-
Comparative Study
Risk scoring system for prediction of contrast-induced nephropathy in patients with pre-existing renal impairment undergoing percutaneous coronary intervention.
- E Chong, L Shen, K K Poh, and H C Tan.
- Alexandra Hospital, Jurong General Health, 378 Alexandra Road, Singapore 159964. ericchong80@hotmail.com
- Singap Med J. 2012 Mar 1;53(3):164-9.
IntroductionBaseline renal impairment is the most recognised risk factor for development of contrast-induced nephropathy (CIN) post percutaneous coronary intervention (PCI). We examined the additional risk factors in this high-risk group and aimed to develop a risk model for prediction of CIN.MethodsA cohort of 770 consecutive patients with existing impaired renal function (estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2), who received routine prophylactic saline hydration and oral N-acetylcysteine treatment while undergoing PCI between May 2005 to October 2008 in our centre, were enrolled. The study endpoint, CIN, was defined as > 25% increase from baseline creatinine within 48 hours post PCI.ResultsDespite routine prophylaxis, CIN occurred in 11.4% of the patients. Important clinical predictors for CIN were age (odds ratio [OR] 1.59, 95% confidence level [CI] 1.0-2.52, p = 0.049), anaemia with haemoglobin < 11 mg/dL (OR 2.26, 95% CI 1.41-3.61, p = 0.001), post-procedure creatinine kinase rise (OR 1.12, 95% CI 1.07-1.16 for every 500 u/L increase, p < 0.001), systolic hypotension with blood pressure < 100 mmHg (OR 2.53, 95% CI 1.16-5.52, p = 0.016) and higher contrast volume. The incidence of CIN was significantly higher in patients with more severe renal failure (6.3%, 17.4% and 40.8% when eGFR was 40-60, 20-40 and < 20 ml/min/1.73 m2 respectively, p < 0.001). A prediction model was developed based on these findings. The incidence of CIN could vary from 2% to > 50% depending on these additional risk profiles.ConclusionPatients with impaired renal function undergoing PCI are at high risk of developing CIN despite traditional prophylaxis. A model of risk prediction could be used to predict its occurrence.
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