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Underberg J, Toth PP, Rodriguez F. LDL-C target attainment in secondary prevention of ASCVD in the United States: barriers, consequences of nonachievement, and strategies to reach goals. Postgrad Med 2022; 134:752-762. [PMID: 36004573 DOI: 10.1080/00325481.2022.2117498] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death in the United States. Elevated low-density lipoprotein cholesterol (LDL-C) is a major causal risk factor for ASCVD. Current evidence overwhelmingly demonstrates that lowering LDL-C reduces the risk of secondary cardiovascular events in patients with previous myocardial infarction or stroke. There is no lower limit for LDL-C: large, randomized studies and meta-analyses have found continuous benefit and no safety concerns in patients achieving LDL-C levels <25 mg/dL. As 'Time is plaque' in patients with ASCVD, early, sustained reductions in LDL-C are critical to slow or halt disease progression. However, despite use of lipid-lowering medications, <30% of patients with ASCVD achieve guideline-recommended reductions in LDL-C, resulting in a substantial societal burden of preventable cardiovascular events and early mortality. LDL-C goals are not met due to several factors: lipid-lowering therapy is not initiated and intensified as directed by clinical guidelines (clinical inertia); most patients do not adhere to prescribed medications; and high-risk patients are frequently denied access to add-on therapies by their insurance providers. Promoting patient and clinician education, multidisciplinary collaboration, and other interventions may help to overcome these barriers. Ultimately, achieving population-level guideline-recommended reductions in LDL-C will require a collaborative effort from patients, clinicians, relevant professional societies, drug manufacturers, and payers.
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Affiliation(s)
| | - Peter P Toth
- Cicarrone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
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3
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Scheinker D, Prahalad P, Johari R, Maahs DM, Majzun R. A New Technology-Enabled Care Model for Pediatric Type 1 Diabetes. NEJM CATALYST INNOVATIONS IN CARE DELIVERY 2022; 3:10.1056/CAT.21.0438. [PMID: 36544715 PMCID: PMC9767424 DOI: 10.1056/cat.21.0438] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
In July 2018, pediatric type 1 diabetes (T1D) care at Stanford suffered many of the problems that plague U.S. health care. Patient outcomes lagged behind those of peer European nations, care was delivered primarily on a fixed cadence rather than as needed, continuous glucose monitors (CGMs) were largely unavailable for individuals with public insurance, and providers' primary access to CGM data was through long printouts. Stanford developed a new technology-enabled, telemedicine-based care model for patients with newly diagnosed T1D. They developed and deployed Timely Interventions for Diabetes Excellence (TIDE) to facilitate as-needed patient contact with the partially automated analysis of CGM data and used philanthropic funding to facilitate full access to CGM technology for publicly insured patients, for whom CGM is not readily available in California. A study of the use of CGM for patients with new-onset T1D (pilot Teamwork, Targets, and Technology for Tight Control [4T] study), which incorporated the use of TIDE, was associated with a 0.5%-point reduction in hemoglobin A1c compared with historical controls and an 86% reduction in screen time for providers reviewing patient data. Based on this initial success, Stanford expanded the use of TIDE to a total of 300 patients, including many outside the pilot 4T study, and made TIDE freely available as open-source software. Next steps include expanding the use of TIDE to support the care of approximately 1,000 patients, improving TIDE and the associated workflows to scale their use to more patients, incorporating data from additional sensors, and partnering with other institutions to facilitate their deployment of this care model.
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Affiliation(s)
- David Scheinker
- Associate Professor, Pediatrics, Stanford University, Stanford, California, USA,Executive Director, Lucile Packard Children’s Hospital Stanford, Palo Alto, California, USA,Faculty, Clinical Excellence Research Center, Stanford University, California, USA
| | - Priya Prahalad
- Associate Professor, Pediatrics, Stanford University, Stanford, California, USA
| | - Ramesh Johari
- Professor, Management Science and Engineering, Stanford University, Stanford, California, USA
| | - David M. Maahs
- Professor, Pediatrics, Stanford University, Stanford, California, USA
| | - Rick Majzun
- Chief Operating Officer, Lucile Packard Children’s Hospital Stanford, Palo Alto, California, USA
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4
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Scheinker D, Gu A, Grossman J, Ward A, Ayerdi O, Miller D, Leverenz J, Hood K, Lee MY, Maahs DM, Prahalad P. Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: A Prospective Evaluation in Clinical Practice (Preprint). JMIR Diabetes 2021; 7:e27284. [PMID: 35666570 PMCID: PMC9210201 DOI: 10.2196/27284] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 05/19/2021] [Accepted: 02/22/2022] [Indexed: 01/04/2023] Open
Abstract
Background The use of continuous glucose monitors (CGMs) is recommended as the standard of care by the American Diabetes Association for individuals with type 1 diabetes (T1D). Few hardware-agnostic, open-source, whole-population tools are available to facilitate the use of CGM data by clinicians such as physicians and certified diabetes educators. Objective This study aimed to develop a tool that identifies patients appropriate for contact using an asynchronous message through electronic medical records while minimizing the number of patients reviewed by a certified diabetes educator or physician using the tool. Methods We used consensus guidelines to develop timely interventions for diabetes excellence (TIDE), an open-source hardware-agnostic tool to analyze CGM data to identify patients with deteriorating glucose control by generating generic flags (eg, mean glucose [MG] >170 mg/dL) and personalized flags (eg, MG increased by >10 mg/dL). In a prospective 7-week study in a pediatric T1D clinic, we measured the sensitivity of TIDE in identifying patients appropriate for contact and the number of patients reviewed. We simulated measures of the workload generated by TIDE, including the average number of time in range (TIR) flags per patient per review period, on a convenience sample of eight external data sets, 6 from clinical trials and 2 donated by research foundations. Results Over the 7 weeks of evaluation, the clinical population increased from 56 to 64 patients. The mean sensitivity was 99% (242/245; SD 2.5%), and the mean reduction in the number of patients reviewed was 42.6% (182/427; SD 10.9%). The 8 external data sets contained 1365 patients with 30,017 weeks of data collected by 7 types of CGMs. The rates of generic and personalized TIR flags per patient per review period were, respectively, 0.15 and 0.12 in the data set with the lowest average MG (141 mg/dL) and 0.95 and 0.22 in the data set with the highest average MG (207 mg/dL). Conclusions TIDE is an open-source hardware-agnostic tool for personalized analysis of CGM data at the clinical population scale. In a pediatric T1D clinic, TIDE identified 99% of patients appropriate for contact using an asynchronous message through electronic medical records while reducing the number of patients reviewed by certified diabetes care and education specialists by 43%. For each of the 8 external data sets, simulation of the use of TIDE produced fewer than 0.25 personalized TIR flags per patient per review period. The use of TIDE to support telemedicine-based T1D care may facilitate sensitive and efficient guideline-based population health management.
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Affiliation(s)
- David Scheinker
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Lucile Packard Children's Hospital, Stanford University, Stanford, CA, United States
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
| | - Angela Gu
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Joshua Grossman
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Andrew Ward
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Oseas Ayerdi
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Daniel Miller
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Jeannine Leverenz
- Lucile Packard Children's Hospital, Stanford University, Stanford, CA, United States
| | - Korey Hood
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
| | - Ming Yeh Lee
- Lucile Packard Children's Hospital, Stanford University, Stanford, CA, United States
| | - David M Maahs
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
- Department of Health Research and Policy, Stanford University, Stanford, CA, United States
| | - Priya Prahalad
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
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5
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Lauffenburger JC, Isaac T, Trippa L, Keller P, Robertson T, Glynn RJ, Sequist TD, Kim DH, Fontanet CP, Castonguay EWB, Haff N, Barlev RA, Mahesri M, Gopalakrishnan C, Choudhry NK. Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol. Implement Sci 2021; 16:9. [PMID: 33413494 PMCID: PMC7792313 DOI: 10.1186/s13012-020-01078-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 12/22/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The prescribing of high-risk medications to older adults remains extremely common and results in potentially avoidable health consequences. Efforts to reduce prescribing have had limited success, in part because they have been sub-optimally timed, poorly designed, or not provided actionable information. Electronic health record (EHR)-based tools are commonly used but have had limited application in facilitating deprescribing in older adults. The objective is to determine whether designing EHR tools using behavioral science principles reduces inappropriate prescribing and clinical outcomes in older adults. METHODS The Novel Uses of Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) project uses a two-stage, 16-arm adaptive randomized pragmatic trial with a "pick-the-winner" design to identify the most effective of many potential EHR tools among primary care providers and their patients ≥ 65 years chronically using benzodiazepines, sedative hypnotic ("Z-drugs"), or anticholinergics in a large integrated delivery system. In stage 1, we randomized providers and their patients to usual care (n = 81 providers) or one of 15 EHR tools (n = 8 providers per arm) designed using behavioral principles including salience, choice architecture, or defaulting. After 6 months of follow-up, we will rank order the arms based upon their impact on the trial's primary outcome (for both stages): reduction in inappropriate prescribing (via discontinuation or tapering). In stage 2, we will randomize (a) stage 1 usual care providers in a 1:1 ratio to one of the up to 5 most promising stage 1 interventions or continue usual care and (b) stage 1 providers in the unselected arms in a 1:1 ratio to one of the 5 most promising interventions or usual care. Secondary and tertiary outcomes include quantities of medication prescribed and utilized and clinically significant adverse outcomes. DISCUSSION Stage 1 launched in October 2020. We plan to complete stage 2 follow-up in December 2021. These results will advance understanding about how behavioral science can optimize EHR decision support to improve prescribing and health outcomes. Adaptive trials have rarely been used in implementation science, so these findings also provide insight into how trials in this field could be more efficiently conducted. TRIAL REGISTRATION Clinicaltrials.gov ( NCT04284553 , registered: February 26, 2020).
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Affiliation(s)
- Julie C Lauffenburger
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA. .,Center for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA.
| | | | - Lorenzo Trippa
- Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Punam Keller
- Tuck School of Business, Dartmouth College, Hanover, NH, USA
| | | | - Robert J Glynn
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA
| | - Thomas D Sequist
- Division of General Internal Medicine and Department of Health Care Policy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Dae H Kim
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA.,Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA
| | - Constance P Fontanet
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA.,Center for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA
| | | | - Nancy Haff
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA.,Center for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA
| | - Renee A Barlev
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA.,Center for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA
| | - Mufaddal Mahesri
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA
| | - Chandrashekar Gopalakrishnan
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA
| | - Niteesh K Choudhry
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA.,Center for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA
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