Howard Zisser

Sansum Diabetes Research Institute, Santa Barbara, California, United States

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Publications (112)213.45 Total impact

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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 11/2014; · 2.09 Impact Factor
  • Diabetes care. 10/2014; 37(10):e228.
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    ABSTRACT: Abstract Background: This study evaluated meal bolus insulin delivery strategies and associated postprandial glucose control while using an artificial pancreas (AP) system. Subjects and Methods: This study was a multicenter trial in 53 patients, 12-65 years of age, with type 1 diabetes for at least 1 year and use of continuous subcutaneous insulin infusion for at least 6 months. Four different insulin bolus strategies were assessed: standard bolus delivered with meal (n=51), standard bolus delivered 15 min prior to meal (n=40), over-bolus of 30% delivered with meal (n=40), and bolus purposely omitted (n=46). Meal carbohydrate (CHO) intake was 1 g of CHO/kg of body weight up to a maximum of 100 g for the first three strategies or up to a maximum of 50 g for strategy 4. Results: Only three of 177 meals (two with over-bolus and one with standard bolus 15 min prior to meal) had postprandial blood glucose values of <60 mg/dL. Postprandial hyperglycemia (blood glucose level >180 mg/dL) was prolonged for all four bolus strategies but was shorter for the over-bolus (41% of the 4-h period) than the two standard bolus strategies (73% for each). Mean postprandial blood glucose level was 15.9 mg/dL higher for the standard bolus with meal compared with the prebolus (baseline-adjusted, P=0.07 for treatment effect over the 4-h period). Conclusions: The AP handled the four bolus situations safely, but at the expense of having elevated postprandial glucose levels in most subjects. This was most likely secondary to suboptimal performance of the algorithm.
    Diabetes Technology &amp Therapeutics 09/2014; · 2.21 Impact Factor
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    ABSTRACT: Abstract Background: The Control to Range Study was a multinational artificial pancreas study designed to assess the time spent in the hypo- and hyperglycemic ranges in adults and adolescents with type 1 diabetes while under closed-loop control. The controller attempted to keep the glucose ranges between 70 and 180 mg/dL. A set of prespecified metrics was used to measure safety. Research Design and Methods: We studied 53 individuals for approximately 22 h each during clinical research center admissions. Plasma glucose level was measured every 15-30 min (YSI clinical laboratory analyzer instrument [YSI, Inc., Yellow Springs, OH]). During the admission, subjects received three mixed meals (1 g of carbohydrate/kg of body weight; 100 g maximum) with meal announcement and automated insulin dosing by the controller. Results: For adults, the mean of subjects' mean glucose levels was 159 mg/dL, and mean percentage of values 71-180 mg/dL was 66% overall (59% daytime and 82% overnight). For adolescents, the mean of subjects' mean glucose levels was 166 mg/dL, and mean percentage of values in range was 62% overall (53% daytime and 82% overnight). Whereas prespecified criteria for safety were satisfied by both groups, they were met at the individual level in adults only for combined daytime/nighttime and for isolated nighttime. Two adults and six adolescents failed to meet the daytime criterion, largely because of postmeal hyperglycemia, and another adolescent failed to meet the nighttime criterion. Conclusions: The control-to-range system performed as expected: faring better overnight than during the day and performing with variability between patients even after individualization based on patients' prior settings. The system had difficulty preventing postmeal excursions above target range.
    Diabetes Technology &amp Therapeutics 07/2014; · 2.21 Impact Factor
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    ABSTRACT: We estimate the effect size of hypoglycemia risk reduction on closed-loop control (CLC) versus open-loop (OL) sensor-augmented insulin pump therapy in supervised outpatient setting.RESEARCH DESIGN AND METHODS: Twenty patients with type 1 diabetes initiated the study at the Universities of Virginia, Padova, and Montpellier and Sansum Diabetes Research Institute; 18 completed the entire protocol. Each patient participated in two 40-h outpatient sessions, CLC versus OL, in randomized order. Sensor (Dexcom G4) and insulin pump (Tandem t:slim) were connected to Diabetes Assistant (DiAs)-a smartphone artificial pancreas platform. The patient operated the system through the DiAs user interface during both CLC and OL; study personnel supervised on site and monitored DiAs remotely. There were no dietary restrictions; 45-min walks in town and restaurant dinners were included in both CLC and OL; alcohol was permitted.RESULTS: The primary outcome-reduction in risk for hypoglycemia as measured by the low blood glucose (BG) index (LGBI)-resulted in an effect size of 0.64, P = 0.003, with a twofold reduction of hypoglycemia requiring carbohydrate treatment: 1.2 vs. 2.4 episodes/session on CLC versus OL (P = 0.02). This was accompanied by a slight decrease in percentage of time in the target range of 3.9-10 mmol/L (66.1 vs. 70.7%) and increase in mean BG (8.9 vs. 8.4 mmol/L; P = 0.04) on CLC versus OL.CONCLUSIONS: CLC running on a smartphone (DiAs) in outpatient conditions reduced hypoglycemia and hypoglycemia treatments when compared with sensor-augmented pump therapy. This was accompanied by marginal increase in average glycemia resulting from a possible overemphasis on hypoglycemia safety.
    Diabetes care. 06/2014;
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    ABSTRACT: The Hypoglycemia-Hyperglycemia Minimizer (HHM) System aims to mitigate glucose excursions by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The "aggressiveness factor" is a key parameter in the HHM System algorithm, affecting how readily the system adjusts insulin infusion in response to changing CGM levels. Twenty adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 26 hours. This analysis focused on the effect of the aggressiveness factor on the insulin dosing characteristics of the algorithm and, to a lesser extent, on the glucose control results observed. As the aggressiveness factor increased from conservative to medium to aggressive: the maximum observed insulin dose delivered by the algorithm-which is designed to give doses that are corrective in nature every 5 minutes-increased (1.00 vs 1.15 vs 2.20 U, respectively); tendency to adhere to the subject's nominal basal dose decreased (61.9% vs 56.6% vs 53.4%); and readiness to decrease insulin below basal also increased (18.4% vs 19.4% vs 25.2%). Glucose analyses by both CGM and Yellow Springs Instruments (YSI) indicated that the aggressive setting of the algorithm resulted in the least time spent at levels >180 mg/dL, and the most time spent between 70-180 mg/dL. There was no severe hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia for any of the aggressiveness values investigated. These analyses underscore the importance of investigating the sensitivity of the HHM System to its key parameters, such as the aggressiveness factor, to guide future development decisions.
    Journal of diabetes science and technology 05/2014;
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    ABSTRACT: Abstract Background: Artificial pancreas (AP) systems are currently an active field of diabetes research. This pilot study examined the attitudes of AP clinical trial participants toward future acceptance of the technology, having gained firsthand experience. Subjects and Methods: After possible influencers of AP technology adoption were considered, a 34-question questionnaire was developed. The survey assessed current treatment satisfaction, dimensions of clinical trial participant motivation, and variables of the technology acceptance model (TAM). Forty-seven subjects were contacted to complete the survey. The reliability of the survey scales was tested using Cronbach's α. The relationship of the factors to the likelihood of AP technology adoption was explored using regression analysis. Results: Thirty-six subjects (76.6%) completed the survey. Of the respondents, 86.1% were either highly likely or likely to adopt the technology once available. Reliability analysis of the survey dimensions revealed good internal consistency, with scores of >0.7 for current treatment satisfaction, convenience (motivation), personal health benefit (motivation), perceived ease of use (TAM), and perceived usefulness (TAM). Linear modeling showed that future acceptance of the AP was significantly associated with TAM and the motivation variables of convenience plus the individual item benefit to others (R(2)=0.26, P=0.05). When insulin pump and continuous glucose monitor use were added, the model significance improved (R(2)=0.37, P=0.02). Conclusions: This pilot study demonstrated that individuals with direct AP technology experience expressed high likelihood of future acceptance. Results support the factors of personal benefit, convenience, perceived usefulness, and perceived ease of use as reliable scales that suggest system adoption in this highly motivated patient population.
    Diabetes Technology &amp Therapeutics 05/2014; · 2.21 Impact Factor
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    ABSTRACT: In this two-part Bench to Clinic narrative, recent advances in both the preclinical and clinical aspects of artificial pancreas (AP) development are described. In the preceding Bench narrative, Kudva and colleagues provide an in-depth understanding of the modified glucoregulatory physiology of type 1 diabetes that will help refine future AP algorithms. In the Clinic narrative presented here, we compare and evaluate AP technology to gain further momentum toward outpatient trials and eventual approval for widespread use. We enumerate the design objectives, variables, and challenges involved in AP development, concluding with a discussion of recent clinical advancements. Thanks to the effective integration of engineering and medicine, the dream of automated glucose regulation is nearing reality. Consistent and methodical presentation of results will accelerate this success, allowing head-to-head comparisons that will facilitate adoption of the AP as a standard therapy for type 1 diabetes.
    Diabetes care 05/2014; 37(5):1191-7. · 7.74 Impact Factor
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    ABSTRACT: The Glucose Rate Increase Detector (GRID), a module of the Health Monitoring System (HMS), has been designed to operate in parallel to the glucose controller to detect meal events and safely trigger a meal bolus. The GRID algorithm was tuned on clinical data with 40-70 g CHO meals and tested on simulation data with 50-100 g CHO meals. Active closed- and open-loop protocols were executed in silico with various treatments, including automatic boluses based on a 75 g CHO meal and boluses based on simulated user input of meal size. An optional function was used to reduce the recommended bolus using recent insulin and glucose history. For closed-loop control of a 3-meal scenario (50, 75, and 100 g CHO), the GRID improved median time in the 80-180 mg/dL range by 17% and in the >180 range by 14% over unannounced meals, using an automatic bolus for a 75 g CHO meal at detection. Under open-loop control of a 75 g CHO meal, the GRID shifted the median glucose peak down by 73 mg/dL and earlier by 120 min and reduced the time >180 mg/dL by 57% over a missed-meal bolus scenario, using a full meal bolus at detection. The GRID improved closed-loop control in the presence of large meals, without increasing late postprandial hypoglycemia. Users of basal-bolus therapy could also benefit from GRID as a safety alert for missed meal corrections.
    Journal of diabetes science and technology 03/2014; 8(2):307-320.
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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: Online glucose prediction which can be used to provide important information of future glucose status is a key step to facilitate proactive management before glucose reaches undesirable concentrations. Based on frequency‐band separation (FS) and empirical modeling approaches, this article considers several important aspects of on‐line glucose prediction for subjects with type 1 diabetes mellitus. Three issues are of particular interest: (1) Can a global (or universal) model be developed from glucose data for a single subject and then used to make suitably accurate on‐line glucose predictions for other subjects? (2) Does a new FS approach based on data filtering provide more accurate models than standard modeling methods? (3) Does a new latent variable modeling method result in more accurate models than standard modeling methods? These and related issues are investigated by developing autoregressive models and autoregressive models with exogenous inputs based on clinical data for two groups of subjects. The alternative modeling approaches are evaluated with respect to on‐line short‐term prediction accuracy for prediction horizons of 30 and 60 min, using independent test data. © 2013 American Institute of Chemical Engineers AIChE J 60: 574–584, 2014
    AIChE Journal 01/2014; 60(2). · 2.58 Impact Factor
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    ABSTRACT: This feasibility study investigated the insulin-delivery characteristics of the Hypoglycemia-Hyperglycemia Minimizer (HHM) System-an automated insulin delivery device-in participants with type 1 diabetes.
    Journal of diabetes science and technology 01/2014; 8(1):35-42.
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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: 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 05/2013; 7(3):789-94.
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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. · 3.77 Impact Factor
  • Diabetes Technology &amp Therapeutics 02/2013; 15 Suppl 1:S96-S106. · 2.21 Impact Factor
  • 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.

Publication Stats

1k Citations
213.45 Total Impact Points


  • 2004–2014
    • Sansum Diabetes Research Institute
      Santa Barbara, California, United States
    • University of California, Santa Barbara
      • Department of Chemical Engineering
      Santa Barbara, California, United States
  • 2013
    • University of Pavia
      Ticinum, Lombardy, Italy
    • York University
      • School of Kinesiology and Health Sciences
      Toronto, Ontario, Canada
  • 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
  • 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