Background. Cardiovascular disease (CVD) is a major cause of death in kidney transplant (KT) recipients. To improve their longterm survival, it is clinically important to estimate the risk of CVD after living donor KT via adequate pre-transplant CVD screening. Methods. A derivation cohort containing 331 KT recipients underwent living donor KT at Kyushu University Hospital from January 2006 to December 2012. A prediction model was retrospectively developed and risk scores were investigated via a Cox proportional hazards regression model. The discrimination and calibration capacities of the prediction model were estimated via the c-statistic and the Hosmer-Lemeshow goodness of fit test. External validation was estimated via the same statistical methods by applying the model to a validation cohort of 300 KT recipients who underwent living donor KT at Tokyo Women'sMedical University Hospital. Results. In the derivation cohort, 28 patients (8.5%) had CVD events during the observation period. Recipient age, CVD history, diabetic nephropathy, dialysis vintage, serum albumin and proteinuria at 12months after KT were significant predictors of CVD. A prediction model consisting of integer risk scores demonstrated good discrimination (c-statistic 0.88) and goodness of fit (Hosmer-Lemeshow test P¼0.18). In a validation cohort, the model demonstrated moderate discrimination (cstatistic 0.77) and goodness of fit (Hosmer-Lemeshow test P¼0.15), suggesting external validity. Conclusions. The above-described simple model for predicting CVD after living donor KT was accurate and useful in clinical situations. VC The Author(s) 2020. Published byOxford University Press on behalf of ERA-EDTA. All rights reserved.
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