OBJECTIVES: To develop an algorithm to identify individuals with limited life expectancy and examine the effect of limited life expectancy on glycemic control and treatment intensification in individuals with diabetes mellitus. DESIGN: Individuals with diabetes mellitus and coexisting congestive heart failure, chronic obstructive pulmonary disease, dementia, end-stage liver disease, and/or primary or metastatic cancer with limited life expectancy were identified. To validate the algorithm, 5-year mortality was assessed in individuals identified as having limited life expectancy. Rates of meeting performance measures for glycemic control between individuals with and without limited life expectancy were compared. In individuals with uncontrolled glycosylated hemoglobin (HbA(1c) ) levels, the effect of limited life expectancy on treatment intensification within 90 days was examined. SETTING: One hundred ten Department of Veterans Affairs facilities; October 2006 to September 2007. PARTICIPANTS: Eight hundred eighty-eight thousand six hundred twenty-eight individuals with diabetes mellitus. MEASUREMENTS: HbA(1c) ; treatment intensification within 90 days of index HbA(1c) reading. RESULTS: Twenty-nine thousand sixteen (3%) participants had limited life expectancy. Adjusting for age, 5-year mortality was five times as high in participants with limited life expectancy than in those without. Participants with limited life expectancy had poorer glycemic control than those without (glycemic control: 77.1% vs 78.1%; odds ratio (OR) = 0.84, 95% confidence interval (CI) = 0.81-0.86) and less-frequent treatment intensification (treatment intensification: 20.9% vs 28.6%; OR = 0.71, 95% CI = 0.67-0.76), even after controlling for patient-level characteristics. CONCLUSION: Participants with limited life expectancy were less likely than those without to have controlled HbA(1c) levels and to receive treatment intensification, suggesting that providers treat these individuals less aggressively. Quality measurement and performance-based reimbursement systems should acknowledge the different needs of this population.
OBJECTIVES: To develop an algorithm to identify individuals with limited life expectancy and examine the effect of limited life expectancy on glycemic control and treatment intensification in individuals with diabetes mellitus. DESIGN: Individuals with diabetes mellitus and coexisting congestive heart failure, chronic obstructive pulmonary disease, dementia, end-stage liver disease, and/or primary or metastatic cancer with limited life expectancy were identified. To validate the algorithm, 5-year mortality was assessed in individuals identified as having limited life expectancy. Rates of meeting performance measures for glycemic control between individuals with and without limited life expectancy were compared. In individuals with uncontrolled glycosylated hemoglobin (HbA(1c) ) levels, the effect of limited life expectancy on treatment intensification within 90 days was examined. SETTING: One hundred ten Department of Veterans Affairs facilities; October 2006 to September 2007. PARTICIPANTS: Eight hundred eighty-eight thousand six hundred twenty-eight individuals with diabetes mellitus. MEASUREMENTS: HbA(1c) ; treatment intensification within 90 days of index HbA(1c) reading. RESULTS: Twenty-nine thousand sixteen (3%) participants had limited life expectancy. Adjusting for age, 5-year mortality was five times as high in participants with limited life expectancy than in those without. Participants with limited life expectancy had poorer glycemic control than those without (glycemic control: 77.1% vs 78.1%; odds ratio (OR) = 0.84, 95% confidence interval (CI) = 0.81-0.86) and less-frequent treatment intensification (treatment intensification: 20.9% vs 28.6%; OR = 0.71, 95% CI = 0.67-0.76), even after controlling for patient-level characteristics. CONCLUSION:Participants with limited life expectancy were less likely than those without to have controlled HbA(1c) levels and to receive treatment intensification, suggesting that providers treat these individuals less aggressively. Quality measurement and performance-based reimbursement systems should acknowledge the different needs of this population.
Authors: Yashashwi Pokharel; Julia M Akeroyd; David J Ramsey; Ravi S Hira; Vijay Nambi; Tina Shah; LeChauncy D Woodard; David E Winchester; Christie M Ballantyne; Laura A Petersen; Salim S Virani Journal: Clin Cardiol Date: 2016-04-05 Impact factor: 2.882
Authors: Hasan Rehman; Julia M Akeroyd; David Ramsey; Sarah T Ahmed; Anwar T Merchant; Sankar D Navaneethan; Laura A Petersen; Salim S Virani Journal: Clin Cardiol Date: 2017-08-25 Impact factor: 2.882
Authors: Dhruv Mahtta; David J Ramsey; Michelle T Lee; Liang Chen; Mahmoud Al Rifai; Julia M Akeroyd; Elizabeth M Vaughan; Michael E Matheny; Karla Rodrigues do Espirito Santo; Sankar D Navaneethan; Carl J Lavie; Yochai Birnbaum; Christie M Ballantyne; Laura A Petersen; Salim S Virani Journal: Diabetes Care Date: 2022-02-01 Impact factor: 19.112
Authors: Erin R Giovannetti; Sydney Dy; Bruce Leff; Christine Weston; Karen Adams; Tom B Valuck; Aisha T Pittman; Caroline S Blaum; Barbara A McCann; Cynthia M Boyd Journal: Am J Manag Care Date: 2013-10-01 Impact factor: 2.229
Authors: LeChauncy Woodard; Nipa Kamdar; Natalie Hundt; Howard S Gordon; Brian Hertz; Amber B Amspoker; Lea Kiefer; Praveen Mehta; Edward Odom; Suja Rajan; Elizabeth Stone; Lindsey Jones; Aanand D Naik Journal: Endocrinol Diabetes Metab Date: 2019-11-18
Authors: LeChauncy D Woodard; Cassie R Landrum; Amber B Amspoker; David Ramsey; Aanand D Naik Journal: Patient Prefer Adherence Date: 2014-07-24 Impact factor: 2.711
Authors: Dhruv Mahtta; David J Ramsey; Mahmoud Al Rifai; Khurram Nasir; Zainab Samad; David Aguilar; Hani Jneid; Christie M Ballantyne; Laura A Petersen; Salim S Virani Journal: JAMA Netw Open Date: 2020-08-03