A time-dependent predictive model for cardiocerebral vascular events in chronic hemodialysis patients: insights from a prospective study - Haowen Zhong; Haowen Zhong; Mengbi Zhang; Mengbi Zhang; Yingye Xie; Yingye Xie; Yuqin Qin; Na Xie; Na Xie; Yuqiu Ye; Yuqiu Ye; Heng Li; et al. (Frontiers in Medicine)
ContextThe conventional risk factors for cardiocerebral vascular events (CVCs) in non-Hemodialysis (HD) patients cannot be directly applied to HD patients due to the unique characteristics of this population. More accurate information on the risk of progression to CVCs is needed for clinical decisions.ObjectiveTo develop and validate time-dependent predictive models for the progression of CVCs in HD patients.Design, setting, and participantsDevelopment and validation of time-dependent predictive models using demographic, clinical, and laboratory data from 3 dialysis centers between 2017 and 2021. These models were developed using time-dependent Cox proportional hazards regression and assessed for discrimination using the concordance index, goodness of fit using the Akaike information criterion and net reclassification improvement.Main outcome measuresCVCs included acute heart failure, acute hematencephalon, cardiac or brain-derived death, acute myocardial infarction, acute cerebral infarction, ischemic cardiomyopathy, unstable angina pectoris, and stable angina pectoris.ResultsThe development and validation cohorts included 233 and 215 patients, respectively. The most accurate model included age, sex, hemoglobin, serum albumin, serum phosphate, white blood cell count, blood flow rate and ultrafiltration volume during HD (C index, 0.704; 95% CI, 0.639–0.768 in the development cohort and 0.775; 95% CI, 0.706–0.843 in the validation cohort). In the validation cohort, this model was more accurate than a model containing variables whose p value in the Cox proportional hazards regression was less than 0.05 (NRI: 0.351, 95% CI: −0.115–0.565).ConclusionA time-dependent model using routinely obtained laboratory tests can accurately predict progression to CVCs in HD patients.