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Updated: Jul 2, 2025
A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
Published on: February 2, 2021
Acute kidney injury prediction model utility in premature myocardial infarction
Fang Tao1, Hongmei Yang2, Wenguang Wang2
1Medical Department, Qinhuangdao First Hospital, Qinhuangdao, Hebei Province 066000, China.
Abstract:
The incidence of premature myocardial infarction (PMI) has been rising and acute kidney injury (AKI) occurring in PMI patients severely impacts prognosis. This study aimed to develop and validate a prediction model for AKI specific to PMI patients. The MIMIC-Ⅲ-CV and MIMIC-Ⅳ databases were utilized for model derivation of PMI patients. Single-center data served for external validation. There were 571 and 182 AKI patients in the training set (n = 937) and external validation set (n = 292) cohorts, respectively. Finally, a 7-variable model consisting of: Sequential Organ Failure Assessment (SOFA) score, coronary artery bypass grafting (CABG), ICU stay time, loop diuretics, estimated glomerular filtration rate (eGFR) HCO3- and Albumin was developed, achieving an AUC of 0.85 (95% CI: 0.83-0.88) in the training set. External validation also confirmed model robustness. This model may assist clinicians in the early identification of patients at elevated risk for PMI. Further validation is warranted before clinical application.
Insights
A new prediction model helps identify patients with premature myocardial infarction (PMI) at high risk for acute kidney injury (AKI). This tool aids early clinical intervention for better patient outcomes.
Area of Science:
- Cardiology
- Nephrology
- Medical Informatics
Background:
- Premature myocardial infarction (PMI) incidence is increasing.
- Acute kidney injury (AKI) in PMI patients significantly worsens prognosis.
- Early identification of AKI risk in PMI is crucial for timely intervention.
Purpose of the Study:
- To develop and validate a predictive model for AKI in patients with premature myocardial infarction.
- To identify key clinical variables associated with AKI development in PMI.
Main Methods:
- Model derivation using MIMIC-Ⅲ-CV and MIMIC-Ⅳ databases.
- External validation with single-center data.
- Development of a 7-variable prediction model.
Main Results:
- The 7-variable model (SOFA score, CABG, ICU stay, loop diuretics, eGFR, HCO3-, Albumin) achieved an AUC of 0.85 in the training set.
- External validation confirmed the model's robustness.
- The model demonstrated strong predictive performance for AKI in PMI patients.
Conclusions:
- A validated prediction model can assist clinicians in early AKI risk identification for PMI patients.
- The model incorporates key clinical factors for AKI prediction.
- Further validation is recommended prior to widespread clinical application.
