Quali novità tecnologiche

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1 Ventennale di attività della Diabetologia di Mariano Comense (CO) Diabete Mellito: uno sguardo sul futuro Quali novità tecnologiche Daniela Bruttomesso Azienda Ospedaliera-Università di Padova

2 Conflitto di interessi Abbott Eli Lilly Novo Nordisk Roche Diagnostics Sanofi-Aventis

3 Agenda Monitoraggio glicemico Somministrazione di insulina

4 Uso clinico del CGM Migliora il controllo glicemico Riduce il numero di eventi ipoglicemici Riduce la variabilità glicemica Educa il paziente alla gestione del diabete Floyd B. et al., J Diabetes Sci Technol 6: , 2012; Poolsup N.et al., Diabetology & Metabolic Syndrome 5: 39-52, 2013; Pickup J C et al. BMJ 2011;343:bmj.d3805; Vigersky RA et al., Diabetes Care 35: 32-38, 2012.

5 Limiti del CGM Accuratezza della misura fatta dal sensore non sempre ottimale Richiesta di calibrazioni con SMBG Durata breve del sensore

6 Quanto è importante l accuratezza? Maggiore è il numero di valori presenti nelle zone A e B, più sono CLINICAMENTE ACCURATI i dispositivi in termini di accuratezza. Clarke WL, et al. Diabetes Care 10: , 1987, Parkes JL, Diabetes Care 23: , 2000,

7 PERFORMANCE DEL SENSORE In base alla MARD < 10% 10-14% 14-18% >18% eccellente buono mediocre scadente

8 DESTINAZIONE D USO/ACCURATEZZA < 10% 10-14% 14-18% >18% eccellente buono mediocre scadente Pancreas artificiale Rileva ipoglicemie e allerta in SAP Rischio ipoglicemia Probabilità di raggiungere il target

9 Accuratezza dei sistemi CGM più recenti Performance parameters LIBRE (Bailey TS et al. Diab Technol Ther 2015) DEXCOM 5 (Bailey TS et al. J Diabetes Sci Technol 2015) ENLITE (Minimed 640G) (Cohen O et al, Abs n 0317, ATTD 2016) CGM vs YSI CGM vs YSI CGM vs YSI MARD (%) ±8.29 MAD hypoglycemia MARD at euglycaemia MARD >180 mg/dl 13.4 mg/dl (< 100 mg/dl) 11.4 (>100 mg/dl) 6.4mg/dl ( 70 mg/dl) 9.7 (70-180mg/dl) 10.6±8.5 ( 75 mg/dl) 10.3±7.5 ( mg/dl) ±4.8 Overall CEG A+B (%) %

10 Senseonics implantable CGM systems Sensore impiantabile sottocutaneo (90-180gg) Trasmettitore rimuovibile e ricaricabile Ricezione dati in app per Android e ios

11 Multisite study of an implantes continuous glucose sensor over 90 days in patients with diabetes mellitus 24 T1 DM 7 in-clinic sessions (day 1,15,30,45,60,90) 3586 CGM vs venous BG ( YSI) Consensus Error Grid Zone A+B: 99.5% Consensus Error Grid Zone A: 87% Overall MARD 11,4% ( mg/dl) Dehennis A et al. J Diabetes Sci Technol 2015, 9:

12 Multisite study of an implantes continuous glucose sensor over 90 days in patients with diabetes mellitus Dehennis A et al. J Diabetes Sci Technol 2015, 9:

13 Implantable Continuous Glucose Sensor in the PRECISE Study: A 180-Day, Prospective, Multicenter, Pivotal Trial 71 pts (T1 e T2DM) CGM at home and in-clinic (8 visits, 8h-24h) Accuracy and longevity MARD ( mg/dl) MARD >75 mg/dl MAD 75 mg/dl 10.1% 11.6 % 11.1% 14.2 mg/dl HbA1c and hypoglycaemia Days HbA1c (%) Time in hypo (%) 7.5± ± ± ± ± ±3.8 Choudary P et al, ADA 2016 DeVries HJ et al, ADA 2016 AES in <15% of patients (in the first 90 days of CGM), by Carlson G et al, ATTD 2015

14 Vantaggi del Senseonics-CGM Durata prolungata Impiantabile Mard buona Assenza di Nocturnal sensor attenuation

15 Agenda Monitoraggio glicemico Somministrazione di insulina

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17 The Hypoglycemia-Hyperglycemia Minimizer (HHM) System Levy BL et al, Eur Endocrinol 2016

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19 Il pancreas artificiale Algoritmo di Controllo Sensore di Glucosio Pompa di Insulina

20 Come funziona l algoritmo predittivo di controllo (MPC) passato k futuro Prevede come sarà la glicemia futura Dati a disposizione Modello matematico Calcola quanta insulina infondere k+1... k+m... k+p tempo

21 Come funziona l algoritmo predittivo di controllo (MPC) passato k+1 futuro Glicemia futura Dati a disposizione modello matematico Calcola insulina da infondere k+2... k+m+1... k+p+1 tempo

22 Come funziona l algoritmo predittivo di controllo (MPC) passato k+2 futuro Glicemia futura Dati a disposizione Modello matematico Calcola insulina da infondere k+3... k+m+2... k+p+2 tempo

23 AP vs CSII/SAP (our experience in multicenter studies) Years Study setting Subjects Lenght of study AP system Hospital, (supervised inpatients) Hotel, camp, (supervised outpatients) # Home, (supervised/ unsupervised outpatients) 147 adults 1 day Laptop computer wired to a CGM and insulin pump 85 adults 30 children 2-5 days Fully portable 62 adults 2-6 months Fully portable Kovatchev et al, Diab Sci Tech, 2010; Breton et al, Diabetes 2012, Luijf et al, Diabetes Care 2013, Zisser et al, DTT Cobelli at al, Diabetes Care 2012, Kovatchev et al, Diabetes Care 2012; Del Favero et al, Diabetes Care 2013, Kovatchev et al, Diabetes Care 2013, Del Favero et al, DOM 2014; Brown et al, DTT 2014; Del Favero S et al, Diabetes Care # Kropff at al, Lancet DE 2015 ; Renard et al, Diabetes Care 2016;

24 Diabetes Assitant (DiAs): the AP wearable platform Remote Monitoring

25 Schermata del DiAs Stato del sistema IPOGLICEMIA Tasti per l uso IPERGLICEMIA

26 Schermata del DiAs Traccia CGM Bolo Profilo Basale Somministr. basale

27 Randomized cross-over study Open Loop 8 weeks Closed Loop Dinner & Night only 8 weeks Open Loop 8 weeks Closed Loop Dinner & Night only 8 weeks Amsterdam Montpellier Padova N=32 Adults Primary endpoint Free-living conditions % of time spent in the target glucose range ( mmol/l) from 20:00 to 08:00 h Secondary outcome HbA1c Kropff et al. The Lancet Diabetes Endocrinology, 2015

28 Randomized cross-over study Open Loop 8 weeks Closed Loop Evening & Night 8 weeks N=32 Adults Open Loop 8 weeks Closed Loop Evening & Night 8 weeks Amsterdam Montpellier Padova Open Loop (n=32) Evening & Night CL (n=32) Kropf et al. The Lancet Diabetes Endocrinology, 2015 p value From dinner until wake-up (20:00-08:00h) Time spent at glucose concentration, % mg/dl < mg/dl < >180 mg/dl < <70 mg/dl < <50 mg/dl Day and Night (24h) HbA1c, change % % +8.7%

29 Randomized study + Incremental follow-up Open Loop Open Loop 8 weeks 8 weeks Closed Loop + 24 h/24h Closed Loop Closed Loop 4 weeks Evening & Night Evening & Night 8 weeks 8 weeks Amsterdam Montpellier Padova N=32 Adults N=20 continuing OL (n=20) E & N CL (n=20) Renard E et al. Diabetes Care,2016 E&N CL and 24h/24h CL perform similarly, both better than SAP 24h/24h CL (n=20) Day and night (24 hours) Time spent at glucose concentration, % mg/dl * >180 mg/dl <70 mg/dl * 1.9* <50 mg/dl * vs OL

30 Nessun evento avverso serio

31 2-Phase Design: Phase 1: 1-Month trial of overnight (2 weeks) and 24/7 closed-loop control (2 weeks); N= 30 T1DM Phase 2: 5-Month extension - 24/7 closed-loop control; N=14 participants; University of Virginia; UC Santa Barbara and the William Sansum Center for Diabetes Research, Santa Barbara, California; Stanford University, California; University of Padova, Italy; Schneider Children s Medical Center, Israel; University of Montpellier, France; Jaeb Center for Health Research

32 6-month CLC Trial Key Results Variable Baseline End of Study P-value A1c [mean ± SD] %CGM < 70mg/dL [median (IQR)] %CGM < 60mg/dL [median (IQR)] %CGM < 50mg/dL [median (IQR)] 7.2 ± ± ( ) 1.3 ( ) < ( ) 0.3 ( ) < ( ) 0.1 ( ) < fold 7 fold 10 fold Kovatchev B et al, submitted

33 Outcomes were Related to System Use Among those who used the system > 70% of the time P=0.02 P=0.01 Closed Loop Open Loop Kovatchev B et al, submitted

34 Safety Outcomes by Subject (Entire Study - 6 patient years of system use) Ketone >1.0 Severe ID mmol/l DKA Hypoglycemia CTR CTR CTR CTR CTR CTR CTR CTR CTR CTR CTR CTR CTR CTR TOTAL 2 0 0

35 Next: Large-Scale Trial University of Virginia Harvard University Mount Sinai School of Medicine Mayo Clinic Barbara Davis Diabetes Center Stanford University William Sansum Diabetes Center Academic Medical Center Amsterdam University of Montpellier University of Padova Coordinated by the Jaeb Center for Health Research Design: N=240 participants in 6-month RCT comparing Control-to-Range vs. SAP Outcomes: HbA1c Incidence of hypoglycemia Objectives: Establish closed-loop control as a viable treatment for type 1 diabetes; Generate safety and efficacy data satisfying requirements by regulatory agencies; Demonstrate clinical effectiveness to facilitate reimbursement.

36 Esperienze di altri gruppi di ricerca

37 Transitional randomised closed-loop studies Reference Study population N Study setting CL- system vs Duration of intervention Outcomes Phillip M et al, 2013 Ly TT et al, 2015 Ly TT et al, 2015 Kovatchev B et al, 2014 Brown SA et al, 2015 Children/ adolescents Children/ adolescents Adolescents/ adults 56 Diabetes camp Single hormone 20 Diabetes camp Single hormone 21 Diabetes camp Single hormone Adults 18 Hotel Single hormone Adolescents/ adults 10 Hotel Single hormone SAP One night hypos* SAP Overnight x 5-6 days Tresho ld suspe nd Day and night x6 days SAP 40 h LBGI* % time in target % time in target SAP Overnight x 5 days % time in target* fasting BG Russel SJ et al, 2014 Russel SJ et al, 2016 Adolescents/ adults 52 Hotel, camp Bihormonal CSII Day and night x 5 days Children 19 Diabetes camp Bihormonal CSII Day and night x 5 days mean S glucose*, % time in hypo mean Sens. gluc* % time in hypo*

38 Home randomised closed-loop studies Reference Study population N Study setting CL- system vs Duration of intervention Outcomes Nimri R et al, 2014 Adolescents/ adults 24 Home Single hormone SAP Overnight x 6 weeks % time in hypo* Leelarathna L et al, 2014 Adults 17 Home Single hormone SAP Day and night x 1 week % time in target* Tauschmann M et al, 2016 Adolescents 12 Home Single hormone SAP Day and night x 1 week % time in target* Kropff J et al, 2015 Adults 32 Home Single hormone SAP Dinner time + nigth x 8 weeks % time in target* Thabit H et al, 2015 Children/ adolescents/ adults 58 Home Single hormone SAP Adults: day and night x 12 weeks. Children/adolescents: overnight x 12 weeks % time in target*

39 Home randomised closed-loop studies Reference Study population N Study setting CL- system vs Duration of intervention Outcomes Nimri R et al, 2014 Leelarathna L et al, 2014 Tauschmann M et al, 2016 Kropff J et al, 2015 Thabit H et al, 2015 Adolescents/ adults 24 Home Single hormone SAP Overnight x 6 weeks Adults Studi 17 di Home transizione Single e a SAP domicilio Day and night hormone x 1 week Adolescents 12 Home Single hormone Il pancreas artificiale: ipoglicemia glicemia media Children/ variabilità adolescents/ glicemica adults tempo in target Adults 32 Home Single hormone 58 Home Single hormone SAP SAP SAP Day and night x 1 week Dinner time + nigth x 8 weeks Adults: day and night x 12 weeks. Children/adolescents: overnight x 12 weeks % time in hypo* % time in target* % time in target* % time in target* % time in target*

40 Pancreas bi-ormonale Co-somministrazione di insulina e glucagone

41 Studio randomizato crossover Durata: 5 giorni Popolazione: 20 adulti (a casa di giorno, in hotel di notte), 32 adolescenti (in un campo scuola)

42 Mean glucose levels Adults 137 vs 158 mg/dl Adolescents 147 vs 158 mg/dl

43 Mean glucose levels Adults Adolescents

44 Pancreas «biormonale» co-somministrazione di insulina e glucagone Benefici Aumenta la protezione dall ipoglicemia Somministrazione di insulina più aggressiva Problemi Glucagone non stabile a T ambiente Rischi somministrazione a lungo termine Due pompe

45 Accettabilità del pancreas da parte del paziente Vantaggi più tempo libero dalla gestione del diabete, ridotta preoccupazione per la glicemia impatto positivo sulla paura dell ipoglicemia e miglioramento di altri indici di qualità della vita sensazione di normalità piuttosto che di malattia Svantaggi dimensione e numero dei devices problemi di connettività problemi di calibrazioni Barnard KD et al, Diabet Med 32: , 2015; Ziegler C, J Diabetes Res 2015: ;

46 Sfide e innovazioni Ritardo nell azione dell insulina Analoghi ultrarapidi dell insulina Insulina prandiale inalatoria Performance/accettabilità del sensore Sensore impiantabile di lunga durata Sensore precalibrato in fabbrica Device unica via per infusione di insulina e misura del glucosio Algoritmi di controllo Adattativi Individualizzati

47 Conclusioni Ampia disponibilità di devices Formazione del medico e del paziente Rimborsabilità

48 Grazie per la vostra attenzione Daniela Bruttomesso Azienda Ospedaliera-Università di Padova