Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.

Journal: International urology and nephrology
PMID: 39477885

Abstract

BACKGROUND: Acute kidney injury (AKI) and contrast-induced nephropathy (CIN) are common complications following percutaneous coronary intervention (PCI) or coronary angiography (CAG), presenting significant clinical challenges. Machine learning (ML) models offer promise for improving patient outcomes through early detection and intervention strategies.

Authors

  • Soroush Najdaghi
    Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Science, Isfahan, Iran. Soroush.najdaghi@yahoo.com.
  • Delaram Narimani Davani
    Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Science, Isfahan, Iran.
  • Davood Shafie
    Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Science, Isfahan, Iran.
  • Azin Alizadehasl
    Echocardiography and Cardiogenetic Research Centers, Cardio-Oncology Department, Rajaie Cardiovascular Medical & Research Center, Tehran, Iran.

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