Evans, Mark L

Golubic, Rajna

English

Apollo (Cambridge)

Technology to overcome clinical inertia in insulin therapy?The evidence that blood glucose lowering in diabetes can reduce risk of complications is well established. Despite this, many people with type 2 diabetes fail to reach recommended glycaemic targets. HYPERLINK \l "_ENREF_1" \o "Selvin, 2016 #10"  ADDIN EN.CITE <EndNote><Cite><Author>Selvin</Author><Year>2016</Year><RecNum>10</RecNum><DisplayText><style face="superscript">1</style></DisplayText><record><rec-number>10</rec-number><foreign-keys><key app="EN" db-id="zzadrzdf15099eeesaw5prrx0ddfz0sxtrs2" timestamp="1550183494">10</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Selvin, E.</author><author>Parrinello, C. M.</author><author>Daya, N.</author><author>Bergenstal, R. M.</author></authors></contributors><auth-address>Department of Epidemiology and Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD eselvin@jhu.edu.&#xD;Department of Epidemiology and Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.&#xD;International Diabetes Center, Park Nicollet Health Services, Minneapolis, MN.</auth-address><titles><title>Trends in Insulin Use and Diabetes Control in the U.S.: 1988-1994 and 1999-2012</title><secondary-title>Diabetes Care</secondary-title><alt-title>Diabetes care</alt-title></titles><periodical><full-title>Diabetes Care</full-title><abbr-1>Diabetes care</abbr-1></periodical><alt-periodical><full-title>Diabetes Care</full-title><abbr-1>Diabetes care</abbr-1></alt-periodical><pages>e33-5</pages><volume>39</volume><number>3</number><dates><year>2016</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>1935-5548 (Electronic)&#xD;0149-5992 (Linking)</isbn><accession-num>26721815</accession-num><urls><related-urls><url>http://www.ncbi.nlm.nih.gov/pubmed/26721815</url></related-urls></urls><custom2>4764038</custom2><electronic-resource-num>10.2337/dc15-2229</electronic-resource-num></record></Cite></EndNote>1 Type 2 diabetes is typically a progressive disease with blood glucose-lowering therapy needing to be increased stepwise to achieve and maintain glucose targets. Data from many health care systems show that there are delays in intensifying therapies in those failing to reach glycaemic targets. ADDIN EN.CITE , As a consequence, many people with diabetes spend years with poor glycaemic control. Why does this happen? There are some obvious challenges. Diabetes care is frequently provided in busy generalist/ primary care services with numerous competing time demands. There may be a reluctance by providers and patients to move from oral to injectable therapy (whether insulin or GLP-1 agonists) and a lack of time, confidence and diabetes education resources available to support training and dose titration. With insulin, there may be additional specific concerns about potential weight gain and risk of hypoglycaemia. One potential solution is to use computerised algorithms to inform and drive insulin dosing decisions more frequently. Reported in this edition of the Lancet, Bergenstal et al examined a handheld glucose meter (d-Nav Insulin Guidance System) with an inbuilt algorithm providing dose advice based on measured blood glucose. HYPERLINK \l "_ENREF_4" \o "Bergenstal, 2019 #87"  ADDIN EN.CITE <EndNote><Cite><Author>Bergenstal</Author><Year>2019</Year><RecNum>87</RecNum><DisplayText><style face="superscript">4</style></DisplayText><record><rec-number>87</rec-number><foreign-keys><key app="EN" db-id="zzadrzdf15099eeesaw5prrx0ddfz0sxtrs2" timestamp="1550184945">87</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bergenstal, R. M.</author></authors></contributors><titles><title>Automated Insulin Dosing Guidance to Optimize Insulin Management in Patients with Type 2 Diabetes; A Multi-Center Randomized-Controlled Trial</title><secondary-title>Lancet</secondary-title></titles><periodical><full-title>Lancet</full-title></periodical><dates><year>2019</year></dates><urls></urls></record></Cite></EndNote>4 The system analyses glucose patterns and provides dosing advice direct to patients at time of injections for four common insulin regimens (once daily basal, twice daily biphasic and basal bolus with or without carbohydrate counting). Here, 181 adults with insulin-treated type 2 diabetes and sub-optimal control (HbA1c > 58 mmol/mol: 7·5%) were studied in a randomised (1:1) parallel arm design. The intervention group used the d-Nav device in combination with health care professional support whereas the control group continued with a standard meter for glucose monitoring but otherwise received identical health care professional contact. The primary outcome was change in HbA1c assessed at six-months.Glycaemic control improved more in the group using the d-Nav system than in the control group (HbA1c decrease of 11 vs 3 mmol/mol or 1·0 vs 0·3%). On average, the algorithm adjusted insulin doses 1·1 times weekly- a frequency of adjustment which would be pragmatically unachievable in routine practice. As expected, this was achieved with a marked increase in insulin administration in the group using the d-Nav with final doses being over 60% greater than the control group. Despite higher insulin doses, weight gain was minimal and the overall rates of hypoglycaemia were low in both groups and no greater in the titration group. Overall, given that the system markedly increased insulin doses, it is interesting that there was no obvious increase in hypoglycaemia. Unlike many other current diabetes studies, continuous glucose monitoring was not performed so hypoglycaemia data are largely based on capturing low glucose values during self-monitoring. Of note, the group randomised to use the d-Nav device performed more glucose tests, perhaps reflecting the users’ perceived value of the system. Importantly for safety, the algorithm can either decrease or increase doses and about 15% of dose titrations during the study were dose reductions. The population studied here had relatively low rates of hypoglycaemia though and an important question for more widespread future use would be whether automated insulin dose titration would result in increased hypoglycaemia in those at higher baseline risk. At face value, the approach looks appealing. The studied population appeared typical for many services and the ability of the system to cope with different insulin regimens (including those who switched insulin regimens during the study) and to provide advice direct to patients is valuable. The reported metabolic outcomes are similar to those reported in an earlier single arm “service evaluation” assessment of the same system in a UK setting HYPERLINK \l "_ENREF_5" \o "Donnelly, 2015 #115"  ADDIN EN.CITE <EndNote><Cite><Author>Donnelly</Author><Year>2015</Year><RecNum>115</RecNum><DisplayText><style face="superscript">5</style></DisplayText><record><rec-number>115</rec-number><foreign-keys><key app="EN" db-id="zzadrzdf15099eeesaw5prrx0ddfz0sxtrs2" timestamp="1550185429">115</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Donnelly, R.</author><author>Carr, S.</author><author>Harper, R.</author></authors></contributors><titles><title>Diabetes Insulin Guidance System: a real-world evaluation of new technology (d-Nav) to achieve glycaemic control in insulin-treated type 2 diabetes</title><secondary-title>Practical Diabetes</secondary-title></titles><periodical><full-title>Practical Diabetes</full-title></periodical><pages>247-252</pages><volume>32</volume><number>7</number><dates><year>2015</year></dates><urls></urls></record></Cite></EndNote>5 and a broadly similar system in which dose advice was fed back to health care providers. HYPERLINK \l "_ENREF_6" \o "Davidson, 2019 #116"  ADDIN EN.CITE <EndNote><Cite><Author>Davidson</Author><Year>2019</Year><RecNum>116</RecNum><DisplayText><style face="superscript">6</style></DisplayText><record><rec-number>116</rec-number><foreign-keys><key app="EN" db-id="zzadrzdf15099eeesaw5prrx0ddfz0sxtrs2" timestamp="1550185526">116</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Davidson, M. B.</author><author>Davidson, S. J.</author></authors></contributors><auth-address>Charles R. Drew University, Los Angeles, CA, USA. mayerdavidson@cdrewu.edu.&#xD;Mellitus Health Inc., Los Angeles, CA, USA. mayerdavidson@cdrewu.edu.&#xD;Mellitus Health Inc., Los Angeles, CA, USA.</auth-address><titles><title>Effect of Remote Glucose Monitoring Utilizing Computerized Insulin Dose Adjustment Algorithms: A Pilot Project</title><secondary-title>Diabetes Ther</secondary-title><alt-title>Diabetes therapy : research, treatment and education of diabetes and related disorders</alt-title></titles><periodical><full-title>Diabetes Ther</full-title><abbr-1>Diabetes therapy : research, treatment and education of diabetes and related disorders</abbr-1></periodical><alt-periodical><full-title>Diabetes Ther</full-title><abbr-1>Diabetes therapy : research, treatment and education of diabetes and related disorders</abbr-1></alt-periodical><dates><year>2019</year><pub-dates><date>Feb 5</date></pub-dates></dates><isbn>1869-6953 (Print)</isbn><accession-num>30721451</accession-num><urls><related-urls><url>http://www.ncbi.nlm.nih.gov/pubmed/30721451</url></related-urls></urls><electronic-resource-num>10.1007/s13300-019-0565-y</electronic-resource-num></record></Cite></EndNote>6As expected in the context of a clinical trial, both groups had frequent contact with study team with seven face to face or telephone contacts over the six month period. The authors are careful to describe the intervention as d-Nav delivered with health care professional support. During contacts, study teams checked overall wellbeing and health changes etc. but also assessed use of the device and could also suggest alterations in insulin doses. In the real world, if this was an essential requirement for success of the system, this frequency of contact would be challenging for many services.  Finally and importantly, how comfortable would patients and clinical teams be in allowing an algorithm to manage insulin dose titration? Generally, participants in this study were comfortable with receiving dose advice from the device. The world of type 1 diabetes with far greater complexity of insulin dosing is already moving rapidly towards automated closed loop insulin delivery.7 Faced with increasing pressures and demands on primary care, it seems likely that there is a significant niche for technology to help in type 2 diabetes. Disclosures:RG reports no conflicts of interest. MLE reports personal fees from Roche, Medtronic, Cellnovo, Dexcom, Abbott Diabetes Care, Eli Lilly, NovoNordisk, Astra Zeneca. MLE is planning a research collaboration with with Mellitus Health (a Company supporting insulin dose titration).References1.	Selvin E, Parrinello CM, Daya N, Bergenstal RM. Trends in Insulin Use and Diabetes Control in the U.S.: 1988-1994 and 1999-2012. Diabetes Care 2016; 39(3): e33-5.2.	Khunti K, Millar-Jones D. Clinical inertia to insulin initiation and intensification in the UK: A focused literature review. Primary Care Diabetes 2017; 11(1): 3-12.3.	Harris S, Yale JF, Dempsey E, Gerstein H. Can family physicians help patients initiate basal insulin therapy successfully?: randomized trial of patient-titrated insulin glargine compared with standard oral therapy: lessons for family practice from the Canadian INSIGHT trial. Canadian Family Physician Medecin de famille Canadien 2008; 54(4): 550-8.4.	Bergenstal RM. Automated Insulin Dosing Guidance to Optimize Insulin Management in Patients with Type 2 Diabetes; A Multi-Center Randomized-Controlled Trial. Lancet 2019.5.	Donnelly R, Carr S, Harper R. Diabetes Insulin Guidance System: a real-world evaluation of new technology (d-Nav) to achieve glycaemic control in insulin-treated type 2 diabetes. Practical Diabetes 2015; 32(7): 247-52.6.	Davidson MB, Davidson SJ. Effect of Remote Glucose Monitoring Utilizing Computerized Insulin Dose Adjustment Algorithms: A Pilot Project. Diabetes Therap 2019. https://doi.org/10.1007/s13300-019-0565-y7. 	Bally L, Thabit H, Kojzar H, Mader JK, Qerimi-Hyseni J, Hartnell S, Tauschmann M, Allen JM, Wilinska ME, Pieber TR, Evans ML & Hovorka R. Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study. Lancet Diabetes Endocrinol 2017; 5: 261-270.2.	Khunti K, Millar-Jones D. Clinical inertia to insulin initiation and intensification in the UK: A focused literature review. Primary Care Diabetes 2017; 11(1): 3-12.3.	Harris S, Yale JF, Dempsey E, Gerstein H. Can family physicians help patients initiate basal insulin therapy successfully?: randomized trial of patient-titrated insulin glargine compared with standard oral therapy: lessons for family practice from the Canadian INSIGHT trial. Canadian Family Physician Medecin de famille Canadien 2008; 54(4): 550-8.4.	Bergenstal RM. Automated Insulin Dosing Guidance to Optimize Insulin Management in Patients with Type 2 Diabetes; A Multi-Center Randomized-Controlled Trial. Lancet 2019.5.	Donnelly R, Carr S, Harper R. Diabetes Insulin Guidance System: a real-world evaluation of new technology (d-Nav) to achieve glycaemic control in insulin-treated type 2 diabetes. Practical Diabetes 2015; 32(7): 247-52.6.	Davidson MB, Davidson SJ. Effect of Remote Glucose Monitoring Utilizing Computerized Insulin Dose Adjustment Algorithms: A Pilot Project. Diabetes Therap 2019. https://doi.org/10.1007/s13300-019-0565-y7. 	Bally L, Thabit H, Kojzar H, Mader JK, Qerimi-Hyseni J, Hartnell S, Tauschmann M, Allen JM, Wilinska ME, Pieber TR, Evans ML & Hovorka R. Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study. Lancet Diabetes Endocrinol 2017; 5: 261-270.

Technology to overcome clinical inertia in insulin therapy.

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Technology to overcome clinical inertia in insulin therapy.

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