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Kidney Week

Abstract: FR-PO1131

Development and Validation of a Risk Score for the Prediction of Cardiovascular Disease in Kidney Transplant Recipients

Session Information

Category: Transplantation

  • 1902 Transplantation: Clinical

Authors

  • Ueki, Kenji, Kyushu University, Fukuoka, Japan
  • Tsuchimoto, Akihiro, Kyushu University, Fukuoka, Japan
  • Matsukuma, Yuta, Kyushu University, Fukuoka, Japan
  • Nakagawa, Kaneyasu, Kyushu University, Fukuoka, Japan
  • Tsujikawa, Hiroaki, Kyushu University, Fukuoka, Japan
  • Masutani, Kosuke, Fukuoka University, Fukuoka, Japan
  • Tanaka, Shigeru, Kyushu University, Fukuoka, Japan
  • Okabe, Yasuhiro, Kyushu University, Fukuoka, Japan
  • Unagami, Kohei, Tokyo Women's Medical University, Shinjuku, ToKyo, Japan
  • Kakuta, Yoichi, Tokyo Women's Medical University, Shinjuku, ToKyo, Japan
  • Okumi, Masayoshi, Tokyo Women's Medical University, Shinjuku, ToKyo, Japan
  • Nakano, Toshiaki, Kyushu University, Fukuoka, Japan
  • Tanabe, Kazunari, Tokyo Women's Medical University, Shinjuku, ToKyo, Japan
  • Kitazono, Takanari, Kyushu University, Fukuoka, Japan

Group or Team Name

  • Japan Academic Consortium of Kidney Transplantation investigators
Background

Cardiovascular disease (CVD) is a major cause of death in kidney transplant (KT) recipients. It is clinically important to estimate the risk of CVD after a KT.

Methods

A derivation cohort contained 387 KT recipients underwent KT at Kyushu University Hospital from January 2006 to December 2012. A prediction model was retrospectively developed and risk scores for CVD were investigated via multivariable logistic regression. The internal validation of the prediction model was estimated via the c-static and the external validation was calibrated via the Hosmer-Lemeshow goodness of fit test using a validation cohort containing 332 KT recipients underwent KT at Tokyo Women’s Medical University Hospital.

Results

In derivation cohort 34 patients (8.8%) had CVD events during the observation period. Age, CVD history, diabetic nephropathy, dialysis vintage, and serum albumin at 12 months after KT were significant predictors of CVD. A prediction model consisting of integer risk scores demonstrated good discrimination (c-statistic 0.80) and goodness of fit (Hosmer-Lemeshow test P = 0.78). In a validation cohort containing 332 KT recipients the model demonstrated moderate discrimination (c-statistic 0.70) and goodness of fit (Hosmer-Lemeshow test P = 0.94), suggesting external validity.

Conclusion

This simple model for predicting CVD after kidney transplantation was moderately accurate and useful in clinical situations. In also suggested that nutritional status was an influential risk factor for CVD in KT recipients.

The receiver operating characteristic curves plotted by the prediction model

The observed and expected incidence rates by the simple prediction risk score