Comparative Efficacy of Statins for Prevention of Contrast-Induced Acute Kidney Injury in Patients With Chronic Kidney Disease: A Network Meta-Analysis.
Xinbin ZhouJin DaiXiaoming XuZhijun WangHaibin XuJie ChenYuangang QiuXiaobing DouPublished in: Angiology (2018)
Contrast-induced acute kidney injury (CI-AKI) is a common complication of iodinated contrast medium administration during cardiac catheterization. Statin treatment has been shown to be associated with reduced risk of CI-AKI; however, the results are inconsistent, especially for patients with chronic kidney disease (CKD). Thus, we conducted a network meta-analysis to evaluate the effects of statins in the prevention of CI-AKI. We systematically searched several databases (including, Embase, PubMed, the Cochrane Library, and ClinicalTrials.gov ) from inception to January 31, 2018. The primary outcome was occurrence of CI-AKI in patients with CKD undergoing cardiac catheterization. Both pairwise and network meta-analysis were performed. Finally, 21 randomized controlled trials with a total of 6385 patients were included. Results showed that statin loading before contrast administration was associated with a significantly reduced risk of CI-AKI in patients with CKD undergoing cardiac catheterization (odds ratio: 0.46; P < .05). Atorvastatin and rosuvastatin administered at high dose may be the most effective treatments to reduce incidence of CI-AKI, with no difference between these 2 agents.
Keyphrases
- acute kidney injury
- cardiac surgery
- chronic kidney disease
- end stage renal disease
- cardiovascular disease
- magnetic resonance
- high dose
- left ventricular
- systematic review
- contrast enhanced
- randomized controlled trial
- diabetic rats
- high glucose
- ultrasound guided
- coronary artery disease
- newly diagnosed
- low dose
- drug induced
- ejection fraction
- type diabetes
- prognostic factors
- computed tomography
- artificial intelligence
- patient reported outcomes
- atrial fibrillation
- replacement therapy
- combination therapy
- machine learning
- big data
- smoking cessation
- case control