Machine-learning model incorporating perioperative hemodynamics predicts moderate-to-severe AKI after heart transplantation

Key result: A machine-learning model incorporating perioperative hemodynamic indices (pulmonary artery systolic pressure, mean arterial pressure, central venous pressure) predicted postoperative stage 2-3 AKI after heart transplant with an AUC of 0.898.
Incidence: 21.9% (25/114) of recipients developed moderate-to-severe AKI.
Best model: Light gradient boosting machine outperformed other algorithms in discrimination and calibration.
Clinical implication: The model may guide early hemodynamic optimization to reduce AKI risk, but requires external validation.

Design	Retrospective cohort study
Setting	Single center in China
Population	114 orthotopic heart transplant recipients
Outcome	Postoperative stage 2-3 acute kidney injury (KDIGO criteria) within 7 days
Predictors	8 factors including pulmonary artery systolic pressure, mean arterial pressure, central venous pressure, and preoperative creatinine
Model performance	LightGBM: AUC 0.898, AUPRC 0.802, Brier score 0.106
Funding	Not reported in source

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

Key result: A machine-learning model incorporating perioperative hemodynamic indices (pulmonary artery systolic pressure, mean arterial pressure, central venous pressure) predicted postoperative stage 2-3 AKI after heart transplant with an AUC of 0.898.
Incidence: 21.9% (25/114) of recipients developed moderate-to-severe AKI.
Best model: Light gradient boosting machine outperformed other algorithms in discrimination and calibration.
Clinical implication: The model may guide early hemodynamic optimization to reduce AKI risk, but requires external validation.

Design	Retrospective cohort study
Setting	Single center in China
Population	114 orthotopic heart transplant recipients
Outcome	Postoperative stage 2-3 acute kidney injury (KDIGO criteria) within 7 days
Predictors	8 factors including pulmonary artery systolic pressure, mean arterial pressure, central venous pressure, and preoperative creatinine
Model performance	LightGBM: AUC 0.898, AUPRC 0.802, Brier score 0.106
Funding	Not reported in source

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