Risk scoring system for prediction of contrast-induced nephropathy in patients with pre-existing renal impairment

E Chong1, L Shen, K K Poh

  • 1Alexandra Hospital, Jurong General Health, 378 Alexandra Road, Singapore 159964. ericchong80@hotmail.com

Insights

Patients with impaired renal function undergoing percutaneous coronary intervention (PCI) face a high risk of contrast-induced nephropathy (CIN). A new risk model identifies predictors like age and anemia to forecast CIN occurrence.

Area of Science:

  • Nephrology
  • Cardiology
  • Radiology

Background:

  • Baseline renal impairment is a primary risk factor for contrast-induced nephropathy (CIN) following percutaneous coronary intervention (PCI).
  • Identifying additional risk factors in high-risk patients is crucial for predicting CIN.
  • This study aimed to develop a predictive risk model for CIN in patients with pre-existing renal dysfunction.

Purpose of the Study:

  • To identify additional risk factors for contrast-induced nephropathy (CIN) in patients with impaired renal function undergoing percutaneous coronary intervention (PCI).
  • To develop a risk prediction model for CIN in this high-risk population.

Main Methods:

  • A cohort of 770 patients with estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2 undergoing PCI were analyzed.
  • Patients received standard prophylactic saline hydration and N-acetylcysteine.
  • CIN was defined as a >25% increase in serum creatinine within 48 hours post-PCI.

Main Results:

  • Despite prophylaxis, CIN occurred in 11.4% of patients.
  • Key predictors of CIN included older age, anemia (hemoglobin < 11 mg/dL), post-procedure creatinine kinase rise, systolic hypotension (< 100 mmHg), and higher contrast volume.
  • CIN incidence increased significantly with worsening renal failure (eGFR < 20 ml/min/1.73 m2 showed 40.8% incidence).

Conclusions:

  • Patients with renal impairment undergoing PCI remain at high risk for CIN, even with standard prophylaxis.
  • A predictive risk model incorporating identified clinical factors can estimate CIN likelihood.
  • The model allows for risk stratification, with potential CIN incidence ranging from 2% to over 50% based on individual risk profiles.
Abstract

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