ISSN: 1859 - 2872
A predictive model for acute kidney injury in patients with septic and septic shock
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Abstract
Objective: This study aimed to establish and validate predictive models based on novel machine learning (ML) algorithms for acute kidney injury (AKI) in patients with sepsis and septic shock. Subject and method: A prospective descriptive study combined with retrospective analysis was conducted on 530 patients with sepsis or septic shock treated at 108 Military Central Hospital from 01/2020 to 01/2024 to develop a machine learning model for predicting acute kidney injury. Result: The machine learning model was built based on 8 factors: Age, gender, portal entry of infection, blood pH, HCO3-, lactate, urea, and creatinine. For predicting acute kidney injury, the model achieved a sensitivity of 82.4%, specificity of 74.4%, and AUC value = 0.84 (0.757 - 0.923). Conclusion: The machine learning-based model for predicting AKI demonstrated a good value in early prognosis of acute kidney injury in patients with sepsis and septic shock.
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References
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