Calculation of threshold and saturation points of sigmoidal baroreflex function curves

Lachlan M. McDowall, Roger A L Dampney*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

65 Citations (Scopus)


The logistic sigmoid function curve provides an accurate description of the baroreflex input-output relationship and is the most commonly used equation for this purpose. The threshold (Thr) and saturation (Sat) values for the baroreflex are commonly defined as the values of mean arterial pressure (MAP) at which the reflexly controlled variable (e.g., heart rate or sympathetic nerve activity) is within 5% of the upper or lower plateau, respectively, of the sigmoid function. These values are referred to here as Thr5% and Sat5%. In many studies, Thr and Sat are calculated with the equations Thr = A3 - 2.0/A2 and Sat = A3 + 2.0/A 2, where A3 is the value of MAP at the point where the reflexly controlled variable is at the midpoint of its range and A2 is the gain coefficient. Although it is commonly stated that the values of Thr and Sat calculated with these equations represent Thr5% and Sat5%, we show here that instead they are significantly greater and less than Thr 5% and Sat5%, respectively. Furthermore, the operating range (difference between Thr and Sat) calculated with these equations is 32% less than the difference between Thr5% and Sat5%. We further show that the equations that provide correct values of Thr5% and Sat5% are Thr5% = A3 - 2.944/A2 and Sat5% = A3 + 2.944/A2. We propose that these be used as the standard equations for calculating threshold and saturation values when a logistic sigmoid function is used to model the open-loop baroreflex function curve.

Original languageEnglish
Pages (from-to)H2003–H2007
Number of pages5
JournalAmerican Journal of Physiology - Heart and Circulatory Physiology
Issue number4
Publication statusPublished - 2006
Externally publishedYes


  • Baroreflex operating range
  • Input-output relationship
  • Mathematical modeling
  • Regression analysis


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