Howard Zisser

University of California, Santa Barbara, Santa Barbara, California, United States

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Publications (100)209.06 Total impact

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    ABSTRACT: The paramount goal in the treatment of type 1 diabetes is the maintenance of normoglycemia. Continuous glucose monitoring (CGM) technologies enable frequent sensing of glucose to inform exogenous insulin delivery timing and dosages. The most commonly available CGMs are limited by the physiology of the subcutaneous (SQ) space in which they reside. The very same advantages of this minimally invasive approach are disadvantages with respect to speed. Because SQ blood flow is sensitive to local fluctuations (e.g., temperature, mechanical pressure), SQ sensing can be slow and variable. We propose the use of a more central, physiologically stable body space for CGM: the intraperitoneal space (IP). We compared the temporal response characteristics of simultaneously placed SQ and IP sensors during intravenous (IV) glucose tolerance tests in eight swine. Using compartmental modeling based on simultaneous IV sensing, blood draws, and intra-arterial sensing, we found that IP kinetics were more than twice as fast as SQ kinetics (mean time constant of 5.6 min IP vs. 12.4 min for SQ). Combined with the known faster kinetics of IP insulin delivery over SQ delivery, our findings suggest that artificial pancreas technologies may be optimized by both sensing glucose and delivering insulin in the IP space.
    Diabetes 03/2014; · 7.90 Impact Factor
  • Diabetes Technology &amp Therapeutics 02/2014; 16(S1):S92-S99. · 2.21 Impact Factor
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    ABSTRACT: Abstract Background: This study was performed to evaluate the safety and efficacy of a fully automated artificial pancreas using zone-model predictive control (zone-MPC) with the health monitoring system (HMS) during unannounced meals and overnight and exercise periods. Subjects and Methods: A fully automated closed-loop artificial pancreas was evaluated in 12 subjects (eight women, four men) with type 1 diabetes (mean±SD age, 49.4±10.4 years; diabetes duration, 32.7±16.0 years; glycosylated hemoglobin, 7.3±1.2%). The zone-MPC controller used an a priori model that was initialized using the subject's total daily insulin. The controller was designed to keep glucose levels between 80 and 140 mg/dL. A hypoglycemia prediction algorithm, a module of the HMS, was used in conjunction with the zone controller to alert the user to consume carbohydrates if the glucose level was predicted to fall below 70 mg/dL in the next 15 min. Results: The average time spent in the 70-180 mg/dL range, measured by the YSI glucose and lactate analyzer (Yellow Springs Instruments, Yellow Springs, OH), was 80% for the entire session, 92% overnight from 12 a.m. to 7 a.m., and 69% and 61% for the 5-h period after dinner and breakfast, respectively. The time spent <60 mg/dL for the entire session by YSI was 0%, with no safety events. The HMS sent appropriate warnings to prevent hypoglycemia via short and multimedia message services, at an average of 3.8 treatments per subject. Conclusions: The combination of the zone-MPC controller and the HMS hypoglycemia prevention algorithm was able to safely regulate glucose in a tight range with no adverse events despite the challenges of unannounced meals and moderate exercise.
    Diabetes Technology &amp Therapeutics 01/2014; · 2.21 Impact Factor
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    ABSTRACT: Prandial glucose regulation is a major challenge for the artificial pancreas using subcutaneous insulin (without a feedforward bolus) due to insulin's slow absorption-peak (50-60 [min]). Intraperitoneal insulin, with a fast absorption peak (20-25 [min]), has been suggested as an alternative to address these limitations. An artificial pancreas using intraperitoneal insulin was designed and evaluated on 100 in silico subjects and compared with two designs using subcutaneous insulin with and without a feedforward bolus, following the three meal (40-70 g-carbohydrates) evaluation protocol. The design using intraperitoneal insulin resulted in a significantly lower postprandial blood glucose peak (38 [mg/dL]) and longer time in the clinically accepted region (13%) compared to the design using subcutaneous insulin without a feedforward bolus and comparable results to a subcutaneous feedforward bolus design. This superior regulation with minimal user interaction may improve the quality of life for people with type 1 diabetes mellitus.
    Computers & Chemical Engineering 01/2014; · 2.09 Impact Factor
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    ABSTRACT: OBJECTIVE To evaluate the feasibility of a wearable artificial pancreas system, the Diabetes Assistant (DiAs), which uses a smart phone as a closed-loop control platform. RESEARCH DESIGN AND METHODS Twenty patients with type 1 diabetes were enrolled at the Universities of Padova, Montpellier, and Virginia and at Sansum Diabetes Research Institute. Each trial continued for 42 h. The United States studies were conducted entirely in outpatient setting (e.g., hotel or guest house); studies in Italy and France were hybrid hospital-hotel admissions. A continuous glucose monitoring/pump system (Dexcom Seven Plus/Omnipod) was placed on the subject and was connected to DiAs. The patient operated the system via the DiAs user interface in open-loop mode (first 14 h of study), switching to closed-loop for the remaining 28 h. Study personnel monitored remotely via 3G or WiFi connection to DiAs and were available on site for assistance. RESULTS The total duration of proper system communication functioning was 807.5 h (274 h in open-loop and 533.5 h in closed-loop), which represented 97.7% of the total possible time from admission to discharge. This exceeded the predetermined primary end point of 80% system functionality. CONCLUSIONS This study demonstrated that a contemporary smart phone is capable of running outpatient closed-loop control and introduced a prototype system (DiAs) for further investigation. Following this proof of concept, future steps should include equipping insulin pumps and sensors with wireless capabilities, as well as studies focusing on control efficacy and patient-oriented clinical outcomes.
    Diabetes care 07/2013; 36(7):1851-1858. · 7.74 Impact Factor
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    ABSTRACT: Advances in diabetes technologies allow patients to manage their diabetes with greater precision and flexibility. Many recent studies show that continuous glucose monitors (CGMs) can be used to tighten glycemic control safely and to ease certain burdens of diabetes self-management. The following summary reflects the most recent findings in CGM and provides an overall review of who would most benefit from CGM use. Benefits of CGM may vary based on age, type of diabetes, pregnancy, health, sleep, or heart rate. Accuracy and reliability are critical in current uses of CGM and especially for new and future systems that automate insulin partially (e.g., low glucose suspend) or entirely (e.g., 'fully closed-loop' artificial pancreas). Clinicians are simultaneously testing available products in new patient groups such as the critically ill and type 2 diabetes patients not using mealtime insulin. In a widening set of circumstances, use of CGM has been shown to promote safer and more effective glycemic control than self-monitoring of blood glucose. Imperfections remain in certain scenarios such as hypoglycemia and in certain populations such as young children. Ongoing research on sensors and calibration software should translate to better systems.
    Current opinion in endocrinology, diabetes, and obesity 04/2013; 20(2):106-11.
  • Diabetes Technology &amp Therapeutics 02/2013; 15 Suppl 1:S96-S106. · 2.21 Impact Factor
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    ABSTRACT: Insulin-on-board (IOB) estimation is used in modern insulin therapy with continuous subcutaneous insulin infusion (CSII) as well as different automatic glucose-regulating strategies (i.e., artificial pancreas products) to prevent insulin stacking that may lead to hypoglycemia. However, most of the IOB calculations are static IOB (sIOB): they are based only on approximated insulin decay and do not take into account diurnal changes in insulin sensitivity. A dynamic IOB (dIOB) that takes into account diurnal insulin sensitivity variation is suggested in this work and used to adjust the sIOB estimations. The dIOB function is used to correct the dosage of insulin boluses in light of this circadian variation. Basal-bolus as applied by pump users and model predictive control therapy with and without dIOB were evaluated using the University of Virginia/Padova metabolic simulator. Three protocols with four meals of 1 g carbohydrate/kg body weight were evaluated: a nominal scenario and two robustness scenarios, one in which insulin sensitivity was 15% greater than estimated and the other where the lunch is 30% less than announced. In the nominal and robustness scenarios, respectively, the dIOB led to 6% and 24% and 40% less hypoglycemia episodes than approaches without IOB. The new approach was also compared with the sIOB to evaluate the improvements with respect to the previous approach. Improved glucose regulation was demonstrated using the dIOB where circadian insulin sensitivity is used to adjust IOB estimation. Use of diurnal variations of insulin sensitivity appears to promote effective and safe insulin therapy using CSII or artificial pancreas. Clinical trials are warranted to determine whether nocturnal hypoglycemia can be reduced using the dIOB approach.
    Journal of diabetes science and technology 01/2013; 7(4):928-40.
  • Journal of diabetes science and technology 01/2013; 7(6):1411-5.
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    ABSTRACT: The objective of this research is an artificial pancreas (AP) that performs automatic regulation of blood glucose levels in people with type 1 diabetes mellitus. This article describes a control strategy that performs algorithmic insulin dosing for maintaining safe blood glucose levels over prolonged, overnight periods of time and furthermore was designed with outpatient, multiday deployment in mind. Of particular concern is the prevention of nocturnal hypoglycemia, because during sleep, subjects cannot monitor themselves and may not respond to alarms. An AP intended for prolonged and unsupervised outpatient deployment must strategically reduce the risk of hypoglycemia during times of sleep, without requiring user interaction. A diurnal insulin delivery strategy based on predictive control methods is proposed. The so-called "periodic-zone model predictive control" (PZMPC) strategy employs periodically time-dependent blood glucose output target zones and furthermore enforces periodically time-dependent insulin input constraints to modulate its behavior based on the time of day. The proposed strategy was evaluated through an extensive simulation-based study and a preliminary clinical trial. Results indicate that the proposed method delivers insulin more conservatively during nighttime than during daytime while maintaining safe blood glucose levels at all times. In clinical trials, the proposed strategy delivered 77% of the amount of insulin delivered by a time-invariant control strategy; specifically, it delivered on average 1.23 U below, compared with 0.31 U above, the nominal basal rate overnight while maintaining comparable, and safe, blood glucose values. The proposed PZMPC algorithm strategically prevents nocturnal hypoglycemia and is considered a significant step toward deploying APs into outpatient environments for extended periods of time in full closed-loop operation.
    Journal of diabetes science and technology 01/2013; 7(6):1446-60.
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    ABSTRACT: Safe and widespread use of diabetes technology is constrained by alarm fatigue: when someone receives so many alarms that he or she becomes less likely to respond appropriately. Alarm fatigue and related usability issues deserve consideration at every stage of alarm system design, especially as new technologies expand the potential number and complexity of alarms. The guiding principle should be patient wellbeing, while taking into consideration the regulatory and liability issues that sometimes contribute to building excessive alarms. With examples from diabetes devices, we illustrate two complementary frameworks for alarm design: a "patient safety first" perspective and a focus on human factors. We also describe opportunities and challenges that will come with new technologies such as remote monitoring, adaptive alarms, and ever-closer integration of glucose sensing with insulin delivery.
    Journal of diabetes science and technology 01/2013; 7(3):789-94.
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    ABSTRACT: Because of the slow pharmacokinetics of subcutaneous (SC) insulin, avoiding postprandial hyperglycemia has been a major challenge for an artificial pancreas (AP) using SC insulin without a meal announcement. A semiautomated AP with Technosphere® Insulin (TI; MannKind Corporation, Valencia, CA) was designed to combine pulmonary and SC insulin. Manual inhalation of 10 U ultrafast-absorbing TI at mealtime delivers the first, or cephalic, phase of insulin, and an SC insulin pump controlled by zone model predictive controller delivers second-phase and basal insulin. This AP design was evaluated on 100 in silico subjects from the University of Virginia/Padova metabolic simulator using a protocol of two 50 g carbohydrate (CHO) meals and two 15 g CHO snacks. Simulation analysis shows that the semiautomated AP with TI provides 32% and 16% more time in the controller target zone (80-140 mg/dl) during the 4 h postprandial period, with 39 and 20 mg/dl lower postprandial blood glucose peak on average than the pure feedback AP and the AP with manual feed-forward SC bolus, respectively. No severe hypoglycemia (<50 mg/dl) was observed in any cases. The semiautomated AP with TI provides maximum time in the clinically accepted region when compared with pure feedback AP and AP with manual feed-forward SC bolus. Furthermore, the semiautomated AP with TI provides a flexible operation (optional TI inhalation) with minimal user interaction, where the controller design can be tailored to specific user needs and abilities to interact with the device.
    Journal of diabetes science and technology 01/2013; 7(1):215-26.
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    ABSTRACT: OBJECTIVE An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden of frequent blood glucose measurements and insulin administration.RESEARCH DESIGN AND METHODS We conducted a pilot clinical trial evaluating an individualized, fully automated AP using commercial devices. Two trials (n = 22, n(subjects) = 17) were conducted using a multiparametric formulation of model predictive control and an insulin-on-board algorithm such that the control algorithm, or "brain," can be embedded on a chip as part of a future mobile device. The protocol evaluated the control algorithm for three main challenges: 1) normalizing glycemia from various initial glucose levels, 2) maintaining euglycemia, and 3) overcoming an unannounced meal of 30 ± 5 g carbohydrates.RESULTSInitial glucose values ranged from 84-251 mg/dL. Blood glucose was kept in the near-normal range (80-180 mg/dL) for an average of 70% of the trial time. The low and high blood glucose indices were 0.34 and 5.1, respectively.CONCLUSIONS These encouraging short-term results reveal the ability of a control algorithm tailored to an individual's glucose characteristics to successfully regulate glycemia, even when faced with unannounced meals or initial hyperglycemia. To our knowledge, this represents the first truly fully automated multiparametric model predictive control algorithm with insulin-on-board that does not rely on user intervention to regulate blood glucose in individuals with type 1 diabetes.
    Diabetes care 11/2012; · 7.74 Impact Factor
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    ABSTRACT: Abstract Background: This pilot trial was designed to determine if an optimal dose of Technosphere(®) insulin (TI) inhalation powder (MannKind Corp., Valencia, CA) could be used regardless of variation in meal carbohydrate (CHO) content. Subjects and Methods: In total, eight subjects (seven men, one woman) with type 2 diabetes were enrolled. Subjects underwent dose optimization meal challenge (MC) visits (100% CHO) and MCs with varied CHO meal contents (50%, 200%, and 0% calculated CHOs). Primary end point was change in postprandial glucose (PPG) excursions. Baseline demographics were 60±7 years of age, diabetes duration of 12.3±4.27 years, hemoglobin A1c (A1C) of 7.82±1.04%, and body mass index of 31.3±5.48 kg/m(2). Results: Maximum mean PPG excursions for the nominal 100% CHO meals were -13±15 mg/dL for breakfast (B) and -14±15 mg/dL for lunch (L), similar to those after 50% CHO meals (B, -17±16 mg/dL; L, +14±10 mg/dL). The largest excursions occurred during 200% CHO meals and remained below American Diabetes Association targets (B, +19±16 mg/dL; L, +32±29 mg/dL). During 15 of the MCs, subjects took their usual TI dose and then had no meal (0% CHO). For the 0% CHO MCs, the largest mean PPG excursion were -33±9 mg/dL at 60 min (B) and -31±10 mg/dL at 60 and 90 min (L). Mean A1C dropped from 7.82±1.04% at the Week 1 visit to 6.18±0.46% (P=0.00091) at the Week 19 visit. Conclusions: Results in eight patients suggest that once an optimal dose of TI is determined, type 2 diabetes patients can ingest meals with a wide range of CHO content or even skip meals without severe hypoglycemia. During this pilot study TI therapy improved A1C by -1.63% (P=0.00091) during 19 weeks of treatment.
    Diabetes Technology &amp Therapeutics 10/2012; · 2.21 Impact Factor
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    Diabetes care 09/2012; 35(9):e65-7. · 7.74 Impact Factor
  • Howard C Zisser
    Diabetes Technology &amp Therapeutics 08/2012; 14(8):649-50. · 2.21 Impact Factor
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    ABSTRACT: The purpose of this study was to develop a method to compare hypoglycemia prediction algorithms and choose parameter settings for different applications, such as triggering insulin pump suspension or alerting for rescue carbohydrate treatment. Hypoglycemia prediction algorithms with different parameter settings were implemented on an ambulatory dataset containing 490 days from 30 subjects with type 1 diabetes mellitus using the Dexcom™ (San Diego, CA) SEVEN™ continuous glucose monitoring system. The performance was evaluated using a proposed set of metrics representing the true-positive ratio, false-positive rate, and distribution of warning times. A prospective, in silico study was performed to show the effect of using different parameter settings to prevent or rescue from hypoglycemia. The retrospective study results suggest the parameter settings for different methods of hypoglycemia mitigation. When rescue carbohydrates are used, a high true-positive ratio, a minimal false-positive rate, and alarms with short warning time are desired. These objectives were met with a 30-min prediction horizon and two successive flags required to alarm: 78% of events were detected with 3.0 false alarms/day and 66% probability of alarms occurring within 30 min of the event. This parameter setting selection was confirmed in silico: treating with rescue carbohydrates reduced the duration of hypoglycemia from 14.9% to 0.5%. However, for a different method, such as pump suspension, this parameter setting only reduced hypoglycemia to 8.7%, as can be expected by the low probability of alarming more than 30 min ahead. The proposed metrics allow direct comparison of hypoglycemia prediction algorithms and selection of parameter settings for different types of hypoglycemia mitigation, as shown in the prospective in silico study in which hypoglycemia was alerted or treated with rescue carbohydrates.
    Diabetes Technology &amp Therapeutics 06/2012; 14(8):719-27. · 2.21 Impact Factor
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    ABSTRACT: Integrated closed-loop control (CLC), combining continuous glucose monitoring (CGM) with insulin pump (continuous subcutaneous insulin infusion [CSII]), known as artificial pancreas, can help optimize glycemic control in diabetes. We present a fundamental modular concept for CLC design, illustrated by clinical studies involving 11 adolescents and 27 adults at the Universities of Virginia, Padova, and Montpellier. We tested two modular CLC constructs: standard control to range (sCTR), designed to augment pump plus CGM by preventing extreme glucose excursions; and enhanced control to range (eCTR), designed to truly optimize control within near normoglycemia of 3.9-10 mmol/L. The CLC system was fully integrated using automated data transfer CGM→algorithm→CSII. All studies used randomized crossover design comparing CSII versus CLC during identical 22-h hospitalizations including meals, overnight rest, and 30-min exercise. sCTR increased significantly the time in near normoglycemia from 61 to 74%, simultaneously reducing hypoglycemia 2.7-fold. eCTR improved mean blood glucose from 7.73 to 6.68 mmol/L without increasing hypoglycemia, achieved 97% in near normoglycemia and 77% in tight glycemic control, and reduced variability overnight. In conclusion, sCTR and eCTR represent sequential steps toward automated CLC, preventing extremes (sCTR) and further optimizing control (eCTR). This approach inspires compelling new concepts: modular assembly, sequential deployment, testing, and clinical acceptance of custom-built CLC systems tailored to individual patient needs.
    Diabetes 06/2012; 61(9):2230-7. · 7.90 Impact Factor
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    ABSTRACT: Modularity plays a key role in many engineering systems, allowing for plug-and-play integration of components, enhancing flexibility and adaptability, and facilitating standardization. In the control of diabetes, i.e., the so-called "artificial pancreas," modularity allows for the step-wise introduction of (and regulatory approval for) algorithmic components, starting with subsystems for assured patient safety and followed by higher layer components that serve to modify the patient's basal rate in real time. In this paper, we introduce a three-layer modular architecture for the control of diabetes, consisting in a sensor/pump interface module (IM), a continuous safety module (CSM), and a real-time control module (RTCM), which separates the functions of insulin recommendation (postmeal insulin for mitigating hyperglycemia) and safety (prevention of hypoglycemia). In addition, we provide details of instances of all three layers of the architecture: the APS© serving as the IM, the safety supervision module (SSM) serving as the CSM, and the range correction module (RCM) serving as the RTCM. We evaluate the performance of the integrated system via in silico preclinical trials, demonstrating 1) the ability of the SSM to reduce the incidence of hypoglycemia under nonideal operating conditions and 2) the ability of the RCM to reduce glycemic variability.
    IEEE transactions on bio-medical engineering 04/2012; 59(11):2986-99. · 2.15 Impact Factor
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    ABSTRACT: Control of blood glucose concentration for patients in intensive care units (ICUs) has been demonstrated to be beneficial in reducing mortality and the incidence of serious complications, for both diabetic and non-diabetic patients. However, the high degree of variability and uncertainty characterizing the physiological conditions of critically ill subjects makes automated glucose control quite difficult; consequently, traditional, nurse-implemented protocols are widely employed. These protocols are based on infrequent glucose measurements, look-up tables to determine the appropriate insulin infusion rates, and bedside insulin administration. In this paper, a novel automatic adaptive control strategy based on frequent glucose measurements and a self-tuning control technique is validated based on a simulation study for 200 virtual patients. The adaptive control strategy is shown to be highly effective in controlling blood glucose concentration despite the large degree of variability in the blood glucose response exhibited by the 200 simulated patients.
    Computer methods and programs in biomedicine 03/2012; · 1.14 Impact Factor

Publication Stats

997 Citations
209.06 Total Impact Points


  • 2004–2014
    • University of California, Santa Barbara
      • Department of Chemical Engineering
      Santa Barbara, California, United States
  • 2013
    • York University
      • School of Kinesiology and Health Sciences
      Toronto, Ontario, Canada
    • University of Pavia
      Ticinum, Lombardy, Italy
  • 2009–2013
    • University of Virginia
      • Department of Systems and Information Engineering
      Charlottesville, Virginia, United States
    • Mills-Peninsula Health Services
      Burlingame, California, United States
    • Polytechnical University of Valencia
      Valenza, Valencia, Spain
  • 2004–2013
    • Sansum Diabetes Research Institute
      Santa Barbara, California, United States
  • 2011–2012
    • University of California, Irvine
      Irvine, California, United States
  • 2008–2012
    • University of Padova
      Padua, Veneto, Italy
  • 2010
    • University of Washington Seattle
      • Department of Pediatrics
      Seattle, WA, United States
    • Universidad Autónoma de San Luis Potosí
      San Luis, San Luis Potosí, Mexico
    • Northeastern University
      • Department of Chemical Engineering
      Boston, MA, United States
  • 2006
    • University of Delaware
      • Department of Chemical and Biomolecular Engineering
      Newark, DE, United States