Serious renal dysfunction after percutaneous coronary interventions can be predicted

Background A prediction rule for determining the post-percutaneous coronary intervention (PCI) risk of developing contrast-induced nephropathy (>= 25% or >= 0.5 mg/dL increase in creatinine) has been reported. However, little work has been done on predicting pre-PCI patient-specific risk for developing more serious renal dysfunction (SRD; new dialysis, >= 2.0 mg/dL absolute increase in creatinine, or a >= 50% increase in creatinine). We hypothesized that preprocedural patient characteristics could be used to predict the risk of post-PCI SRD. Methods Data were prospectively collected on a consecutive series of 11 141 patients undergoing PC] without dialysis in northern New England from 2003 to 2005. Multivariate logistic regression model was used to identify the combination of patient characteristics most predictive of developing post-PCI SRD. The ability of the model to discriminate was quantified using a bootstrap validated C-Index (area under the receiver operating characteristic [ROC] curve). Its calibration was tested with a Hosmer-Lemeshow statistic. The model was validated on PCI procedures in 2006. Results Serious renal dysfunction occurred in 0.74% of patients (83/11141) with an associated inhospital mortality of 19.3% versus 0.9% in those without SRD. The model discriminated well between patients who did and did not develop SRD after PCI (ROC 0.87, 95% Cl 0.82-0.91). Preprocedural creatinine (37%), congestive heart failure (24%), and diabetes (15%) accounted for 76% of the predictive ability of the model. The other factors contributed 24%: urgent and emergent priority (10%), preprocedural intra-aortic balloon pump use (8%), age >= 80 years (5%), and female sex (1%). Validation of the model was successful with ROC: 0.84 (95% Cl 0.80-0.89). Conclusions Although infrequent, the occurrence of SRD after PCI is associated with a very high inhospital mortality. We developed and validated a robust clinical prediction rule to determine which patients are at high risk for SRD. Use of this model may help physicians perform targeted interventions to reduce this risk.