A novel risk score model for prediction of contrast-induced nephropathy after emergent percutaneous coronary intervention

Abstract

Background

A few studies developed simple risk model for predicting CIN with poor prognosis after emergent PCI. The study aimed to develop and validate a novel tool for predicting the risk of contrast-induced nephropathy (CIN) in patients undergoing emergent percutaneous coronary intervention (PCI).

Methods

692 consecutive patients undergoing emergent PCI between January 2010 and December 2013 were randomly (2:1) assigned to a development dataset (n = 461) and a validation dataset (n = 231). Multivariate logistic regression was applied to identify independent predictors of CIN, and established CIN predicting model, whose prognostic accuracy was assessed using the c-statistic for discrimination and the Hosmere Lemeshow test for calibration.

Results

The overall incidence of CIN was 55(7.9%). A total of 11 variables were analyzed, including age > 75 years old, baseline serum creatinine (SCr) > 1.5 mg/dl, hypotension and the use of intra-aortic balloon pump(IABP), which were identified to enter risk score model (Chen). The incidence of CIN was 32(6.9%) in the development dataset (in low risk (score = 0), 1.0%, moderate risk (score:1–2), 13.4%, high risk (score ≥ 3), 90.0%). Compared to the classical Mehran's and ACEF CIN risk score models, the risk score (Chen) across the subgroup of the study population exhibited similar discrimination and predictive ability on CIN (c-statistic:0.828, 0.776, 0.853, respectively), in-hospital mortality, 2, 3-years mortality (c-statistic:0.738.0.750, 0.845, respectively) in the validation population.

Conclusions

Our data showed that this simple risk model exhibited good discrimination and predictive ability on CIN, similar to Mehran's and ACEF score, and even on long-term mortality after emergent PCI.

Introduction

Contrast-induced nephropathy (CIN) develops as a complication after coronary diagnostic and interventional procedures, and is associated with increased mortality, longer hospitalization stays, increased health care costs, and higher risk of adverse clinical outcomes, especially among patients undergoing emergent or primary percutaneous coronary intervention (PCI) [1], [2], [3], [4], [5]. The risk of CIN after emergent PCI is significantly higher than after elective PCI [6], [7], [8].

Current clinical guidelines suggest that patients should be assessed for the risk of CIN after PCI [9], [10] with CIN predictive model included patient-related risk factors (advanced age, renal insufficiency, hypotension, intra-aortic balloon pump, diabetes mellitus) or procedure-related risk factors (large contrast volume, insufficient hydration and high osmolality agents) [11], [12], [13]. The hemodynamic unstable markers, including reduced ejection fraction, cardiogenic shock, hypotension were reported as the most import risk factors of CIN after emergent PCI [14], [15], [16], [17].

Although the combination of above risk factors is common, a cumulative risk score model for prediction for CIN and prognosis among patients undergoing emergent PCI is limited in clinical practice. The most commonly used Mehran's risk score model comprised 8 variables, which was established and validated in patients without acute myocardial infarction (AMI) [11]. And majority of other models use variables that are not known at the time of an emergent PCI [12], [13]. There is a need to develop a simple and rapid risk prediction tool for patient with ST-elevation-myocardial infarction (STEMI) or non-ST-elevation acute coronary syndromes (NSTE-ACS), who are at higher risk of undergoing emergent PCI. Therefore, in this prospective study, the purpose was to develop a simple risk score model that could be rapidly applied to evaluate the risk of CIN following emergent PCI.