ArticleRenal failure2023
Construction and validation of a risk prediction model for acute kidney injury in patients after cardiac arrest.
Article in Renal failure, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 3 citations in OpenAlex.
- Development and validation of machine learning models to predict 30-day mortality in patients with cardiac arrest complicated by acute kidney injury.Clinics (Sao Paulo, Brazil) · 2026Article
- Association between C-reactive protein-to-platelet ratio and acute kidney injury in cardiac arrest survivors.BMC cardiovascular disorders · 2026Article
- Development of a Clinical Prediction Model for Acute Kidney Injury Among In-Hospital Cardiac Arrest Patients During Intensive Care Unit Hospitalization.Reviews in cardiovascular medicine · 2026Article
- Dual-phase CT radiomics for acute kidney injury prediction after out-of-hospital cardiac arrest.Frontiers in radiology · 2026Article
- Transparent AI-driven personalized risk prediction system for acute kidney injury after total hip arthroplasty.Scientific reports · 2025Article
- Development and Validation of a Nomogram Prediction Model for In-hospital Mortality in Patients with Cardiac Arrest: A Retrospective Study.Reviews in cardiovascular medicine · 2025Article
- A machine learning model for prediction of cardiac arrest-associated acute kidney injury in the ICU: an internal and external validation study.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIdentifying patients at high risk for cardiac arrest-associated acute kidney injury (CA-AKI) helps in early preventive interventions. This study aimed to establish and validate a high-risk nomogram for CA-AKI.
methodsIn this retrospective dataset, 339 patients after cardiac arrest (CA) were enrolled and randomized into a training or testing dataset. The Student's
resultsIn total, 150 patients (44.2%) were diagnosed with CA-AKI. Four independent risk predictors were identified and integrated into the nomogram: chronic kidney disease, albumin level, shock, and heart rate. Receiver operating characteristic (ROC) analyses showed that the nomogram had a good discrimination performance for CA-AKI in the training dataset 0.774 (95%CI, 0.715-0.833) and testing dataset 0.763 (95%CI, 0.670-0.856). The AUC values for the two groups were calculated and compared using the Hanley-McNeil test. No statistically significant differences were observed between the groups. The calibration curve demonstrated good agreement between the predicted outcome and actual observations. Good clinical usefulness was identified using DCA and CIC.
conclusionAn easy-to-use nomogram for predicting CA-AKI was established and validated, and the prediction efficiency of the clinical model has reasonable clinical practicability.
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