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Risk Factors for Hypoglycemia-Related Hospitalization in Patients With Type 2 Diabetes: A Nested Case–Control Study
Clinical Therapeutics/Volume 33, Number 11, 2011
Risk Factors for Hypoglycemia-Related Hospitalization in Patients With Type 2 Diabetes: A Nested Case–Control Study Brian J. Quilliam, PhD; Jason C. Simeone, PhD*; and A. Burak Ozbay, PhD† College of Pharmacy, University of Rhode Island, Kingston, Rhode Island ABSTRACT Background: Hypoglycemia requiring hospitalization remains a serious and costly limitation to treatment of type 2 diabetes with antidiabetic medications. Objective: We identified risk factors for hypoglycemia hospitalization in patients with type 2 diabetes treated with oral antidiabetic drugs (OADs). Methods: In the 2004 to 2008 MarketScan database, we identified patients with type 2 diabetes taking OADs with ⬎12 months of enrollment. We conducted a nested case– control study, selecting cases with an inpatient admission for hypoglycemia (first event). Using the index date of the cases, we conducted incidence density sampling to identify controls (10:1 matching) with continued eligibility during that month, further matching on date of cohort entry (⫾1 month). The final sample was 1339 cases and 13,390 controls. We assessed patterns of OAD availability (creating 3 groups: continuous, intermittent, and nonavailability), other medication availability, previous visits for hypoglycemia, complications of diabetes, and other comorbidities in the previous 180 days. A conditional logistic regression model identified predictors of hypoglycemia hospitalization. Results: Mean (SD) age of cases was 56.4 (7.0) years compared with 54.6 (7.8) years in the controls. Overall, cases had more comorbidities than controls. In multivariable modeling, previous emergency department hypoglycemia visits (odds ratio [OR] ⫽ 9.48; 95% CI, 4.95–18.15) and previous outpatient hypoglycemia visits (OR ⫽ 7.88; 95% CI, 5.68 –10.93) were strongly predictive of inpatient hypoglycemia admission. Continuous metformin availability had a 38% lower rate of inpatient hypoglycemia admission (OR ⫽ 0.62; 95% CI, 0.53– 0.73) and intermittent metformin availability a 24% lower rate (OR ⫽ 0.76; 95% CI, 0.64 – 0.92) than nonavailability of metformin. Relative to nonavailability, continuous (OR ⫽ 2.25; 95% CI, 1.93–2.63) and intermittent sulfonylurea availability (OR ⫽ 2.28; 95% CI, 1.90 –2.74) had increased rates of hypoglycemia hospitalization. Intermittent thiazolidinedione availability had a slightly in-
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creased rate of hypoglycemia hospitalization (OR ⫽ 1.22; 95% CI, 1.01–1.47). Continuous availability of thiazolidinediones and continuous or intermittent use of other OADs were not predictive of hypoglycemia admission. Conclusions: Previous outpatient or emergency department visits for hypoglycemia and continuous or intermittent sulfonylurea availability were found to be predictive of costly inpatient hypoglycemia admissions. Although this observational study may not be generalizable to all patients with type 2 diabetes and assessed medication availability rather than actual consumption, previous outpatient visits and prescription for OADs should serve as points of intervention and patient education. (Clin Ther. 2011;33:1781–1791) © 2011 Elsevier HS Journals, Inc. All rights reserved. Key words: hospitalization, hypoglycemia, inpatient, oral antidiabetic medication, predictors, type 2 diabetes.
INTRODUCTION Research identifying the importance of tight glycemic control for preventing microvascular complications of type 2 diabetes has been widely available for ⬎10 years. In the late 1990s, the United Kingdom Prospective Diabetes Study (UKPDS) demonstrated that intensive goal-oriented therapy with sulfonylureas or insulin prevented microvascular complications of type 2 diabetes.1 More recent studies reinforced the benefits of intensive antidiabetic drug therapy, but failed to show a similar reduction in macrovascular complications of diabetes after intensive glucose-lowering ther*Current affiliation: United BioSource Corporation, Lexington, Massachusetts. † Current affiliation: HealthCore, Inc, Andover, Massachusetts. Accepted for publication September 14, 2011. doi:10.1016/j.clinthera.2011.09.020 0149-2918/$ - see front matter © 2011 Elsevier HS Journals, Inc. All rights reserved.
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PATIENTS AND METHODS Data Source We utilized health care claims from the 2004 to 2008 MarketScan database (Thomson Reuters HI, Ann Arbor, Michigan) to conduct this nested case– control study. We utilized the most recent data available (2008) at the time of the study inception and included 4 additional years of data (2004 –2007) to ensure an adequate sample size to achieve our specific aim. The size of the database, coupled with the diversity of contributing health plans, allows for the conduct of large research studies that are representative of Americans with work-based health insurance.
Cohort Identification We assembled a cohort of adults (⬎18 years of age) with at least 2 outpatient or inpatient claims for diabetes (International Classification of Diseases-9th Edition [ICD-9] 250.XX) during 2004 to 2008 and further restricted the cohort to those taking at least 1 OAD. From this eligible population, we first excluded participants who did not have at least 12 months of continuous eligibility within a noncapitated health plan after the initial fill date of an OAD, and secondly those with ⬎1 medical claim (inpatient or outpatient) for type 1 (ICD-9 250.x1 or 250.x3) or gestational diabetes (ICD-9 648.0X) during the study period. The final sample in the cohort was 536,581 patients.
Case Identification Within the larger cohort, we conducted a nested case– control study and identified hypoglycemic events
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requiring inpatient medical intervention using an ICD-9 based algorithm developed by Ginde et al.5 We identified the first hospitalization indicative of hypoglycemia (ICD-9: 251.0, 251.1, 251.2, 270.3, 962.3) or ICD-9 250.8 in the absence of other contributing diagnoses (ICD-9: 259.8, 272.7, 681.XX, 682.XX, 686.9X, 707.1-707.9, 709.3, 730.0-730.2 or 731.8). As we were interested in factors preceding the incident event, we excluded all potential cases with an inpatient hypoglycemic event in the first 180 days of cohort entry. Each selected case was then assigned an index date based on the date of service associated with the first identified inpatient claim for a hypoglycemic event. During follow up, there were a total of 1761 inpatient admissions for hypoglycemia among 1676 study participants. After excluding 337 persons with inpatient admissions for hypoglycemia in the first 6 months, the final sample of cases eligible for inclusion was 1339.
Control Selection To identify controls representing the exposure distribution that gave rise to the cases, we employed incidence density sampling with replacement. For each case, we randomly selected 10 controls that remained continuously eligible during the calendar month covering the cases index date. Because length of follow up might have been related to disease severity, we further required that eligible controls entered the study cohort within ⫾1 month of the matched case and assigned each control an index date corresponding to the date of admission for their matched case. The final sample eligible for study inclusion was 1339 cases and 13,390 matched controls.
Power Analysis We performed a post hoc power analysis using calculations by Lachin.6 With 1390 identified cases, a 10:1 case/control matching ratio, and a 2-sided ␣ level of 0.05, our study had 92% power to detect an odds ratio (OR) of 1.10 (ie, a 10% difference between cases and controls).
Exposure History Medications The MarketScan database contains deidentified claims information from retail pharmacies, as well as mail order and specialty pharmacy transactions. We examined medication exposure history in the 180 days preceding the index date and identified all OAD pharmacy transactions (metformin, sulfonylureas, thiazoli-
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B.J. Quilliam et al. dinediones [TZD], meglitinides, ␣-glucosidase inhibitors, and dipeptidyl peptidase 4 [DPP-4] inhibitors). As changes in OAD use throughout time were probable, we evaluated the availability of each of these classes in the six 30-day intervals preceding the index date. We identified all OADs that were filled either during each of the 6 intervals or before the interval with a supply that ended during or beyond the interval. Using this information, we created medication availability flags for each class of medication during the 6 intervals and created 3 groups for each medication class, including continuous availability, intermittent availability, and nonavailability. To have continuous availability, the participant must have had medication coverage in all of the six 30-day periods preceding the index date. We defined intermittent availability as those having medication coverage in at least 1 of the preceding 6 intervals. Finally, nonavailability was defined as no medication coverage during the 6 intervals preceding the index date. After preliminary analyses, ␣-glucosidase inhibitors, meglitinides, and DPP-4 inhibitors were combined into 1 category, other OADs, due to limited sample size. In addition, we evaluated pharmacy claims for availability of other agents (in the previous 30 days) known to be associated with hypoglycemia, including angiotensinconverting enzyme (ACE) inhibitors, angiotensin-2 receptor blockers (ARB), allopurinol, benzodiazepines, -blockers, fibrates, fluoroquinolones, insulin, nonsteroidal anti-inflammatory drugs (NSAIDs), trimethoprim, warfarin, and other injectable antidiabetic agents (ie, exenatide and pramlintide).
Demographic Data and Comorbidity Within the 180 days before the index date, we characterized demographic characteristics, including age, gender, and geographic region. Using ICD-9 codes from medical inpatient and outpatient claims data, we assessed the presence of other comorbid conditions that might be associated with hypoglycemic events. We identified the presence of Addison’s disease, hypopituitarism, hypothyroidism, liver disease, microvascular and macrovascular complications of diabetes (utilizing methods employed in other studies),7 previous instances of outpatient hypoglycemia (medical office or emergency department visits using the aforementioned algorithm utilized to identify inpatient events),5 and an overall marker of comorbidity.8 Lastly, we identified
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study participants who used self-monitoring of blood glucose using pharmacy claims for test strips or lancets.
Analytic Plan We first conducted bivariate analyses to assess comparability between cases and controls utilizing the CochranMantel-Haenszel test statistic for categorical variables9 and a paired t-test for continuous variables. Any factor that differed between the cases and controls by at least 5% was considered a potential independent predictor and flagged for additional consideration. Next, we developed a conditional logistic regression model initiated by inclusion of all factors preliminarily identified as potential predictors during the bivariate analyses. We then manually removed variables sequentially that were not predictive of hypoglycemic events (through Wald P value assessment) with confirmation of removal through likelihood ratio testing. Results are presented as crude (unadjusted) and multivariable (adjusted) ORs, with their respective 95% CIs. Because we employed incidence density sampling, the OR derived from this conditional logistic model approximates the incidence rate ratio and was therefore interpreted as such.10 All statistical tests were conducted with a 2-tailed ␣ of 0.05 and performed using SAS software (version 9.2; SAS Institute Inc, Cary, North Carolina). This study was reviewed and approved as exempt by the University of Rhode Island’s Institutional Review Board.
RESULTS As presented in Table I, cases and matched controls were of similar age, gender, and resided in the same geographic regions. The mean (SD) age of cases was 56.4 (7.0) years compared with 54.6 (7.8) years in the controls, with 82.6% of cases and 74.5% of controls aged 50 to 64 years. Overall, the prevalence of evaluated comorbidities was higher in the cases: the mean Charlson comorbidity index was 1.7 in the cases compared with 0.4 in the controls. When evaluating the individual Charlson factors (data not shown), cases were more likely than controls to have diabetes with complications (13.4% in cases and 5.3% in controls). Cases were more likely than controls to have a history of hypothyroidism (7.3% compared with 4.7%) and liver disease (2.8% vs 1.0%), whereas other diseases were rare in both groups. Cases were also more likely than controls to have had an outpatient hypoglycemiarelated visit (12.5% in cases vs 0.9% in controls) and
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Table I. Demographic and clinical characteristics. Characteristic
Cases (n ⫽1339)
Controls (n ⫽ 13,390) Mean (SD)
Age, y Charlson comorbidity index
56.4 (7.0) 1.7 (2.5)
54.6 (7.8) 0.4 (0.9) No. (%)
Age, y 18–34 35–49 50–64 ⱖ65
18 (1.3) 178 (13.3) 1106 (82.6) 37 (2.8)
280 (2.1) 2822 (21.1) 9971 (74.5) 317 (2.4)
Gender Female Male
659 (49.2) 680 (50.8)
6196 (46.3) 7194 (53.7)
Geographic region* Northeast Midwest South West
115 (8.6) 419 (31.3) 651 (48.6) 148 (11.1)
864 (6.5) 4003 (29.9) 6828 (51.0) 1587 (11.9)
Clinical characteristics† Self-monitoring of blood glucose Hypoglycemia–outpatient visit Hypothyroidism Hypoglycemia–emergency department visit Liver disease Hypopituitarism Addison’s disease
414 (30.9) 167 (12.5) 98 (7.3) 83 (6.2) 38 (2.8) 2 (0.2) 1 (0.07)
4098 (30.6) 115 (0.9) 631 (4.7) 18 (0.1) 136 (1.0) 4 (0.03) 1 (0.01)
Macrovascular diabetes complications† Coronary artery disease Heart failure Peripheral vascular disease Arrhythmia Stroke
281 (21.0) 187 (14.0) 103 (7.7) 91 (6.8) 45 (3.4)
1049 (7.8) 202 (1.5) 231 (1.7) 185 (1.4) 48 (0.4)
Microvascular diabetes complications† Chronic renal pathophysiology Acute renal failure Retinopathy Ulcer Nephropathy End-stage renal disease Dialysis Amputation
113 (8.4) 111 (8.3) 85 (6.4) 86 (6.4) 63 (4.7) 13 (1.0) 2 (0.2) 0 (0)
147 (1.1) 77 (0.6) 447 (3.3) 184 (1.4) 148 (1.1) 2 (0.01) 5 (0.04) 6 (0.04)
*Missing data on 6 (0.5%) cases and 108 (0.8%) controls. † As identified in inpatient and outpatient claims in the 6 months before the index date.
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B.J. Quilliam et al. an emergency department hypoglycemia-related visit (6.2% of cases compared with 0.1% of controls). The prevalence of previous microvascular and macrovascular diabetes complications is displayed in Table I. Coronary artery disease (CAD) was present in 21.0% of cases compared with 7.8% of controls, and heart failure occurred in 14.0% of cases and 1.5% of controls. Other macrovascular complications were of lower prevalence, but occurred more frequently in the cases than the controls. Microvascular complications of diabetes were also higher in the cases than the controls. Acute renal failure occurred in 8.3% of the cases and ⬍1% of the controls. Similarly, chronic renal pathophysiology was noted more frequently in the cases than the controls (8.4% vs 1.1%). Other microvascular complications were lower and remained elevated in the cases relative to the controls. Within this population of patients with type 2 diabetes, 16.8% of cases and 6.7% of controls had an indication of insulin availability in the 30 days before the index date. The prevalence of other antidiabetic injectable agents (exenatide and pramlintide) were much lower and comparable between the cases (2.6%) and the controls (3.7%). Cardiovascular medications associated with hypoglycemia were the most frequently identified in the cases and controls. Within the cases, 35.9% had medication availability of an ACE inhibitor, which was comparable to the controls (33.9%). Similarly, 20.3% of cases and 19.7% of controls had an ARB available in the 30 days before the index date. The prevalence of -blockers in the previous 30 days among cases was much higher that of controls (35.1% vs 21.3%). All other medications assessed, including allopurinol, benzodiazepines, fibrates, fluoroquinolones, trimethoprim, NSAIDs, and warfarin were more common in cases than controls. Patterns of use of the 6 classes of OADs are detailed in Table II. Overall, ␣-glucosidase inhibitors, meglitinides, and DPP-4 inhibitors were utilized infrequently by the study population. Cases were more likely to have continuous availability of sulfonylureas (41.1%) than were controls (30.0%), whereas controls were more likely to have continuous availability of metformin (47.9%) compared with cases (34.1%). Continuous TZD availability was 22.9% in cases and 23.8% in controls. The proportion of cases and controls with intermittent metformin availability was comparable, while patterns for intermittent sulfonylurea and TZD availability mirrored those of continuous availability, with a lower frequency.
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The results of the conditional logistic regression model designed to identify independent predictors of inpatient hypoglycemia are presented in Table III. The strongest predictor of an inpatient hypoglycemic admission was a previous emergency department visit for hypoglycemia (OR ⫽ 9.48; 95% CI, 4.95–18.15). Similarly, previous outpatient visits for hypoglycemia had an ⬃8-fold increase in the odds of inpatient hypoglycemia admission (OR ⫽ 7.88; 95% CI, 5.68 – 10.93). After adjusting for other factors, age was not associated with inpatient hypoglycemic admission. Men had a 16% lower rate of inpatient hypoglycemiarelated admission than women (OR ⫽ 0.84; 95% CI, 0.73– 0.96). All categories of metformin availability (relative to nonavailability) had lower rates of inpatient hypoglycemic episodes. Continuous availability of metformin had a 38% lower rate of inpatient admissions for hypoglycemia (OR ⫽ 0.62; 95% CI, 0.53– 0.73), and intermittent availability of metformin had a 24% lower rate (OR ⫽ 0.76; 95% CI 0.64 – 0.92) than nonavailability of metformin. Conversely, continuous availability of sulfonylureas (OR ⫽ 2.25; 95% CI, 1.93–2.63) and intermittent availability of sulfonylureas (OR ⫽ 2.28; 95% CI, 1.90 –2.74) had increased rates of inpatient hypoglycemia-related admissions compared with nonavailability of sulfonylureas. Intermittent TZD availability had a slightly increased rate of inpatient hypoglycemia (OR ⫽ 1.22; 95% CI, 1.01– 1.47). Continuous TZD availability or other OADs were not associated with inpatient hypoglycemic admissions. Availability of all other evaluated medication classes in the previous 30 days was associated with an increased rate of inpatient hypoglycemia admission. Insulin availability had a 2-fold increased rate (OR ⫽ 2.23; 95% CI, 1.83–2.72) of inpatient hypoglycemic admission relevant to nonavailability of insulin. As shown in Table III, availability of allopurinol, benzodiazepines, -blockers, fluoroquinolones, NSAIDs, and trimethoprim all demonstrated an increased rate of inpatient admission (relative to nonavailability). A 1-U change in the Charlson comorbidity index was associated with a ⬃40% increase in the rate of inpatient admission (OR ⫽ 1.37 per 1 U change; 95% CI, 1.32–1.44). Lastly, individual microvascular and macrovascular complications of diabetes were independent predictors of inpatient hypoglycemia-related admission. Arrhythmia, CAD, heart failure, stroke, acute renal failure, chronic renal pathophysiology, and the
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Table II. Prescription patterns of oral antidiabetic drug (OAD) availability in the 180 days before the index date. Medication
Cases (n ⫽ 1339)
Controls (n ⫽ 13,390) n (%)
Sulfonylureas Continuous availability* Intermittent availability† Nonavailability‡
550 (41.1) 336 (25.1) 453 (33.8)
4010 (30.0) 1950 (14.6) 7430 (55.5)
Metformin Continuous availability* Intermittent availability† Nonavailability‡
457 (34.1) 318 (23.8) 564 (42.1)
6411 (47.9) 3114 (23.3) 3865 (28.9)
Thiazolidinediones Continuous availability* Intermittent availability† Nonavailability‡
307 (22.9) 226 (16.9) 806 (60.2)
3181 (23.8) 1844 (13.8) 8365 (62.5)
DPP-4 inhibitors Continuous availability* Intermittent availability† Nonavailability‡
24 (1.8) 41 (3.1) 1274 (95.2)
292 (2.2) 359 (2.7) 12,739 (95.1)
Meglitinides Continuous availability* Intermittent availability† Nonavailability‡
14 (1.1) 28 (2.1) 1297 (96.9)
128 (1.0) 175 (1.3) 13,087 (97.7)
Alpha-glucosidase inhibitors Continuous availability* Intermittent availability† Nonavailability‡
2 (0.2) 7 (0.5) 1330 (99.3)
35 (0.3) 41 (0.3) 13,314 (99.4)
DPP-4 ⫽ dipeptidyl peptidase-4. *Participants with continuous availability had medication coverage in each of all six 30 day periods preceding the index date. † Participants with intermittent availability had medication coverage in at least 1 of the preceding 6 intervals. ‡ Participants with nonavailability did not have medication coverage during any of the intervals preceding the index date.
presence of a diabetic ulcer all had increased rates of inpatient admission (relative to the absence of each factor).
DISCUSSION We conducted a large, nested case– control study in a population of patients with type 2 diabetes taking OADs to identify independent predictors of hypoglycemia-related inpatient admissions. In addition to traditional risk factors associated with hypoglycemia, we evaluated the presence of microvascular and macrovascular complications of diabetes as potential independent predictors
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of inpatient admission. Further, we assessed the association of patterns of OAD availability (in the previous 6 months) with inpatient admission. Previous studies largely assessed hypoglycemia as a comprehensive end point (ie, outpatient and inpatient visits combined); therefore, our study added important knowledge of risk factors for severe hypoglycemic events resulting in hospitalization. This study highlighted important areas for health care intervention and provided a reminder for vigilance when known risk factors for hypoglycemia are present.
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Table III. Independent predictors of inpatient hypoglycemia admissions. Controls, % (n ⫽ 13,390)
Crude OR
95% CI
Adjusted OR*
95% CI
Gender Female Male
49.2 50.8
46.3 53.7
1.00 0.89
N/A 0.80–0.99
1.00 0.84
N/A 0.73–0.96
Age, y 18–34 35–49 50–64 ⱖ65
1.3 13.3 82.6 2.8
2.1 21.1 74.5 2.4
1.00 0.99 1.75 1.88
N/A 0.60–1.63 1.08–2.84 1.04–3.39
1.00 1.01 1.14 0.91
N/A 0.58–1.79 0.66–1.97 0.46–1.81
Oral diabetes medications†,‡ Sulfonylureas: Continuous availability§ Sulfonylureas: Intermittent availability Metformin: Continuous availability§ Metformin: Intermittent availability Thiazolidinediones: Continuous availability§ Thiazolidinediones: Intermittent availability Other OAD: Continuous availability§,¶ Other OAD: Intermittent availability,¶
41.1 25.1 34.1 23.8 22.9 16.9 4.5 3.7
30.0 14.6 47.9 23.3 23.8 13.8 3.9 3.2
2.36 2.88 0.48 0.70 1.00 1.27 1.15 1.17
2.06–2.70 2.48–3.35 0.42–0.55 0.60-0.81 0.87–1.15 1.09–1.49 0.88–1.52 0.86–1.59
2.25 2.28 0.62 0.76 1.06 1.22 1.11 1.09
1.93–2.63 1.90–2.74 0.53–0.73 0.64-0.92 0.90–1.24 1.01–1.47 0.80–1.55 0.75–1.59
Other medications# Allopurinol Benzodiazepine Beta-blocker Blood glucose monitoring supplies Fluoroquinolone Insulin NSAID Trimethoprim
5.5 14.6 35.1 30.9 10.7 16.8 13.8 3.3
2.6 6.2 21.3 30.6 2.5 6.7 10.4 0.9
2.15 2.57 2.01 1.02 4.69 2.84 1.38 3.81
1.66–2.78 2.17–3.03 1.78–2.26 0.90–1.15 3.82–5.77 2.42–3.33 1.17–1.63 2.68–5.41
1.54 1.90 1.20 0.83 2.59 2.23 1.27 1.97
1.13–2.12 1.55–2.33 1.03–1.40 0.71–0.96 1.99–3.39 1.83–2.72 1.05–1.54 1.26–3.08
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Cases, % (n ⫽ 1339)
Variable
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1788 Table III (continued). Variable Comorbid conditions Previous outpatient visit for hypoglycemia Previous ED visit for hypoglycemia Macrovascular complications Arrhythmia Coronary artery disease Heart failure Stroke Microvascular complications Acute renal failure Chronic renal pathophysiology Ulcer Charlson comorbidity (per 1 U change)
Cases, % (n ⫽ 1339)
Controls, % (n ⫽ 13,390)
Crude OR
95% CI
Adjusted OR*
95% CI
12.5 6.2
0.9 0.1
16.17 48.53
12.60–20.76 28.80–81.78
7.88 9.48
5.68–10.93 4.95–18.15
6.8 21.0 14.0 3.4
1.4 7.8 1.5 0.4
5.25 3.12 10.99 9.62
4.05–6.81 2.69–3.61 8.86–13.64 6.37–14.52
1.69 1.48 2.33 2.78
1.17–2.44 1.21–1.81 1.72–3.15 1.62–4.77
8.3 8.4 6.4 –
0.6 1.1 1.4 –
15.43 8.37 4.98 1.72
11.43–20.83 6.49–10.81 3.82–6.49 1.66–1.79
3.10 2.22 1.71 1.37
2.05–4.67 1.56–3.15 1.20–2.44 1.32-1.44
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ED ⫽ emergency department; NSAID ⫽ nonsteroidal anti-inflammatory drug; OAD ⫽ oral antidiabetic drugs; OR ⫽ odds ratio. *Adjusted for all factors listed in the table. † As identified in pharmacy claims in the 6 months before the index date. ‡ Nonavailability of the medication/class of medication is the referent group. § Participants with continuous availability had medication coverage in each of all six 30-day periods preceding the index date. Participants with intermittent availability had medication coverage in at least 1 of the preceding 6 intervals. ¶ Includes persons taking ␣-glucosidase inhibitors, dipeptidyl peptidase-4 inhibitors, or meglitinides. # Defined as medication availability in the previous 30 days.
B.J. Quilliam et al. Direct comparison of other studies assessing risk factors of hypoglycemia to our study was problematic as definitions of serious hypoglycemia11 and settings studied varied widely. In addition, other studies included limited study populations,12,13 were restricted to insulin users,14,15 or focused on a limited set of OADs.16,17 Our study included a broader range of patients with type 2 diabetes taking OADs with or without insulin. An important finding from our study was the increased rate of inpatient admission after outpatient medical or emergency department visits for hypoglycemia in the previous 6 months. In our study, the relative rate for previous emergency department visits for hypoglycemia was 9.5, and for previous outpatient visits for hypoglycemia, the relative rate was 7.9. In a prospective cohort study of community dwelling patients with type 2 diabetes by Davis et al,18 a history of hypoglycemia requiring hospitalization was associated with a hazard ratio of 5.66 (95% CI, 2.21–14.50) for subsequent severe hypoglycemic episodes. In a small cross-sectional study conducted in patients with type 2 diabetes and attending specialty outpatient diabetes clinics, Miller et al19 reported a ⬎2-fold increase (OR ⫽ 2.65; 95% CI, 1.80 –3.80) in the occurrence of hypoglycemia (mild, moderate, or severe) associated with a history of hypoglycemia at baseline after adjustment for age, gender, race, duration of diabetes, body mass index, glycosated hemoglobin, and OAD use. In this same study, there was no association between history of hypoglycemia and incident severe hypoglycemia, although the absolute number of severe events was small (n ⫽ 5). Another study found that discharge orders for patients with hypoglycemia had a low frequency of educating patients to avoid recurring episodes of hypoglycemia (only 3% received such a warning), and only 2% were informed about obtaining a glucagon emergency kit.20 An important finding from our study was that ⬎12% of cases had at least 1 outpatient visit for hypoglycemia and nearly 6% had an emergency department visit for hypoglycemia in the previous 6 months. As educational interventions have been encouraged by the Task Force for the National Standards for Diabetes Self Management Education,21 more efforts should focus on the development of educational materials and systems for early intervention in patients being seen in the outpatient setting for hypoglycemia.
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Although the association between insulin use and hypoglycemia has been well-studied in patients with type 1 diabetes, more recent studies assessed the impact of insulin use in type 2 diabetes. In a crosssectional study of patients with type 2 diabetes, Akram et al14 found that rates of hypoglycemia were approximately one-third of the rates in patients with type 1 diabetes. In our study, insulin availability in the previous 30 days was associated with a ⬎2-fold increase in the rate of inpatient admission for hypoglycemia. A study by Davis et al18 found an increased crude association between severe hypoglycemia and insulin use that did not remain after adjustment for other factors. Another study reported 3 times the risk of hypoglycemia (OR ⫽ 3.44; 95% CI, 2.07–5.73) associated with insulin use after adjusting for concurrent sulfonylurea use.19 A more recent study found similar associations with insulin use after adjustment for simultaneous use of other OADs.22 In our sample, 16.8% of cases and 6.7% of controls had insulin availability in the 30 days before their index date. As the use of insulin therapy is becoming more commonplace in patients with type 2 diabetes, heightened awareness of the signs, symptoms, and precipitating factors for hypoglycemic episodes is essential among physicians, caregivers, and patients. Within our study, we assessed the potential for time-varying effects of OAD agents by creating exposure groups for each class of OAD agents in the 180 days before the index date. Other studies of serious hypoglycemic events assessed current use of OAD agents,17,18,23 and 1 study assessed duration of use.22 In our study, availability of sulfonylureas (continuously or intermittently) in the previous 6 months had a 2 times higher rate of admission for hypoglycemia than patients without sulfonylurea availability. In addition, the 2 categories of metformin availability (each relative to no metformin availability) were associated with a significantly reduced rate (28%–30%) of inpatient hypoglycemia admission. Participants with continuous and intermittent availability had varying levels of cumulative exposure in the 180 days before the index date. Our measures of drug exposure were not sensitive enough to detect daily dose or adherence, and suggested that future endeavors should focus on understanding more detailed patterns of use that might increase or decrease rates of hypoglycemia.
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Study Limitations The data utilized for this study were from a large health care database, generally containing workplacesponsored insurance plans. Enrollees included within this database were typically of working age and therefore may not be generalizable to all persons with type 2 diabetes. In addition, we only measured inpatient admissions for hypoglycemia and might have missed more severe cases that resulted in death before hospital admission. Lastly, we created flags for antidiabetic medication availability during several exposure windows preceding the index date. We derived these flags based on dates of medication dispensing and the days supply dispensed. Although this measure was consistent with many health care database analyses, it did not necessarily identify actual consumption by study participants.
CONCLUSIONS Our study focused on risk factors for inpatient hypoglycemia hospitalization, adding important knowledge of risk factors associated with these events. Previous outpatient and emergency department visits for hypoglycemia were significant predictors of inpatient hypoglycemia admission. In addition, insulin availability and continuous or intermittent sulfonylurea availability had elevated rates of inpatient admission, whereas both categories of metformin availability had decreased rates of hypoglycemia hospitalization. As inpatient hospitalizations for hypoglycemia are both costly and can have significant impact on a patient’s quality of life, appropriate risk management plans should be developed to prevent these serious complications of therapy from occurring. Outpatient or emergency department visits related to hypoglycemia may be indicative of patients at higher risk for inpatient admission and therefore should serve as points of intervention and education.
ACKNOWLEDGMENTS This research was funded by a grant from Takeda Pharmaceuticals America, Inc. Dr. Quilliam is a consultant for Ortho-McNeill Janssen Scientific Affairs, LLC, conducting studies unrelated to the content of this article (pharmacological management of pain). Dr. Simeone was a postdoctoral fellow at the University of Rhode Island at the time this study was conducted; he is currently an employee of United BioSource Corporation. Dr. Ozbay was a doctoral student at the Uni-
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versity of Rhode Island at the time this study was conducted; he is currently an employee of HealthCore, Inc. The authors have indicated that they have no other conflicts of interest regarding the content of this article. Dr. Quilliam’s contributions included data acquisition, literature evaluation, study design, data interpretation, and drafting and revising of the manuscript. Dr. Simeone’s contributions included literature evaluation, study design, data interpretation and drafting of the manuscript. Dr. Ozbay’s contributions included literature evaluation, data analysis, data interpretation and drafting of the manuscript.
REFERENCES 1. UKPDS Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352:837– 853. 2. Patel A, MacMahon S, Chalmers J, et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008;358:2560 –2572. 3. Davis S, Alonso MD. Hypoglycemia as a barrier to glycemic control. J Diabetes Complications. 2004;18:60 – 68. 4. Curkendall SM, Natoli JL, Alexander CM, et al. Economic and clinical impact of inpatient diabetic hypoglycemia. Endocr Pract. 2009;15:302–312. 5. Ginde AA, Blanc PG, Lieberman RM, Camargo CA, Jr.Validation of ICD-9-CM coding algorithm for improved identification of hypoglycemia visits. BMC Endocr Disord. 2008;8:4. 6. Lachin JM. Sample size evaluation for a multiply matched case-control study using the score test from a conditional logistic (discrete Cox PH) regression model. Stat Med. 2008;27:2509 –2523. 7. Meduru P, Helmer D, Rajan M, et al. Chronic illness with complexity: implications for performance measurement of optimal glycemic control. J Gen Intern Med. 2007;22(Suppl 3):408 – 418. 8. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613– 619. 9. Agresti A. An Introduction to Categorical Data Analysis. Hoboken, NJ: John Wiley and Sons, Inc; 2007. 10. Pearce N. What does the odds ratio estimate in a casecontrol study? Int J Epidemiol. 1993;22:1189 –1192. 11. Zammitt NN, Frier BM. Hypoglycemia in type 2 diabetes: pathophysiology, frequency, and effects of different treatment modalities. Diabetes Care. 2005;28:2948 –2961. 12. Greco D, Pisciotta M, Gambina F, Maggio F. Severe hypoglycaemia leading to hospital admission in type 2
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13.
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diabetic patients aged 80 years or older. Exp Clin Endocrinol Diabetes. 2010;118:215–219. Sugarman JR. Hypoglycemia associated hospitalizations in a population with a high prevalence of noninsulin-dependent diabetes mellitus. Diabetes Res Clin Pract. 1991;14:139 – 147. Akram K, Pedersen-Bjergaard U, Carstensen B, et al. Frequency and risk factors of severe hypoglycaemia in insulin-treated type 2 diabetes: a cross-sectional survey. Diabet Med. 2006;23:750 –756. Monami M, Marchionni N, Mannucci E. Long-acting insulin analogues versus NPH human insulin in type 2 diabetes: a meta-analysis. Diabetes Res Clin Pract. 2008;81:184 – 189. Leese GP, Wang J, Broomhall J, et al. Frequency of severe hypoglycemia requiring emergency treatment in type 1 and type 2 diabetes: a populationbased study of health service resource use.DiabetesCare.2003;26:1176–1180. Stahl M, Berger W. Higher incidence of severe hypoglycaemia leading to hospital admission in type 2 diabetic patients treated with long-acting versus short-acting sulphonylureas. Diabet Med. 1999;16:586 –590. Davis TM, Brown SG, Jacobs IG, et al. Determinants of severe hypoglycemia complicating type 2 diabetes: the Fremantle diabetes study. J Clin EndocrinolMetab.2010;95:2240–2247. Miller CD, Phillips LS, Ziemer DC, et al. Hypoglycemia in patients with type 2 diabetes mellitus. Arch Intern Med. 2001;161:1653–1659. Ginde AA, Pallin DJ, Camargo CA Jr. Hospitalization and discharge education of emergency department patients with hypoglycemia. Diabetes Educ. 2008;34:683– 691. Funnell MM, Brown TL, Childs BP, et al. National standards for diabetes self-management education. Diabetes Care. 2010;33(Suppl 1):S89 –96. Bodmer M, Meier C, Krahenbuhl S, et al. Metformin, sulfonylureas, or
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other antidiabetes drugs and the risk of lactic acidosis or hypoglycemia: a nested case-control analysis. Diabetes Care. 2008;31:2086 –2091. 23. Miller ME, Bonds DE, Gerstein HC, et al. The effects of baseline characteris-
tics,glycaemiatreatmentapproach,and glycatedhaemoglobinconcentrationon the risk of severe hypoglycaemia: post hoc epidemiological analysis of the ACCORD study. BMJ. 2010;340: b5444.
Address correspondence to: Brian J. Quilliam, PhD, University of Rhode Island, College of Pharmacy, 41 Lower College Road, Kingston, RI 02881. E-mail: [email protected]
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Risk Factors for Hypoglycemia-Related Hospitalization in Patients With Type 2 Diabetes: A Nested Case–Control Study Brian J. Quilliam, PhD; Jason C. Simeone, PhD*; and A. Burak Ozbay, PhD† College of Pharmacy, University of Rhode Island, Kingston, Rhode Island ABSTRACT Background: Hypoglycemia requiring hospitalization remains a serious and costly limitation to treatment of type 2 diabetes with antidiabetic medications. Objective: We identified risk factors for hypoglycemia hospitalization in patients with type 2 diabetes treated with oral antidiabetic drugs (OADs). Methods: In the 2004 to 2008 MarketScan database, we identified patients with type 2 diabetes taking OADs with ⬎12 months of enrollment. We conducted a nested case– control study, selecting cases with an inpatient admission for hypoglycemia (first event). Using the index date of the cases, we conducted incidence density sampling to identify controls (10:1 matching) with continued eligibility during that month, further matching on date of cohort entry (⫾1 month). The final sample was 1339 cases and 13,390 controls. We assessed patterns of OAD availability (creating 3 groups: continuous, intermittent, and nonavailability), other medication availability, previous visits for hypoglycemia, complications of diabetes, and other comorbidities in the previous 180 days. A conditional logistic regression model identified predictors of hypoglycemia hospitalization. Results: Mean (SD) age of cases was 56.4 (7.0) years compared with 54.6 (7.8) years in the controls. Overall, cases had more comorbidities than controls. In multivariable modeling, previous emergency department hypoglycemia visits (odds ratio [OR] ⫽ 9.48; 95% CI, 4.95–18.15) and previous outpatient hypoglycemia visits (OR ⫽ 7.88; 95% CI, 5.68 –10.93) were strongly predictive of inpatient hypoglycemia admission. Continuous metformin availability had a 38% lower rate of inpatient hypoglycemia admission (OR ⫽ 0.62; 95% CI, 0.53– 0.73) and intermittent metformin availability a 24% lower rate (OR ⫽ 0.76; 95% CI, 0.64 – 0.92) than nonavailability of metformin. Relative to nonavailability, continuous (OR ⫽ 2.25; 95% CI, 1.93–2.63) and intermittent sulfonylurea availability (OR ⫽ 2.28; 95% CI, 1.90 –2.74) had increased rates of hypoglycemia hospitalization. Intermittent thiazolidinedione availability had a slightly in-
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creased rate of hypoglycemia hospitalization (OR ⫽ 1.22; 95% CI, 1.01–1.47). Continuous availability of thiazolidinediones and continuous or intermittent use of other OADs were not predictive of hypoglycemia admission. Conclusions: Previous outpatient or emergency department visits for hypoglycemia and continuous or intermittent sulfonylurea availability were found to be predictive of costly inpatient hypoglycemia admissions. Although this observational study may not be generalizable to all patients with type 2 diabetes and assessed medication availability rather than actual consumption, previous outpatient visits and prescription for OADs should serve as points of intervention and patient education. (Clin Ther. 2011;33:1781–1791) © 2011 Elsevier HS Journals, Inc. All rights reserved. Key words: hospitalization, hypoglycemia, inpatient, oral antidiabetic medication, predictors, type 2 diabetes.
INTRODUCTION Research identifying the importance of tight glycemic control for preventing microvascular complications of type 2 diabetes has been widely available for ⬎10 years. In the late 1990s, the United Kingdom Prospective Diabetes Study (UKPDS) demonstrated that intensive goal-oriented therapy with sulfonylureas or insulin prevented microvascular complications of type 2 diabetes.1 More recent studies reinforced the benefits of intensive antidiabetic drug therapy, but failed to show a similar reduction in macrovascular complications of diabetes after intensive glucose-lowering ther*Current affiliation: United BioSource Corporation, Lexington, Massachusetts. † Current affiliation: HealthCore, Inc, Andover, Massachusetts. Accepted for publication September 14, 2011. doi:10.1016/j.clinthera.2011.09.020 0149-2918/$ - see front matter © 2011 Elsevier HS Journals, Inc. All rights reserved.
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PATIENTS AND METHODS Data Source We utilized health care claims from the 2004 to 2008 MarketScan database (Thomson Reuters HI, Ann Arbor, Michigan) to conduct this nested case– control study. We utilized the most recent data available (2008) at the time of the study inception and included 4 additional years of data (2004 –2007) to ensure an adequate sample size to achieve our specific aim. The size of the database, coupled with the diversity of contributing health plans, allows for the conduct of large research studies that are representative of Americans with work-based health insurance.
Cohort Identification We assembled a cohort of adults (⬎18 years of age) with at least 2 outpatient or inpatient claims for diabetes (International Classification of Diseases-9th Edition [ICD-9] 250.XX) during 2004 to 2008 and further restricted the cohort to those taking at least 1 OAD. From this eligible population, we first excluded participants who did not have at least 12 months of continuous eligibility within a noncapitated health plan after the initial fill date of an OAD, and secondly those with ⬎1 medical claim (inpatient or outpatient) for type 1 (ICD-9 250.x1 or 250.x3) or gestational diabetes (ICD-9 648.0X) during the study period. The final sample in the cohort was 536,581 patients.
Case Identification Within the larger cohort, we conducted a nested case– control study and identified hypoglycemic events
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requiring inpatient medical intervention using an ICD-9 based algorithm developed by Ginde et al.5 We identified the first hospitalization indicative of hypoglycemia (ICD-9: 251.0, 251.1, 251.2, 270.3, 962.3) or ICD-9 250.8 in the absence of other contributing diagnoses (ICD-9: 259.8, 272.7, 681.XX, 682.XX, 686.9X, 707.1-707.9, 709.3, 730.0-730.2 or 731.8). As we were interested in factors preceding the incident event, we excluded all potential cases with an inpatient hypoglycemic event in the first 180 days of cohort entry. Each selected case was then assigned an index date based on the date of service associated with the first identified inpatient claim for a hypoglycemic event. During follow up, there were a total of 1761 inpatient admissions for hypoglycemia among 1676 study participants. After excluding 337 persons with inpatient admissions for hypoglycemia in the first 6 months, the final sample of cases eligible for inclusion was 1339.
Control Selection To identify controls representing the exposure distribution that gave rise to the cases, we employed incidence density sampling with replacement. For each case, we randomly selected 10 controls that remained continuously eligible during the calendar month covering the cases index date. Because length of follow up might have been related to disease severity, we further required that eligible controls entered the study cohort within ⫾1 month of the matched case and assigned each control an index date corresponding to the date of admission for their matched case. The final sample eligible for study inclusion was 1339 cases and 13,390 matched controls.
Power Analysis We performed a post hoc power analysis using calculations by Lachin.6 With 1390 identified cases, a 10:1 case/control matching ratio, and a 2-sided ␣ level of 0.05, our study had 92% power to detect an odds ratio (OR) of 1.10 (ie, a 10% difference between cases and controls).
Exposure History Medications The MarketScan database contains deidentified claims information from retail pharmacies, as well as mail order and specialty pharmacy transactions. We examined medication exposure history in the 180 days preceding the index date and identified all OAD pharmacy transactions (metformin, sulfonylureas, thiazoli-
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B.J. Quilliam et al. dinediones [TZD], meglitinides, ␣-glucosidase inhibitors, and dipeptidyl peptidase 4 [DPP-4] inhibitors). As changes in OAD use throughout time were probable, we evaluated the availability of each of these classes in the six 30-day intervals preceding the index date. We identified all OADs that were filled either during each of the 6 intervals or before the interval with a supply that ended during or beyond the interval. Using this information, we created medication availability flags for each class of medication during the 6 intervals and created 3 groups for each medication class, including continuous availability, intermittent availability, and nonavailability. To have continuous availability, the participant must have had medication coverage in all of the six 30-day periods preceding the index date. We defined intermittent availability as those having medication coverage in at least 1 of the preceding 6 intervals. Finally, nonavailability was defined as no medication coverage during the 6 intervals preceding the index date. After preliminary analyses, ␣-glucosidase inhibitors, meglitinides, and DPP-4 inhibitors were combined into 1 category, other OADs, due to limited sample size. In addition, we evaluated pharmacy claims for availability of other agents (in the previous 30 days) known to be associated with hypoglycemia, including angiotensinconverting enzyme (ACE) inhibitors, angiotensin-2 receptor blockers (ARB), allopurinol, benzodiazepines, -blockers, fibrates, fluoroquinolones, insulin, nonsteroidal anti-inflammatory drugs (NSAIDs), trimethoprim, warfarin, and other injectable antidiabetic agents (ie, exenatide and pramlintide).
Demographic Data and Comorbidity Within the 180 days before the index date, we characterized demographic characteristics, including age, gender, and geographic region. Using ICD-9 codes from medical inpatient and outpatient claims data, we assessed the presence of other comorbid conditions that might be associated with hypoglycemic events. We identified the presence of Addison’s disease, hypopituitarism, hypothyroidism, liver disease, microvascular and macrovascular complications of diabetes (utilizing methods employed in other studies),7 previous instances of outpatient hypoglycemia (medical office or emergency department visits using the aforementioned algorithm utilized to identify inpatient events),5 and an overall marker of comorbidity.8 Lastly, we identified
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study participants who used self-monitoring of blood glucose using pharmacy claims for test strips or lancets.
Analytic Plan We first conducted bivariate analyses to assess comparability between cases and controls utilizing the CochranMantel-Haenszel test statistic for categorical variables9 and a paired t-test for continuous variables. Any factor that differed between the cases and controls by at least 5% was considered a potential independent predictor and flagged for additional consideration. Next, we developed a conditional logistic regression model initiated by inclusion of all factors preliminarily identified as potential predictors during the bivariate analyses. We then manually removed variables sequentially that were not predictive of hypoglycemic events (through Wald P value assessment) with confirmation of removal through likelihood ratio testing. Results are presented as crude (unadjusted) and multivariable (adjusted) ORs, with their respective 95% CIs. Because we employed incidence density sampling, the OR derived from this conditional logistic model approximates the incidence rate ratio and was therefore interpreted as such.10 All statistical tests were conducted with a 2-tailed ␣ of 0.05 and performed using SAS software (version 9.2; SAS Institute Inc, Cary, North Carolina). This study was reviewed and approved as exempt by the University of Rhode Island’s Institutional Review Board.
RESULTS As presented in Table I, cases and matched controls were of similar age, gender, and resided in the same geographic regions. The mean (SD) age of cases was 56.4 (7.0) years compared with 54.6 (7.8) years in the controls, with 82.6% of cases and 74.5% of controls aged 50 to 64 years. Overall, the prevalence of evaluated comorbidities was higher in the cases: the mean Charlson comorbidity index was 1.7 in the cases compared with 0.4 in the controls. When evaluating the individual Charlson factors (data not shown), cases were more likely than controls to have diabetes with complications (13.4% in cases and 5.3% in controls). Cases were more likely than controls to have a history of hypothyroidism (7.3% compared with 4.7%) and liver disease (2.8% vs 1.0%), whereas other diseases were rare in both groups. Cases were also more likely than controls to have had an outpatient hypoglycemiarelated visit (12.5% in cases vs 0.9% in controls) and
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Table I. Demographic and clinical characteristics. Characteristic
Cases (n ⫽1339)
Controls (n ⫽ 13,390) Mean (SD)
Age, y Charlson comorbidity index
56.4 (7.0) 1.7 (2.5)
54.6 (7.8) 0.4 (0.9) No. (%)
Age, y 18–34 35–49 50–64 ⱖ65
18 (1.3) 178 (13.3) 1106 (82.6) 37 (2.8)
280 (2.1) 2822 (21.1) 9971 (74.5) 317 (2.4)
Gender Female Male
659 (49.2) 680 (50.8)
6196 (46.3) 7194 (53.7)
Geographic region* Northeast Midwest South West
115 (8.6) 419 (31.3) 651 (48.6) 148 (11.1)
864 (6.5) 4003 (29.9) 6828 (51.0) 1587 (11.9)
Clinical characteristics† Self-monitoring of blood glucose Hypoglycemia–outpatient visit Hypothyroidism Hypoglycemia–emergency department visit Liver disease Hypopituitarism Addison’s disease
414 (30.9) 167 (12.5) 98 (7.3) 83 (6.2) 38 (2.8) 2 (0.2) 1 (0.07)
4098 (30.6) 115 (0.9) 631 (4.7) 18 (0.1) 136 (1.0) 4 (0.03) 1 (0.01)
Macrovascular diabetes complications† Coronary artery disease Heart failure Peripheral vascular disease Arrhythmia Stroke
281 (21.0) 187 (14.0) 103 (7.7) 91 (6.8) 45 (3.4)
1049 (7.8) 202 (1.5) 231 (1.7) 185 (1.4) 48 (0.4)
Microvascular diabetes complications† Chronic renal pathophysiology Acute renal failure Retinopathy Ulcer Nephropathy End-stage renal disease Dialysis Amputation
113 (8.4) 111 (8.3) 85 (6.4) 86 (6.4) 63 (4.7) 13 (1.0) 2 (0.2) 0 (0)
147 (1.1) 77 (0.6) 447 (3.3) 184 (1.4) 148 (1.1) 2 (0.01) 5 (0.04) 6 (0.04)
*Missing data on 6 (0.5%) cases and 108 (0.8%) controls. † As identified in inpatient and outpatient claims in the 6 months before the index date.
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B.J. Quilliam et al. an emergency department hypoglycemia-related visit (6.2% of cases compared with 0.1% of controls). The prevalence of previous microvascular and macrovascular diabetes complications is displayed in Table I. Coronary artery disease (CAD) was present in 21.0% of cases compared with 7.8% of controls, and heart failure occurred in 14.0% of cases and 1.5% of controls. Other macrovascular complications were of lower prevalence, but occurred more frequently in the cases than the controls. Microvascular complications of diabetes were also higher in the cases than the controls. Acute renal failure occurred in 8.3% of the cases and ⬍1% of the controls. Similarly, chronic renal pathophysiology was noted more frequently in the cases than the controls (8.4% vs 1.1%). Other microvascular complications were lower and remained elevated in the cases relative to the controls. Within this population of patients with type 2 diabetes, 16.8% of cases and 6.7% of controls had an indication of insulin availability in the 30 days before the index date. The prevalence of other antidiabetic injectable agents (exenatide and pramlintide) were much lower and comparable between the cases (2.6%) and the controls (3.7%). Cardiovascular medications associated with hypoglycemia were the most frequently identified in the cases and controls. Within the cases, 35.9% had medication availability of an ACE inhibitor, which was comparable to the controls (33.9%). Similarly, 20.3% of cases and 19.7% of controls had an ARB available in the 30 days before the index date. The prevalence of -blockers in the previous 30 days among cases was much higher that of controls (35.1% vs 21.3%). All other medications assessed, including allopurinol, benzodiazepines, fibrates, fluoroquinolones, trimethoprim, NSAIDs, and warfarin were more common in cases than controls. Patterns of use of the 6 classes of OADs are detailed in Table II. Overall, ␣-glucosidase inhibitors, meglitinides, and DPP-4 inhibitors were utilized infrequently by the study population. Cases were more likely to have continuous availability of sulfonylureas (41.1%) than were controls (30.0%), whereas controls were more likely to have continuous availability of metformin (47.9%) compared with cases (34.1%). Continuous TZD availability was 22.9% in cases and 23.8% in controls. The proportion of cases and controls with intermittent metformin availability was comparable, while patterns for intermittent sulfonylurea and TZD availability mirrored those of continuous availability, with a lower frequency.
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The results of the conditional logistic regression model designed to identify independent predictors of inpatient hypoglycemia are presented in Table III. The strongest predictor of an inpatient hypoglycemic admission was a previous emergency department visit for hypoglycemia (OR ⫽ 9.48; 95% CI, 4.95–18.15). Similarly, previous outpatient visits for hypoglycemia had an ⬃8-fold increase in the odds of inpatient hypoglycemia admission (OR ⫽ 7.88; 95% CI, 5.68 – 10.93). After adjusting for other factors, age was not associated with inpatient hypoglycemic admission. Men had a 16% lower rate of inpatient hypoglycemiarelated admission than women (OR ⫽ 0.84; 95% CI, 0.73– 0.96). All categories of metformin availability (relative to nonavailability) had lower rates of inpatient hypoglycemic episodes. Continuous availability of metformin had a 38% lower rate of inpatient admissions for hypoglycemia (OR ⫽ 0.62; 95% CI, 0.53– 0.73), and intermittent availability of metformin had a 24% lower rate (OR ⫽ 0.76; 95% CI 0.64 – 0.92) than nonavailability of metformin. Conversely, continuous availability of sulfonylureas (OR ⫽ 2.25; 95% CI, 1.93–2.63) and intermittent availability of sulfonylureas (OR ⫽ 2.28; 95% CI, 1.90 –2.74) had increased rates of inpatient hypoglycemia-related admissions compared with nonavailability of sulfonylureas. Intermittent TZD availability had a slightly increased rate of inpatient hypoglycemia (OR ⫽ 1.22; 95% CI, 1.01– 1.47). Continuous TZD availability or other OADs were not associated with inpatient hypoglycemic admissions. Availability of all other evaluated medication classes in the previous 30 days was associated with an increased rate of inpatient hypoglycemia admission. Insulin availability had a 2-fold increased rate (OR ⫽ 2.23; 95% CI, 1.83–2.72) of inpatient hypoglycemic admission relevant to nonavailability of insulin. As shown in Table III, availability of allopurinol, benzodiazepines, -blockers, fluoroquinolones, NSAIDs, and trimethoprim all demonstrated an increased rate of inpatient admission (relative to nonavailability). A 1-U change in the Charlson comorbidity index was associated with a ⬃40% increase in the rate of inpatient admission (OR ⫽ 1.37 per 1 U change; 95% CI, 1.32–1.44). Lastly, individual microvascular and macrovascular complications of diabetes were independent predictors of inpatient hypoglycemia-related admission. Arrhythmia, CAD, heart failure, stroke, acute renal failure, chronic renal pathophysiology, and the
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Table II. Prescription patterns of oral antidiabetic drug (OAD) availability in the 180 days before the index date. Medication
Cases (n ⫽ 1339)
Controls (n ⫽ 13,390) n (%)
Sulfonylureas Continuous availability* Intermittent availability† Nonavailability‡
550 (41.1) 336 (25.1) 453 (33.8)
4010 (30.0) 1950 (14.6) 7430 (55.5)
Metformin Continuous availability* Intermittent availability† Nonavailability‡
457 (34.1) 318 (23.8) 564 (42.1)
6411 (47.9) 3114 (23.3) 3865 (28.9)
Thiazolidinediones Continuous availability* Intermittent availability† Nonavailability‡
307 (22.9) 226 (16.9) 806 (60.2)
3181 (23.8) 1844 (13.8) 8365 (62.5)
DPP-4 inhibitors Continuous availability* Intermittent availability† Nonavailability‡
24 (1.8) 41 (3.1) 1274 (95.2)
292 (2.2) 359 (2.7) 12,739 (95.1)
Meglitinides Continuous availability* Intermittent availability† Nonavailability‡
14 (1.1) 28 (2.1) 1297 (96.9)
128 (1.0) 175 (1.3) 13,087 (97.7)
Alpha-glucosidase inhibitors Continuous availability* Intermittent availability† Nonavailability‡
2 (0.2) 7 (0.5) 1330 (99.3)
35 (0.3) 41 (0.3) 13,314 (99.4)
DPP-4 ⫽ dipeptidyl peptidase-4. *Participants with continuous availability had medication coverage in each of all six 30 day periods preceding the index date. † Participants with intermittent availability had medication coverage in at least 1 of the preceding 6 intervals. ‡ Participants with nonavailability did not have medication coverage during any of the intervals preceding the index date.
presence of a diabetic ulcer all had increased rates of inpatient admission (relative to the absence of each factor).
DISCUSSION We conducted a large, nested case– control study in a population of patients with type 2 diabetes taking OADs to identify independent predictors of hypoglycemia-related inpatient admissions. In addition to traditional risk factors associated with hypoglycemia, we evaluated the presence of microvascular and macrovascular complications of diabetes as potential independent predictors
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of inpatient admission. Further, we assessed the association of patterns of OAD availability (in the previous 6 months) with inpatient admission. Previous studies largely assessed hypoglycemia as a comprehensive end point (ie, outpatient and inpatient visits combined); therefore, our study added important knowledge of risk factors for severe hypoglycemic events resulting in hospitalization. This study highlighted important areas for health care intervention and provided a reminder for vigilance when known risk factors for hypoglycemia are present.
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Table III. Independent predictors of inpatient hypoglycemia admissions. Controls, % (n ⫽ 13,390)
Crude OR
95% CI
Adjusted OR*
95% CI
Gender Female Male
49.2 50.8
46.3 53.7
1.00 0.89
N/A 0.80–0.99
1.00 0.84
N/A 0.73–0.96
Age, y 18–34 35–49 50–64 ⱖ65
1.3 13.3 82.6 2.8
2.1 21.1 74.5 2.4
1.00 0.99 1.75 1.88
N/A 0.60–1.63 1.08–2.84 1.04–3.39
1.00 1.01 1.14 0.91
N/A 0.58–1.79 0.66–1.97 0.46–1.81
Oral diabetes medications†,‡ Sulfonylureas: Continuous availability§ Sulfonylureas: Intermittent availability Metformin: Continuous availability§ Metformin: Intermittent availability Thiazolidinediones: Continuous availability§ Thiazolidinediones: Intermittent availability Other OAD: Continuous availability§,¶ Other OAD: Intermittent availability,¶
41.1 25.1 34.1 23.8 22.9 16.9 4.5 3.7
30.0 14.6 47.9 23.3 23.8 13.8 3.9 3.2
2.36 2.88 0.48 0.70 1.00 1.27 1.15 1.17
2.06–2.70 2.48–3.35 0.42–0.55 0.60-0.81 0.87–1.15 1.09–1.49 0.88–1.52 0.86–1.59
2.25 2.28 0.62 0.76 1.06 1.22 1.11 1.09
1.93–2.63 1.90–2.74 0.53–0.73 0.64-0.92 0.90–1.24 1.01–1.47 0.80–1.55 0.75–1.59
Other medications# Allopurinol Benzodiazepine Beta-blocker Blood glucose monitoring supplies Fluoroquinolone Insulin NSAID Trimethoprim
5.5 14.6 35.1 30.9 10.7 16.8 13.8 3.3
2.6 6.2 21.3 30.6 2.5 6.7 10.4 0.9
2.15 2.57 2.01 1.02 4.69 2.84 1.38 3.81
1.66–2.78 2.17–3.03 1.78–2.26 0.90–1.15 3.82–5.77 2.42–3.33 1.17–1.63 2.68–5.41
1.54 1.90 1.20 0.83 2.59 2.23 1.27 1.97
1.13–2.12 1.55–2.33 1.03–1.40 0.71–0.96 1.99–3.39 1.83–2.72 1.05–1.54 1.26–3.08
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B.J. Quilliam et al.
Cases, % (n ⫽ 1339)
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1788 Table III (continued). Variable Comorbid conditions Previous outpatient visit for hypoglycemia Previous ED visit for hypoglycemia Macrovascular complications Arrhythmia Coronary artery disease Heart failure Stroke Microvascular complications Acute renal failure Chronic renal pathophysiology Ulcer Charlson comorbidity (per 1 U change)
Cases, % (n ⫽ 1339)
Controls, % (n ⫽ 13,390)
Crude OR
95% CI
Adjusted OR*
95% CI
12.5 6.2
0.9 0.1
16.17 48.53
12.60–20.76 28.80–81.78
7.88 9.48
5.68–10.93 4.95–18.15
6.8 21.0 14.0 3.4
1.4 7.8 1.5 0.4
5.25 3.12 10.99 9.62
4.05–6.81 2.69–3.61 8.86–13.64 6.37–14.52
1.69 1.48 2.33 2.78
1.17–2.44 1.21–1.81 1.72–3.15 1.62–4.77
8.3 8.4 6.4 –
0.6 1.1 1.4 –
15.43 8.37 4.98 1.72
11.43–20.83 6.49–10.81 3.82–6.49 1.66–1.79
3.10 2.22 1.71 1.37
2.05–4.67 1.56–3.15 1.20–2.44 1.32-1.44
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ED ⫽ emergency department; NSAID ⫽ nonsteroidal anti-inflammatory drug; OAD ⫽ oral antidiabetic drugs; OR ⫽ odds ratio. *Adjusted for all factors listed in the table. † As identified in pharmacy claims in the 6 months before the index date. ‡ Nonavailability of the medication/class of medication is the referent group. § Participants with continuous availability had medication coverage in each of all six 30-day periods preceding the index date. Participants with intermittent availability had medication coverage in at least 1 of the preceding 6 intervals. ¶ Includes persons taking ␣-glucosidase inhibitors, dipeptidyl peptidase-4 inhibitors, or meglitinides. # Defined as medication availability in the previous 30 days.
B.J. Quilliam et al. Direct comparison of other studies assessing risk factors of hypoglycemia to our study was problematic as definitions of serious hypoglycemia11 and settings studied varied widely. In addition, other studies included limited study populations,12,13 were restricted to insulin users,14,15 or focused on a limited set of OADs.16,17 Our study included a broader range of patients with type 2 diabetes taking OADs with or without insulin. An important finding from our study was the increased rate of inpatient admission after outpatient medical or emergency department visits for hypoglycemia in the previous 6 months. In our study, the relative rate for previous emergency department visits for hypoglycemia was 9.5, and for previous outpatient visits for hypoglycemia, the relative rate was 7.9. In a prospective cohort study of community dwelling patients with type 2 diabetes by Davis et al,18 a history of hypoglycemia requiring hospitalization was associated with a hazard ratio of 5.66 (95% CI, 2.21–14.50) for subsequent severe hypoglycemic episodes. In a small cross-sectional study conducted in patients with type 2 diabetes and attending specialty outpatient diabetes clinics, Miller et al19 reported a ⬎2-fold increase (OR ⫽ 2.65; 95% CI, 1.80 –3.80) in the occurrence of hypoglycemia (mild, moderate, or severe) associated with a history of hypoglycemia at baseline after adjustment for age, gender, race, duration of diabetes, body mass index, glycosated hemoglobin, and OAD use. In this same study, there was no association between history of hypoglycemia and incident severe hypoglycemia, although the absolute number of severe events was small (n ⫽ 5). Another study found that discharge orders for patients with hypoglycemia had a low frequency of educating patients to avoid recurring episodes of hypoglycemia (only 3% received such a warning), and only 2% were informed about obtaining a glucagon emergency kit.20 An important finding from our study was that ⬎12% of cases had at least 1 outpatient visit for hypoglycemia and nearly 6% had an emergency department visit for hypoglycemia in the previous 6 months. As educational interventions have been encouraged by the Task Force for the National Standards for Diabetes Self Management Education,21 more efforts should focus on the development of educational materials and systems for early intervention in patients being seen in the outpatient setting for hypoglycemia.
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Although the association between insulin use and hypoglycemia has been well-studied in patients with type 1 diabetes, more recent studies assessed the impact of insulin use in type 2 diabetes. In a crosssectional study of patients with type 2 diabetes, Akram et al14 found that rates of hypoglycemia were approximately one-third of the rates in patients with type 1 diabetes. In our study, insulin availability in the previous 30 days was associated with a ⬎2-fold increase in the rate of inpatient admission for hypoglycemia. A study by Davis et al18 found an increased crude association between severe hypoglycemia and insulin use that did not remain after adjustment for other factors. Another study reported 3 times the risk of hypoglycemia (OR ⫽ 3.44; 95% CI, 2.07–5.73) associated with insulin use after adjusting for concurrent sulfonylurea use.19 A more recent study found similar associations with insulin use after adjustment for simultaneous use of other OADs.22 In our sample, 16.8% of cases and 6.7% of controls had insulin availability in the 30 days before their index date. As the use of insulin therapy is becoming more commonplace in patients with type 2 diabetes, heightened awareness of the signs, symptoms, and precipitating factors for hypoglycemic episodes is essential among physicians, caregivers, and patients. Within our study, we assessed the potential for time-varying effects of OAD agents by creating exposure groups for each class of OAD agents in the 180 days before the index date. Other studies of serious hypoglycemic events assessed current use of OAD agents,17,18,23 and 1 study assessed duration of use.22 In our study, availability of sulfonylureas (continuously or intermittently) in the previous 6 months had a 2 times higher rate of admission for hypoglycemia than patients without sulfonylurea availability. In addition, the 2 categories of metformin availability (each relative to no metformin availability) were associated with a significantly reduced rate (28%–30%) of inpatient hypoglycemia admission. Participants with continuous and intermittent availability had varying levels of cumulative exposure in the 180 days before the index date. Our measures of drug exposure were not sensitive enough to detect daily dose or adherence, and suggested that future endeavors should focus on understanding more detailed patterns of use that might increase or decrease rates of hypoglycemia.
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Clinical Therapeutics
Study Limitations The data utilized for this study were from a large health care database, generally containing workplacesponsored insurance plans. Enrollees included within this database were typically of working age and therefore may not be generalizable to all persons with type 2 diabetes. In addition, we only measured inpatient admissions for hypoglycemia and might have missed more severe cases that resulted in death before hospital admission. Lastly, we created flags for antidiabetic medication availability during several exposure windows preceding the index date. We derived these flags based on dates of medication dispensing and the days supply dispensed. Although this measure was consistent with many health care database analyses, it did not necessarily identify actual consumption by study participants.
CONCLUSIONS Our study focused on risk factors for inpatient hypoglycemia hospitalization, adding important knowledge of risk factors associated with these events. Previous outpatient and emergency department visits for hypoglycemia were significant predictors of inpatient hypoglycemia admission. In addition, insulin availability and continuous or intermittent sulfonylurea availability had elevated rates of inpatient admission, whereas both categories of metformin availability had decreased rates of hypoglycemia hospitalization. As inpatient hospitalizations for hypoglycemia are both costly and can have significant impact on a patient’s quality of life, appropriate risk management plans should be developed to prevent these serious complications of therapy from occurring. Outpatient or emergency department visits related to hypoglycemia may be indicative of patients at higher risk for inpatient admission and therefore should serve as points of intervention and education.
ACKNOWLEDGMENTS This research was funded by a grant from Takeda Pharmaceuticals America, Inc. Dr. Quilliam is a consultant for Ortho-McNeill Janssen Scientific Affairs, LLC, conducting studies unrelated to the content of this article (pharmacological management of pain). Dr. Simeone was a postdoctoral fellow at the University of Rhode Island at the time this study was conducted; he is currently an employee of United BioSource Corporation. Dr. Ozbay was a doctoral student at the Uni-
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versity of Rhode Island at the time this study was conducted; he is currently an employee of HealthCore, Inc. The authors have indicated that they have no other conflicts of interest regarding the content of this article. Dr. Quilliam’s contributions included data acquisition, literature evaluation, study design, data interpretation, and drafting and revising of the manuscript. Dr. Simeone’s contributions included literature evaluation, study design, data interpretation and drafting of the manuscript. Dr. Ozbay’s contributions included literature evaluation, data analysis, data interpretation and drafting of the manuscript.
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Address correspondence to: Brian J. Quilliam, PhD, University of Rhode Island, College of Pharmacy, 41 Lower College Road, Kingston, RI 02881. E-mail: [email protected]
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