Christopher D Miller1,2, David C Ziemer1, Paul Kolm3, Imad M El-Kebbi1, Curtiss B Cook1,2, Daniel L Gallina1, Joyce P Doyle4, Catherine S Barnes1, Lawrence S Phillips1. 1. The Divisions of Endocrinology, Department of Medicine, Emory University School of Medicine and Diabetes Clinic, Grady Health Systems, Atlanta, Georgia (Drs Miller, Ziemer, El-Kebbi, Cook, Gallina, Barnes, Phillips) 2. Dr Miller is currently based at the Bond Clinic, Winter Haven, Florida, and Dr Cook is based at the Mayo Clinic, Scottsdale, Arizona 3. Cardiology, Department of Medicine, Emory University School of Medicine and Diabetes Clinic, Grady Health Systems, Atlanta, Georgia (Dr Kolm) 4. General Internal Medicine, Department of Medicine, Emory University School of Medicine and Diabetes Clinic, Grady Health Systems, Atlanta, Georgia (Dr Doyle).
Abstract
PURPOSE: The purpose of this study was to determine whether an algorithm that recommended individualized changes in therapy would help providers to change therapy appropriately and improve glycemic control in their patients. METHODS: The algorithm recommended specific doses of oral agents and insulin based on a patient's medications and glucose or A1C levels at the time of the visit. The prospective observational study analyzed the effect of the algorithm on treatment decisions and A1C levels in patients with type 2 diabetes. RESULTS: The study included 1250 patients seen in pairs of initial and follow-up visits during a 7-month baseline and/or a subsequent 7-month algorithm period. The patients had a mean age of 62 years, body mass index of 33 kg/m(2), duration of diabetes of 10 years, were 94% African American and 71% female, and had average initial A1C level of 7.7%. When the algorithm was available, providers were 45% more likely to intensify therapy when indicated (P = .005) and increased therapy by a 20% greater amount (P < .001). A1C level at follow-up was 90% more likelyto be <7% in the algorithm group, even after adjusting for differences in age, sex, body mass index, race, duration of diabetes and therapy, glucose, and A1C level at the initial visit (P < .001). CONCLUSIONS: Use of an algorithm that recommends patient-specific changes in diabetes medications improves both provider behavior and patient A1C levels and should allow quantitative evaluation of provider actions for that provider's patients.
PURPOSE: The purpose of this study was to determine whether an algorithm that recommended individualized changes in therapy would help providers to change therapy appropriately and improve glycemic control in their patients. METHODS: The algorithm recommended specific doses of oral agents and insulin based on a patient's medications and glucose or A1C levels at the time of the visit. The prospective observational study analyzed the effect of the algorithm on treatment decisions and A1C levels in patients with type 2 diabetes. RESULTS: The study included 1250 patients seen in pairs of initial and follow-up visits during a 7-month baseline and/or a subsequent 7-month algorithm period. The patients had a mean age of 62 years, body mass index of 33 kg/m(2), duration of diabetes of 10 years, were 94% African American and 71% female, and had average initial A1C level of 7.7%. When the algorithm was available, providers were 45% more likely to intensify therapy when indicated (P = .005) and increased therapy by a 20% greater amount (P < .001). A1C level at follow-up was 90% more likelyto be <7% in the algorithm group, even after adjusting for differences in age, sex, body mass index, race, duration of diabetes and therapy, glucose, and A1C level at the initial visit (P < .001). CONCLUSIONS: Use of an algorithm that recommends patient-specific changes in diabetes medications improves both provider behavior and patientA1C levels and should allow quantitative evaluation of provider actions for that provider's patients.
Authors: Arshiya A Baig; Abigail E Wilkes; Andrew M Davis; Monica E Peek; Elbert S Huang; Douglas S Bell; Marshall H Chin Journal: Med Care Res Rev Date: 2010-07-30 Impact factor: 3.929
Authors: David C Ziemer; Imad M El-Kebbi; Guillermo E Umpierrez; Mary K Rhee; Lawrence S Phillips; Curtiss B Cook Journal: Ethn Dis Date: 2008 Impact factor: 1.847