Increasing Lipid Profile Screening in Youth With Type 2 Diabetes

Abstract

Quality Improvement Success Stories are published by the American Diabetes Association in collaboration with the American College of Physicians and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes an initiative to improve the rate of lipid profile screening in youth with type 2 diabetes at a tertiary children’s hospital in Southern California.

Describe your practice setting and location.

This project was conducted at Rady Children’s Hospital, San Diego (RCHSD), a tertiary children’s hospital in Southern California, affiliated with the University of California, San Diego. Our center serves a diverse patient population, with a broad catchment area that includes San Diego County, parts of Imperial County, and Southern Riverside County and extends beyond the United States–Mexico border. We provide care for 265 youth with type 2 diabetes, of whom 88% are publicly insured.

The patient population for this project consisted of youth with type 2 diabetes who were ≤18 years of age, had a completed office visit at the RCHSD diabetes clinic between 1 May 2023 and 31 May 2024, and had at least one prior visit in the preceding 12 months. The demographic composition of patients seen during this time was as follows: 77.4% Hispanic, 8.5% non-Hispanic Black, 3.9% Asian, and 6.8% non-Hispanic White. The remaining patients identified their race/identity as “other” or did not specify their race/ethnicity. Our diabetes program is part of the T1D Exchange Quality Improvement Collaborative (T1DX-QI), a network of diabetes centers from various regions across the United States committed to sharing best practices and setting clinical benchmarks to improve outcomes for people with type 1 or type 2 diabetes (1).

Describe the specific quality gap addressed through the initiative.

It is crucial to identify comorbidities such as dyslipidemia early; therefore, our project focused on improving the rates of lipid profile screening completion in youth with type 2 diabetes. The aim of our quality improvement (QI) project was to increase the percentage of patients with type 2 diabetes who had lipid profile completed in the past 12 months from a baseline of 74% to 90% between 1 May 2023 and 31 May 2024.

The International Society for Pediatric and Adolescent Diabetes (ISPAD) and the American Diabetes Association (ADA) have published guidelines on lipid screening for youth with type 2 diabetes; recommendations include screening for dyslipidemia soon after diagnosis, ideally once glycemia has improved, and annually thereafter (2,3).

These recommendations are based on evidence that type 2 diabetes in youth is associated with significant increased risk of microvascular (e.g., retinopathy, nephropathy, and neuropathy) and macrovascular (e.g., myocardial infarction and stroke) complications and heightened cardiovascular risk factors (e.g., hypertension and hyperlipidemia). Additionally, progression of β-cell failure and risks of complications are more severe in type 2 diabetes in youth (<18 years of age) compared with the adult population. Some youth may already have complications at the time of diagnosis, and the progression of the vascular abnormalities is more pronounced in this population (4). Findings from the TODAY (Treatment Options of Diabetes in Adolescents and Youth) study showed that the risk of microvascular complications in participants with youth-onset diabetes increased over time, with a cumulative incidence of any microvascular complication of 50.0% by 9 years and 80.1% by 15 years (4). Macrovascular complications such as dyslipidemia were seen in 20.8% of participants at baseline, with a cumulative incidence of 51.6% by 15 years after diagnosis (4). Therefore, it is crucial to identify these comorbidities as soon as possible for appropriate management.

How did you identify this quality gap? In other words, where did you get your baseline data?

Data on baseline lipid profile completion rates were extracted from the electronic health record (EHR; Epic) with the assistance of a physician informaticist (M.B.). All patients seen in our diabetes program are part of a registry that is maintained in the EHR system. This registry allows us to monitor metrics of interest, such as completion of recommended screenings, in real time. For this project, an additional web-based dashboard (Qlik Sense) was developed outside of the EHR to display weekly completion rates for all recommended screenings for patients with diabetes (e.g., microalbumin, thyroid function, and lipid tests). This dashboard was built to automatically query the EHR database on a weekly basis, calculate screening rates, and flag patients who had missed a recommended screening based on the time elapsed since the last time the screening was completed. The historical data from the dashboard were subsequently compiled (by P.S. and M.B.) to create reports of monthly lipid profile completion rates for patients with type 2 diabetes who had office visits completed in the preceding 5 months, from December 2022 to April 2023, to determine the baseline rate of lipid profile screening completion. Data presented include unique patient encounters (i.e., if the same patient had a clinic visit in December 2022 and again in April 2023, these are reported as separate encounters).

Summarize the initial data for your practice (before the improvement initiative).

Our center line was derived from data from December 2022 to April 2023 and reflects lipid profile completion rates for patients with type 2 diabetes who were ≤18 years of age, were seen between December 2022 to April 2023, and had at least one additional clinic visit in the preceding 12 months. Our baseline lipid profile completion rate was 74% (120 patient encounters compliant out of 162 total patient encounters) (Supplementary Table S1).

What was the time frame from initiation of your QI initiative to its completion?

The time frame for this project was 12 months from 1 May 2023 to 31 May 2024.

Describe your core QI team. Who served as project leader, and why was this person selected? Who else served on the team?

The QI team included two pediatric endocrinologists and three certified diabetes care and education specialist nurse educators. A pediatric endocrinologist was selected as the project lead, bringing her experience from the RCHSD QI Leaders (QIL) program and her role as a member of the T1DX-QI team at RCHSD. The RCHSD QIL program offers a structured curriculum for physician and nurse leaders aimed at guiding QI projects and facilitating institution-wide feedback. The second pediatric endocrinologist acted as the clinical mentor, offering her in-depth expertise as a QI coach and the principal investigator for the T1DX-QI initiative at RCHSD. The team worked together to identify key change concepts and carried out several plan-do-study-act (PDSA) cycles to drive improvements.

Describe the structural changes you made to your practice through this initiative.

Through the use of a report of patients in our diabetes registry with upcoming visits (created by M.B.), patients with type 2 diabetes who were overdue for lipid screening were identified on a weekly basis before their upcoming appointments (Supplementary Figure S1). This report is an extension of our diabetes registry that has been built out to monitor diabetes-specific metrics, as previously described. This report allows clinic staff to track at the individual patient level compliance with lipid, retinopathy, thyroid function, and microalbuminuria screening rates. These items are highlighted by the report, indicating which tests are overdue for a given patient. This information is also reflected in the Health Maintenance activity in our EHR system.

Before the start date of this project, diabetes providers were using an EHR order set (created by C.D.-B.). However, the first intervention was an educational session presented to all pediatric endocrinology clinicians (physicians and nurse practitioners) that outlined ISPAD and ADA recommendations for lipid screening and reviewed the utility of nonfasting blood samples for lipid screening. Although fasting lipid profiles are preferred, there is still utility in nonfasting cholesterol screening. Non-HDL cholesterol is relative unaffected by nonfasting status and can be a reliable screening measure in children (5). Additionally, in special populations such as youth with diabetes, there is increased potential for fasting-induced hypoglycemia (6).

In addition, clinicians and clinic staff were instructed on how to use the Health Maintenance activity and its associated reports within the EHR that allow the systematic identification of patients with overdue screenings. The Health Maintenance activity is a standard function in our EHR system that can be used to identify whether patients with a specific condition have completed a test or a procedure. In our population of patients with diabetes, we have focused on whether specific laboratory tests (e.g., for lipid levels, thyroid function, and urine microalbumin concentration) have been completed within the recommended time frame. Patients are identified based on their inclusion in our diabetes registry, which draws on information about specific ICD-10 codes used in patient encounters, patients’ problem lists, and whether patients have been seen in our endocrinology department. For each patient, the Health Maintenance activity flags items as being completed, due soon, or overdue based on ADA guidelines if there is no identifiable result in the EHR that fulfills the screening. An example of the Health Maintenance activity is shown in Supplementary Figure S2.

To keep track of which of the indicated screening tests needed to be performed on a given day for a particular patient, we used a customized patient-tracking tool that we built in the EHR. This tool was used in conjunction with the upcoming appointments report during pre-charting so the medical assistants (MAs) could mark the tests a patient would need on the day of the visit. We have previously worked on a policy that allows our MAs to place orders for screening tests to be completed if patients meet the criteria based on ADA guidelines. This same tool is also used in our standard clinic workflow to notify clinicians about which additional team members are needed for visits (e.g., social worker, registered dietitian, or endocrinology nurse) (Supplementary Figure S3).

An English-language educational handout was developed for patients and caregivers to emphasize the importance of lipid screening. This handout, available both as a printed copy and electronically within the EHR, provides comprehensive information about cholesterol, including an explanation of what cholesterol is, recommended screening timelines, target cholesterol levels, and strategies to lower cholesterol levels in the event of abnormalities.

The QI project lead (P.S.) successfully sent reminders to individual clinicians about patients who were overdue for their annual lipid screening. This process was made possible through a similar report to the one described above. This report would list all patients seen in the clinic within the past year who are part of the diabetes registry, along with the date of their last lipid profile and other recommended screenings. The report also included information on patients’ most recent clinic visit as well as the date of their upcoming visit. Templated reminders were then sent securely to clinicians via messaging within the EHR. These reminders were sent weekly by the project lead (P.S.) to clinicians who were scheduled to see patient with overdue tests in the coming week.

In April 2024, implementation of a point-of-care testing (POCT) lipid analyzer in the diabetes clinic enabled the performance of timely lipid screening testing for patients during clinic visits. The POCT lipid machine used by our clinic is the Cholestech LDX Analyzer from Abbott Laboratories, which meets National Cholesterol Education Program performance goals for lipid testing. Additionally, it is certified by the Centers for Disease Control and Prevention’s Lipid Standardization Program and the Cholesterol Reference Method Laboratory Method Network program. Thus, it meets accuracy standards for lipid testing (7).

Describe the most important changes you made to your process of care delivery.

The most important process changes included EHR utilization to identify patients overdue for an annual lipid panel screening ahead of upcoming diabetes visits. This process was achieved through the use of reports within the EHR, as well as the Health Maintenance activity, to highlight patients’ lipid overdue status.

Through implementation of a POCT lipid machine in the clinic, in conjunction with the pre-charting done by MAs to update the patient-tracking tool, lipid panel tests could be completed successfully on the same day as a clinic appointment. Furthermore, this process improvement made lipid screening results more readily available for clinicians to review and discuss with patients and caregivers during visits. Clinicians could then provide guidance on next steps, such as dietary modifications or medication options, if warranted. The baseline and current updated clinic workflows are shown in Supplementary Figure S4A and B, respectively.

Summarize your final outcome data (at the end of the improvement initiative) and how they compared with your baseline data.

The data represent the monthly percentage of patients with type 2 diabetes (≤18 years of age) who attended a clinic visit during that month, had been seen at least once in the preceding 12 months, and had a completed lipid profile screening. Through the implementation of the interventions described above, we achieved our target lipid screening completion rate of 90% by the end of May 2024, an improvement from a baseline rate of 74% (Supplementary Figure S5).

Since the conclusion of this project in May 2024, the initiative has demonstrated sustained success. From June 2024 to June 2025, lipid profile screening rates remained at or above 90% during 11 of 13 months, with the only exceptions occurring in August and November 2024. Notably, the screening rate reached 100% in June 2024 (Supplementary Table S1). The key driver diagram for this project is shown in Supplementary Figure S6.

What are your next steps?

Balancing measures of our QI project included the additional time required to room patients for their diabetes clinic visits because MAs must now complete POCT along with other diabetes health maintenance tasks. Because there is currently only one POCT lipid machine in our clinic, delays in rooming can occur when multiple patients are due for lipid screening on the same day. To address this problem, the clinic is identifying patients who need lipid screening and staggering their testing to minimize delays. With the addition of POCT lipid screening, there was the concern that completion of other health maintenance items (e.g., retinopathy and urine microalbumin screenings) would be negatively affected. However, such was not the case. Therefore, we plan to purchase and implement a second POCT lipid machine. Other future steps include further optimizing patient rooming and MA workflows, continuously training new staff, and reinforcing patient education tools.

What lessons did you learn through your QI process that you would like to share with others?

This QI project demonstrates how data tracking and health informatics can be used effectively to identify patients who are overdue for routine screenings, enabling targeted interventions that will lead to early recognition of macrovascular complications and improved health outcomes. The most significant improvement was observed after the integration of the POCT lipid machine into the clinic workflow. The project also highlights the success of QI methodology in enhancing lipid profile screening completion rates within a tertiary children’s hospital diabetes clinic.

This article contains supplementary material online at https://doi.org/10.2337/figshare.30190111.

Acknowledgments

The authors acknowledge Chelsea Fernandez, MA, who assisted with the creation and formatting of the P-chart.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

P.S. wrote the manuscript and served as lead of the QI project. C.B.-M., K.M., A.H., and J.R. reviewed and edited the manuscript. M.B. assisted with data informatics, data collection, and the diabetes dashboard. C.D.-B. served as project mentor and reviewed and edited the manuscript. P.S. is the guarantor of this work and, as such, had full access to all data in this study and takes responsibility for the integrity of the data and the accuracy of the data analysis.