Automatic control of arterial blood pressure: computer, benchtop, and animal models
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Updated: Oct 31, 2026

Long-term Blood Pressure Measurement in Freely Moving Mice Using Telemetry
Published on: May 17, 2016
Raghav Sriram1,2, Bijan Abar1, Siri Gullapalli3
1Department of Emergency Medicine, School of Medicine, Duke University, Durham, NC, United States.
Background:
Timely, accurate, and stable control of blood pressure (BP) in critical illness is essential for good clinical outcomes. The current clinical standard entails human operators intervening between the monitor and infusion pump, introducing delay, potential error, and risk of over- and undershoot of therapeutic goals. We developed and tested a closed-loop, automatic control system for BP using computer, benchtop, and animal models.
Methods:
All models employed a proportional-integral-derivative (PID) algorithm to control mean arterial pressure (MAP). The three components are summed to determine the final output, corresponding to the infusion rate of fluids or vasoactive drugs. The computer model simulated an automatically controlled drug infusion to regulate MAP. The benchtop model added physical components (clinical cardiac monitor, microcontroller, medication pump, and pressure transducer), adjusting the hydrostatic pressure in a closed system to meet a target. In the animal model, the MAP of anesthetized swine undergoing surgery was regulated using an automatically controlled norepinephrine or clevidipine infusion. For the animal model, statistical inference and comparison between trials could not be made given the sequential, non-independent design; quantities are descriptive only, not inferential. We applied common control system metrics including rise time, settling time, and on-target percentage (OTP).
Results:
The computer model rapidly reached and maintained target MAP ± 5 mmHg with OTP of 90% (95% CI 89.4%, 90.6%) at 240 simulated seconds (ss) and 95% (95% CI 94.6%, 95.4%) at 294 ss. The benchtop system reached and maintained target MAP with a mean rise time of 0.47 ± SD 0.15 min, mean settling time of 0.62 ± SD 0.18 min, and mean OTP (using a tolerance of MAP ± 5 mmHg) of 91.25 ± 9.68%. In a swine model, automatic norepinephrine augmented MAP by > 15 mmHg with mean rise time of 2.25 (SD 0.65) min and mean OTP of 56.9% (SD 22.7%). No trial maintained an equilibrium value of target MAP ± 5 mmHg; therefore, no settling time could be calculated. Clevidipine decreased MAP by >15 mmHg with mean rise time of 1.00 (SD 0.40) min, mean settling time of 2.19 (SD 1.45) min, and mean OTP of 93.8% (SD 7.18%). The results of the swine model were confounded by concurrent non-survival surgery. Within this limitation, the swine model demonstrated that the automatic control system was technically feasible.
Conclusions:
These proof-of-concept studies utilizing computer, benchtop, and animal models provide initial evidence that an automatic PID algorithm has potential to control BP across a range of starting BP. Future studies can optimize the system and compare its performance with human operators in advance of potential human clinical trials.
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