Validation of pre-operative risk scores of contrast-induced acute kidney injury in a Chinese cohort

Abstract

Background: Pre-operative risk scores are more valuable than post-procedure risk scores because of lacking effective treatment for contrast-induced acute kidney injury (CI-AKI). A number of pre-operative risk scores have been developed, but due to lack of effective external validation, most of them are also difficult to apply accurately in clinical practice. It is necessary to review and validate the published pre-operative risk scores for CI-AKI. Materials and methods: We systematically searched PubMed and EMBASE databases for studies of CI-AKI preoperative risk scores and assessed their calibration and discriminatory in a cohort of 2669 patients undergoing coronary angiography or percutaneous coronary intervention (PCI) from September 2007 to July 2017. The definitions of CI-AKI may affect the validation results, so three definition were included in this study, CI-AKI broad1 was defined as an increase in serum creatinine (Scr) of 44.2 μmol/L or 25%; CI-AKI broad2, an increase in Scr of 44.2 μmol/L or 50%; and CI-AKI-narrow, an increase in Scr of 44.2 μmol/L. The calibration of the model was assessed with the Hosmer-Lemeshow test and the discriminatory capacity was identified by C-statistic. Results: Of the 8 pre-operative risk scores for CI-AKI identified, 7 were single-center study and only 1 was based on multi-center study. In addition, 7 of the scores were just validated internally and only Chen score was externally validated. In the validation cohort of 2669 patients, the incidence of CI-AKI ranged from 3.0%(Liu) to 16.4%(Chen) for these scores. Furthermore, the incidence of CI-AKI was 6.59% (178) for CI-AKI broad1, 1.44% (39) for CI-AKI broad2, and 0.67% (18) for CI-AKI-narrow. For CI-AKI broads, C-statistics varied from 0.44 to 0.57. For CI-AKI-narrow, the Maioli score had the best discrimination and calibration, what's more, the C-statistics of Maioli, Chen, Liu and Ghani was ≥0.7. Conclusion: Most pre-operative risk scores were established based on single-center studies and most of them lacked external validation. For CI-AKI broads, the prediction accuracy of all risk scores was low. The Maioli score had the best discrimination and calibration, when using the CI-AKI-narrow definition.

Contrast-induced acute kidney injury and its contemporary prevention

“…None of the models passed past C-statistic ≥0.8. Yin et al ( 54 ) later assessed the calibration and discriminatory value of risk scores on patients undergoing percutaneous coronary intervention or coronary angiography. The best predictive value according to Yin et al has the risk model by Chen et al ( 55 ).…”

Just give the contrast? Appraisal of guidelines on intravenous iodinated contrast media use in patients with kidney disease

“…On the other hand, many risk models and tools have been developed for PC-AKI prediction [ 33 , 36 , 39 – 41 ]. The validation studies were still lacking for the selection of these risk models to guide renal function testing [ 47 – 49 ]. Second, it is still dependent on clinical judgment whether repeated renal function is necessary or emergency contrast‐enhanced CT without renal function results are appropriate [ 34 , 50 , 51 ].…”

Age-Related Effect of Uric Acid on Contrast-Induced Acute Kidney Injury of Patients Undergoing Coronary Angiography

“…Multinomial multiple logistic regression analyses were performed to examine between the UA and CI-AKI adjusted for age, gender,intra-aortic balloon pump (IABP), congestive heart failure (CHF), anemia, DM, pre-SCr, contrast medium volume, AMI and PCI, which were based on the classic Mehran CIN risk score 4 and relevant literature. 11,12 Furthermore, restricted cubic spline (RCS) was used to investigate the linear tendency of the association between UA value and the risk of CI-AKI. In this method, we selected four SUA values as knots based on UA percentiles, tested the linear and non-linear associations between knots using a cubic function, and presented the smoothly integrated graph.…”