Development and validation of machine learning models to predict 30-day mortality in patients with cardiac arrest complicated by acute kidney injury.

Meng Yuan, Lei Zhong, Jie Min, Mingxia Ni, Yufeng Yao

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Article in Clinics (Sao Paulo, Brazil), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Meng YuanDepartment of Intensive Care Unit, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang, China; Department of Anesthesiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang, China.

Lei ZhongDepartment of Intensive Care Unit, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang, China; Department of Anesthesiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang, China.

Jie MinDepartment of Intensive Care Unit, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang, China; Department of Anesthesiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang, China.

Mingxia NiDepartment of Anesthesiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang, China; Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Zhejiang, China.

Yufeng YaoDepartment of Operating Room, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang, China; Department of Anesthesiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, Zhejiang, China. Electronic address: 13754200911@163.com.

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6 · The paper itself

Abstract

backgroundCardiac Arrest (CA) often leads to Acute Kidney Injury (AKI), presenting a major health issue. This study aims to develop and validate machine learning models predicting 30-day mortality in CA patients with AKI.

methodsA retrospective study was conducted on 1,121 adult ICU patients diagnosed with CA and AKI using data from the MIMIC-IV database. Data from 2008‒2016 (n = 900) formed the training cohort, while 2017‒2019 data (n = 221) served as the temporal validation cohort. Feature selection utilized LASSO regression, followed by the application of six machine learning algorithms. Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis, with comparisons made against the APACHE II and SOFA scores.

resultsSeven key features were identified, namely, APACHE II score, lactate, anion gap, RDW, oliguria, norepinephrine, and temporary pacemaker implantation. The LASSO-Logistic Regression (LASSO-LR) model demonstrated the most stable performance in validation (AUROC = 0.751), significantly outperforming APACHE II (0.583, p < 0.001) and SOFA (0.660, p = 0.009), with a Brier score of 0.209. SHAP analysis revealed oliguria and RDW as the most significant predictors.

conclusionsThe LASSO-LR model offers a modest but statistically significant improvement over conventional scoring systems for predicting 30-day mortality in patients with CA and AKI, with acceptable calibration and interpretability. However, the clinical meaningfulness of this incremental benefit requires prospective validation before clinical implementation.

Indexed as

Acute kidney injuryCardiac arrestMachine learningMortalityPredictive model

Identifiers

PMID42710354

DOI10.1016/j.clinsp.2026.101129

PMCPMC13573679

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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.