Volume 22 Issue 5
May  2024
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ZHAO Xin, LIU Qianqian, LI Hangyu. Study on cardiovascular disease risk prediction model for hemodialysis patients[J]. Chinese Journal of General Practice, 2024, 22(5): 742-745. doi: 10.16766/j.cnki.issn.1674-4152.003493
Citation: ZHAO Xin, LIU Qianqian, LI Hangyu. Study on cardiovascular disease risk prediction model for hemodialysis patients[J]. Chinese Journal of General Practice, 2024, 22(5): 742-745. doi: 10.16766/j.cnki.issn.1674-4152.003493
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Study on cardiovascular disease risk prediction model for hemodialysis patients

doi: 10.16766/j.cnki.issn.1674-4152.003493

  • Department of Nephrology, Tongren Hospital, Affiliated to Southeast University School of Medicine, Nanjing, Jiangsu 211102, China

Funds:

 81503381

  • Received Date: 2023-07-16
    Available Online: 2024-07-20
    Abstract
  •   Objective  To evaluate the derivation and validation of cardiovascular disease risk prediction scores in hemodialysis patients and to provide a reference for clinical diagnosis and treatment.  Methods  A total of 388 patients who underwent routine hemodialysis for more than 3 months from January to December 2020 were selected and followed up until May 2023.All participants were used as the training dataset to obtain the predicted scores, and the guided validation dataset was used for validation.Evaluate the discriminative ability of predicted scores using the area under the subject's work characteristic curve (AUC).  Results  Among 388 patients without cardiovascular disease at baseline, 132 had a first cardiovascular event during a mean follow-up of (3.27±1.01) years.Among the 26 clinical parameters, age, hypertension, diabetes and abnormal WBC count were identified as significant predictors and included in the prediction model.Compared with those without these risk factors, those with scores of 1, 2, and 3-4 had an increased risk of cardiovascular disease.The adjusted hazard ratio and 95%CI were 3.293(1.174-9.260), 7.429(2.681-20.516) and 15.434(5.441-43.759), respectively.The model shows good discriminative ability on both training and validation data sets[AUC values of 0.703(95%CI: 0.652-0.753) and 0.688(95%CI: 0.655-0.720), respectively].  Conclusion  We have derived and validated a predictive model for cardiovascular risk in hemodialysis patients.With the rapidly increasing number of hemodialysis patients, this simple model can be used to identify high-risk individuals in clinical practice for more accurate and effective personalized treatment.

     

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