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A New Murine Model of Endovascular Aortic Aneurysm Repair
Published on: July 7, 2013
Validating the use of contrast-induced nephropathy prediction models in endovascular aneurysm repairs
Evelyn Lixuan Cheng1, Qiantai Hong1, Enming Yong1
1Vascular Surgery Service, Department of General Surgery, Tan Tock Seng Hospital, Singapore.
Insights
Existing contrast-induced nephropathy (CIN) models, developed for cardiac procedures, were validated for endovascular aneurysm repair (EVAR). Five models showed good predictive ability for CIN in EVAR patients, aiding risk assessment.
Area of Science:
- Nephrology
- Vascular Surgery
- Radiology
Background:
- Contrast-induced nephropathy (CIN) risk models are primarily based on percutaneous coronary interventions.
- No existing CIN models have been validated for vascular procedures like endovascular aneurysm repair (EVAR).
Purpose of the Study:
- To validate established contrast-induced nephropathy (CIN) prediction models in patients undergoing EVAR.
- To assess the utility of existing CIN models for identifying at-risk patients in a vascular surgery context.
Main Methods:
- Retrospective review of 216 patients who underwent EVAR between January 2008 and December 2015.
- Evaluation of acute kidney injury incidence at 24, 48, 72 hours, and follow-up.
- Validation of 8 selected CIN prediction models using C-statistics.
Main Results:
- Eight of 12 evaluated CIN prediction models were suitable for EVAR patients.
- Five models demonstrated good discriminative ability (C-statistics >0.70).
- The Mehran and Tziakas models achieved the highest C-statistics (0.75).
Conclusions:
- Five of the 12 evaluated CIN prediction models are useful for identifying patients at risk of CIN after EVAR.
- Validation of CIN models is crucial for accurate risk stratification in vascular procedures.
Background:
Existing risk prediction models for contrast-induced nephropathy (CIN) are based on studies for percutaneous coronary interventions, with none validated for use in vascular procedures. We aim to validate existing CIN prediction models in patients who underwent aortic endovascular aneurysm repair (EVAR).
Methods:
A retrospective review of 216 patients who underwent EVAR between January 2008 and December 2015 was undertaken. Incidence of acute kidney injuries at 24, 48, and 72 hours and at follow-up were evaluated. Of 12 CIN prediction models within the literature, 8 were suitable for validation in patients who underwent EVAR and validation was performed with C-statistics.
Results:
There were 216 EVARs performed within the study period. The mean patients age was 73 years and 162 (75%) were performed in an elective setting. Percentage of preoperative chronic kidney disease stages 1 to 5 were 16%, 42%, 31%, 6%, and 5%, respectively. The mean intraprocedure contrast volume used was 280 mL. Incidence of acute kidney injuries at 24, 48, and 72 hours and at follow-up were 8%, 12%, 11%, and 6%, respectively. Three percent of patients became dialysis dependent. Validation of the eight existing CIN predication models reveal area under curve C-statistics between 0.61 and 0.75 (P = .026 to P < .001). Five of the 8 had good discriminative ability (C-statistics of >0.70) and the CIN prediction models by Mehran and Tziakas had the highest C-statistics at 0.75 (P < .001).
Conclusions:
In our study population, 8 of 12 CIN prediction models within the literature were validated for use in patients undergoing EVAR and five are useful in identifying patients at risk for CIN.
