Evidence map›Paper›PMID 40124680›Full record

ArticleFrontiers in medicine2025

Development and validation of a dynamic nomogram for acute kidney injury prediction in ICU patients with acute heart failure.

Lu-Huai Feng, Tingting Su, Lina Huang, Tianbao Liao, Yang Lu, Lili Wu

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 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.

Lu-Huai Feng *Department of Endocrinology and Metabolism Nephrology, Guangxi Medical University Cancer Hospital, Nanning, China.
Tingting Su *Department of ECG Diagnostics, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Lina HuangDepartment of Endocrinology and Metabolism Nephrology, Guangxi Medical University Cancer Hospital, Nanning, China.
Tianbao LiaoDepartment of President's Office, Youjiang Medical University for Nationalities, Baise, China.
Yang LuDepartment of International Medical, Guangxi Medical University Cancer Hospital, Nanning, China.
Lili WuDepartment of Endocrinology and Metabolism Nephrology, Guangxi Medical University Cancer Hospital, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Developing and validating a simple and clinically useful dynamic nomogram for predicting early acute kidney injury (AKI) in patients with acute heart failure (AHF) admitted to the intensive care unit (ICU). Methods: Clinical data from patients with AHF were obtained from the Medical Information Mart for Intensive Care IV database. The patients with AHF were randomly allocated into derivation and validation sets. The independent predictors for AKI development in AHF patients were identified through least absolute shrinkage and selection operator and multivariate logistic regression analyses. A nomogram was developed based on the results of the multivariable logistic regression to predict early AKI onset in AHF patients, which was subsequently implemented as a web-based calculator for clinical application. An evaluation of the nomogram was conducted using discrimination, calibration curves, and decision curve analyses (DCA). Results: After strict screening, 1,338 patients with AHF were included in the derivation set, and 3,129 in the validation set. Sepsis, use of human albumin, age, mechanical ventilation, aminoglycoside administration, and serum creatinine levels were identified as predictive factors for AKI in patients with AHF. The discrimination of the nomogram in both the derivation and validation sets was 0.81 (95% confidence interval: 0.78-0.83) and 0.79 (95% confidence interval: 0.76-0.83). Additionally, the calibration curve demonstrated that the predicted outcomes aligned well with the actual observations. Ultimately, the DCA curves indicated that the nomogram exhibited favorable clinical applicability. Conclusion: The nomogram that integrates clinical risk factors and enables the personalized prediction of AKI in patients with AHF upon admission to the ICU, which has the potential to assist in identifying AHF patients who would derive the greatest benefit from interventions aimed at preventing and treating AKI.

Indexed as

acute heart failureacute kidney injuryintensive care unitnomogramprediction

Identifiers

PMID40124680
PMCPMC11927719

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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.