been recommended as a patient-centered endpoint for clinical trials involving AKI.

objectiveThis study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.

methodsA total of 4266 older patients (aged ≥ 65 years) with AKI admitted to the Second Xiangya Hospital of Central South University from January 1, 2015, to December 31, 2020, were included and randomly divided into a training set and an internal test set in a ratio of 7:3. An additional cohort of 11,864 eligible patients from the Medical Information Mart for Intensive Care Ⅳ database served as an external test set. The Boruta algorithm was used to select the most important predictor variables from 53 candidate variables. The eXtreme Gradient Boosting algorithm was applied to establish a prediction model for MAKE30. Model discrimination was evaluated by the area under the receiver operating characteristic curve (AUROC). The SHapley Additive exPlanations method was used to interpret model predictions.

resultsThe overall incidence of MAKE30 in the 2 study cohorts was 28.3% (95% CI 26.9%-29.7%) and 26.7% (95% CI 25.9%-27.5%), respectively. The prediction model for MAKE30 exhibited adequate predictive performance, with an AUROC of 0.868 (95% CI 0.852-0.881) in the training set and 0.823 (95% CI 0.798-0.846) in the internal test set. Its simplified version achieved an AUROC of 0.744 (95% CI 0.735-0.754) in the external test set. The SHapley Additive exPlanations method showed that the use of vasopressors, mechanical ventilation, blood urea nitrogen level, red blood cell distribution width-coefficient of variation, and serum albumin level were closely associated with MAKE30.

conclusionsAn interpretable eXtreme Gradient Boosting model was developed and validated to predict MAKE30, which provides opportunities for risk stratification, clinical decision-making, and the conduct of clinical trials involving AKI.

trial registrationChinese Clinical Trial Registry ChiCTR2200061610; https://tinyurl.com/3smf9nuw.

Indexed as

Acute Kidney InjuryHospitalizationMachine LearningAgedAged, 80 and overFemaleHumansMaleacute kidney injurymachine learningmajor adverse kidney events within 30 daysolderprediction model

Identifiers

PMID39752664
PMCPMC11748444

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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 OpenQuestion graph.