Clinical Researcher—August 2022 (Volume 36, Issue 4)

SCIENCE & SOCIETY

Mac Bonafede, PhD, MPH

 

Clinical patient disease registries collect information on large numbers of people in diverse clinical practices. Diversity applies to both the types of clinical practices and the patients themselves, since researchers consider hard-to-reach patients{1} as potential clinical trial participants from outside typical research recruitment settings.

With patient disease registries, life science researchers also find a pathway for deeper understanding of:

• Variations in a disease’s treatment and outcomes
• Variations in care delivery, quality of care, and care effectiveness
• Safety signals and opportunities for enhanced surveillance
• Factors that influence disease prognosis and associated quality of life for patients

These registries are powerful tools{2} for better understanding of distinct therapeutic areas of interest—from cardiovascular disease, diabetes, and hypertension to other chronic conditions that require longitudinal views of patient data. They also provide an efficient avenue for custom data collection or site recruitment and engagement to support pharmacovigilance and other real-world evidence (RWE) generation activities.

Incorporating data from patient disease registries in a real-world data (RWD) mix offers four impactful benefits for clinical researchers in terms of facilitating improved diversity in research, accessing hard-to-reach patients, making more-informed public policy decisions, and presenting opportunities for better health outcomes. Let’s look at each of these separately in the following sections.

Improved Geographic and Demographic Diversity

RWD obtained from patient disease registries allow research beyond what is possible{3} with randomized controlled trials (RCTs).

In RCTs, researchers attempt to reduce bias by a) randomizing the medical intervention delivered to each patient, and b) using strict inclusion and exclusion criteria for selection of the trial patient population. However, this reduction of bias is frequently obtained at the expense of generalizability; that is, how research findings apply to a larger population or different setting. Results in a trial patient population almost certainly represent a restricted subset of patients seen in real-world practice.{4}

For example, recent research{5} revealed patient populations enrolled in studies with the greatest impact on current heart failure treatment differ significantly from patients observed in clinical practice. Most heart failure clinical trials have been conducted in white, male patients with a mean age of 60 years. However, in most developed countries, patients affected by heart failure are typically older and more balanced between male and female.

Similarly, RCTs frequently exclude older adults.{6} Age has a clear influence on clinical outcomes. Medication efficacy and optimal dosing are often uncertain in the elderly, whose drug metabolism and clearance rates may be diminished; who may have lower drug tolerance; and in whom there is the potential for drug-drug interactions.

Access to Hard-to-Reach Patients

Patient disease registries have emerged as an important means of gaining insight into the effects of medical interventions in more diverse clinical settings than can be achieved in clinical trials. They can:

• Provide access to RWD from research naïve, geographically diverse sites, across multiple electronic health record (EHR) and practice management platforms.
• Produce results complementary to those obtained in RCTs.
• Obtain data on large numbers of patients at significantly reduced costs and with quicker timelines.

More-Informed Public Policy Decision Making

Patient disease registries offer data to better understand how diverse populations with diabetes, cardiovascular disease, hypertension, and other chronic conditions responded to the virus and subsequent treatments. For example, limited access{7} to COVID-19 vaccines and treatments for hard-to-reach patients surfaced as the pandemic expanded across the globe, casting a spotlight on existing imbalances.

Care of women before, during, and after pregnancy in the U.S. presents mental and basic care challenges{8} that are often addressed inadequately or totally ignored in underserved populations, such as care provided by a regular doctor or in a regular location.

Understanding these disparities requires reported outcomes data that highlight the lack of care for women or specific patient populations. Clinical patient disease registries offer such data so that researchers can pinpoint specific population health needs. Policy makers then access those data in their effort to establish local, state, and federal health policies.

Opportunities for Better Health Outcomes

The three factors addressed above—greater geographic and population diversity, improved access to hard-to-reach patients, and more-informed public policy decision making—all lead to opportunities for better health outcomes using data from patient disease registries.

Registries can fill in gaps{9} where efficacy for specific, defined RCT populations cannot be generalized to patients seen in clinical practice, making them particularly valuable for cardiovascular, cardiometabolic, and diabetes research on population health management and treatments.

The Power of Patient Data Registries

Research across populations, geographic locations, and disease states has become even more vital to understand what treatments life sciences researchers can identify and advance. The power of data analytics coupled with advancement of interoperable data sharing{10} across digital EHR systems benefits users of clinical patient disease registries.

References

1. Heath S. 2022. Top Challenges Impacting Patient Access to Healthcare. Patient EngagementHIT. https://patientengagementhit.com/news/top-challenges-impacting-patient-access-to-healthcare
2. Research Report: Registries for Evaluating Patient Outcomes: A User’s Guide: 4th Edition. Effective Health Care Program. Agency for Healthcare Research and Quality, Rockville, Md. https://effectivehealthcare.ahrq.gov/products/registries-guide-4th-edition/users-guide
3. Atkins D, Best D, Briss PA, et al. 2004. Grading Quality of Evidence and Strength of Recommendations. BMJ 328(7454):1490. doi:10.1136/bmj.328.7454.1490. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC428525/
4. Seeger JD, Nunes A, Loughlin AM. 2020. Using RWE Research to Extend Clinical Trials in Diabetes: An Example with Implications for the Future. Diabetes Obes Metab 22(Suppl. 3):35–44. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7216829/pdf/DOM-22-35.pdf
5. Vogel B, Acevedo M, Appelman Y, Merz CNB, Chieffo A, Figtree GA, Guerrero M, Kunadian V, Lam CSP, Maas AHEM, Mihailidou AS, Olszanecka A, Poole JE, Saldarriaga C, Saw J, Zühlke L, Mehran R. 2021. The Lancet Women and Cardiovascular Disease Commission: Reducing the Global Burden by 2030. The Lancet 397(10292):2385–438. ISSN 0140-6736. https://www.sciencedirect.com/science/article/pii/S014067362100684X
6. Bourgeois FT, Orenstein L, Ballakur S, Mandl KD, Ioannidis J. 2017. Exclusion of Elderly People from Randomized Clinical Trials of Drugs for Ischemic Heart Disease. Journal of the American Geriatrics Society 65(11):2354–61. https://doi.org/10.1111/jgs.14833
7. Bambra C, Riordan R, Ford J, Matthews F. 2020. The COVID-19 Pandemic and Health Inequalities. Journal of Epidemiology and Community Health 74(11):964–68. https://doi.org/10.1136/jech-2020-214401
8. Gliadkovskaya, A. 2022. U.S. Women Face Worse Outcomes, Higher Costs than in Other Wealthy Countries. Fierce Healthcare. https://www.fiercehealthcare.com/providers/commonwealth-fund-report-us-women-poor-care-outcomes
9. Roberts MH, Ferguson GT. 2021. Real-World Evidence: Bridging Gaps in Evidence to Guide Payer Decisions. PharmacoEconomics Open 5:3–11. https://doi.org/10.1007/s41669-020-00221-y
10. Delivering on the Promise of Health Information Technology in 2022. 2022. Health Affairs Forefront. doi:10.1377/forefront.20220217.71427. https://www.healthaffairs.org/do/10.1377/forefront.20220217.71427/

Mac Bonafede, PhD, MPH, is Vice President of Real-World Evidence at Veradigm, an Allscripts Company, where he leads a team of data scientists, statisticians, and epidemiologists in real-world evidence generation using Veradigm’s PINNACLE and Diabetes Collaborative Registries.