Evidence map›Paper›PMID 40059274›Full record

ArticleJournal of nephrology2025

Development and validation of a prediction model for 90-day mortality among critically ill patients with AKI undergoing CRRT.

Tingting Wang, Sha Xu, Yufei Yuan, Wenbin Guo, Hongliang Zhang, Jiajun Sun

Abstract readValidation Study
In one paragraph

Article in Journal of nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Tingting WangDepartment of Intensive Care Unit, The Second People's Hospital of Liaocheng, Linqing, 252600, Shandong Province, China. wangtingting869079@126.com.ORCID http://orcid.org/0000-0002-3824-6183
Sha XuDepartment of Cardiology, The Second People's Hospital of Liaocheng, Linqing, 252600, Shandong Province, China.
Yufei YuanDepartment of Nephrology, The Second People's Hospital of Liaocheng, Linqing, 252600, Shandong Province, China.
Wenbin GuoDepartment of Intensive Care Unit, The Second People's Hospital of Liaocheng, Linqing, 252600, Shandong Province, China.
Hongliang ZhangDepartment of Intensive Care Unit, The Second People's Hospital of Liaocheng, Linqing, 252600, Shandong Province, China.
Jiajun SunDepartment of Intensive Care Unit, The Second People's Hospital of Liaocheng, Linqing, 252600, Shandong Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute kidney injury (AKI) is frequent among intensive care unit (ICU) patients and is linked with high morbidity and mortality. In the absence of specific pharmacological treatments for AKI, continuous renal replacement therapy (CRRT) is a primary treatment option. This study aimed to develop and validate a predictive model for 90-day mortality in critically ill patients with AKI undergoing CRRT.

methodsClinical data from DATADRYAD were used. We randomly divided 1121 adult patients receiving CRRT for AKI into training (80%, n = 897) and validation (20%, n = 224) cohorts. A nomogram prediction model was developed using Cox proportional hazards regression with the training set, and was validated internally. Model performance was evaluated based on calibration, discrimination, and clinical utility.

resultsThe model, incorporating seven predictors-SOFA score, serum creatinine, blood urea nitrogen, albumin levels, Charlson comorbidity index, mean arterial pressure at CRRT initiation, and phosphate levels 24 h after CRRT initiation-demonstrated robust performance. It achieved a C-index of 0.810 in the training set and 0.794 in the validation set.

conclusionsWe developed and validated a predictive model based on seven key clinical predictors, showing excellent performance in identifying high-risk patients for 90-day mortality in AKI patients undergoing CRRT.

Indexed as

Acute Kidney InjuryContinuous Renal Replacement TherapyNomogramsAgedBlood Urea NitrogenCreatinineCritical IllnessFemaleHumansIntensive Care UnitsMaleMiddle AgedOrgan Dysfunction ScoresPhosphatesPredictive Value of TestsReproducibility of ResultsCreatininePhosphatesAcute kidney injuryContinuous renal replacement therapyMortalityPrediction model

Identifiers

PMID40059274
PMCPMC12165874

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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.