Original Research

Predictors of Suboptimal Glycemic Control for Hospitalized Patients with Diabetes: Targets for Clinical Action

Journal of Clinical Outcomes Management. 2015 April;22(4)

Author(s):

Miriam Bender, PhD, RN

Tyler C. Smith, MS, PhD

Jacqueline Thompson, RN, MAS, CDE

Ameen Koucheki, MS

Kalman Holdy, MD

References

1. Umpierrez GE, Hellman R, Korytkowski MT, et al; Endocrine Society. Management of hyperglycemia in hospitalized patients in non-critical care setting: an endocrine society clinical practice guideline. J Clin Endocrinol Metab 2012;97:16–38.

2. Moghissi ES, Korytkowski MT, DiNardo M, et al; American Association of Clinical Endocrinologists; American Diabetes Association. American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Diabetes Care2009;32:1119–31.

3. Coursin DB, Connery LE, Ketzler JT. Perioperative diabetic and hyperglycemic management issues. Crit Care Med 2004;32(4 Suppl):S116–25.

4. Pandit MK, Burke J, Gustafson AB, et al. Drug-induced disorders of glucose tolerance. Ann Intern Med 1993;118:529–39.

5. Donihi AC, Raval D, Saul M, et al. Prevalence and predictors of corticosteroid-related hyperglycemia in hospitalized patients. Endocr Pract 2006;12:358–62.

6. McHugh MD, Shang J, Sloane DM, Aiken LH. Risk factors for hospital-acquired ‘poor glycemic control’: a case-control study. Int J Qual Health Care 2011;23:44–51.

7. McCowen KC, Malhotra A, Bistrian BR. Stress-induced hyperglycemia. Crit Care Clin 2001;17:107–24.

8. Rady MY, Johnson DJ, Patel BM, et al. Influence of individual characteristics on outcome of glycemic control in intensive care unit patients with or without diabetes mellitus. Mayo Clin Proc 2005;80:1558–67.

9. Selvin E, Steffes MW, Zhu H, et al. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med 2010;362:800–11.

10. Kim KS, Kim SK, Lee YK, et al. Diagnostic value of glycated haemoglobin HbA(1c) for the early detection of diabetes in high-risk subjects. Diabet Med 2008;25:997–1000.

11. Quan H, Li B, Couris CM, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol 2011;173:676–82.

12. Kirk JK, Oldham EC. Hyperglycemia management using insulin in the acute care setting: therapies and strategies for care in the non-critically ill patient. Ann Pharmacother 2010;44:1222–30.

13. Draznin B, Gilden J, Golden SH, et al. Pathways to quality inpatient management of hyperglycemia and diabetes: a call to action. Diabetes Care 2013;36:1807–14.

14. Boord JB, Greevy RA, Braithwaite SS, et al. Evaluation of hospital glycemic control at US academic medical centers. J Hosp Med 2009;4:35–44.

15. Neubauer KM, Schaupp L, Plank J, et al. Failure to control hyperglycemia in noncritically ill diabetes patients despite standard glycemic management in a hospital setting. J Diabetes Sci Technol 2013;7:402–9.

16. Schnipper JL, Barsky EE, Shaykevich S, et al. Inpatient management of diabetes and hyperglycemia among general medicine patients at a large teaching hospital. J Hosp Med 2006;1:145–50.

17. Rohlfing CL, Wiedmeyer HM, Little RR, et al. Defining the relationship between plasma glucose and HbA(1c): analysis of glucose profiles and HbA(1c) in the Diabetes Control and Complications Trial. Diabetes Care 2002;25:275–8.

18. Nathan DM, Kuenen J, Borg R, et al; A1c-Derived Average Glucose Study Group. Translating the A1C assay into estimated average glucose values. Diabetes Care 2008;31:1473–8.

From Sharp HealthCare, San Diego, CA (Ms. Thompson, Mr. Koucheki, Dr. Holdy), National University, San Diego, CA (Dr. Smith), and University of California, Irvine (Dr. Bender).

Abstract

Objective: Suboptimal glycemic control (SGC) puts hospitalized patients with diabetes at risk for poor outcomes. The purpose of this study was to quantify factors with predictive capacity to identify patients at risk for SGC during hospitalization.
Methods: 32 baseline and demographic variables were extracted from the electronic records of 23,100 patients with diabetes hospitalized between 2009 and 2012. The rate of blood glucose values between 70 and 180 mg/dL was calculated for each patient. A predictive model for SGC was developed using regression modeling, standardized coefficients, and classification tree analysis. Odds ratios (ORs) were calculated to isolate adjusted odds of SGC for top predictors.
Results: The final predictive model included 13 variables (C statistic = 0.88). HbA1c (OR, 0.60 [95% confidence interval {CI}, 0.58–0.61]), admission blood glucose (OR, 0.91 [CI, 0.91–0.92]), and steroid use (OR, 0.06 [CI 0.04–0.08]) were the highest-ranking predictors of SGC. HbA1c and SGC had a strong linear relationship (R ²= 0.99), with increasing odds for SGC as HbA1c increased. Admission blood glucose and SGC had a polynomial relationship (R ² = 0.95); increasing odds for SGC until 240 mg/dL; then odds started decreasing. Steroid use showed a steady threefold increase in odds for SGC across all rates of use.
Conclusions: Poor preadmission diabetes control and inpatient steroid use strongly predict SGC. A range of thresholds for these predictors was empirically determined, providing a basis for targeted therapies on admission. Guidelines incorporating empirically derived thresholds should enhance the ability to achieve optimal glycemic control for hospitalized patients with diabetes.

Current recommendations for glycemic control of hospitalized patients include use of multidisciplinary diabetic care teams and standardized insulin order sets [1,2], yet there is still uncertainty how best to target such protocols for patients with diabetes at risk for suboptimal glycemic control [2,3]. Many factors are theorized to hinder optimal inpatient glycemic control, such as steroid use [4,5],comorbid states [6],severity of illness [7,8], and preadmission glycemic control [9,10]. However, there currently exists little evidence of these factors’ ability to predict suboptimal glycemic control (SGC). Identifying straightforward predictive factors of SGC would provide a clinically meaningful basis for targeted therapy. The purpose of this study was to describe the prevalence of a range of potential risk factors in a diverse hospitalized patient population with a secondary diagnosis of diabetes (types 1 and 2), and to determine which factors were predictive of SGC.

Methods

A retrospective cohort study design was used to identify factors predictive of inpatient SGC for patients admitted to any of 3 hospitals aligned with Sharp HealthCare (“Sharp”), a community-based, nonprofit integrated health system headquartered in San Diego, California, that serves more than 27% of the county’s 3 million-plus residents each year.

Inclusion Criteria

We extracted data for 23,100 patients hospitalized between January 2009 and December 2012 with a secondary diagnosis of diabetes (types 1 and 2), a length of stay (LOS) ≥ 3 days, and a minimum of 2 point-of-care (POC) blood glucose tests per day. The LOS and blood glucose minimum are standard criteria for Sharp glycemic monitoring to ensure a minimum quantity of blood glucose monitoring for glycemic management.