Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes

被引:9
|
作者
Ozaslan, Basak [1 ]
Deshpande, Sunil [1 ]
Doyle III, Francis J. [1 ]
Dassau, Eyal [1 ]
机构
[1] Harvard Univ, Harvard John A Paulson Sch Engn & Appl Sci, Boston, MA USA
来源
基金
美国国家卫生研究院;
关键词
type; 1; diabetes; model predictive control; automated insulin delivery; pregnancy; in-silico verification; MODEL-PREDICTIVE CONTROL; CLOSED-LOOP CONTROL; ARTIFICIAL PANCREAS; WOMEN; SAFETY; SETTINGS;
D O I
10.3389/fendo.2021.768639
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Type 1 diabetes (T1D) increases the risk for pregnancy complications. Increased time in the pregnancy glucose target range (63-140 mg/dL as suggested by clinical guidelines) is associated with improved pregnancy outcomes that underscores the need for tight glycemic control. While closed-loop control is highly effective in regulating blood glucose levels in individuals with T1D, its use during pregnancy requires adjustments to meet the tight glycemic control and changing insulin requirements with advancing gestation. In this paper, we tailor a zone model predictive controller (zone-MPC), an optimization-based control strategy that uses model predictions, for use during pregnancy and verify its robustness in-silico through a broad range of scenarios. We customize the existing zone-MPC to satisfy pregnancy-specific glucose control objectives by having (i) lower target glycemic zones (i.e., 80-110 mg/dL daytime and 80-100 mg/dL overnight), (ii) more assertive correction bolus for hyperglycemia, and (iii) a control strategy that results in more aggressive postprandial insulin delivery to keep glucose within the target zone. The emphasis is on leveraging the flexible design of zone-MPC to obtain a controller that satisfies glycemic outcomes recommended for pregnancy based on clinical insight. To verify this pregnancy-specific zone-MPC design, we use the UVA/Padova simulator and conduct in-silico experiments on 10 subjects over 13 scenarios ranging from scenarios with ideal metabolic and treatment parameters for pregnancy to extreme scenarios with such parameters that are highly deviant from the ideal. All scenarios had three meals per day and each meal had 40 grams of carbohydrates. Across 13 scenarios, pregnancy-specific zone-MPC led to a 10.3 +/- 5.3% increase in the time in pregnancy target range (baseline zone-MPC: 70.6 +/- 15.0%, pregnancy-specific zone-MPC: 80.8 +/- 11.3%, p < 0.001) and a 10.7 +/- 4.8% reduction in the time above the target range (baseline zone-MPC: 29.0 +/- 15.4%, pregnancy-specific zone-MPC: 18.3 +/- 12.0, p < 0.001). There was no significant difference in the time below range between the controllers (baseline zone-MPC: 0.5 +/- 1.2%, pregnancy-specific zone-MPC: 3.5 +/- 1.9%, p = 0.1). The extensive simulation results show improved performance in the pregnancy target range with pregnancy-specific zone MPC, suggest robustness of the zone-MPC in tight glucose control scenarios, and emphasize the need for customized glucose control systems for pregnancy.
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页数:18
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