Predicting hemodialysis mortality utilizing blood pressure trends

Mean Systolic Blood Pressure (SBP) is a predictor of mortality in hemodialysis (HD) patients. The hypothesis is that transforming SBP measurements to reflect trends would improve the quality of predictions.

Data consisted of 4,500 patients from a dialysis provider in the US with at least six months follow-up. Relative Difference in Percentage yielded six transformed variables, representing SBP trends. Models were constructed using Support Vector Machine (SVM). RESULTS were compared to a baseline model utilizing six-month mean SBP. All models included age, gender, race, diabetes, vintage, and BMI. Pooling of repeated observations incorporated all repeated observations in a generalized person-month approach.

The AUC for the model using transformed variables on unseen data was 0.70, compared to 0.63 for the baseline model (p<0.00001). The AUC was 0.69 when modeling a pooled data set.

The use of SBP trends significantly improved mortality prediction in HD patients.