Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas.
To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions.



After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70-180 mg/dL.



Mean time in range 70-180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period.



This first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.
William Sansum Diabetes Center, Santa Barbara, CA.
Jordan E
JE
Pinsker
William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA.
Joon Bok
JB
Lee
William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.
Eyal
E
Dassau
William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA.
Dale E
DE
Seborg
William Sansum Diabetes Center, Santa Barbara, CA.
Paige K
PK
Bradley
William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA.
Ravi
R
Gondhalekar
William Sansum Diabetes Center, Santa Barbara, CA.
Wendy C
WC
Bevier
William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA.
Lauren
L
Huyett
William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA.
Howard C
HC
Zisser
William Sansum Diabetes Center, Santa Barbara, CA Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA frank_doyle@seas.harvard.edu.
Francis J
FJ
Doyle
D000328:Adult; D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000187:drug effects / Q000378:metabolism; D018592:Cross-Over Studies; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000628:therapy*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007003:Hypoglycemia / Q000517:prevention & control; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000009:adverse effects; D007328:Insulin / Q000008:administration & dosage / Q000009:adverse effects; D007332:Insulin Infusion Systems; D008297:Male; D062407:Meals; D008875:Middle Aged; D019397:Pancreas, Artificial / Q000009:adverse effects; D010865:Pilot Projects; D057285:Precision Medicine / Q000379:methods*; D055815:Young Adult
D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin