title: Predicting hemodialysis mortality utilizing blood pressure trends.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  The use of SBP trends significantly improved mortality prediction in HD patients.
authors.affiliation: Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Ronilda
authors.initials: R
authors.lastname: Lacson
mesh_terms: D001795:Blood Pressure Determination / Q000379:methods* / Q000706:statistics & numerical data; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006801:Humans; D015994:Incidence; D007676:Kidney Failure, Chronic / Q000175:diagnosis* / Q000401:mortality / Q000517:prevention & control; D016016:Proportional Hazards Models; D006435:Renal Dialysis / Q000401:mortality*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D015996:Survival Rate; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology