David M. Mosen, PhD, MPH

: We studied the independent association of adher- ence to oral hypoglycemic medications with poor glycemic con- trol among a population of adults with type 2 diabetes (T2D), adjusting for demographics, health behaviors, and clinical and treatment characteristics.

: This was a retrospective cohort design.

: We studied a population of Kaiser Permanente Northwest (KPNW) members with T2D who either had: 1) good glycemic control (glycated hemoglobin [A1C] <8.0%; n = 15,891) or 2) poor glycemic control (A1C >9.0%; n = 3709). The primary independent variable was medication adherence to 1 or more oral hypoglycemic medications. High medication adherence was defined as at least 80% of days covered in the 12 months prior to the A1C test date (yes vs no). Multiple logistic regression was used to analyze the independent association of medication adherence with poor glycemic control, adjusting for demographics, health behaviors, medical comorbidities, healthcare utilization, receipt of diabetes care management services, and intensity of diabetes treatments. All measures were constructed via KPNW’s electronic heath record.

: Increased adherence to oral hypoglycemic medications was associated with a lower likelihood (OR, 0.54; 95% CI, 0.50-0.59; P <.0001) of having poor glycemic control, after adjusting for demographics, health behaviors, comorbidities, healthcare utilization, receipt of diabetes care management services, and intensity of diabetes treatments.

: Higher adherence to oral hypoglycemic medica- tions is independently associated with a lower likelihood of having poor glycemic control among an adult population with T2D. Studies of the effects of measures to improve medication adherence on population-level glycemic control are needed.

Type 2 diabetes (T2D) is a chronic, progressive condition characterized by the failure of insulin secretion to compensate for resistance to insulin’s actions, resulting in hyperglycemia. The long-term complications of diabetes contribute to its status as a leading cause of premature illness and mortality.1 In 2012, 29.1 million individuals in the United States had diabetes (approximately 90% of the patients had T2D), and the prevalence appears to have increased in the past 2 decades.2 In 2012, diabetes resulted in $245 billion in costs, including $176 billion in direct medical costs and $69 billion in reduced productivity.3

The long-term control of hyperglycemia is essential in order to improve health outcomes for populations with diabetes. Poor glycemic control has been associated with microvascular complications4-6 and mortality,7,8 whereas good control reduces the development of microvascular and long-term cardiovascular complications.9 Current clinical guidelines to manage glycemic control utilize glycated hemoglobin (A1C) target levels, with adjustment for age and length of illness10; however, A1C levels greater than 75 mmol/mol (9.0%) are universally considered to reflect poor control.11 Of particular concern, according to data from a recent National Health and Nutrition Examination Survey, the proportion of adults with diabetes with poor glycemic control (A1C >9.0%) increased from 17.9% in the 2005-to-2008 period to 21.0% in the 2009-to-2012 period.12

Despite clinical evidence of the efficacy of several glucose- lowering medications, adherence levels often remain sub- optimal.13-17 Moreover, several studies have found that lower medication adherence can be associated with poorer glycemic control outcomes.14,18-23 However, these studies have several limitations, including small sample sizes and analyses not conducted on a population level.24,25

With this background, the primary objective of this study was to analyze the independent association of adherence to oral hypoglycemic medications with poor glycemic control among adults with T2D on a population level. Such research has significant clinical implications—given that medication adherence is a potentially modifiable factor&mdash;that may be influenced by quality improvement initiatives. A secondary objective was to identify demographics, health behaviors, comorbidities, healthcare utilization, diabetes care management services, and treatment intensity mea- sures associated with poor glycemic control, with an emphasis on factors that were modifiable.

The institutional review board of Kaiser Permanente Northwest (KPNW) in Portland, Oregon, approved the study protocol and waived the need for patient consent for data use, as is the protocol for data-only studies.

ThestudywasconductedatKPNW,anonprofit,group-model healthcare system in northwest Oregon and southwest Washington. The system consists of 15 medical clinics and 2 hospitals that serve more than 500,000 members. KPNW membership demographics (age, sex, race/ethnicity) are similar to the larger population in the geographic area. We used the electronic health record (EHR) (Epic Systems Corporation; Madison, Wisconsin) and administrative data systems to ascertain information on patient membership; demographics; clinical data, including height and weight; laboratory results; and medical healthcare utilization.

We conducted a retrospective cohort of KPNW members who met all the following inclusion

fasting glucose >125 mg/dL, nonfasting glucose >200 mg/ dL), or 3) 1 or more elevated A1C test results (>6.5%). In addition, those with T2D had an International Classification of Diseases, Ninth Revision (ICD-9)-related encounter (in any medical setting) as far back as membership existed. T2D ICD-9 codes include any of the following: 250.00, 250.02, 250.12, 250.20, 250.22, 250.30, 250.32, 250.40, 250.42, 250.52,250.60,250.62,250.70,250.72,250.80,250.82, 250.90, and 250.02.

Uncontrolled and Good Glycemic Control Groups

The process flow describing the study population is shown in the FIGURE. We identified 37,514 members who met the initial inclusion criteria. Of this population, 4759 members had an A1C value greater than 9.0% and formed the poor glycemic control group, while 27,514 had an A1C value less than 8.0% and made up the good glycemic control group. Another 5250 members with A1C values between 8.0% and 9.0% were not included in the analysis. The final analytic population (n = 19,600) included poor (3709) and good (15,891) glycemic control populations that received 1 or

criteria: 1) had an A1C test between July 1, 2014, and June 30, 2015; 2) were 18 years or older on the A1C test date; 3) were on the KPNW diabetes registry with a T2D indication at the time of the A1C test; and 4) had continuous health plan enrollment for 12 or more months prior to the A1C test date, which was defined as the index date. If the results of multiple A1C tests were available between July 1, 2014, and June 30, 2015, the latest value was used in the analysis.

KPNW members enter the diabetes registry through any of 3 ways: 1) receipt of treatment with oral hypo- glycemic medications, 2) 2 or more abnormal fasting or nonfasting glucose test results in the previous 3 years (eg, more oral hypoglycemic medications in the 12 months prior to the A1C test. Generally, adherence to oral hypoglycemic medications and other study covariates was measured in the year prior to the A1C test date, for a complete study period between July 1, 2013, and June 30, 2015.

. The primary dependent variable was poor glycemic control (A1C >9.0%) versus good glycemic control (A1C <8.0%).

. Adherence to oral hypoglycemic medications was the primary independent variable in the analysis. We used the proportion of days covered (PDC) to construct medication adherence—a measure well described in the literature26 and considered to be more consistent than another adherence measure commonly used with electronic pharmacy information, such as the medication possession ratio.27 The denominator included the total number of days between the first hypoglycemic medication fill and the end of the 365-day observation period, prior to the A1C test date. The numerator included total days covered by prescription fills during the denominator period. High medication adher- ence was defined as 80% or more days covered during the denominator period. If multiple medications were prescribed, a measure of total average days covered was constructed. No insulin use was included in the adherence measure.

. We identified 6 groups of covariate measures: demographics, health behaviors, comorbidities, healthcare utilization, use of diabetes care management services, and diabetes treatment-intensity measures. The rationale for selecting these measures was 2-fold. First, they are important factors previously associated with poor glycemic control in the literature. Second, adjusting for these measures on a population level increased the ability to better assess the independent association of medication adherence with poor glycemic control.

. Using KPNW’s EHR, we measured age (continuous: assessed upon A1C test date), sex (female vs male), race/ethnicity, and primary language status (non—English speaker: yes vs no).

. Two health behavior measures were included. The first, current tobacco use (yes vs no), was assessed at the time closest to the A1C test, with a 1-year look-back period. Second, current physical activity status—whether an individual exercised 4 or more times per week (yes vs no)&mdash;was the most recent measurement assessed closest to the A1C test date. This information, systematically assessed at routine KPNW office visits and asked in person by clinic staff, was the most recent update in the member’s EHR.

. Four comorbidity measures were analyzed. The first was obesity, as reflected by a body mass index (BMI) of 30 or higher (yes vs no) if height and weight were available, using the most recent assessment with a look-back period of 60 months (5 years). The Charlson Comorbidity Index (CCI) score was used to assess patient- level comorbidities. CCI score (continuous), an established risk of mortality score in the medical literature,28,29 was constructed using utilization of inpatient and outpatient services in the year prior to the A1C test date. The duration of diabetes was assessed by measuring months on the diabetes registry (continuous) as far back as membership existed.

. Four dichotomous (yes vs no) variables assessed healthcare utilization in the year prior to the A1C assessment date: 1) primary care visits (1 or more visits vs none), 2) specialty care visits (excluding visits to internal medicine, family practices or pediatrics; 1 or more visits vs none), 3) hospital admission (1 or more admissions vs none), and 4) emergency department (ED) utilization (1 or more visits vs none).

. Enrollment in diabetes care management services was assessed by any contact with a diabetes care management program (yes vs no) in the year prior to the A1C assessment date.

. Two diabetes treatment measures were assessed in the year prior to the A1C test date: 1) use of oral hypoglycemic medications (1 medication vs 2 or more medications) and 2) use of insulin (any use vs no use).

We first examined the association of adherence to oral hypoglycemic medications and other covariate measures with poor glycemic control using χ2 analysis. Variables that were significant in the bivariate analysis (P <.05) were included in a final logistic regression model. The model examined the independent association of adherence to oral hypoglycemic medications and other covariate measures with poor glycemic control. Odds ratios (ORs) and 95% confidence intervals (CIs) were constructed for medication adherence and each covariate measure. The final logistic regression model included all covariate measures with the exception of: 1) ED utilization, which was not significant in the bivariate analysis, and 2) proteinuria. The proteinuria measure, although significant in the bivariate analysis, was dropped from the final logistic regression model because of a high level of missing information. The final model included the following variables: age (continuous), sex (female vs male [reference group]), and race/ethnicity (Hispanic, African American, Asian American/Pacific Islander, other race [Native American/Aleutian/Eskimo] vs white [reference group]).

Bivariate results are presented in TABLE 1 and multivariate results are in TABLE 2. Significant multivariate results are presented below.

Adherence to Oral Hypoglycemic Medications 

Those with high adherence to oral hypo- glycemic medications were less likely to have poor glycemic control (OR, 0.54; 95% CI, 0.50-0.59) compared with those with low medication adherence, adjusting for other study covariates.

Association of Covariate Measures With Poor Glycemic Control 

. Of the demographic factors, age, sex, and race/ethnicity were significantly associated with poor glycemic control. Specifically, younger age (OR, 0.96; 95% CI, 0.95-0.96) was associated with poorer glycemic con- trol, while women (OR, 0.88; 95% CI, 0.81-0.96) were less likely to have poor glycemic control than men. Lastly, African Americans (OR, 1.40; 95% CI, 1.16-1.69), Hispanics (OR, 1.62; 95% CI, 1.40-1.87), Asian Americans/Pacific Islanders (OR, 1.31; 95% CI, 1.11-1.55), and those of other race (OR, 1.44; 95% CI, 1.17-1.77) were more likely to have poor glycemic control compared with non-Hispanic whites.

. One of 2 health behaviors, exercise status was signifi- cantly associated with glycemic control in the multivariate analysis. Those who exercised 4 or more times per week were less likely to have poor glycemic control (OR, 0.75; 95% CI, 0.68-0.82) compared with those who exercised 3 or fewer times per week.

. Two comorbidity measures were associ- ated with poor glycemic control. Specifically, those with a BMI of 30 or greater (OR, 1.18; 95% CI, 1.07-1.30) were more likely to have poor glycemic control compared with those with a BMI of less than 30. Moreover, longer duration on the diabetes care registry (OR, 1.06; 95% CI, 1.05-1.07) was associated with increased likelihood of having poor glycemic control.

. Three of 4 utilization variables were significantly associated with glycemic control. Having a primary care visit (OR, 0.95; 95% CI, 0.94-0.97) or specialty care visit (OR, 0.98; 95% CI, 0.98-0.99) was associated with a lower likelihood of poor glycemic control compared with no primary or specialty care utilization. Interestingly, those with 1 or more hospital admissions were less likely to have poor glycemic control (OR, 0.85; 95% CI, 0.79-0.92) compared with those with no hospital admissions, adjusting for patient-level covariates.

Diabetes care management services and treatment

. Perhaps serving as a proxy for disease severity, those with 1 or more diabetes care management visits (OR, 2.80; 95% CI, 2.55-3.07) were more likely to have poor glycemic control compared with those with no contact. Similarly, those using more than 1 oral hypoglycemic medication (OR, 2.09; 95% CI, 1.93-2.27) and any insulin (OR, 2.31; 95% CI, 2.09-2.55) were more likely to have poor glycemic control compared with those using 1 oral hypoglycemic medication and no insulin, respectively.

Improving glycemic control is a central goal of contemporary diabetes management. Nevertheless, many patients with diabetes do not succeed in reaching goal A1C levels. This can be a source of significant frustration for patients and clinicians and portends a greater disease burden upon health systems. Although a range of demographic, behavioral, and clinical factors were associated with poorer glycemic control, we found that medication adherence, as reflected by PDC of at least 80%, was the most significant factor associated with satisfactory glycemic control. In our study, we found that those with high adherence to oral hypoglycemic medications were 46% less likely to have poor glycemic control compared with those with low adherence. Our study results are unique, given that we found this relationship among a population of adults with T2D receiving care in an integrated system (serving more than 500,000 individuals) after adjusting for a comprehensive set of patient-level covariate measures.

Surprisingly, receipt of diabetes care management services was associated with poor glycemic control. Rather than showing the potential ineffectiveness of care management services, the receipt of these services is likely a marker for higher disease severity. Similar results were found for insulin use and higher use of oral hypoglycemic medications.

Our findings are consistent with other recent research findings that examined medication adherence and glycemic control. A study by Feldman and colleagues30 of the EHRs of more than 200,000 adults with diabetes in Israel found that lower medication adherence was associated with a greater likelihood of having poor glycemic control (A1C >9%). Similarly, a retrospective cohort study by Farmer and colleagues31 using the Clinical Practice Research Datalink found that reduced medication adherence was associated with smaller reductions in A1C values compared with greater medication adherence.

Our results have direct applicability to clinical practice, given that medication adherence is a modifiable factor that could improve. A recent study by de Vries McClintock and colleagues32 found that a brief in-person and telephone-based intervention program improved adherence to diabetes medi- cations, which was associated with a subsequent improve- ment in glycemic control. Our results, coupled with previous research findings, provide a focus for quality improvement opportunities to improve medication adherence.

We also found interesting relationships between several covariate measures and glycemic control that have direct applicability to clinical care. First, we found that African Americans and other ethnic minorities were more likely to have poor glycemic control compared with non-Hispanic whites, even after adjusting for medication adherence and other patient-level covariates. These results are similar to research findings from Lafata and colleagues33 that found that African American—Caucasian differences in glycemic control could not be explained by medication adherence. These results give the larger KPNW delivery system an opportunity to develop quality improvement interventions to further address racial disparities in glycemic control.

In addition, 2 potentially modifiable factors were found to be associated with glycemic control. First, we found that increased exercise (4 or more times per week vs 3 or fewer times) was independently associated with a lower likelihood of having poor glycemic control after adjusting for medication adherence and other study covariates. Second, a BMI of 30 or higher (vs BMI <30) was associated with a greater likelihood of poor glycemic control. These results reinforce the importance of lifestyle interventions to improve glycemic control and identify another important quality improvement opportunity, specifically, interventions that target weight loss and improvement in medication adherence for adults with T2D.

Articles

Among a population of adult patients with type 2 diabetes, adherence to oral hypoglycemic medications is independently associated with poor glycemic control, even after adjusting for patient-level covariates.

Assessing the Association Between Medication Adherence and Glycemic Control

: Perioperative hyperglycemia is a risk factor for increased surgical morbidity and mortality. Pharmacy-led management teams may improve glycemic control and postoperative outcomes. We sought to determine whether a pharmacist-led glycemic control team is associated with improved glycemic control and reduced postdischarge utilization and medical costs.

: We assessed patient-level outcomes during a 12-month pre-intervention period and compared them at years 1 and 2 post implementation at a tertiary care multi-specialty medical center. The patients were noncritically ill postoperative surgical patients followed 72 hours post surgery (days 1-3). Measurements were proportion of patients with good glycemic control (capillary blood glucose [CBG] 70-180 mg/dL) (day 1), hypoglycemia (CBG <70 mg/dL) (days 1-3), 90-day postdischarge utilization, and 6-month per patient per month (PPPM) medical costs.

: Glycemic control significantly improved in year 1 (odds ratio [OR], 2.24; 95% CI, 1.85-2.72) and year 2 (OR, 2.19; 95% CI, 1.81-2.66) post implementation; hypoglycemia declined significantly in year 1 (OR, 0.38; 95% CI, 0.31-0.46) and year 2 (OR, 0.31; 95% CI, 0.25-0.38). In addition, postdischarge hospital readmissions declined in year 1 (OR, 0.69; 95% CI, 0.56-0.86) and year 2 (OR, 0.67; 95% CI, 0.54-0.84) post implementation, and emergency department utilization declined in year 1 (OR, 0.71; 95% CI, 0.60-0.84) and year 2 (OR, 0.72; 95% CI, 0.60-0.85) post intervention. Finally, PPPM costs declined significantly in year 1 (beta coefficient = —208.8) and year 2 (beta coefficient = –283.5) post implementation.

: The intervention was associated with improved glycemic control outcomes, reduced utilization, and lowered postdischarge medical costs.

Implementation of a pharmacist-led glycemic control team was associated with improved glycemic control and utilization outcomes in a population of noncritically ill surgical patients.

Good glycemic control improved in years 1 and 2 post implementation.

Hypoglycemia declined in year 1 post implementation.

Postdischarge hospital admissions, emergency department utilization, and per patient per month medical costs declined in years 1 and 2 post implementation.

Patients with diabetes and stress hyperglycemia are frequently hospitalized for surgical procedures,

 and recent estimates indicate that 30% to 50% of US inpatients have diabetes and/or hyperglycemia.

 Multiple studies have documented the risks of perioperative hyperglycemia, including poor surgical outcomes and higher readmission rates.

 However, improved glycemic control leads to reductions in hospital complications, length of stay, and mortality.

Multiple professional societies, agencies, and task forces have issued guidelines recommending methods to achieve safe and effective glycemic control in hospitalized patients.

 Although the optimal target glucose range for hospitalized patients continues to evolve based on results of recent clinical trials,

 achieving moderate glycemic control is also beneficial to surgical patients.

 Surgical populations—as opposed to medically ill patient populations&mdash;seem to be at lower risk for hypoglycemia and are able to derive benefit from inpatient glucose control.

Despite the potential benefits of improved glucose control for surgery patients, patients admitted to hospitals for any cause, including surgical services, may not receive optimal glycemic management. The challenges to creating and implementing a perioperative glycemic control program are numerous and well-recognized.

 The clinical expertise necessary for prescribing and adjusting appropriate insulin regimens in patients with rapidly changing needs may not be readily available to all surgical patients.

 Patient safety advocates predict that expanding the roles of nonphysician health professionals (eg, pharmacists) will be central to the success of new healthcare delivery models in optimizing quality and providing timely evidence-based care.

In an effort to improve care in our dysglycemic surgical population, we created a dedicated glycemic control team (GCT) responsible for writing insulin orders and coordinating all aspects of glucose control perioperatively.

 The objective of this implementation research analysis was to evaluate the effectiveness of the GCT with respect to: a) inpatient glycemic control; and b) post surgical outcomes, including wound infection and all-cause hospital readmissions, postdischarge emergency department (ED) utilization, and postdischarge medical costs. Glycemic control in the perioperative setting at our hospital had been managed at the discretion of each patient’s individual surgeon and/or anesthesiologist, and approaches to glucose control were highly variable in the management of perioperative hyperglycemia when it was identified.

This study was reviewed and approved by the Kaiser Permanente Northwest (KPNW) Center for Health Research Institutional Review Board.

We developed and implemented a GCT at Kaiser Sunnyside Medical Center (KSMC) in Clackamas, Oregon. KSMC is a tertiary care hospital and part of KPNW, a nonprofit health maintenance organization with about 485,000 members in southwest Washington and the Portland, Oregon, metropolitan area. KPNW’s regional EpicCare-based electronic medical record (EMR) provides highly sensitive integrated data on patient membership, demographics, primary care assignment, and clinical data (including weight and height, laboratory results, and healthcare utilization).

The study population included all surgical inpatients admitted to the post anesthesia care unit (PACU) who also had 2 or more point-of-care test (POCT) glucose measurements during the 24-hour period (day 1) following the index PACU admission. To assess hypoglycemia and hyperglycemia, we measured POCT glucose measurements during days 1 to 3 following the PACU admission date. By identifying patients using PACU admission, we included all surgical patients being admitted to the hospital, except for 2 groups: cardiovascular surgery patients and critically ill surgical patients, since in our hospital, those patients are admitted directly to the intensive care unit from the operation room. Our evaluation population is thus congruent with the GCT’s target population (ie, noncardiovascular, noncritically ill, postoperative patients with glycemic control opportunities).

Cardiovascular surgery patients and critically ill patients were excluded from the evaluation analysis.

The development and implementation of the GCT pharmacist program and protocol for management of hyperglycemia has been described previously. The glycemic control team was deployed in January 2009. As described by Mularski and colleagues (2012)

“The GCT protocol was created by a pharmacy clinical coordinator who worked closely with physicians experienced in diabetes management from the Endocrinology, Hospitalist, and Surgery Departments. The protocol was designed to direct the safe and appropriate use of intravenous (IV) and subcutaneous insulin in surgical and medical patients with diabetes or risk of developing hospital hyperglycemia. Important components of the protocol included recommendations on when to begin and discontinue IV insulin, how to transition from IV therapy, how to calculate subcutaneous insulin doses, how to adjust insulin doses, when to resume oral agents, how to manage patients on tube feedings and total parental nutritional, and discharge planning. The GCT protocol allows the GCT pharmacist to provide comprehensive inpatient glycemic management. In addition to writing and adjusting daily insulin orders, the GCT can also order relevant labs (glycated hemoglobin [A1C]), place consultation requests for a registered dietician and/or diabetes educator, and via verbal collaboration with clinicians place discharge orders for insulin and diabetic supplies.”

Trained inpatient pharmacists were available, on a consultation basis, 7 days a week, to any surgical patient requiring perioperative glycemic control management.

The GCT was usually consulted in the immediate post anesthesia phase upon recognition of stress hyperglycemia or for ongoing management of known diabetes. Regarding day-to-day work flow, Mularski (2012) notes

“After reviewing the patient’s chart and meeting with the patient’s nurse, the GCT pharmacist enters insulin orders into the EMR order entry system. All of the following are available to the GCT pharmacist: the POCT glucose results, laboratory test values, electronic medication administration record, all clinician notes, and outpatient records (including medication orders, chart notes, and laboratory test results). After entering necessary orders for all patients, the GCT pharmacist meets with most of the patients to obtain history from new patients or, especially for patients without known diabetes, to explain why blood glucose is tested, and why they are receiving insulin during their hospitalization. The GCT pharmacist also gathers home blood glucose control information and identifies barriers to good outpatient control.”

Mularski et al also describes the hospital discharge process, noting:

“Discharge glycemic control needs are addressed as early in the hospitalization as clinically feasible to allow for needed patient education and planning patients’ transition regimens. Patients who have discharge needs (eg, patients with new insulin starts or patients with poor control) receive interventions by the GCT pharmacist and coordination of care from the health care team. In general, the GCT pharmacist is responsible for: communicating the discharge plan to the patient; providing basic education and reviewing signs or symptoms of hypoglycemia and its management; constructing a discharge instruction sheet for new insulin starts; obtaining authorization from the attending physician regarding the discharge plan; entering discharge orders for outpatient regimens; electronically routing progress notes to the primary care physician; communicating with floor nurses to provide glucometer teaching (if indicated); and communicating with the certified diabetes educator; who provides more in-depth, individualized diabetes education.”

We considered the period between January 1 and June 30, 2009, to be the implementation phase when key staff became familiar with the GCT program. By July 1, 2009, the GCT program was fully implemented, with the pharmacist team using 2 full-time inpatient pharmacists and operating 7 days a week, 10 hours per day. After hours, an on-call hospitalist addressed new consults and urgent issues, with subsequent care and follow-up assumed by the GCT pharmacists the following morning. Hospitalist or endocrinologist supervision was available and was required for complex or unusual cases that did not fall clearly under the KPNW protocol during all hours of the day. Inpatient nurses performed bedside POCT glucose testing and administered scheduled insulin per orders. Nurses followed established written protocols for IV insulin adjustments on an hourly basis. GCT pharmacists reviewed POCT glucose results at least daily and made appropriate adjustments to ordered insulin regimens as needed during daytime hours.

We used a retrospective, observational study design to evaluate the effectiveness of the intervention on study outcomes. The pre-implementation period was designated as the 12 months prior to the GCT implementation: January 1 through December 31, 2008. The post implementation period—after the GCT team was fully implemented at KSMC&mdash;included the 24 months from July 1, 2009, through June 30, 2011.

We obtained all data elements from KPNW’s EMR.

 We analyzed 2 measures of glycemic control whose definitions were reported elsewhere21: a) good glycemic control: defined as having all, or all but one, POCT glucose values between 70 and 180 mg/dL in the first 24-hour period (day 1) following the PACU admission date; b) hypoglycemia: defined as having any POCT glucose value less than 70 mg/dL during days 1 to 3 following the PACU admission date.

 We defined 3 clinical patient-centered outcome measures related to utilization: a) wound-infection—related hospital readmissions: any wound-infection hospital readmission within 90 days after index surgical hospital discharge date (

International Classification of Diseases, Ninth Revision, Clinical Modification

 codes 998.59, 674.30, 674.32, or 674.34; primary/secondary diagnosis); b) all-cause hospital readmissions within 90 days after the index surgical hospital discharge date; c) ED utilization within 90 days after the index surgical hospital discharge date.

 We measured total medical costs in the 6 months (182 days) after index hospital discharge date. All cost information was measured in 2011 US$ and is reported as postdischarge per patient per month (PPPM) costs. Actual health plan costs for each person were extracted from administrative data; the methods for this analysis have been reported elsewhere.

 To adjust for possible effects of comorbidity and contributory factors, we constructed the following covariate measures from existing comprehensive EMR data: a) demographics and socioeconomic status (SES) (ie, age as of index date [continuous], gender, race [white, nonwhite, unknown]), and an approximation of low SES designated as poverty status (assessed using 2010 Census information, measured as a dichotomous variable based on income/federal poverty level (FPL) (household income ≤100% FPL vs >100% FPL); b) severity of illness (adjusted by the Charlson comorbidity index [CCI] score—a well-established comorbidity measure

) and average length of stay (ALOS) for index hospitalization; c) prior utilization (2 measures of prior healthcare utilization including hospital admissions [≥1 admission vs none] and ED visits [≥1 visit vs none]; d) to approximate for case risk and differences in care across different subspecialty groups, we developed a surgery-service categorization using 10 groups (general surgery, orthopedics, gynecology, urology, spine, thoracic, podiatric, neurosurgical, vascular, and other), which were constructed by reviewing frequencies of Current Procedural Terminology codes conducted at KSMC and grouping surgeries into subspecialty categories; and e) to assess risk of perioperative hyperglycemia, we applied a preexisting approximation for prior hyperglycemia using A1C status, operationalizing a dichotomous variable from A1C value taken closest to index hospital admission date and categorized into 2 groups: A1C ≥6.5% vs A1C< 6.5% or untested, suggesting lack of underlying identified risk of hyperglycemic state.

 analysis to assess differences in glycemic control and utilization metrics between the post implementation periods (year 1 and year 2) versus the pre-implementation period. To account for the effect of potential confounders, we further performed logistic regression analysis to measure the independent effect of the GCT intervention with study outcomes using 2 approaches.

For the 2 glycemic control outcome measures, we adjusted logistic models for: age (continuous), gender, race/ethnicity (nonwhite vs white [reference group]), poverty status (≤100% FPL vs >100% FPL [reference group]), CCI score (continuous), surgery type (ie, orthopedic, gynecology, urology, spine, thoracic, neurosurgery, vascular, other/unknown vs general surgery [reference group]), ≥1 prior ED visits (vs none [reference group]), ≥1 prior hospital admissions (vs none [reference group]), ALOS for index hospitalization (continuous), and risk status for perioperative hyperglycemia

(A1C ≥6.5% vs A1C <6.5%/untested [reference group]). The same covariate measures were included for the 3 utilization outcome measures, with the addition of good glycemic control (day 1) versus poor control (reference group).

The study population totaled 11,553 unique patients: 1277 in the pre-intervention period, 4811 in year 1 post intervention, and 5465 in year 2 post intervention. 

 presents demographics, clinical characteristics, utilization, and type of surgery classification among the study population. In the post intervention periods, the average age was approximately 4 years younger and a slightly higher proportion was female compared with the pre-intervention period. Also, both the CCI index and utilization (hospital admissions and ED utilization) 12 months prior to index admission were lower in the post intervention periods (year 1 and year 2), compared with the pre-intervention period. Similarly, there was a lower proportion of patients with A1C values ≥6.5% in the post intervention periods (year 1 and year 2), compared to the pre-intervention period. Last, there were fewer general surgeries in the post intervention period, offset by more orthopedic, gynecologic, urologic, and spine surgeries in the post intervention periods (year 1 and year 2).

 presents a bivariate analysis of the study outcomes, and 

 present multivariate results. The proportion of patients with good glycemic control increased significantly in both bivariate and multivariate analysis. Patients were more likely to have good glycemic control (day 1) in year 1 (OR, 2.24; 95% CI, 1.85-2.72; 

 <.0001) and year 2 (OR, 2.19; 95% CI, 1.81-2.66; 

 <.0001) after implementation of the GCT, compared to the preintervention period after adjusting for covariates. In addition, the proportion of patients with hypoglycemia in days 1 to 3 declined significantly in both bivariate and multivariate analysis. Patients were less likely to have hypoglycemia in year 1 (OR, 0.38; 95% CI, 0.31-0.46; 

 <.0001) and year 2 (OR, 0.31; 95% CI, 0.25-0.38; 

 <.0001) post intervention, compared with the pre-intervention period, even after adjusting for covariates.

The utilization metrics also showed significant reductions upon both bivariate and multivariate analysis. Compared to the pre-intervention period, wound-infection—related readmissions declined significantly in year 2 (OR, 0.51; 95% CI, 0.29-0.91; 

 = .031), all-cause hospital readmissions declined in year 1 (OR, 0.69; 95% CI, 0.56-0.86; 

 = .03) and year 2 (OR, 0.67; 95% CI, 0.54-0.85; 

 = .008), and all-cause postdischarge ED utilization declined significantly in year 1 (OR, 0.71; 95% CI, 0.60-0.84; 

 = .003) and year 2 (OR, 0.72; 95% CI, 0.60-0.85; 

 = .009), compared to the pre-intervention period and adjusting for covariate measures. Finally, postdischarge PPPM costs were lower in both bivariate and multivariate analyses. PPPM costs were lower in year 1 (parameter estimate = —208.8; 

 <.0001) and year 2 (parameter estimate = —283.5; 

 = .010) post intervention compared with the pre-intervention period and adjusting for covariate measures.

Web Exclusive: Pharmacist Glycemic Control Team Associated With Improved Perioperative Glycemic and Utilization Outcomes

We analyzed 2 measures of glycemic control whose definitions were reported elsewhere21: a) good glycemic control: defined as having all, or all but one, POCT glucose values between 70 and 180 mg/dL in the first 24-hour period (day 1) following the PACU admission date; b) hypoglycemia: defined as having any POCT glucose value less than 70 mg/dL during days 1 to 3 following the PACU admission date.

We defined 3 clinical patient-centered outcome measures related to utilization: a) wound-infection—related hospital readmissions: any wound-infection hospital readmission within 90 days after index surgical hospital discharge date (

International Classification of Diseases, Ninth Revision, Clinical Modification 

codes 998.59, 674.30, 674.32, or 674.34; primary/secondary diagnosis); b) all-cause hospital readmissions within 90 days after the index surgical hospital discharge date; c) ED utilization within 90 days after the index surgical hospital discharge date.

We measured total medical costs in the 6 months (182 days) after index hospital discharge date. All cost information was measured in 2011 US$ and is reported as postdischarge per patient per month (PPPM) costs. Actual health plan costs for each person were extracted from administrative data; the methods for this analysis have been reported elsewhere.

To adjust for possible effects of comorbidity and contributory factors, we constructed the following covariate measures from existing comprehensive EMR data: a) demographics and socioeconomic status (SES) (ie, age as of index date [continuous], gender, race [white, nonwhite, unknown]), and an approximation of low SES designated as poverty status (assessed using 2010 Census information, measured as a dichotomous variable based on income/federal poverty level (FPL) (household income ≤100% FPL vs >100% FPL); b) severity of illness (adjusted by the Charlson comorbidity index [CCI] score—a well-established comorbidity measure

The study population totaled 11,553 unique patients: 1277 in the pre-intervention period, 4811 in year 1 post intervention, and 5465 in year 2 post intervention.

presents demographics, clinical characteristics, utilization, and type of surgery classification among the study population. In the post intervention periods, the average age was approximately 4 years younger and a slightly higher proportion was female compared with the pre-intervention period. Also, both the CCI index and utilization (hospital admissions and ED utilization) 12 months prior to index admission were lower in the post intervention periods (year 1 and year 2), compared with the pre-intervention period. Similarly, there was a lower proportion of patients with A1C values ≥6.5% in the post intervention periods (year 1 and year 2), compared to the pre-intervention period. Last, there were fewer general surgeries in the post intervention period, offset by more orthopedic, gynecologic, urologic, and spine surgeries in the post intervention periods (year 1 and year 2).

presents a bivariate analysis of the study outcomes, and 

present multivariate results. The proportion of patients with good glycemic control increased significantly in both bivariate and multivariate analysis. Patients were more likely to have good glycemic control (day 1) in year 1 (OR, 2.24; 95% CI, 1.85-2.72; 

<.0001) and year 2 (OR, 2.19; 95% CI, 1.81-2.66; 

<.0001) after implementation of the GCT, compared to the preintervention period after adjusting for covariates. In addition, the proportion of patients with hypoglycemia in days 1 to 3 declined significantly in both bivariate and multivariate analysis. Patients were less likely to have hypoglycemia in year 1 (OR, 0.38; 95% CI, 0.31-0.46; 

<.0001) and year 2 (OR, 0.31; 95% CI, 0.25-0.38; 

<.0001) post intervention, compared with the pre-intervention period, even after adjusting for covariates.

= .031), all-cause hospital readmissions declined in year 1 (OR, 0.69; 95% CI, 0.56-0.86; 

= .03) and year 2 (OR, 0.67; 95% CI, 0.54-0.85; 

= .008), and all-cause postdischarge ED utilization declined significantly in year 1 (OR, 0.71; 95% CI, 0.60-0.84; 

= .003) and year 2 (OR, 0.72; 95% CI, 0.60-0.85; 

= .009), compared to the pre-intervention period and adjusting for covariate measures. Finally, postdischarge PPPM costs were lower in both bivariate and multivariate analyses. PPPM costs were lower in year 1 (parameter estimate = —208.8; 

<.0001) and year 2 (parameter estimate = —283.5; 

= .010) post intervention compared with the pre-intervention period and adjusting for covariate measures.

Our previously published report showed that improvements in the quality and safety of perioperative glycemic control could be achieved by implementing a pharmacist-run glycemic management team.

 In this longitudinal analysis, our results suggest sustained improvement in measures of glycemic control in the second year following implementation of the team. The achieved level of control and low hypoglycemia rate in our study compare favorably with published results of hospitalized patients in other systems.

Moreover, we found reduced postoperative ED visits, hospital readmissions, and total medical costs in the post implementation period, compared with the pre-implementation period. Reduced post surgical ED and hospital utilization and costs likely reflect an overall reduction in postoperative complications. These results may confirm the success of continuing to meet quality and safety goals with the GCT initiative. They may also demonstrate important correlations with patient-centered outcomes such as postdischarge problems requiring ED visits or repeat hospitalization, as well as reduced cost.

Results from our study are the first that we are aware of that may demonstrate the effectiveness of a pharmacist-run team to improve both: a) glycemic control measures and b) utilization and cost outcomes for surgical inpatients. Prior studies documented reduced utilization, namely improved hospital length of stay (LOS) for an inpatient population with diabetes,

 but they did not examine costs. Other research found cost savings due to implementation of IV insulin with a target postoperative glucose <150 mg/dL after coronary bypass surgery ($680 per patient

) but did not examine utilization differences. Last, other research found that implementation of a comprehensive glucose management program reduced hyperglycemia and decreased LOS, but hospital costs were not changed.

Our results have implications for dissemination and implementation of a glycemic control pharmacist program modeled after our GCT to other institutions. Research shows that implementation of evidence-based guidelines and recommended treatments are highly variable and inherently slow and gradual among clinicians and hospitals.

 The results of our analysis demonstrate that, by developing a specific team of pharmacists whose sole purpose is to focus on inpatient glycemic management, perioperative and postoperative dysglycemia management can be improved.

Our quality-improvement evaluation has some important limitations. First, the population in the post intervention periods had slightly less acuity compared with the pre-intervention period, and it is possible that temporal changes account for some aspects of the improved outcomes, though multivariate analysis controlled for some patient-level variance. However, our concerns about these limitations are mitigated for several reasons. First, we robustly adjusted statistically for differences in the patient population over time, and our analyses demonstrated persistent main effects that remained statistically significant. Second, we found improvements in glycemic control (post intervention) that remained significant in a sub-analysis of patients with A1C >6.5% (results not shown). Additionally, a sensitivity analysis looking at all surgical patients during parallel time frames demonstrated outcome improvements proportional to the ratio of study population to all surgical patients, suggesting that other temporal effects do not account for the main beneficial effects. Nevertheless, it is possible that differences observed in key outcome measures may be due to imbalances between the 2 study population periods (post implementation vs pre-implementation), rather than the intervention per se. The imbalances between the 2 groups may also suggest unmeasured factors such as patient behaviors that are not possible to assess through the EMR. Nevertheless, despite rigorously adjusting for differences in the 2 study populations, the retrospective prepost observational design of the study limits the ability to assess the effectiveness of the GCT intervention. A prospective, randomized trial design would be ideal in future studies to assess the effectiveness of pharmacist-based GCT interventions.

Secondly, it is possible that the pharmacist team helped achieve improved outcomes due to factors that were not related to glycemic control. For instance, improved medication reconciliation and patient education for nonglycemic-related medications might have benefitted patients being followed by the GCT. However, our pharmacists were not tasked to provide these broader services under the protocol, and, given their limited scope, this seems unlikely to have had a major effect on nor be a detrimental effect of the program. In addition, because we did not have a control group available for analysis, results may have been attributed to secular trends, rather than the intervention per se.

A final limitation is that our intervention and evaluation were conducted at a single medical center within an integrated delivery system and our results may not apply to other settings or care delivery systems. This limitation is mitigated by anticipated generalizability to other hospital settings (eg, academic medical centers, community hospitals), given that the intervention was conducted in the inpatient setting where fewer differences exist across institutions. The inpatient pharmacists and staff who conducted the intervention would be found in most hospital settings in the United States.

Based on the initial success of the GCT, future research should examine the effectiveness of a glycemic pharmacist team intervention for nonsurgical patient populations with unmet glycemic control management needs in the hospital, such as oncology patients on high-dose steroid regimens. Future research should also study the effectiveness of the intervention across a variety of hospital systems (eg, Veterans Administration, academic medical centers) that serve more socioeconomically and ethnically diverse patient populations. Other ways to implement standardized, guideline-based treatment of perioperative hyperglycemia (such as automated nurse-initiated protocols) could also be explored. In addition, future research is needed to examine whether glycemic control outcomes differ among populations with stress hyperglycemia, compared with diabetes. In addition, more research is needed to understand the effect of GCT programs on future glycemic control post hospital discharge. Finally, prospective, randomized pragmatic and effectiveness trials are needed to refine our understanding of the ideal target glucose range for surgical patients, as well as of the best medical regimens (IV insulin, subcutaneous insulin, and noninsulin medications) to achieve glucose control targets for surgical subpopulations.

Implementation of a pharmacist-led glycemic control team was associated with improved glycemic control and utilization outcomes in a population of noncritically ill surgical patients. 