title: Computational analysis of blood volume curves and risk of intradialytic morbid events in hemodialysis.
abstract: BACKGROUND
  Blood volume (BV) curves have been used to prevent intradialytic morbid events (IMEs) caused by hypotensive episodes in hemodialysis treatment. However, no standardized parameter is available to describe BV dynamics and to enable online interference with ultrafiltration rates in unselected patients. Moreover, only time-dependent BV reduction and absolute hematocrit threshold, but not BV variability, have been suggested as markers of pending hypotension. The present study therefore deals with a computer-aided analysis of indices characterizing both BV reduction per time and BV variability in treatments of nonselected maintenance hemodialysis patients.
  
  
  METHODS
  The methodology uses indices obtained by mathematical analysis of BV curves and was designed to potentially enable automatic interference with ultrafiltration.
  
  
  RESULTS
  In 46 out of 380 treatments (12.1%), IMEs occurred. In these treatments, the indices for long- and short-term variability and slope of the curves were significantly lower than in treatments without IMEs. Moreover, the last 10 minutes before an IME were characterized by additionally decreased variability and slope. In a risk analysis of long-term variability and IMEs, we established an index below 16 to be associated with the highest risk of IMEs.
  
  
  CONCLUSIONS
  Using these kind of index thresholds and online analysis of BV curves, automatic management of ultrafiltration by BV dynamics could be a promising concept to avoid intradialytic morbidity.
authors.affiliation: Department of Internal Medicine, Division of Endocrinology and Nephrology, Universitätsklinikum Benjamin Franklin, Freie Universität Berlin, Berlin, Germany. jobeige@zedat.fu-berlin.de
authors.forename: J
authors.initials: J
authors.lastname: Beige
authors.forename: J
authors.initials: J
authors.lastname: Sone
authors.forename: A M
authors.initials: AM
authors.lastname: Sharma
authors.forename: M
authors.initials: M
authors.lastname: Rudwaleit
authors.forename: G
authors.initials: G
authors.lastname: Offermann
authors.forename: A
authors.initials: A
authors.lastname: Distler
authors.forename: L
authors.initials: L
authors.lastname: Preuschof
mesh_terms: D000328:Adult; D000368:Aged; D001794:Blood Pressure / Q000502:physiology; D001810:Blood Volume / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D006801:Humans; D007022:Hypotension / Q000209:etiology / Q000517:prevention & control*; D007676:Kidney Failure, Chronic / Q000503:physiopathology / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D006435:Renal Dialysis / Q000009:adverse effects*; D018570:Risk Assessment; D012984:Software