Literature DB >> 25347226

A decision support tool for appropriate glucose-lowering therapy in patients with type 2 diabetes.

F Javier Ampudia-Blasco1, Pierre Yves Benhamou, Guillaume Charpentier, Agostino Consoli, Michaela Diamant, Baptist Gallwitz, Kamlesh Khunti, Chantal Mathieu, Martin Ridderstråle, Jochen Seufert, Cees Tack, Tina Vilsbøll, Tra-Mi Phan, Herman Stoevelaar.   

Abstract

BACKGROUND: Optimal glucose-lowering therapy in type 2 diabetes mellitus requires a patient-specific approach. Although a good framework, current guidelines are insufficiently detailed to address the different phenotypes and individual needs of patients seen in daily practice. We developed a patient-specific decision support tool based on a systematic analysis of expert opinion.
MATERIALS AND METHODS: Based on the American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) 2012 position statement, a panel of 12 European experts rated the appropriateness (RAND/UCLA Appropriateness Method) of treatment strategies for 930 clinical scenarios, which were permutations of clinical variables considered relevant to treatment choice. These included current treatment, hemoglobin A1c difference from individualized target, risk of hypoglycemia, body mass index, life expectancy, and comorbidities. Treatment options included addition of a second or third agent, drug switches, and replacement by monotherapies if the patient was metformin-intolerant. Treatment costs were not considered. Appropriateness (appropriate, inappropriate, uncertain) was based on the median score and expert agreement. The panel recommendations were embedded in an online decision support tool (DiaScope(®); Novo Nordisk Health Care AG, Zürich, Switzerland).
RESULTS: Treatment appropriateness was associated with (combinations of) the patient variables mentioned above. As second-line agents, dipeptidyl peptidase-4 inhibitors were considered appropriate in all scenarios, followed by glucagon-like peptide-1 receptor agonists (50%), insulins (33%), and sulfonylureas (25%), but not pioglitazone (0%). Ratings of third-line combinations followed a similar pattern. Disagreement was highest for regimens including pioglitazone, sulfonylureas, or insulins and was partly due to differences in panelists' opinions and in drug availability and reimbursement across European countries (although costs were disregarded in the rating process).
CONCLUSIONS: A novel decision support tool based on the ADA/EASD 2012 position statement and a systematic analysis of expert opinion has been developed to help healthcare professionals to individualize glucose-lowering therapy in daily clinical situations.

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Year:  2014        PMID: 25347226      PMCID: PMC4346378          DOI: 10.1089/dia.2014.0260

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  27 in total

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Authors:  S E Inzucchi; R M Bergenstal; J B Buse; M Diamant; E Ferrannini; M Nauck; A L Peters; A Tsapas; R Wender; D R Matthews
Journal:  Diabetologia       Date:  2012-04-20       Impact factor: 10.122

2.  Saxagliptin and cardiovascular outcomes in patients with type 2 diabetes mellitus.

Authors:  Benjamin M Scirica; Deepak L Bhatt; Eugene Braunwald; P Gabriel Steg; Jaime Davidson; Boaz Hirshberg; Peter Ohman; Robert Frederich; Stephen D Wiviott; Elaine B Hoffman; Matthew A Cavender; Jacob A Udell; Nihar R Desai; Ofri Mosenzon; Darren K McGuire; Kausik K Ray; Lawrence A Leiter; Itamar Raz
Journal:  N Engl J Med       Date:  2013-09-02       Impact factor: 91.245

Review 3.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

Authors:  Amit X Garg; Neill K J Adhikari; Heather McDonald; M Patricia Rosas-Arellano; P J Devereaux; Joseph Beyene; Justina Sam; R Brian Haynes
Journal:  JAMA       Date:  2005-03-09       Impact factor: 56.272

Review 4.  The appropriateness method has acceptable reliability and validity for assessing overuse and underuse of surgical procedures.

Authors:  Elise H Lawson; Melinda Maggard Gibbons; Clifford Y Ko; Paul G Shekelle
Journal:  J Clin Epidemiol       Date:  2012-11       Impact factor: 6.437

5.  Personalized decision support in type 2 diabetes mellitus: current evidence and future directions.

Authors:  Michael J Wilkinson; Aviva G Nathan; Elbert S Huang
Journal:  Curr Diab Rep       Date:  2013-04       Impact factor: 4.810

6.  Design of a decision support system to help clinicians manage glycemia in patients with type 2 diabetes mellitus.

Authors:  David Rodbard; Robert A Vigersky
Journal:  J Diabetes Sci Technol       Date:  2011-03-01

7.  Changes in time to insulin initiation in type 2 diabetes patients: a retrospective database analysis in Germany and UK (2005-2010).

Authors:  Karel Kostev; Wolfgang Rathmann
Journal:  Prim Care Diabetes       Date:  2013-04-20       Impact factor: 2.459

Review 8.  Computerized decision support systems in primary care for type 2 diabetes patients only improve patients' outcomes when combined with feedback on performance and case management: a systematic review.

Authors:  Frits G W Cleveringa; Kees J Gorter; Maureen van den Donk; Juliette van Gijsel; Guy E H M Rutten
Journal:  Diabetes Technol Ther       Date:  2013-02       Impact factor: 6.118

9.  Association between hypoglycemia and dementia in a biracial cohort of older adults with diabetes mellitus.

Authors:  Kristine Yaffe; Cherie M Falvey; Nathan Hamilton; Tamara B Harris; Eleanor M Simonsick; Elsa S Strotmeyer; Ronald I Shorr; Andrea Metti; Ann V Schwartz
Journal:  JAMA Intern Med       Date:  2013-07-22       Impact factor: 21.873

10.  The management of iron deficiency in inflammatory bowel disease--an online tool developed by the RAND/UCLA appropriateness method.

Authors:  W Reinisch; Y Chowers; S Danese; A Dignass; F Gomollón; O Haagen Nielsen; P L Lakatos; C W Lees; S Lindgren; M Lukas; G J Mantzaris; P Michetti; B Moum; L Peyrin-Biroulet; M Toruner; J van der Woude; G Weiss; H Stoevelaar
Journal:  Aliment Pharmacol Ther       Date:  2013-09-17       Impact factor: 8.171

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  4 in total

1.  Individualised treatment targets in patients with type-2 diabetes and hypertension.

Authors:  Roland E Schmieder; Diethelm Tschöpe; Cornelia Koch; Taoufik Ouarrak; Anselm K Gitt
Journal:  Cardiovasc Diabetol       Date:  2018-01-22       Impact factor: 9.951

2.  Development of a clinical decision support system for diabetes care: A pilot study.

Authors:  Livvi Li Wei Sim; Kenneth Hon Kim Ban; Tin Wee Tan; Sunil Kumar Sethi; Tze Ping Loh
Journal:  PLoS One       Date:  2017-02-24       Impact factor: 3.240

3.  Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis.

Authors:  Rung-Ching Chen; Hui Qin Jiang; Chung-Yi Huang; Cho-Tsan Bau
Journal:  J Healthc Eng       Date:  2017-10-26       Impact factor: 2.682

4.  Efficacy and Safety of Switching Patients Inadequately Controlled on Basal Insulin to Insulin Glargine 300 U/mL: The TRANSITION 2 Study.

Authors:  Pierre Gourdy; Amar Bahloul; Zahra Boultif; Didier Gouet; Bruno Guerci
Journal:  Diabetes Ther       Date:  2019-11-28       Impact factor: 2.945

  4 in total

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