Network Collaborator(s): Yale-New Haven Hospital, Mayo Clinic
Leveraging patient-generated health data, this project will assess the impact of a mobile-delivered, prescription digital therapeutic (PDT) (Somryst) delivering Cognitive Behavioral Therapy for Insomnia (CBT-I) for real-world patients with chronic insomnia.
Insomnia is one of the most prevalent health concerns and imposes a significant physical, behavioral, and financial burden on patients’ lives. Up to 50% of the general adult population experience insomnia symptoms, with 12-20% meeting criteria for chronic insomnia. Adults suffering from insomnia also have a higher likelihood of comorbid conditions such as depression, resulting in a reduced quality-of-life and higher rates of morbidity and mortality. CBT for insomnia is the recommended first line treatment for chronic insomnia, but many patients encounter treatment barriers, including a limited number of trained providers. The goal of this Test-Case is to conduct a multi-center randomized controlled trial to collect and evaluate real-world data alongside clinically-validated measures of insomnia to yield a multidimensional analysis of patient benefit from a mobile-delivered CBT-I prescription digital therapeutic.
Using Hugo Health, a patient-centered health data sharing platform for data collection and aggregation, this study will gather and collate patient-reported outcomes, healthcare utilization data, and data from personal digital devices. 100 patients with chronic insomnia will be randomly allocated to receive the PDT delivering CBT-I or usual care and will be followed for 61 weeks. The primary study endpoints are self-reported online ratings of insomnia severity. Secondary endpoints include rate of healthcare utilization, change in sleep outcomes, and change in patient-reported outcomes of sleep quality.
Network Collaborator(s): Yale-New Haven Hospital, Mayo Clinic, Duke University
The objective of this Test-Case is to assess the effect of the Apple Watch’s ECG and irregular rhythm notification detection software features on both patient-reported outcomes and clinical utilization. The pace of innovation for digital health technologies is accelerating. However, several potential risks are inherent to these new technologies, including misinterpretation and/or overreliance on the device-generated data, as well as false negatives and false positives. Therefore, the clinical impact of these devices must be assessed through active surveillance to guide future labeling and risk mitigation strategies.
Using Hugo, a patient-centered health data sharing platform, this study will gather and collate patient-reported outcomes, healthcare utilization data, and data from personal digital devices. 150 patients with atrial fibrillation who undergo planned outpatient cardioversion will be randomly allocated to receive the Apple Watch or a control device and will be followed for six months. The primary study endpoint is the validated Atrial Fibrillation Effect on QualiTy-of-life (AFEQT) questionnaire global score at 6 months compared to baseline. Secondary endpoints include additional clinical treatment for atrial fibrillation and differences in clinical utilization between participants who receive the Apple Watch and the control group. Finally, we will assess the Apple Watch’s ability to serve as a tool for post-market ECG surveillance monitoring by examining its accuracy compared to 12-lead ECGs obtained in routine clinical care.
- Click here for BMJ 2021 publication
Network Collaborator(s): Yale-New Haven Hospital, One Florida Health Systems
This Test-Case will assess the feasibility of NESTcc Data Network collaborators using electronic health records (EHRs) to gather relevant real-world data (RWD) to better characterize the number of, and outcomes experienced by, patients with uncontrolled hypertension. The project is focused on understanding the population of patients potentially eligible for multi-electrode renal denervation system, which is a minimally invasive, catheter-based therapy that delivers energy to the nerves leading to the kidneys that helps regulate blood pressure.
Hypertension (HTN) is the single largest modifiable contributor to death and poses a significant clinical and economic burden to patients and communities. Patients with HTN are at an increased risk for heart attack, stroke, heart failure, and kidney failure. It is estimated that in the United States, 1 in 2 adults have HTN. Despite availability of effective antihypertensive medications, less than half of patients with HTN achieve blood pressure control.
This study will use RWD from health systems to retrospectively identify adult patients with uncontrolled HTN, understand their demographic and clinical characteristics, medication treatment patterns, and both short-term and long-term clinical outcomes.
The project seeks to leverage RWD to better characterize the population that may benefit most from identification and better management of poorly-controlled hypertension.
Network Collaborator(s): Vanderbilt University Medical Center, Lahey Hospital & Medical Center, Mayo Clinic, Weill-Cornell (MDEpiNet), Yale-New Haven Hospital
The objectives of this project are to assess the capacity of routinely collected electronic health record (EHR) data to be used to evaluate long-term (>2 years) adverse events following synthetic surgical mesh implantation (mid-urethral slings) for female stress urinary incontinence (SUI). Synthetic surgical mesh used in transvaginal pelvic organ prolapse (POP) repair surgeries are Class III devices, and synthetic surgical mesh for all other gynecological indications (i.e., transabdominal POP repair and SUI) are Class II devices.
Stress urinary incontinence (SUI) is a common condition among women, and negatively impacts patient quality of life, with an estimated 50% of patients experiencing some symptoms during their lifetime. When conservative treatments are inadequate, implantation of a mesh mid-urethral sling is a recognized minimally invasive surgical treatment for SUI. There is uncertainty about adverse event rates for this procedure and indication, with reports of chronic pain, urethral fistula, voiding problems, and mesh breaking through the skin. Because of adverse outcomes, re-operation rates can reach 8% to 9%.
NESTcc was approached by the U.S. Food and Drug Administration (FDA) to engage in a collaborative consortium to pursue concerns around the risk of mesh use for SUI. Multiple academic medical centers, including Vanderbilt University Medical Center, Lahey Hospital & Medical Center, Mayo Clinic, Weill-Cornell, and Yale-New Haven Hospital, were recruited to collect and analyze patient data regarding mesh implantations for SUI for the outcomes of re-operation, mesh erosion, all-cause chronic pain, and continued voiding symptoms that occur past one year after the surgery.
This study has the potential to increase medical knowledge regarding the safety of SUI devices. Working with the NESTcc is important for this project in order to coordinate multiple data sites for execution of the study. In addition, NESTcc is operating as a central point of contact between FDA and other sponsors to find the appropriate data sites and investigators to pursue the use case of interest.
Network Collaborator(s): Mercy Health System, Mayo Clinic, Yale-New Haven Hospital
This Test-Case assessed the ability of the NESTcc Data Network to reliably and validly capture data on class III surgical devices to study the safety and effectiveness outcomes for an indication expansion.
Currently, the standard treatment for cardiac arrhythmias includes cardiac ablation with a catheter to destroy a small area of heart tissue that is causing rapid and irregular heartbeats. Catheters have been generally approved by the FDA for use in the treatment of specific cardiac arrhythmias, such as paroxysmal atrial fibrillation and ischemic ventricular tachycardia. Catheters vary in which of these cardiac arrhythmias the FDA has approved their use. There are currently no catheters that are indicated for the treatment of persistent atrial fibrillation.
This project explored the feasibility of generating evidence for label expansions on the use of cardiac ablation catheters to treat cardiac arrhythmias. The feasibility assessment examined if the NESTcc Data Network Collaborators capture the necessary data elements, and if the data are of appropriate quality (e.g., reliability and relevance) and there is a sufficient population for a representative sample to support a robust and rigorous study for label extensions.
- Click here for JAMIA 2021 publication
- Click here for BMJ Surgery, Interventions, & Health Technologies 2021 publication
- Click here for JAMA Network Open 2022 publication
Evaluation of Uptake of Unique Device Identifiers by Health Systems and Health Plans
Network Collaborator(s): Mayo Clinic; University of California at San Francisco; Yale New Haven Hospital; Mercy Health System
This project aims to characterize unique device identifier (UDI) adoption and use, including identification of barriers and facilitators, through site visits and key informant interviews at all NEST network collaborating health systems, as well as to survey a random sample of U.S. health systems of varying sizes and locations on these same issues.
Adoption and use of UDIs across healthcare systems can and should be used to enhance our ability to generate high-quality real-world evidence for regulated medical devices, including postmarket evaluations of safety and effectiveness. Furthermore, integration of UDIs at the point-of-care in structured electronic health records (EHRs) can and should support local and national safety surveillance and tracking efforts by facilities, clinicians, manufacturers, and regulators, including management of product recalls and purchasing. However, numerous high-level clinical, information technology, manufacturer, and support challenges have limited UDI implementation, including limited understanding and use for clinical purposes, information system interoperability challenges, and lack of collaboration among manufacturers, hospital leadership and regulators. The goal of this test case is to improve evidence generation from routinely collected real-world data sources, such as health system EHRs, and a critical component to achieving success is understanding the barriers and facilitators to UDI adoption and implementation with health system electronic information and EHRs.
This study implements a mixed methods approach to characterize UDI adoption and use through site visits and key informant interviews at all NEST network collaborating health systems, along with a survey of a random sample of U.S. health systems of varying sizes and locations. The results of this project will further advance the understanding around the availability of UDIs as well as gain a better understanding of UDI implementation in healthcare systems. Routine availability of UDIs would significantly enhance the ability to generate real-world evidence for regulatory decision making.