J S Skyler

Yale University, New Haven, Connecticut, United States

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Publications (216)1317.06 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: We assessed whether type 1 diabetes (T1D) can be diagnosed earlier using a new approach based on prediction and natural history in autoantibody-positive individuals. Diabetes Prevention Trial-Type 1 (DPT-1) and TrialNet Natural History Study (TNNHS) participants were studied. A metabolic index, the T1D Diagnostic Index60 (Index60), was developed from 2-h oral glucose tolerance tests (OGTTs) using the log fasting C-peptide, 60-min C-peptide, and 60-min glucose. OGTTs with Index60 ≥2.00 and 2-h glucose <200 mg/dL (Ind60+Only) were compared with Index60 <2.00 and 2-h glucose ≥200 mg/dL (2hglu+Only) OGTTs as criteria for T1D. Individuals were assessed for C-peptide loss from the first Ind60+Only OGTT to diagnosis. Areas under receiver operating characteristic curves were significantly higher for Index60 than for the 2-h glucose (P < 0.001 for both DPT-1 and the TNNHS). As a diagnostic criterion, sensitivity was higher for Ind60+Only than for 2hglu+Only (0.44 vs. 0.15 in DPT-1; 0.26 vs. 0.17 in the TNNHS) OGTTs. Specificity was somewhat higher for 2hglu+Only OGTTs in DPT-1 (0.97 vs. 0.91) but equivalent in the TNNHS (0.98 for both). Positive and negative predictive values were higher for Ind60+Only OGTTs in both studies. Postchallenge C-peptide levels declined significantly at each OGTT time point from the first Ind60+Only OGTT to the time of standard diagnosis (range -22 to -34% in DPT-1 and -14 to -27% in the TNNHS). C-peptide and glucose patterns differed markedly between Ind60+Only and 2hglu+Only OGTTs. An approach based on prediction and natural history appears to have utility for diagnosing T1D. © 2014 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.
    Diabetes Care 12/2014; DOI:10.2337/dc14-1813 · 8.57 Impact Factor
  • Jay S Skyler
    Diabetes 11/2014; 63(11):3578-80. DOI:10.2337/db14-1160 · 7.90 Impact Factor
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    ABSTRACT: Objective Type 1 diabetes (T1D) results from the inflammatory destruction of pancreatic β-cells. In this study, we investigated the effect of docosahexaenoic acid (DHA) supplementation on stimulated inflammatory cytokine production in white blood cells (WBC) from infants with a high genetic risk for T1D.Research design and methodsThis was a multicenter, two-arm, randomized, double-blind pilot trial of DHA supplementation, beginning either in the last trimester of pregnancy (41 infants) or in the first 5 months after birth (57 infants). Levels of DHA in infant and maternal red blood cell (RBC) membranes and in breast milk were analyzed by gas chromatography/mass spectrometry. Inflammatory cytokines were assayed from whole blood culture supernatants using the Luminex multiplex assay after stimulation with high dose lipopolysaccharide (LPS), 1 µg/mL.ResultsThe levels of RBC DHA were increased by 61–100% in treated compared to control infants at ages 6–36 months. There were no statistically significant reductions in production of the inflammatory cytokines, IL-1β, TNFα, or IL-12p40 at any of the six timepoints measured. The inflammatory marker, high-sensitivity C-reactive protein (hsCRP), was significantly lower in breast-fed DHA-treated infants compared to all formula-fed infants at the age of 12 months. Three infants (two received DHA) were removed from the study as a result of developing ≥two persistently positive biochemical islet autoantibodies.Conclusions This pilot trial showed that supplementation of infant diets with DHA is safe and fulfilled the pre-study goal of increasing infant RBC DHA levels by at least 20%. Inflammatory cytokine production was not consistently reduced.
    Pediatric Diabetes 07/2014; DOI:10.1111/pedi.12170 · 2.13 Impact Factor
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    ABSTRACT: Aims: Prandial treatment with human regular insulin for diabetes may result in early postprandial hyperglycaemia and late hypoglycaemia due to its slow onset and long duration of action. This study compared injections of recombinant human insulin (rHI) formulated with recombinant human hyaluronidase [rHuPH20] (INSULIN-PH20) to insulin lispro for prandial treatment in subjects with type 1 diabetes (T1D). Methods: After a 1-month run-in period using twice-daily insulin glargine (or usual basal insulin therapy for pump users) with prandial lispro, 46 subjects with T1D (42 +/- 13 years; body mass index: 26 +/- 4 kg/m(2); A1c: 6.8 +/- 0.5%) were assigned to INSULIN-PH20 or lispro in a random sequence for two consecutive, 12-week periods as the prandial insulin in an intensive treatment regimen. Results: The mean glycaemic excursion for INSULIN-PH20 (0.96 +/- 2.00 mmol/l) was comparable (p=0.322) to lispro (0.80 +/- 1.95 mmol/l). The 8-point self-monitored blood glucose profiles were also comparable in the two groups. Good glycaemic control (A1c) was maintained for both treatments at 12 weeks (INSULIN-PH20: 7.0 +/- 0.5%; lispro: 6.9 +/- 0.6%). Overall rates of hypoglycaemia (<= 3.9 mmol/l) were 24 events per patient per 4 weeks for INSULIN-PH20 and 22 events for lispro. There were no significant differences in adverse events or immunogenicity between treatments and both treatments were well tolerated. Conclusions: Unlike commercially available formulations of regular human insulin, a formulation of rHI with rHuPH20 was comparable to lispro for postprandial glucose excursions in a basal-bolus treatment regimen for T1D patients. Glycaemic control, safety and tolerability profiles were comparable for both treatments.
    Diabetes Obesity and Metabolism 05/2014; 16(11). DOI:10.1111/dom.12315 · 5.18 Impact Factor
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    ABSTRACT: Recent advances in insulins, insulin pumps, continuous glucose-monitoring systems, and control algorithms have resulted in an acceleration of progress in the development of artificial pancreas devices. This review discusses progress in the development of external systems that are based on subcutaneous drug delivery and subcutaneous continuous glucose monitoring. There are two major system-level approaches to achieving closed-loop control of blood glucose in diabetic individuals. The unihormonal approach uses insulin to reduce blood glucose and relies on complex safety mitigation algorithms to reduce the risk of hypoglycemia. The bihormonal approach uses both insulin to lower blood glucose and glucagon to raise blood glucose, and also relies on complex algorithms to provide for safety of the user. There are several major strategies for the design of control algorithms and supervision control for application to the artificial pancreas: proportional–integral–derivative, model predictive control, fuzzy logic, and safety supervision designs. Advances in artificial pancreas research in the first decade of this century were based on the ongoing computer revolution and miniaturization of electronic technology. The advent of modern smartphones has created the ability to utilize smartphone technology as the engineering centerpiece of an artificial pancreas. With these advances, an artificial or bionic pancreas is within reach.
    Annals of the New York Academy of Sciences 04/2014; 1311(1). DOI:10.1111/nyas.12431 · 4.38 Impact Factor
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    ABSTRACT: OBJECTIVE We studied the utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of type 1 diabetes (T1D) risk classification in TrialNet Natural History Study (TNNHS) participants.RESEARCH DESIGN AND METHODS The cumulative incidence of T1D was compared between normoglycemic individuals with DPTRS values >7.00 and dysglycemic individuals in the TNNHS (n = 991). The cumulative incidence was compared between individuals with DPTRS values <7.00 and >7.00 among those with dysglycemia and those with multiple autoantibodies in the TNNHS. DPTRS values >7.00 were compared with dysglycemia for characterizing risk in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 670) and TNNHS participants. The reliability of DPTRS values >7.00 was compared with dysglycemia in the TNNHS.RESULTSThe cumulative incidence of T1D for normoglycemic TNNHS participants with DPTRS values >7.00 was comparable to those with dysglycemia. Among those with dysglycemia, the cumulative incidence was much higher (P < 0.001) for those with DPTRS values >7.00 than for those with values <7.00 (3-year risks: 0.16 for <7.00 and 0.46 for >7.00). Dysglycemic individuals in DPT-1 were at much higher risk for T1D than those with dysglycemia in the TNNHS (P < 0.001); there was no significant difference in risk between the studies among those with DPTRS values >7.00. The proportion in the TNNHS reverting from dysglycemia to normoglycemia at the next visit was higher than the proportion reverting from DPTRS values >7.00 to values <7.00 (36 vs. 23%).CONCLUSIONSDPTRS thresholds can improve T1D risk classification accuracy by identifying high-risk normoglycemic and low-risk dysglycemic individuals. The 7.00 DPTRS threshold characterizes risk more consistently between populations and has greater reliability than dysglycemia.
    Diabetes care 02/2014; 37(4). DOI:10.2337/dc13-2359 · 7.74 Impact Factor
  • Jay S Skyler
    Diabetes Technology &amp Therapeutics 02/2014; 16(S1):S85-S91. DOI:10.1089/dia.2014.1510 · 2.29 Impact Factor
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    ABSTRACT: Objective We previously reported that two years of co-stimulation modulation with abatacept slowed decline of beta-cell function in recent-onset type 1 diabetes mellitus (T1DM). Subsequently, abatacept was discontinued, and subjects followed to determine whether there was persistence of effect.Research Design and Methods In 112 subjects (ages 6-36) with T1DM, 77 received abatacept and 35 received placebo infusions intravenously for 27 infusions over two years. The primary outcome - baseline-adjusted geometric mean 2-hour area under the curve (AUC) serum C-peptide during a mixed meal tolerance test (MMTT) at two years - showed higher C-peptide with abatacept versus placebo. Subjects were followed an additional year, off treatment, with MMTTs performed at 30 and 36 months.ResultsC-peptide AUC means, adjusted for age and baseline C-peptide, at 36 months were 0.217 (95% CI: 0.168, 0.268) and 0.141 (95% CI: 0.071, 0.215) nmol/L for abatacept and placebo groups, respectively (p=0.046). The C-peptide decline from baseline remained parallel with an estimated 9.5 months' delay with abatacept. Moreover, HbA1c levels remainded lower in the abatacept group than in the placebo group. The slightly lower (non-significant) mean total insulin dose among the abatacept group reported at 2 years was the same as the placebo group by 3 years.Conclusions Co-stimulation modulation with abatacept slowed decline of beta-cell function and improved HbA1c in recent-onset T1DM. The beneficial effect was sustained for at least one year after cessation of abatacept infusions, or three years from T1DM diagnosis.
    Diabetes care 12/2013; 37(4). DOI:10.2337/dc13-0604 · 7.74 Impact Factor
  • Jay S Skyler
    12/2013; 1(4):265-6. DOI:10.1016/S2213-8587(13)70087-1
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    Jay S Skyler
    Diabetes 11/2013; 62(11):3656-7. DOI:10.2337/db13-1157 · 7.90 Impact Factor
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    ABSTRACT: Objective We previously reported that selective depletion of B-lymphocytes with rituximab, an anti-CD20 monoclonal antibody, slowed decline of beta-cell function in recent-onset type 1 diabetes mellitus (T1DM) at one year. Subjects were followed further to determine whether there was persistence of effect.Research Design and Methods Eighty-seven subjects (ages 8-40) were randomly assigned to, and 81 received, infusions of rituximab or placebo on days 1, 8, 15, and 22. The primary outcome - baseline-adjusted mean 2-hour area under the curve (AUC) serum C-peptide during a mixed meal tolerance test (MMTT) at one year - showed higher C-peptide AUC with rituximab versus placebo. Subjects were further followed with additional MMTTs every 6 months.ResultsThe rate of decline of C-peptide was parallel between groups, but shifted by 8.2 months in rituximab treated subjects. Over 30 months, AUC, insulin dose, and HbA1c were similar for rituximab and placebo. However, in evaluating change in C-peptide over the entire follow-up period, the rituximab group means were significantly larger as compared within assessment times to the placebo group means using a global test (p = 0.03). Odds ratio for loss of C-peptide to < 0.2 nmol/L following rituximab was 0.565 (p= 0.064). B-lymphocytes recovered to baseline values by 18 months. Serum IgG levels were maintained in the normal range but IgM levels were depressed.Conclusions Like several other immunotherapeutic approaches tested, in recent-onset T1DM, rituximab delays the fall in C-peptide, but does not appear to fundamentally alter the underlying pathophysiology of the disease.
    Diabetes care 09/2013; 37(2). DOI:10.2337/dc13-0626 · 7.74 Impact Factor
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    ABSTRACT: OBJECTIVE We assessed whether a risk score that incorporates levels of multiple islet autoantibodies could enhance the prediction of type 1 diabetes (T1D).RESEARCH DESIGN AND METHODS TrialNet Natural History Study participants (n = 784) were tested for three autoantibodies (GADA, IA-2A, and mIAA) at their initial screening. Samples from those positive for at least one autoantibody were subsequently tested for ICA and ZnT8A. An autoantibody risk score (ABRS) was developed from a proportional hazards model that combined autoantibody levels from each autoantibody along with their designations of positivity and negativity.RESULTS The ABRS was strongly predictive of T1D (hazard ratio [with 95% CI] 2.72 [2.23-3.31], P < 0.001). Receiver operating characteristic curve areas (with 95% CI) for the ABRS revealed good predictability (0.84 [0.78-0.90] at 2 years, 0.81 [0.74-0.89] at 3 years, P < 0.001 for both). The composite of levels from the five autoantibodies was predictive of T1D before and after an adjustment for the positivity or negativity of autoantibodies (P < 0.001). The findings were almost identical when ICA was excluded from the risk score model. The combination of the ABRS and the previously validated Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) predicted T1D more accurately (0.93 [0.88-0.98] at 2 years, 0.91 [0.83-0.99] at 3 years) than either the DPTRS or the ABRS alone (P 0.01 for all comparisons).CONCLUSIONS These findings show the importance of considering autoantibody levels in assessing the risk of T1D. Moreover, levels of multiple autoantibodies can be incorporated into an ABRS that accurately predicts T1D.
    Diabetes Care 08/2013; 36(9):2615-2620. DOI:10.2337/dc13-0425 · 8.57 Impact Factor
  • Evis Harja, Jonathan Lord, Jay S Skyler
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    ABSTRACT: Abstract A recent autopsy analysis asserted that incretin drugs have the potential of increasing the risk for pancreatic cancer and for pancreatic neuroendocrine tumors. We examined the Network for Pancreatic Organ Donors with Diabetes (nPOD) database from which that analysis was derived. Our findings raise important questions about the comparability of the two groups of diabetes patients used for the analysis. Our review of the data available on the nPOD Web site and our reading of the earlier article lead us to the conclusion that the data, and the implications of the data, as expressed by the authors of the autopsy analysis are vastly overstated and are a misrepresentation of the information available.
    Diabetes Technology &amp Therapeutics 08/2013; DOI:10.1089/dia.2013.0177 · 2.29 Impact Factor
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    ABSTRACT: We studied the change in the first-phase insulin response (FPIR) during the progression to type 1 diabetes (T1D). Seventy-four oral insulin trial progressors to T1D of the Diabetes Prevention Trial-Type 1 with at least one FPIR measurement after baseline and before diagnosis were studied. The FPIR was examined longitudinally in 26 progressors who had FPIR measurements during each of the 3 years before diagnosis. The association between the change from the baseline FPIR to the last FPIR and time to diagnosis was studied in the remainder (n=48). The 74 progressors had lower baseline FPIR values than non-progressors (n=270) with adjustments for age and BMI. In the longitudinal analysis of the 26 progressors, there was a greater decline in the FPIR from 1.5 to 0.5 years before diagnosis than from 2.5 to 1.5 years before diagnosis. This accelerated decline was also evident in a regression analysis of the 48 remaining progressors in whom the rate of decline became more marked with the approaching diagnosis. The patterns of decline were similar between the longitudinal and regression analyses. There is an acceleration of decline in the FPIR during the progression to T1D which becomes especially marked between 1.5 and 0.5 years before diagnosis.
    Diabetes 07/2013; 62(12). DOI:10.2337/db13-0656 · 7.90 Impact Factor
  • Jay S Skyler, Jay M Sosenko
    JAMA The Journal of the American Medical Association 06/2013; 309(23):2491-2. DOI:10.1001/jama.2013.6286 · 29.98 Impact Factor
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    Alberto Pugliese, Jay S Skyler
    Diabetes care 06/2013; 36(6):1437-1442. DOI:10.2337/dc13-0753 · 7.74 Impact Factor
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    ABSTRACT: Integrating patient-centered diabetes care and algorithmic medicine poses particular challenges when optimized basal insulin fails to maintain glycemic control in patients with type 2 diabetes. Multiple entwined physiological, psychosocial, and systems barriers to insulin adherence are not easily studied and are not adequately considered in most treatment algorithms. Moreover, the limited number of alternatives to add-on prandial insulin therapy has hindered shared decision-making, a central feature of patient-centered care. This article considers how the addition of a GLP-1 analogue to basal insulin may provide new opportunities at this stage of treatment, especially for patients concerned about weight gain and risk of hypoglycemia. A flexible framework for patient-clinician discussions is presented to encourage development of decision-support tools applicable to both specialty and primary care practice.
    Diabetes Obesity and Metabolism 05/2013; 16(3). DOI:10.1111/dom.12136 · 5.18 Impact Factor
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    ABSTRACT: BACKGROUND: Innate immunity contributes to the pathogenesis of autoimmune diseases, such as type 1 diabetes, but until now no randomised, controlled trials of blockade of the key innate immune mediator interleukin-1 have been done. We aimed to assess whether canakinumab, a human monoclonal anti-interleukin-1 antibody, or anakinra, a human interleukin-1 receptor antagonist, improved β-cell function in recent-onset type 1 diabetes. METHODS: We did two randomised, placebo-controlled trials in two groups of patients with recent-onset type 1 diabetes and mixed-meal-tolerance-test-stimulated C peptide of at least 0·2 nM. Patients in the canakinumab trial were aged 6-45 years and those in the anakinra trial were aged 18-35 years. Patients in the canakinumab trial were enrolled at 12 sites in the USA and Canada and those in the anakinra trial were enrolled at 14 sites across Europe. Participants were randomly assigned by computer-generated blocked randomisation to subcutaneous injection of either 2 mg/kg (maximum 300 mg) canakinumab or placebo monthly for 12 months or 100 mg anakinra or placebo daily for 9 months. Participants and carers were masked to treatment assignment. The primary endpoint was baseline-adjusted 2-h area under curve C-peptide response to the mixed meal tolerance test at 12 months (canakinumab trial) and 9 months (anakinra trial). Analyses were by intention to treat. These studies are registered with ClinicalTrials.gov, numbers NCT00947427 and NCT00711503, and EudraCT number 2007-007146-34. FINDINGS: Patients were enrolled in the canakinumab trial between Nov 12, 2010, and April 11, 2011, and in the anakinra trial between Jan 26, 2009, and May 25, 2011. 69 patients were randomly assigned to canakinumab (n=47) or placebo (n=22) monthly for 12 months and 69 were randomly assigned to anakinra (n=35) or placebo (n=34) daily for 9 months. No interim analyses were done. 45 canakinumab-treated and 21 placebo-treated patients in the canakinumab trial and 25 anakinra-treated and 26 placebo-treated patients in the anakinra trial were included in the primary analyses. The difference in C peptide area under curve between the canakinumab and placebo groups at 12 months was 0·01 nmol/L (95% CI -0·11 to 0·14; p=0·86), and between the anakinra and the placebo groups at 9 months was 0·02 nmol/L (-0·09 to 0·15; p=0·71). The number and severity of adverse events did not differ between groups in the canakinumab trial. In the anakinra trial, patients in the anakinra group had significantly higher grades of adverse events than the placebo group (p=0·018), which was mainly because of a higher number of injection site reactions in the anakinra group. INTERPRETATION: Canakinumab and anakinra were safe but were not effective as single immunomodulatory drugs in recent-onset type 1 diabetes. Interleukin-1 blockade might be more effective in combination with treatments that target adaptive immunity in organ-specific autoimmune disorders. FUNDING: National Institutes of Health and Juvenile Diabetes Research Foundation.
    The Lancet 04/2013; DOI:10.1016/S0140-6736(13)60023-9 · 39.21 Impact Factor
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    ABSTRACT: Decreased treatment adherence in patients with diabetes mellitus type 1 (type 1 DM) may reflect impairments in decision-making and underlying associated deficits in working memory and executive functioning. Other factors, including comorbid major depression, may also interfere with decision-making. The authors sought to review the clinically relevant characteristics of decision-making in type 1 DM by surveying the literature on decision-making by patients with type 1 DM. Deficiencies in decision-making in patients with type 1 DM or their caregivers contribute to treatment nonadherence and poorer metabolic control. Animal models of type 1 DM reveal deficits in hippocampal-dependent memory tasks, which are reversible with insulin. Neurocognitive studies of patients with type 1 DM reveal lowered performance on ability to apply knowledge to solve problems in a new situation and acquired scholarly knowledge, psychomotor efficiency, cognitive flexibility, visual perception, speed of information-processing, and sustained attention. Other factors that might contribute to poor decision-making in patients with type 1 DM, include "hypoglycemia unawareness" and comorbid major depression (given its increased prevalence in type 1 DM). Future studies utilizing novel treatment strategies to help patients with type 1 DM make better decisions about their disease may improve their glycemic control and quality of life, while minimizing the impact of end-organ disease.
    The Journal of neuropsychiatry and clinical neurosciences 03/2013; 25(1):40-50. DOI:10.1176/appi.neuropsych.12010016 · 2.34 Impact Factor
  • Jay S Skyler
    Diabetes Technology &amp Therapeutics 02/2013; 15 Suppl 1:S88-95. DOI:10.1089/dia.2013.1510 · 2.29 Impact Factor

Publication Stats

6k Citations
1,317.06 Total Impact Points


  • 2014
    • Yale University
      New Haven, Connecticut, United States
  • 1984–2014
    • University of Miami
      • • Department of Medicine
      • • Behavioral Medicine Research Center
      • • Department of Psychology
      • • Department of Pediatrics
      كورال غيبلز، فلوريدا, Florida, United States
  • 1986–2013
    • University of Miami Miller School of Medicine
      • • Diabetes Research Institute (DRI)
      • • Department of Medicine
      • • Division of Hospital Medicine
      • • Department of Epidemiology and Public Health
      Miami, Florida, United States
  • 2012
    • Benaroya Research Institute
      Seattle, Washington, United States
  • 2010
    • University of South Florida
      • Department of Pediatrics
      Tampa, FL, United States
    • University of Colorado
      • Barbara Davis Center for Childhood Diabetes
      Denver, CO, United States
  • 2009
    • Indiana University-Purdue University Indianapolis
      Indianapolis, Indiana, United States
  • 2006–2007
    • Columbia University
      New York, New York, United States
  • 2003
    • University of Leeds
      Leeds, England, United Kingdom
  • 2002
    • Diabetes Australia, Victoria
      Melbourne, Victoria, Australia
  • 1999
    • University at Buffalo, The State University of New York
      • Department of Social and Preventive Medicine
      Buffalo, NY, United States
  • 1997
    • Rochester Center for Behavioral Medicine
      Rochester Hills, Michigan, United States
  • 1994
    • University Of Miami Hospital
      Miami, Florida, United States
  • 1976
    • Duke University Medical Center
      Durham, North Carolina, United States