Key result
Risk prediction model shows excellent discrimination and calibration for identifying high-risk postoperative AKI patients.
Why the study?
Can a risk prediction model accurately identify high-risk patients for postoperative acute kidney injury in a broad surgical population?
Can a risk prediction model accurately identify high-risk patients for postoperative acute kidney injury in a broad surgical population?
Answers are grounded in this paper and cite their sources.
A risk prediction model demonstrated excellent discrimination and calibration for identifying surgical patients at high risk for postoperative acute kidney injury.
The model demonstrated excellent discrimination and calibration, particularly in the temporal validation set. The model has potential to help identify high-risk patients preoperatively, guiding perioperative interventions to reduce AKI incidence and the associated healthcare costs. Further studies are needed to validate, refine, and study real-world applications of the model in diverse clinical settings.
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Shotwell et al. (2026) studied this question. The risk prediction model showed excellent discrimination and calibration in identifying patients at high risk for postoperative acute kidney injury.
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