Work

Predicting hemodialysis mortality utilizing blood pressure trends.

Year: 2008

Type: conference-paper

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

Source: PubMed

Author: Ronilda Lacson

Institution: Brigham and Women's Hospital

Language: English

Topic: Dialysis and Renal Disease Management

Subfield: Nephrology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, U.S. National Library of Medicine

Awards: Assessing quality of individual predictions in medical decision support systems, Biomedical Informatics and Data Science Research Training Program (BIRT)