Prognostication of Acute Kidney Injury Following Percutaneous Coronary Intervention: A Machine Learning Approach
Authors' conclusion
In this single-center study, we developed a machine learning model using preprocedural variables that predicts post-PCI AKI. Although external validation is required, this model could support individualized risk stratification and preventive strategies.
Study context
- Population
- Patients undergoing percutaneous coronary intervention (PCI) at a tertiary medical center (2004–2022), routine electronic health record dataPCI procedures from 17,943 patients
- Participants
- 23,728 patients
- Exposure
- Machine learning model using preprocedural variables against acute kidney injury (AKI) defined by Kidney Disease: Improving Global Outcomes criteria
- Outcome
- AKI (absolute serum creatinine increase ≥0.3 mg/dL or relative increase ≥50% within 48 hours)
- Follow-up
- Within 48 hours
Methodological detailssingle-center, tertiary medical center, machine learning model (CatBoost), held-out test set (20%)
- Single-center
- Tertiary medical center
- Machine learning model (CatBoost)
- Held-out test set (20%)
Main finding
As reported in the abstract
Area under the receiver operating characteristic curve (held-out test set)
0.85
95% CI 0.82–0.87
Precision-recall curve (held-out test set)
0.38
95% CI 0.32–0.43
Observational data: associations, not effects.
Further findings
AKI prevalence
- AKI prevalence
- 7.0%
Calibration
- Integrated calibration index
- 0.017
- Calibration was excellent
Sensitivity at screening threshold (probability 0.041)
- Sensitivity
- 0.83 (95% CI: 0.80-0.87)
Specificity at rule-in threshold (probability 0.199)
- Specificity
- 0.92 (95% CI: 0.91-0.93)
Key predictors
- Baseline creatinine, hemoglobin, uric acid, and white blood cell count
Original publication
Prognostication of Acute Kidney Injury Following Percutaneous Coronary Intervention: A Machine Learning Approach. JACC Adv. Published 21 July 2026. doi:10.
Summarised from the published abstract; every number is checked against it