Abdominal aortic calcification is an independent predictor of cardiovascular events in peritoneal dialysis patients

Francesca Martino1, Pierluigi Di Loreto, Dario Giacomini

  • 1Nephrology, Dialysis and Transplantation, San Bortolo Hospital, Vicenza, Italy. francesca.martino.k@gmail.com

Insights

Abdominal aortic calcification (AAC) predicts cardiovascular events in peritoneal dialysis patients. Assessing AAC via abdominal X-rays offers a cost-effective risk stratification tool for managing cardiovascular disease in this population.

Area of Science:

  • Nephrology
  • Cardiology
  • Radiology

Background:

  • Abdominal aortic calcification (AAC) is a known cardiovascular event predictor in the general population and hemodialysis patients.
  • Data on AAC's prognostic role in peritoneal dialysis patients is currently lacking.

Purpose of the Study:

  • To investigate the predictive value of abdominal aortic calcification (AAC) score for cardiovascular events in patients undergoing peritoneal dialysis.
  • To establish AAC as a prognostic marker in peritoneal dialysis.

Main Methods:

  • Seventy-four peritoneal dialysis patients were included in this prospective study.
  • Abdominal aortic calcification (AAC) was quantified using a semi-quantitative method on baseline radiographs.
  • Cardiovascular events were tracked over a median follow-up of 30.5 months, analyzed with Kaplan-Meier and Cox regression models.

Main Results:

  • A total of 29 cardiovascular events (39.2%) occurred during follow-up.
  • Univariable analysis identified age, urine output, and AAC tertiles as significant predictors.
  • Multivariable analysis confirmed AAC score, stratified by tertiles, as the sole independent predictor of cardiovascular events (P<0.001).

Conclusions:

  • Abdominal aortic calcification (AAC) score is demonstrated as an independent predictor of cardiovascular events in peritoneal dialysis patients.
  • AAC assessment offers a valuable, cost-effective method for cardiovascular risk stratification in this population, utilizing existing radiographic data.

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