Transitions of Care for People with Type 2 Diabetes: Utilization of Antihyperglycemic Agents Pre- and Post-Hospitalization

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Abstract

Introduction

Little research to date has examined antihyperglycemic agent (AHA) utilization among patients with type 2 diabetes mellitus (T2DM) around transitions of care from inpatient to outpatient settings. Discontinuity of care between inpatient and outpatient settings has been associated with adverse clinical outcomes, so a better understanding of AHA treatment patterns is important.

Methods

This retrospective study assessed AHA utilization among a sample of United States adults with a T2DM diagnosis listed on an inpatient admission during 2010–2012 in the MarketScan ^® Hospital Drug database (Truven Health Analytics). AHA use while hospitalized was measured from inpatient medication administration records in that database. AHA use pre- and post-hospitalization was assessed from outpatient retail and mail order pharmacy claims in the MarketScan Commercial and Medicare Supplemental databases, which contain de-identified insurance claims from large employers and health plans. The hospital and claims databases are linked, allowing patients to be followed across transitions of care.

Results

The study sample ( N = 8144) was 53% male, with a mean age of 66 years. Twenty-one percent had no T2DM diagnosis or claims for AHAs in the 90-day pre-hospitalization period suggesting they may have been newly diagnosed at the time of admission. Most (83%) patients used AHAs while hospitalized, but the proportions with AHA claims 30 days pre- and post-hospitalization were only 53% and 40%, respectively. Biguanides and sulfonylureas were the most common outpatient agents. Most (70%) patients who had no AHA utilization pre-hospitalization continued to have no AHA utilization post-hospitalization. About half the patients with AHA claims pre-hospitalization did not have any AHA claims post-discharge.

Conclusion

Further research is warranted to explore the reasons why AHAs are not continued following hospital discharge. Inadequate treatment of T2DM remains an issue before and after hospitalization; inpatient stays represent an important and frequently missed opportunity to assess and optimize care for these patients.

Funding

Janssen Scientific Affairs, LLC.

Electronic supplementary material

The online version of this article (doi:10.1007/s13300-015-0148-5) contains supplementary material, which is available to authorized users.

Related collections

Most cited references 8

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Validation of ICD-9-CM coding algorithm for improved identification of hypoglycemia visits

Adit Ginde, Phillip G. Blanc, Rebecca Lieberman … (2008)

Background Accurate identification of hypoglycemia cases by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes will help to describe epidemiology, monitor trends, and propose interventions for this important complication in patients with diabetes. Prior hypoglycemia studies utilized incomplete search strategies and may be methodologically flawed. We sought to validate a new ICD-9-CM coding algorithm for accurate identification of hypoglycemia visits. Methods This was a multicenter, retrospective cohort study using a structured medical record review at three academic emergency departments from July 1, 2005 to June 30, 2006. We prospectively derived a coding algorithm to identify hypoglycemia visits using ICD-9-CM codes (250.3, 250.8, 251.0, 251.1, 251.2, 270.3, 775.0, 775.6, and 962.3). We confirmed hypoglycemia cases by chart review identified by candidate ICD-9-CM codes during the study period. The case definition for hypoglycemia was documented blood glucose 3.9 mmol/l or emergency physician charted diagnosis of hypoglycemia. We evaluated individual components and calculated the positive predictive value. Results We reviewed 636 charts identified by the candidate ICD-9-CM codes and confirmed 436 (64%) cases of hypoglycemia by chart review. Diabetes with other specified manifestations (250.8), often excluded in prior hypoglycemia analyses, identified 83% of hypoglycemia visits, and unspecified hypoglycemia (251.2) identified 13% of hypoglycemia visits. The absence of any predetermined co-diagnosis codes improved the positive predictive value of code 250.8 from 62% to 92%, while excluding only 10 (2%) true hypoglycemia visits. Although prior analyses included only the first-listed ICD-9 code, more than one-quarter of identified hypoglycemia visits were outside this primary diagnosis field. Overall, the proposed algorithm had 89% positive predictive value (95% confidence interval, 86–92) for detecting hypoglycemia visits. Conclusion The proposed algorithm improves on prior strategies to identify hypoglycemia visits in administrative data sets and will enhance the ability to study the epidemiology and design interventions for this important complication of diabetes care.