HEDIS Glycemic Goal Achieved Using Control-IQ Technology

ABSTRACT

Objectives: This analysis evaluated changes in glucose management indicator (GMI) at 12 months from baseline hemoglobin A_1c (HbA_1c)—which are both used interchangeably in the Healthcare Effectiveness Data and Information Set (HEDIS) quality measures for glycemic status—in individuals with type 1 diabetes (T1D) who switched from multiple daily insulin injections (MDI) to Control-IQ technology, an automated insulin delivery (AID) therapy.

Study Design: US-based retrospective analysis that used data from the Tandem Diabetes Care, Inc t:connect web application and customer relationship management database.

Methods: Inclusion criteria were having had prior treatment with MDI, having started Control-IQ at least 1 year prior to the study end date, having a most recent recorded baseline HbA_1c measurement within 6 months prior to AID initiation, and having at least 70% continuous glucose monitoring use during the 12-month postinitiation period. The primary outcome was the change in number and proportion of individuals who met the HEDIS quality measure (HbA_1c or GMI of < 8% [control] or > 9.0% [poor control]) and American Diabetes Association (ADA) glycemic target (< 7%) according to payer type (commercial, Medicaid, Medicare) during the 12-month observation.

Results: The analysis included 12,522 individuals with T1D. Following AID initiation, the number and proportion of individuals who met the HEDIS quality standard increased from 6205 (49.6%) to 11,632 (92.9%) at 12 months (∆ = 87.5%). Similar improvements were observed among those who achieved less than 7% GMI. Within all payer groups, the number of patients with baseline HbA_1c levels greater than 9% decreased from 3431 to 15 (∆ = –99.6%).

Conclusions: A greater proportion of individuals can achieve the HEDIS and ADA target goals for glycemic status with Control-IQ use compared with MDI use across all payer types.

Am J Manag Care. 2025;31(12):In Press

Takeaway Points

• Our study assessed the impact of using Control-IQ technology, an automated insulin delivery system, on achievement of a Healthcare Effectiveness Data and Information Set (HEDIS) quality measure (glucose management indicator < 8%), which is a surrogate for hemoglobin A_1c.
• At 12 months, a greater proportion of individuals who switched from multiple daily insulin injections (MDI) to Control-IQ therapy achieved the HEDIS quality measure compared with when they were on MDI therapy.
• This study offers valuable insights for payers and providers, aiding in prioritization of strategies aimed at optimizing outcomes in diabetes care.
• These strategies encompass a range of options, such as exploring alternative pharmaceutical treatments, leveraging medical device technologies, and implementing complementary population health management services.

The growing global prevalence of diabetes has challenged clinicians and health care systems to address the clinical, emotional, and financial burden of this disease. The number of individuals with diabetes is expected to grow to an estimated 643 million worldwide by 2030, up from 537 million in 2021.¹ The number of individuals with type 1 diabetes (T1D) was approximately 8.4 million in 2021 and is expected to increase rapidly.² A recent meta-analysis of 34 studies³ found that the majority of the 5466 individuals comprising the sample had hemoglobin A_1c (HbA_1c) levels well above the American Diabetes Association (ADA) target of less than 7%.⁴

During the past decade, ongoing innovations in glucose monitoring and insulin delivery technologies have led to the development of advanced tools designed to help people overcome the burden of diabetes and actuate optimal diabetes management. Continuous glucose monitoring (CGM) is increasingly being used. Unlike traditional finger-stick blood glucose testing, CGM shows trends of hypoglycemia, hyperglycemia, and glucose variability in real time that can allow for more timely adjustments and therapy changes when needed.

Standardized CGM metrics include time in range (%TIR; 70-180 mg/dL), time below range (%TBR; level 1 hypoglycemia [< 70 mg/dL] and level 2 or severe hypoglycemia [< 54 mg/dL]), time above range (%TAR; 181-250 mg/dL and > 250 mg/dL), and glucose management indicator (GMI).⁵ GMI is an established metric that provides an estimated measure of HbA_1c levels based on CGM data and is reported as a percentage like HbA_1c.⁶ However, unlike HbA_1c, GMI is not impacted by medications such as a long-term salicylate, opioid ingestion, or any condition that is associated with decreased red cell turnover such as iron deficiency anemia that can falsely elevate an HbA_1c level.^7,8 A secondary end point that was recently established is time in tight range (%TITR; 70-140 mg/dL).⁹ These clinical measures were recommended by a consensus panel and have been endorsed by various diabetes associations. A minimum of 10 to 14 days of CGM data is recommended for a reliable 3-month estimate, with at least 14 days providing a better representation of glycemic performance.^5,6

With the pairing of CGM to insulin pumps, automated insulin delivery (AID) systems have become possible. AID systems, also known as hybrid closed-loop systems, are insulin pump systems that use an algorithm to automatically increase or decrease basal insulin delivery in response to CGM glucose values. Advanced AID systems are also available, which have the added capability to deliver automated correction boluses.¹⁰ In randomized controlled trials (RCTs), AID has been shown to improve glycemic control and reduce the risk of hypoglycemia in adult, adolescent, and pediatric patients with T1D.^11,12 A meta-analysis of RCTs by Beck et al demonstrated significant improvement in mean glucose, HbA_1c, hyperglycemia metrics, and hypoglycemia metrics in individuals with T1D using an AID system.¹² Additionally, improved TIR was observed across the full age range (2- 72 years) regardless of race/ethnicity, household income, prior CGM use, or prior insulin delivery method; the greatest benefit in HbA_1c and TIR was seen in individuals with the worst baseline control.¹²

Importantly, the ADA updated its recommendations in 2020 to include an assessment of glycemic metrics, which includes GMI as a measure presented in the Ambulatory Glucose Profile (AGP) with a goal of less than 7%.⁴ According to the AGP developers, GMI is a direct measure of glucose and provides the HbA_1c level that would usually be expected from a large population of individuals with diabetes who have the same average CGM glucose level.⁶ GMI is estimated from a linear regression model that utilizes laboratory-measured HbA_1c and CGM-measured mean glucose data from 4 RCTs.⁶ Conversely, although the HbA_1c test is considered to be a useful measure of population health,¹³ it may not accurately reflect an individual’s true glycemic status.^7,8

The National Committee for Quality Assurance (NCQA) included the use of the GMI for measurement year 2024 as a surrogate for HbA_1c in its Healthcare Effectiveness Data and Information Set (HEDIS) diabetes-related measures,¹⁴ which aligns with updated ADA clinical guidelines and new technologies.¹⁵ HEDIS is a set of diverse measures that assess health care effectiveness, access, and enrollee experience and is used by more than 90% of health plans.¹⁶

The HEDIS Glycemic Status Assessment for Patients With Diabetes assesses an individual’s most recent HbA_1c or GMI result against 2 thresholds of less than 8% and greater than 9%,¹⁴ previously defined as control and poor control, respectively.¹⁷ The metric uses the most recent result (GMI or HbA_1c). The formal target ADA goal for HbA_1c level is less than 7% for most individuals,⁴ and less than 7% is an implied goal for GMI as presented in an ADA report showing key points in a standard AGP.⁴

There are currently several commercially available AID systems in the US. This study assessed the real-world performance of the t:slim X2 insulin pump with Control-IQ technology (hereafter, Control-IQ; Tandem Diabetes Care, Inc) within a large population of patients with T1D using HEDIS and ADA thresholds for glycemic control.

METHODS

Study Design and Population

This US-based, retrospective analysis evaluated changes in glycemic status over 12 months among children, adolescents, and adults with T1D who initiated therapy with the Control-IQ system following therapy with multiple daily insulin injections (MDI). Uploaded glycemic information for January 15, 2020, to March 14, 2024, was obtained from the Tandem Diabetes Care t:connect web application, which limited our investigation to Tandem devices. Baseline patient characteristics were obtained from the Tandem customer relationship management database. Inclusion criteria were having had prior treatment with MDI, having started Control-IQ at least 1 year prior to the study end date, having a most recent recorded baseline HbA_1c measurement within 6 months prior to Control-IQ initiation, and having at least 70% CGM use during each 3-month interval (quarter [Q] 1, Q2, Q3, Q4) during the 12-month postinitiation period.⁵ Results were stratified by key payer type (ie, commercial, Medicaid, and Medicare). GMI was calculated for each 3-month interval in the 12-month postinitiation period and compared with baseline HbA_1c levels. Figure 1 presents the study flow diagram.

Outcome Measures

The primary outcome was the change in the number and proportion of all individuals who aligned with the HEDIS quality measures (< 8% and > 9%)¹⁸ and a more aggressive metric (GMI of < 7%) according to payer type during the 12-month observation period. Baseline CGM data were not available, so CGM metrics during the preindex baseline period were not calculated. Calculated CGM metrics included %TIR (70-180 mg/dL), %TITR (70-140 mg/dL), %TBR (both in level 1 hypoglycemia [< 70 mg/dL] and level 2 hypoglycemia [< 54 mg/dL]), and %TAR (> 180 mg/dL). The goals for these metrics are greater than 70% for %TIR, greater than 70% for %TITR, less than 4% for %TBR less than 70 mg/dL, less than 1% for %TBR less than 54 mg/dL, and less than 25% for %TAR.⁵

Statistical Analysis

GMI was calculated using the following formula:

GMI = 3.31 + 0.02392 × mean glucose in mg/dL⁶

and compared with baseline HbA_1c level. Changes in glycemic outcomes were analyzed using paired t tests. The proportions of patients meeting national thresholds were analyzed using McNemar χ² tests.

RESULTS

Study Population Characteristics

The analysis included 12,522 individuals with T1D. Baseline demographic characteristics are presented in the Table. Most participants were female, and a plurality were aged 18 to 44 years; 26.5% were younger than 18 years. The majority of individuals were covered by commercial insurance, followed by Medicaid and Medicare.

Achievement of HEDIS and ADA Quality Standards

At baseline, 6205 (49.6%) individuals met the HEDIS quality standard for glycemic status (GMI < 8%) and 2842 (22.7%) individuals achieved a GMI less than 7%. By Q4, significant improvements were seen in the percentage of individuals meeting HEDIS or ADA quality standards. The percentage of patients meeting the HEDIS measure of GMI less than 8% increased to 92.9% (∆ = 87.5%) and the percentage achieving a GMI less than 7% increased to 34.4% (∆ = 51.4%).

The largest increase in the proportion of individuals meeting the HEDIS threshold (< 8%) was observed among patients covered by Medicaid, followed by those covered by commercial insurers and Medicare (Figure 2 [A]). The largest increase in the proportion of individuals achieving a GMI less than 7% was observed in those with Medicare coverage (Figure 2 [B]).

Reduction of Suboptimal Glycemic Status per HEDIS Quality Standards (HbA_1c or GMI > 9%)

A total of 3431 patients with a baseline HbA_1c level greater than 9% were identified (commercial: n = 2289; Medicaid: n = 867; Medicare: n = 275). The number of individuals with HbA_1c greater than 9% decreased from 3431 to 15 (∆ = –99.6%) (Figure 3). The largest percentage reduction was observed among patients covered by commercial plans.

Changes in GMI per Quarter From Baseline to Q4

A significant reduction in GMI from baseline HbA_1c levels was observed across the full cohort within the first 2 weeks of Contro1-IQ use and was sustained at each quarterly measurement throughout the 12-month observation period (P < .0001 at all increments) (Figure 4). There was an observed difference between mean GMI for the first 2 weeks and Q4 that was statistically significant (P < .0001); however, the effect size was small (Δ = 0.1%).

Secondary Postinitiation Observations

Within the full cohort, the mean (SD) %TIR (70-180 mg/dL) and %TITR (70-140 mg/dL) decreased from Q1 through Q4 from 69.3% (14.2%) and 43.2% (15.0%) to 65.5% (15.4%) and 39.5% (15.5%), respectively. The mean (SD) %TAR (> 180 mg/dL) increased from 28.8% (13.5%) to 32.2% (14.7%). Increases in mean (SD) %TBR (< 70 mg/dL and < 54 mg/dL) were also observed from Q1 to Q4 (< 70 mg/dL: 1.9% [2.3%] to 2.2% [2.7%]; < 54 mg/dL, 1.0% [1.9%] to 1.2% [2.4%]).

DISCUSSION

The inclusion of GMI as an HbA_1c surrogate has expanded access to clinical outcomes through CGM, enhancing managed care plans’ compliance with quality metrics. This study shows how combined HbA_1c and GMI outcomes improve HEDIS and ADA measures in individuals with T1D using the Control-IQ after prior MDI therapy, serving as a prototype for including GMI in quality metrics for various diabetes treatments. This study reports on a significantly greater proportion of individuals with T1D who meet national quality standards (HEDIS/ADA) for target glycemic status after switching from MDI to Control-IQ. A significant reduction in GMI from baseline HbA_1c was observed in the full cohort during the first 2 weeks of Control-IQ use (from 8.30% to 7.06%; Δ = –1.24%; P < .0001) and sustained over the 12-month period. The reduction in GMI was achieved in all individuals regardless of their insurance coverage type. These findings are consistent with a meta-analysis from 3 RCTs among Control-IQ users by Beck et al, which showed significant improvement in mean glucose and HbA_1c levels in individuals with T1D.¹²

Recently, a similar study by Forlenza et al was conducted among 5575 Medicaid and Medicare beneficiaries new to Control-IQ with T1D (n = 5075) or type 2 diabetes (n = 500).¹⁹ At the 12-month follow-up, significant improvements in %TIR were observed in both the Medicaid and Medicare populations (from 46% to 60% and from 64% to 74%, respectively; both P < .0001).¹⁹ The study also assessed the percentage of Control-IQ users who met the ADA guidelines for CGM glucometrics (ie, GMI < 7% with TIR > 70% and either TBR of < 4% for those < 65 years or a TBR of < 1% for those ≥ 65 years). Forlenza et al found that the percentage of users meeting ADA guidelines improved for the Medicare group (12.8% vs 26.3%; P < .001) and Medicaid group (5.7% vs 13.4%; P < .001).¹⁹ The magnitude of improvement was lower in the study by Forlenza et al than in this study; however, it is important to note that the Forlenza et al study cohort included individuals with CGM data at baseline and follow-up and individuals with prior AID experience, whereas this study only included individuals with prior MDI therapy.

Results from this study showed significant increases in the percentage of individuals meeting HEDIS and ADA standards for target glycemic status of diabetes after initiation of Control-IQ after MDI use. Although clinicians and health plans aim to provide high-quality care to achieve the best clinical outcomes, there are also other incentives for high performance on these national quality standards. HEDIS scores are used to monitor quality performance and inform value-based provider agreements and accreditation for private health plans, managed Medicaid plans, and provider organizations.²⁰ CMS, the largest payer in the US, uses HEDIS data for its Medicare Star Ratings to measure the quality of health services provided to Medicare beneficiaries in Medicare Advantage and Part D plans, with 1 star being the lowest rating and 5 stars being the highest rating for quality. As reported by CMS, contracts with plans with consistently low ratings can be terminated. Star ratings also impact reimbursement rates to Medicare Advantage plans. High performance (ie, ≥ 4 stars) can have significant financial implications, with substantial increases in quality bonus payments that can amount to millions of dollars depending on plan size. High performance also impacts market positioning with increased member enrollment for Medicare Advantage plans. A recent review by Borrelli et al found that star ratings directly impact both enrollment and disenrollment in a given plan.²¹

Use of diabetes technology, including CGM and AID systems, provides real-time and more readily accessible glycemic information compared with laboratory HbA_1c readings. Standardized glycemic metrics such as TIR and GMI have been recommended by various diabetes associations, including the ADA, and used by the NCQA HEDIS and clinicians for diabetes management. Additionally, standardized reports including these measures have been developed for practicing clinicians. The AGP report was developed by the International Diabetes Center²² and is recommended by the ADA for glucose assessment.¹⁵ The AGP reports display glucose data in a standardized format intended to reduce clinician burden by making the data easy to interpret and comparable across all devices.²³ Most CGM and AID system manufacturers include variations of the AGP format, first introduced as a 1-page report, in their health care professional software portals.²⁴ Individuals using these technologies can share reports with their physician or other health care professionals, including medication therapy management pharmacists and certified diabetes educators, as well as their caregivers. To further streamline clinician workflows and efficient use of these metrics to ultimately improve patient outcomes, the Diabetes Technology Society initiated the Integration of Continuous Glucose Monitoring Data into the Electronic Health Record, or iCoDE, Project to facilitate automated uploading and integration of CGM data into the electronic health record.²⁵

This study is unique in that it measured the performance of an AID system among users switching from MDI therapy; other studies compared individuals with prior pump experience, including experience with insulin pumps that were not connected to CGMs or earlier versions of hybrid closed-loop pumps. Furthermore, this study focused on performance on HEDIS and ADA glycemic standards across different payer types. This study analyzed population-level glycemic performance over a period of 1 year on a large sample size, allowing for an assessment of the sustainability of glycemic improvement associated with the use of the Control-IQ system. Evaluating performance based on payer type offers meaningful context from the quality improvement, value, and population health perspectives to payers. This study offers valuable insights for payers and providers, aiding in prioritization of strategies aimed at optimizing outcomes in diabetes care. These strategies encompass a range of options, such as exploring alternative pharmaceutical treatments, leveraging medical device technologies, and implementing complementary population health management services. Population health management services may include medication therapy management, care management, and the integration of digital health solutions including engaging in technological innovations that seek to alleviate points of friction for patients, caregivers, and providers.

Additionally, our findings demonstrated how rapidly glycemic status improves when AID treatment is initiated. Rapid improvement in glycemic status assessed by %TIR has been reported in recent studies. For example, Sherr et al reported rapid improvements in %TIR and other metrics in a cohort of 11 adults, 10 adolescents, and 15 children with T1D.²⁶ During the first 5 days of the evaluation, the %TIR (70-180 mg/dL) increased during AID use compared with standard therapy, reaching significance for adolescents (79.0% vs 60.6%; P = .01) and children (69.2% vs 54.9%; P = .003), with a notable improvement among adults (73.7% vs 68.0%; P = .08).²⁶ Similar improvements in %TIR from baseline were recently reported in a cohort of 368 children and adolescents with T1D during the first 15 days of AID use in automode (from 62.8% to 75.4%; P < .001).²⁷ Beato-Víbora et al also reported an increase in %TIR with AID use, from 67.3% to 79.6% at 1 month (P = .001).²⁸ Absent blinded CGM data at baseline, using the first 2 weeks of unblinded data allows for a more accurate assessment of glycemic change than after 3 months of CGM use.^26-28

Limitations

This study is not without limitations. Baseline levels of %TIR, %TBR, and %TAR were not available for comparisons with the follow-up period. Specifically, the Q1 and subsequent quarters of data following Control-IQ initiation reflect only postinitiation observations without reference to the magnitude of change from baseline. The inability to measure performance of individuals for whom sufficient data was not available, according to the minimum necessary CGM data (≥ 70%) per international consensus guidelines,⁵ is also a limitation. Finally, it is important to recognize that HbA_1c and GMI measure 2 different glycemic characteristics. Whereas HbA_1c measures the amount of circulating glucose that has attached to hemoglobin over the past 2 to 3 months,²⁹ the GMI is derived from the glucose values captured during at least 14 days of CGM use and provides only an estimate of the HbA_1c level.⁶ Although at an individual level there can be higher discordance between these measures, at the population level the relationship has been established via multiple studies, resulting in an equation to translate and compare the 2 measures. When comparing HbA_1c with GMI, there is frequent discordance between the two on an individual patient level.³⁰ However, in a large cohort study,this discordance will “wash out.” Moreover, unlike HbA_1c measurement, which can be affected by long-term salicylate use, opioid ingestion, and other conditions that can falsely raise or lower HbA_1c results,^7,8 GMI is not impacted by these factors. Use of GMI also has other advantages over HbA_1c. Because the GMI is automatically calculated by CGM download software, laboratory costs are avoided and the results are readily available to clinicians and health plans. Moreover, individuals are not burdened by the pain and inconvenience of presenting at the laboratory or clinic for blood draws. Further, with the adoption of GMI by the NCQA for performance measurement, understanding the impact to quality improvement scores using CGM-derived measurements is necessary.

CONCLUSIONS

In this retrospective database analysis of children, adolescents, and adults with T1D who switched from MDI therapy to Control-IQ, a significantly greater percentage of individuals achieved the HEDIS goal for glycemic status across all payer types. The NCQA’s decision to include GMI in its quality measures is an important first step in recognizing the value and utility of CGM data for assessing the quality of diabetes care. Democratizing CGM metrics and reporting in electronic health records for broader use in clinical platforms that are available to payers (eg, medication therapy management and care management) should be considered to potentially further improve patient outcomes. The challenge of addressing the increasing prevalence of diabetes and the clinical and economic consequences of suboptimal glycemic status can be improved by increased awareness of and access to AID technology.

Acknowledgments

The authors thank Christopher Parkin, MS, CGParkin Communications, Inc, for providing editorial support in the development of this manuscript.

Author Affiliations: Tandem Diabetes Care, Inc (BVP, SMW, LHM, JEP, MRP), San Diego, CA; University of Washington School of Medicine (IBH), Seattle, WA; University of California, San Francisco (LE), San Francisco, CA; University of California, San Diego (SE), San Diego, CA; University of Utah (DIB), Salt Lake City, UT; Stemple Medical Consulting (CS), Montgomery, OH.

Source of Funding: Tandem Diabetes Care.

Author Disclosures: Drs Patel, Wang, Messer, and Pinsker and Ms Polin are employed by Tandem Diabetes Care. Dr Hirsch has received consulting or advisory fees from Abbott, Hager, Roche, and Vertex and has received grants from Dexcom, MannKind, and Tandem. Dr Ekhlaspour receives salary support from the National Institute of Diabetes and Digestive and Kidney Diseases; has served on the advisory boards of Abbott, Diabetes Center Berne, Mannkind, Medtronic, and Sequel; has received consulting fees from Jaeb and Tandem Diabetes Care; and has received honorarium fees from Insulet, Med Learning Group (Sanofi-sponsored grant), Medtronic, and Tandem Diabetes Care; her institution has received research support from Abbott, Breakthrough T1D, Mannkind, Medtronic, and Tandem Diabetes Care. Dr Edelman has received consulting or advisory fees from Embecta. Dr Brixner has received consulting or advisory fees from Embecta, Sanofi, and Tandem. Dr Stemple reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (BVP, SMW); acquisition of data (BVP, SMW, MRP); analysis and interpretation of data (BVP, SMW, LHM, JEP, MRP, IBH, LE, SE, DIB, CS); drafting of the manuscript (BVP, SMW, LHM, JEP, IBH, LE, SE, DIB, CS); critical revision of the manuscript for important intellectual content (BVP, SMW, LHM, JEP, MRP, IBH, LE, SE, DIB, CS); and statistical analysis (BVP, SMW, MRP).

Address Correspondence to: Sharon M. Wang, PharmD, MS, Tandem Diabetes Care, 12400 High Bluff Dr, San Diego, CA 92130. Email: swang@tandemdiabetes.com.

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