Development and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts

Aug 5, 2026

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Retrospective cohort study develops and validates a real-time machine learning model to predict persistent acute kidney injury (AKI) stage 3 in intensive care units using multiple international datasets. Adult ICU patients with AKI stage 2 or 3 were selected per Kidney Disease: Improving Global Outcomes criteria, with a primary endpoint of stage 3 lasting at least 72 hours. A calibrated explainable tree regressor was trained on two single-center databases and externally validated on two multi-center cohorts. Results included 7759 patients, with AUROC up to 0.94 in US and 0.85 in Italian cohorts. The approach supports improved AKI management in clinical practice.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Background

Persistent vs transient AKI and clinical relevance

Methods

Patient selection and endpoint definition

Model training, calibration, and explainability

Internal and external validation cohorts

Results

Cohort size and incidence of persistent stage 3 AKI

Predictive performance (AUROC and confidence intervals)

Conclusions

Clinical potential for ICU AKI management

POLITECNICO DI TORINO Repository ISTITUZIONALE Development and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts Original Development and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts / Zappalà, Simone; Alfieri, Francesca; Ancona, Andrea; Taccone, Fabio Silvio; Maviglia, Riccardo; Cauda, Valentina; Finazzi, Stefano; Dell'Anna, Antonio Maria. -In: CRITICAL CARE. -ISSN 1364-8535. -ELETTRONICO. -28:1(2024) . [10 . 1186/s13054-024-04954-8] Availability: This version is available at: 11583/2995250 since: 2024-12-12T13:30:38Z Publisher: BioMed Central Ltd Published DOI:10.1186/s13054-024-04954-8 Terms of use: This article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository Publisher copyright (Article begins on next page) 17 February 2025 Zappalà et al. Critical Care (2024) 28:189 [https://doi.org/10.1186/s13054-024-04954-8](https://doi.org/10.1186/s13054-024-04954-8) Critical Care RESEARCH Open Access Development and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts Simone Zappalà1, Francesca Alfieri1, Andrea Ancona1, Fabio Silvio Taccone2, Riccardo Maviglia3, Valentina Cauda1,4, Stefano Finazzi5 and Antonio Maria Dell’Anna3* Abstract Background The aim of this retrospective cohort study was to develop and validate on multiple international datasets a real-time machine learning model able to accurately predict persistent acute kidney injury (AKI) in the intensive care unit (ICU) . Methods We selected adult patients admitted to ICU classified as AKI stage 2 or 3 as defined by the “Kidney Disease: Improving Global Outcomes” criteria. The primary endpoint was the ability to predict AKI stage 3 lasting for at least 72 h while in the ICU. An explainable tree regressor was trained and calibrated on two tertiary, urban, academic, singlecenter databases and externally validated on two multi-centers databases. Results A total of 7759 ICU patients were enrolled for analysis. The incidence of persistent stage 3 AKI varied from 11 to 6% in the development and internal validation cohorts, respectively and 19% in external validation cohorts. The model achieved area under the receiver operating characteristic curve of 0.94 (95% CI 0.92–0. 95) in the US external validation cohort and 0.85 (95% CI 0.83–0. 88) in the Italian external validation cohort. Conclusions A machine learning approach fed with the proper data pipeline can accurately predict onset of Persistent AKI Stage 3 during ICU patient stay in retrospective, multi-centric and international datasets. This model has the potential to improve management of AKI episodes in ICU if implemented in clinical practice. Keywords Artificial intelligence, Acute kidney injury, Biomarker, Intensive care unit *Correspondence: Antonio Maria Dell’Anna [antoniomaria.dellanna@policlinicogemelli.it](antoniomaria.dellanna@policlinicogemelli.it) Full list of author information is available at the end of the article © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your

FAQs

What clinical condition does the model aim to predict, and how is it defined?

The model predicts persistent acute kidney injury (AKI) stage 3 in ICU patients. Persistence is defined as stage 3 lasting at least 72 hours while in the ICU.

How was the machine learning model developed and validated?

An explainable tree regressor was trained and calibrated using two tertiary, urban, academic single-center databases, then externally validated on two multi-center databases.

What predictive performance did the model achieve in external validation?

In external validation, the model achieved an AUROC of 0.94 (95% CI 0.92–0.95) in the US cohort and 0.85 (95% CI 0.83–0.88) in the Italian cohort.

Aug 5, 2026

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