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Cited by 29Diabetes Management and ResearchMetabolism and Genetic DisordersNeurological and metabolic disorders

Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023

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Worth, Chris, Hoskyns, Lucy, Salomon-Estebanez, Maria, Nutter, Paul W., Harper, Simon, Derks, Terry G.J, Beardsall, Kathy, Banerjee, Indraneel·2023·Frontiers in Endocrinology
DOI 10.3389/fendo.2023.1116864PMC PMC9900115
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Abstract

In 2023, childhood hypoglycaemia remains a major public health problem and significant risk factor for consequent adverse neurodevelopment. Irrespective of the underlying cause, key elements of clinical management include the detection, prediction and prevention of episodes of hypoglycaemia. These tasks are increasingly served by Continuous Glucose Monitoring (CGM) devices that measure subcutaneous glucose at near-continuous frequency. While the use of CGM in type 1 diabetes is well established, the evidence for widespread use in rare hypoglycaemia disorders is less than convincing. However, in the few years since our last review there have been multiple developments and increased user feedback, requiring a review of clinical application. Despite advances in device technology, point accuracy of CGM remains low for children with non-diabetes hypoglycaemia. Simple provision of CGM devices has not replicated the efficacy seen in those with diabetes and is yet to show benefit. Machine learning techniques for hypoglycaemia prevention have so far failed to demonstrate sufficient prediction accuracy for real world use even in those with diabetes. Furthermore, access to CGM globally is restricted by costs kept high by the commercially-driven speed of technical innovation. Nonetheless, the ability of CGM to digitally phenotype disease groups has led to a better understanding of natural history of disease, facilitated diagnoses and informed changes in clinical management. Large CGM datasets have prompted re-evaluation of hypoglycaemia incidence and facilitated improved trial design. Importantly, an individualised approach and focus on the behavioural determinants of hypoglycaemia has led to real world reduction in hypoglycaemia. In this state of the art review, we critically analyse the updated evidence for use of CGM in non-diabetic childhood hypoglycaemia disorders since 2020 and provide suggestions for qualified use.

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  • In a recent review by Worth et al., hypoglycaemia sensitivity of continuous glucose monitoring reduces as the threshold for hypoglycaemia decreases.

    “In a recent review by Worth et al. on the use of CGM, they found that hypoglycaemia sensitivity reduces as the threshold for hypoglycaemia decreases (61).”

    From Hypoglycaemia in adrenal insufficiency (2023) · cites this paper as [61]

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  1. [1]Kristensen K Quitmann J Witt S. Health-related quality of life of children and adolescents with congenital hyperinsulinism – a scoping review. Front Endocrinol (Lausanne) (2021) 12: 784932. doi: 10.3389/fendo.2021.784932 34925243 PMC8678977 · DOI
  2. [2]Pasquini TLS Mesfin M Schmitt J Raskin J. Global registries in congenital hyperinsulinism. Front Endocrinol (Lausanne) (2022) 13: 876903. doi: 10.3389/fendo.2022.876903 35721728 PMC9201947 · DOI
  3. [3]Eljamel S Griffiths A Evans J Banerjee I Hussain K Thompson R. The burden of congenital hyperinsulinism in the united kingdom: A cost of illness study. Orphanet J Rare Dis (2018) 13. doi: 10.1186/s13023-018-0867-6 PMC6054726 30029695 · DOI
  4. [4]Wild D von Maltzahn R Brohan E Christensen T Clauson P Gonder-Frederick L. A critical review of the literature on fear of hypoglycemia in diabetes: Implications for diabetes management and patient education. Patient Educ Couns (2007) 68: 10–5. doi: 10.1016/J.PEC.2007.05.003 17582726 · DOI · Paper page
  5. [5]Nordfeldt S Jonsson D. Short-term effects of severe hypoglycaemia in children and adolescents with type 1 diabetes. A cost-of-illness study Acta Pædiatrica (2001) 90: 137–42. doi: 10.1111/J.1651-2227.2001.TB00274.X 11236041 · DOI
  6. [6]Raskin J Pasquini TLS Bose S Tallis D Schmitt J. Congenital hyperinsulinism international: A community focused on improving the lives of people living with congenital hyperinsulinism. Front Endocrinol (Lausanne) (2022) 13: 886552. doi: 10.3389/fendo.2022.886552 35573986 PMC9097272 · DOI
  7. [7]Shah R Dai DWT Alsweiler JM Brown GTL Chase JG Gamble GD. Association of neonatal hypoglycemia with academic performance in mid-childhood. Jama (2022) 327: 1158. doi: 10.1001/jama.2022.0992 35315886 PMC8941348 · DOI
  8. [8]McKinlay CJD Alsweiler JM Ansell JM Anstice NS Chase JG Gamble GD. Neonatal glycemia and neurodevelopmental outcomes at 2 years. N Engl J Med (2015) 373: 1507–18. doi: 10.1056/NEJMoa1504909 PMC4646166 26465984 · DOI
  9. [10]Lucas A Morley R Cole TJ. Adverse neurodevelopmental outcome of moderate neonatal hypoglycaemia. (1988), British Medical Journal, Vol 297 1304–8. doi: 10.1136/bmj.297.6659.1304 PMC1834933 2462455 · DOI
  10. [11]Lord K Radcliffe J Gallagher PR Adzick NS Stanley CA De León DD. High risk of diabetes and neurobehavioral deficits in individuals with surgically treated hyperinsulinism. J Clin Endocrinol Metab (2015) 100: 4133–9. doi: 10.1210/jc.2015-2539 PMC4702456 26327482 · DOI
  11. [12]Worth C Yau D Salomon Estebanez M O’Shea E Cosgrove K Dunne M. Complexities in the medical management of hypoglycaemia due to congenital hyperinsulinism. Clin Endocrinol (Oxf) (2020) 92: 387–95. doi: 10.1111/cen.14152 31917867 · DOI
  12. [13]Worth C Dunne M Ghosh A Harper S Banerjee I. Continuous glucose monitoring for hypoglycaemia in children: Perspectives in 2020. Pediatr Diabetes (2020) 21: 1– 10. doi: 10.1111/pedi.13029 32315515 · DOI · Paper page
  13. [16]Wright EE Kerr MSD Reyes IJ Nabutovsky Y Miller E. Use of flash continuous glucose monitoring is associated with A1C reduction in people with type 2 diabetes treated with basal insulin or noninsulin therapy. Diabetes Spectr (2021) 34: 184. doi: 10.2337/DS20-0069 34149259 PMC8178717 · DOI · Paper page
  14. [17]Tauschmann M Hermann JM Freiberg C Papsch M Thon A Heidtmann B. Reduction in diabetic ketoacidosis and severe hypoglycemia in pediatric type 1 diabetes during the first year of continuous glucose monitoring: A multicenter analysis of 3,553 subjects from the DPV registry. Diabetes Care (2020) 43: e40–2. doi: 10.2337/DC19-1358 31969340 · DOI
  15. [18]Wan W Skandari MR Minc A Nathan AG Winn A Zarei P. Cost-effectiveness of continuous glucose monitoring for adults with type 1 diabetes compared with self-monitoring of blood glucose: The DIAMOND randomized trial. Diabetes Care (2018) 41: 1227–34. doi: 10.2337/DC17-1821 PMC5961392 29650803 · DOI
  16. [19]Roze S Isitt J Smith-Palmer J Javanbakht M Lynch P. Long-term cost-effectiveness of dexcom G6 real-time continuous glucose monitoring versus self-monitoring of blood glucose in patients with type 1 diabetes in the U.K. Diabetes Care (2020) 43: 2411–7. doi: 10.2337/DC19-2213 32647050 · DOI
  17. [21]Beardsall K Ogilvy-Stuart AL Ahluwalia J Thompson M Dunger DB. The continuous glucose monitoring sensor in neonatal intensive care. Arch Dis Child Fetal Neonatal Ed (2005) 90: 307–10. doi: 10.1136/adc.2004.051979 PMC1721924 16036889 · DOI
  18. [22]Beardsall K Vanhaesebrouck S Ogilvy-Stuart AL Vanhole C VanWeissenbruch M Midgley P. Validation of the continuous glucose monitoring sensor in preterm infants. Arch Dis Child Fetal Neonatal Ed (2013) 98: 136–40. doi: 10.1136/archdischild-2012-301661 22791467 · DOI · Paper page
  19. [23]Win M Beckett R Thomson L Thankamony A Beardsall K. Continuous glucose monitoring in the management of neonates with persistent hypoglycaemia and congenital hyperinsulinism. J Clin Endocrinol Metab (2020) 107: 1– 18. doi: 10.1210/clinem/dgab601 PMC8830056 34407200 · DOI
  20. [24]Vijayanand S Stevenson PG Grant M Choong CS Davis EA Abraham MB. The utility of continuous glucose monitoring systems in the management of children with persistent hypoglycaemia. J Pediatr Endocrinol Metab (2021) 34: 1567–72. doi: 10.1515/jpem-2021-0414 34520640 · DOI
  21. [25]Alsaffar H Turner L Yung Z Didi M Senniappan S. Continuous flash glucose monitoring in children with congenital hyperinsulinism; first report on accuracy and patient experience. Int J Pediatr Endocrinol (2018) 2018. doi: 10.1186/s13633-018-0057-2 PMC5870486 29599801 · DOI
  22. [27]Worth C Dunne MJ Salomon-Estebanez M Harper S Nutter PW Dastamani A. The hypoglycaemia error grid: a UK-wide consensus on CGM accuracy assessment in hyperinsulinism. Front Endocrinol (Lausanne) (2022). doi: 10.3389/fendo.2022.1016072 PMC9666389 36407313 · DOI
  23. [28]Kasapkara CS Cinasal Demir G Hasanoğlu A Tümer L. Continuous glucose monitoring in children with glycogen storage disease type i. Eur J Clin Nutr (2014) 68: 101–5. doi: 10.1038/ejcn.2013.186 24149443 · DOI
  24. [29]Herbert M Pendyal S Rairikar M Halaby C Benjamin RW Kishnani PS. Role of continuous glucose monitoring in the management of glycogen storage disorders. J Inherit Metab Dis (2018) 41: 917–27. doi: 10.1007/s10545-018-0200-5 29802555 · DOI
  25. [30]Rossi A Venema A Haarsma P Feldbrugge L Burghard R Rodriguez-Buritica D. A prospective study on continuous glucose monitoring in glycogen storage disease type ia: Toward glycemic targets. J Clin Endocrinol Metab (2022) 107: e3612–23. doi: 10.1210/clinem/dgac411 PMC9387687 35786777 · DOI
  26. [31]Fernández Martínez M del M Llorente JLG de Cabo JM López MAV Porcel M del CO Rubio JDD. Monitoring the frequency and duration of hypoglycemia in preterm infants and identifying associated factors. Fetal Pediatr Pathol (2021) 40: 131–41. doi: 10.1080/15513815.2019.1692111 31738633 · DOI
  27. [32]Beardsall K Thomson L Guy C Iglesias-Platas I van Weissenbruch MM Bond S. Real-time continuous glucose monitoring in preterm infants (REACT): an international, open-label, randomised controlled trial. Lancet Child Adolesc Heal (2021) 5: 265–73. doi: 10.1016/S2352-4642(20)30367-9 PMC7970623 33577770 · DOI
  28. [33]Denkboy Ongen Y Eren E Saglam H. Maltodextrin may be a promising treatment modality after near-total pancreatectomy in infants younger than six months with persistent hyperinsulinism: A case report. J Clin Res Pediatr Endocrinol (2012) 0: 0– 0. doi: 10.4274/jcrpe.galenos.2021.2021.0121 PMC9976159 34477356 · DOI
  29. [34]Kiuchi K Sato T Nakano S Ishii T Hasegawa T. Continuous glucose monitoring in an infant with panhypopituitarism having hypoglycemia on growth hormone therapy. J Pediatr Endocrinol Metab (2022) 2975: 1– 4. doi: 10.1515/jpem-2022-0224 36113970 · DOI
  30. [35]Worth C Nutter PW Salomon-Estebanez M Auckburally S Dunne MJ Banerjee I. The behaviour change behind a successful pilot of hypoglycaemia reduction with HYPO-CHEAT. Under Rev Digit Heal (2022). PMC10403985 37545627
  31. [36]White FJ Jones SA. The use of continuous glucose monitoring in the practical management of glycogen storage disorders. J Inherit Metab Dis (2011) 34: 631–42. doi: 10.1007/s10545-011-9335-3 21556835 · DOI
  32. [37]Du CQ Wei H Zhang C Zhang M Hu MH Hou L. [The application of continuous glucose monitoring in the management of hepatic glycogen storage disease]. Chin J Pediatr (2021) 59: 452–8. doi: 10.3760/cma.j.cn112140-20210321-00235 34102817 · DOI
  33. [38]Kaiser N Gautschi M Bosanska L Meienberg F Baumgartner MR Spinas GA. Glycemic control and complications in glycogen storage disease type I: Results from the Swiss registry. Mol Genet Metab (2019) 126: 355–61. doi: 10.1016/j.ymgme.2019.02.008 30846352 · DOI
  34. [39]Esper BS Carson M Galvin-Parton P Lane AH Tafuri KS. Continuous glucose monitoring in the management of hypoglycemia in TANGO2. J Endocr Soc (2021) 5: A699–9. doi: 10.1210/jendso/bvab048.1423 · DOI
  35. [40]Ng SM Dearman S Fisher M Mushtaq T. Case for funding of continuous glucose monitoring systems for patients with recurrent hypoglycaemia. Arch Dis Child (2022), 1– 2. doi: 10.1136/archdischild-2022-323872 36202595 · DOI
  36. [41]Peeks F Boonstra WF De BL Carøe C. Research priorities for liver glycogen storage disease: An international priority setting partnership with the. J Inherit Metab Dis (2020) 43: 279–89. doi: 10.1002/jimd.12178 PMC7079148 31587328 · DOI
  37. [42]Peeks F Hoogeveen IJ Feldbrugge RL Burghard R de Boer F Fokkert-Wilts MJ. A retrospective in-depth analysis of continuous glucose monitoring datasets for patients with hepatic glycogen storage disease: Recommended outcome parameters for glucose management. J Inherit Metab Dis (2021) 44: 1136–50. doi: 10.1002/jimd.12383 PMC8519135 33834518 · DOI
  38. [43]Auckburally S Worth C Salomon-Estebanez M Nutter PW Harper S Nicholson J. Families’ experiences of continuous glucose monitoring in the management of congenital hyperinsulinism: A thematic analysis. Front Endocrinol (Lausanne) (2022). doi: 10.3389/fendo.2022.894559 PMC9343578 35928891 · DOI
  39. [46]Bila R Varo R Madrid L Sitoe A Bassat Q. Continuous glucose monitoring in resource-constrained settings for hypoglycaemia detection: Looking at the problem from the other side of the coin. Biosensors (2018) 8: 2– 7. doi: 10.3390/bios8020043 PMC6023081 29693557 · DOI
  40. [49]Anderson S Shakya KN Shrestha LN De AM. Hypoglycaemia: A common problem among uncomplicated newborn infants in Nepal. J Trop Pediatr (1993) 39: 273–7. doi: 10.1093/tropej/39.5.273 8271333 · DOI
  41. [50]Maynard KR Causey L Kawaza K Dube Q Lufesi N Oden ZM. New technologies for essential newborn care in under-resourced areas: What is needed and how to deliver it. Paediatr Int Child Health (2015) 35: 192– 205. doi: 10.1179/2046905515Y.0000000034 26053669 · DOI
  42. [51]Burns CM Rutherford MA Boardman JP Cowan FM. Patterns of cerebral injury and neurodevelopmental outcomes after symptomatic neonatal hypoglycemia. Pediatrics (2008) 122: 65– 74. doi: 10.1542/peds.2007-2822 18595988 · DOI
  43. [52]Kariuki SM Thomas PT Newton CR. Epilepsy stigma in children in low-income and middle-income countries. Lancet Child Adolesc Heal (2021) 5: 314–6. doi: 10.1016/S2352-4642(21)00090-0 PMC7613551 33864740 · DOI
  44. [53]Dua T De Boer HM Prilipko LL Saxena S. Epilepsy care in the world: Results of an ILAE/IBE/WHO global campaign against epilepsy survey. Epilepsia (2006) 47: 1225–31. doi: 10.1111/j.1528-1167.2006.00595.x 16886987 · DOI
  45. [54]Smythe T Adelson JD Polack S. Systematic review of interventions for reducing stigma experienced by children with disabilities and their families in low- and middle-income countries: state of the evidence. Trop Med Int Heal (2020) 25: 508–24. doi: 10.1111/tmi.13388 32145136 · DOI
  46. [56]Venema A Peeks F Rossi A Jager EA Derks TGJ. Towards values-based healthcare for inherited metabolic disorders: An overview of current practices for persons with liver glycogen storage disease and fatty acid oxidation disorders. J Inherit Metab Dis (2022) 45: 1018–27. doi: 10.1002/jimd.12555 PMC9828459 36088581 · DOI
  47. [57]Schopman S Kalchthaler K Malthern R Mehta K Butler P. Ruggedising biomedical devices for field-testing in resource-constrained environments: Context, issues and solutions. J Humanit Eng (2013) 2: 17– 26. doi: 10.36479/jhe.v2i1.17 · DOI
  48. [58]Howitt P Darzi A Yang GZ Ashrafian H Atun R Barlow J. Technologies for global health. Lancet (2012) 380: 507–35. doi: 10.1016/S0140-6736(12)61127-1 22857974 · DOI
  49. [59]Free MJ. Achieving appropriate design and widespread use of health care technologies in the developing world. overcoming obstacles that impede the adaptation and diffusion of priority technologies for primary health care. Int J Gynecol Obstet (2004) 85: 3– 13. doi: 10.1016/j.ijgo.2004.01.009 15147849 · DOI
  50. [60]Kasali BA Gururaj A Batra M. Newborn care technology investments for LMIC settings: a CPAP approach. BMJ Innov (2021) 7: 519–22. doi: 10.1136/bmjinnov-2020-000598 · DOI
  51. [61]Ahmed I Jiang N Shao X Elsherif M Alam F Salih A. Recent advances in optical sensors for continuous glucose monitoring. Sensors Diagnostics (2022). doi: 10.1039/d1sd00030f · DOI
  52. [62]Zafar H Channa A Jeoti V Stojanović GM. Comprehensive review on wearable sweat-glucose sensors for continuous glucose monitoring. Sensors (2022) 22: 1– 35. doi: 10.3390/s22020638 PMC8781973 35062598 · DOI
  53. [63]Kumar Das S Nayak KK Krishnaswamy PR Kumar V Bhat N. Review–electrochemistry and other emerging technologies for continuous glucose monitoring devices. ECS Sensors Plus (2022) 1: 031601. doi: 10.1149/2754-2726/ac7abb · DOI
  54. [65]Leelarathna L English SW Thabit H Caldwell K Allen JM Kumareswaran K. Feasibility of fully automated closed-loop glucose control using continuous subcutaneous glucose measurements in critical illness: A randomized controlled trial. Crit Care (2013) 17. doi: 10.1186/cc12838 PMC4056260 23883613 · DOI
  55. [66]Beardsall K Thomson L Elleri D Dunger DB Hovorka R. Feasibility of automated insulin delivery guided by continuous glucose monitoring in preterm infants. Arch Dis Child Fetal Neonatal Ed (2020) 105: F279–84. doi: 10.1136/archdischild-2019-316871 PMC7363782 31399480 · DOI
  56. [67]Bertachi A Viñals C Biagi L Contreras I Vehí J Conget I. Prediction of nocturnal hypoglycemia in adults with type 1 diabetes under multiple daily injections using continuous glucose monitoring and physical activity monitor. Sensors 2020 Vol 20 Page 1705 (2020) 20: 1705. doi: 10.3390/S20061705 PMC7147466 32204318 · DOI
  57. [68]Amar Y Shilo S Oron T Amar E Phillip M Segal E. Clinically accurate prediction of glucose levels in patients with type 1 diabetes(2020). Available at: https://home.liebertpub.com/dia. 31928415
  58. [69]Elhadd T Mall R Bashir M Palotti J Fernandez-Luque L Farooq F. Artificial intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST – IT Ramadan study). Diabetes Res Clin Pract (2020) 169: 108388. doi: 10.1016/J.DIABRES.2020.108388 32858096 · DOI
  59. [70]Dave D DeSalvo DJ Haridas B McKay S Shenoy A Koh CJ. Feature-based machine learning model for real-time hypoglycemia prediction. J Diabetes Sci Technol (2020) 15 (4), 842–855. doi: 10.1177/1932296820922622 PMC8258517 32476492 · DOI
  60. [71]Mujahid O Contreras I Vehi J. Machine learning techniques for hypoglycemia prediction: Trends and challenges. Sensors (Switzerland) (2021) 21: 1– 21. doi: 10.3390/s21020546 PMC7828835 33466659 · DOI
  61. [72]Kodama S Fujihara K Shiozaki H Horikawa C Yamada MH Sato T. Ability of current machine learning algorithms to predict and detect hypoglycemia in patients with diabetes mellitus: meta-analysis. JMIR Diabetes (2021) 6. doi: 10.2196/22458 PMC7880810 33512324 · DOI
  62. [73]Tyler NS Mosquera-Lopez CM Wilson LM Dodier RH Branigan DL Gabo VB. An artificial intelligence decision support system for the management of type 1 diabetes. Nat Metab (2020) 2: 612–9. doi: 10.1038/s42255-020-0212-y PMC7384292 32694787 · DOI
  63. [74]Liu C Avari P Leal Y Wos M Sivasithamparam K Georgiou P. A modular safety system for an insulin dose recommender: A feasibility study. J Diabetes Sci Technol (2020) 14: 87– 96. doi: 10.1177/1932296819851135 31117804 PMC7189144 · DOI
  64. [75]Tyler NS Jacobs PG. Artificial intelligence in decision support systems for type 1 diabetes. Sensors (Switzerland) (2020) 20: 1– 26. doi: 10.3390/s20113214 PMC7308977 32517068 · DOI
  65. [76]Nano J Carinci F Okunade O Whittaker S Walbaum M Barnard-Kelly K. A standard set of person-centred outcomes for diabetes mellitus: results of an international and unified approach. Diabetes Med (2020) 37: 2009–18. doi: 10.1111/dme.14286 32124488 · DOI
  66. [77]Agiostratidou G Anhalt H Ball D Blonde L Gourgari E Harriman KN. Standardizing clinically meaningful outcome measures beyond HbA1c for type 1 diabetes: A consensus report of the American association of clinical endocrinologists, the American association of diabetes educators, the American diabetes association, the endo. Diabetes Care (2017) 40: 1622–30. doi: 10.2337/dc17-1624 PMC5864122 29162582 · DOI · Paper page
  67. [79]Flores M Glusman G Brogaard K Price ND Hood L. P4 medicine: How systems medicine will transform the healthcare sector and society. Per Med (2013) 10: 565–76. doi: 10.2217/pme.13.57 PMC4204402 25342952 · DOI
  68. [80]Worth C Harper S Salomon-Estebanez M O’Shea E Nutter PW Dunne MJ. Indraneel Banerjee,MBBS m. clustering of hypoglycemia events in patients with hyperinsulinism: Extension of the digital phenotype through retrospective data analysis. J Med Internet Res (2021) 23: 1– 14. doi: 10.2196/26957 PMC8590184 34435596 · DOI
  69. [81]Worth C Nutter PW Dunne MJ Salomon-Estebanez M Banerjee I Harper S. HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia. Digit Heal (2022) 8: 1– 22. doi: 10.1177/20552076221129712 PMC9580093 36276186 · DOI
  70. [82]Worth C Tropeano Y Gokul PR Cosgrove KE Salomon-Estebanez M Senniappan S. Insight into hypoglycemia frequency in congenital hyperinsulinism: evaluation of a large UK CGM dataset. BMJ Open Diabetes Res Care (2022) 10: e002849. doi: 10.1136/BMJDRC-2022-002849 PMC9185472 35675953 · DOI
  71. [84]Woldaregay AZ Årsand E Walderhaug S Albers D Mamykina L Botsis T. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Artif Intell Med (2019) 98: 109–34. doi: 10.1016/j.artmed.2019.07.007 31383477 · DOI · Paper page
  72. [85]Ehrhardt N Al Zaghal E. Behavior modification in prediabetes and diabetes: Potential use of real-time continuous glucose monitoring. J Diabetes Sci Technol (2019) 13: 271–5. doi: 10.1177/1932296818790994 PMC6399786 30066574 · DOI
  73. [86]Ehrhardt N Al Zaghal E. Continuous glucose monitoring as a behavior modification tool. Clin Diabetes (2020) 38: 126–31. doi: 10.2337/cd19-0037 PMC7164990 32327884 · DOI
  74. [87]Schembre SM Liao Y Jospe MR. Continuous glucose monitors as wearable lifestyle behavior change tools in obesity and diabetes. Obes Diabetes (2020), 591– 603. doi: 10.1007/978-3-030-53370-0_43 · DOI
  75. [90]Dubose SN Kanapka LG Bradfield B Sooy M Beck RW Steck AK. Continuous glucose monitoring profiles in healthy, nondiabetic young children. J Endocr Soc (2022) 6: 1– 7. doi: 10.1210/jendso/bvac060 PMC9049110 35506147 · DOI
  76. [91]Rossi A Rutten MGS van Dijk TH Bakker BM Reijngoud DJ Oosterveer MH. Dynamic methods for childhood hypoglycemia phenotyping: A narrative review. Front Endocrinol (Lausanne) (2022) 13: 858832. doi: 10.3389/fendo.2022.858832 35789807 PMC9249565 · DOI
  77. [92]Logel SN Connor EL Hsu DA Fenske RJ Paloian NJ De Vivo DC. Exploring diazoxide and continuous glucose monitoring as treatment for Glut1 deficiency syndrome. Ann Clin Transl Neurol (2021) 8: 2205–9. doi: 10.1002/ACN3.51462 PMC8607448 34612610 · DOI
  78. [93]Sadhwani A Asaro LA Goldberg CS Ware J Butcher J Gaies M. Impact of tight glycemic control and hypoglycemia after pediatric cardiac surgery on neurodevelopmental outcomes at three years of age: Findings from a randomized clinical trial. BMC Pediatr (2022) 22: 1– 10. doi: 10.1186/s12887-022-03556-z 36071424 PMC9450419 · DOI
  79. [94]Banerjee I De Leon DD Kendall DM Birch S Bøge E Ivkovic J. Dasiglucagon treatment over 21 days in infants with congenital hyperinsulinism results in glycaemic stability and reduces requirement for intravenous glucose IN 60th annual meeting of the European society for paediatric endocrinology (ESPE). Horm Res Paediatr (2022) 95(2). doi: 10.1159/000525606 · DOI
  80. [95]Demirbilek H Melikyan M Galcheva S Dastamani A Thornton P De Leon D. Results from a global, multi-center, phase 2b study (RIZE) in congenital hyperinsulinism: Characterization of a high unmet treatment need and glycemic response to RZ358 IN 60th annual meeting of the European society for paediatric endocrinology (ESPE). Horm Res Paediatr (2022) 95(2). doi: 10.1159/000525606 · DOI

Authors
Worth, C., Hoskyns, L., Salomon-Estebanez, M., Nutter, P. W., Harper, S., Derks, T. G. J., Beardsall, K., & Banerjee, I.
Affiliations
  • University of Manchester (GB)
  • Royal Manchester Children's Hospital (GB)
  • University of Groningen (NL)
Corresponding author
Worth, Chris
ORCID
Venue
Frontiers in Endocrinology
Publisher
Frontiers Media SA
Published
2023-01-23
Volume
14
Pages
1116864-1116864
ISSN
1664-2392 (ISSN-L)
DOI
10.3389/fendo.2023.1116864
PMC
PMC9900115
OpenAlex
W4317722328
Cited by
29
References
90
Citations by year
2026: 8 · 2025: 3 · 2024: 11 · 2023: 7
FWCI
1.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Endocrinology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Explorer (The University of Manchester) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo
Funders
National Institute for Health and Care Research (11/133/07)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Neurological and metabolic disorders
Keywords
continuous glucose monitoring, hypoglycemia prevention, type 1 diabetes, machine learning, CGM accuracy, neurodevelopmental risk, hypoglycaemia incidence, digital phenotyping
MeSH terms
Blood Glucose, Diabetes Mellitus, Type 1, Humans, Hypoglycemia, Risk Factors, Blood Glucose Self-Monitoring

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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