Prevention of dialysis hypotension episodes using fuzzy logic control system

Elena Mancini, Emanuele Mambelli, Mina Irpinia, Danila Gabrielli, Carmelo Cascone, Ferruccio Conte, Gina Meneghel, Fosco Cavatorta, Alessandro Antonelli, Giuseppe Villa, Antonio Dal Canton, Leonardo Cagnoli, Filippo Aucella, Fulvio Fiorini, Enzo Gaggiotti, Giorgio Triolo, Vitale Nuzzo, Antonio Santoro

Research output: Contribution to journalArticlepeer-review


Background. Automatic systems for stabilizing blood pressure (BP) during dialysis are few and only control those variables indirectly related to BP. Due to complex BP regulation under dynamic dialysis conditions, BP itself appears to be the most consistent input parameter for a device addressed to preventing dialysis hypotension (DH). Methods. An automatic system (ABPS, automatic blood pressure stabilization) for BP control by fluid removal feedback regulation is implemented on a dialysis machine (Dialog Advanced, Braun). A fuzzy logic (FL) control runs in the system, using instantaneous BP as the input variable governing the ultrafiltration rate (UFR) according to the BP trend. The system is user-friendly and just requires the input of two data: critical BP (individually defined as the possible level of DH risk) and the highest UFR applicable (percentage of the mean UFR). We evaluated this system's capacity to prevent DH in 55 RDT hypotension-prone patients. Sessions with (treatment A) and without (treatment B) ABPS were alternated one-by-one for 30 dialysis sessions per patient (674 with ABPS vs 698 without). Results. Despite comparable treatment times and UF volumes, severe DH appeared in 8.3% of sessions in treatment A vs 13.8% in treatment B (-39%, P = 0.01). Mild DH fell non-significantly (-12.3%). There was a similar percentage of sessions in which the planned body weight loss was not achieved and dialysis time was prolonged. Conclusions. In conclusion, FL may be suited to interpreting and controlling the trend of a determined multi-variable parameter like BP. The medical knowledge of the patient and the consequent updating of input parameters depending on the patient's clinical conditions seem to be the main factors for obtaining optimal results.

Original languageEnglish
Pages (from-to)1420-1427
Number of pages8
JournalNephrology Dialysis Transplantation
Issue number5
Publication statusPublished - May 2007


  • Biofeedback control
  • Blood pressure
  • Dialysis hypotension
  • Fuzzy control
  • Haemodynamics

ASJC Scopus subject areas

  • Nephrology
  • Transplantation


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