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Kim MJ, Song H, Koh Y, Lee H, Park HE, Choi SH, Yoon JW, Choi SY. Clonal hematopoiesis as a novel risk factor for type 2 diabetes mellitus in patients with hypercholesterolemia. Front Public Health 2023; 11:1181879. [PMID: 37457265 PMCID: PMC10345505 DOI: 10.3389/fpubh.2023.1181879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/31/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction Clonal hematopoiesis of indeterminate potential (CHIP) is associated with atherosclerosis and cardiovascular disease. It has been suggested that CHIP may be related to diabetes, so we investigated the association between CHIP and new-onset type 2 diabetes. Methods This study included 4,047 subjects aged >=40 years without diabetes. To detect CHIP, targeted gene sequencing of genomic DNA from peripheral blood cells was performed. The incidence of new-onset type 2 diabetes during the follow-up period was evaluated. Results Of the total subjects, 635 (15.7%) had CHIP. During the median follow-up of 5.1 years, the incidence of new-onset diabetes was significantly higher in CHIP carriers than in subjects without CHIP (11.8% vs. 9.1%, p = 0.039). In a univariate analysis, CHIP significantly increased the risk of new-onset diabetes (HR 1.32, 95% CI 1.02-1.70, p = 0.034), but in a multivariate analysis, it was not significant. The CHIP-related risk of new onset diabetes differed according to LDL cholesterol level. In the hyper-LDL cholesterolemia group, CHIP significantly increased the risk of diabetes (HR 1.64, 95% CI 1.09-2.47, p = 0.018), but it did not increase the risk in the non-hyper-LDL cholesterolemia group. The subjects with CHIP and hyper-LDL-cholesterolemia had approximately twice the risk of diabetes than subjects without CHIP and with low LDL cholesterol (HR 2.05, 95% CI 1.40-3.00, p < 0.001). Conclusion The presence of CHIP was a significant risk factor for new-onset type 2 diabetes, especially in subjects with high LDL cholesterol. These results show the synergism between CHIP and high LDL cholesterol as a high-risk factor for diabetes.
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Affiliation(s)
- Min Joo Kim
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Han Song
- Genome Opinion Incorporation, Seoul, Republic of Korea
| | - Youngil Koh
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Genome Opinion Incorporation, Seoul, Republic of Korea
| | - Heesun Lee
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyo Eun Park
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sung Hee Choi
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Ji Won Yoon
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Su-Yeon Choi
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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Dang J, Lal A, Montgomery A, Flurin L, Litell J, Gajic O, Rabinstein A. Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit. BMC Neurol 2023; 23:161. [PMID: 37085850 PMCID: PMC10121414 DOI: 10.1186/s12883-023-03192-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/30/2023] [Indexed: 04/23/2023] Open
Abstract
INTRODUCTION Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create a model of the Earth for climate research. While digital twins hold much promise for the neurocritical care unit, the question remains on how to best establish the rules that govern these models. This model will expand on our group's existing digital twin model for the treatment of sepsis. METHODS The authors of this project collaborated to create a Direct Acyclic Graph (DAG) and an initial series of 20 DELPHI statements, each with six accompanying sub-statements that captured the pathophysiology surrounding the management of acute ischemic strokes in the practice of Neurocritical Care (NCC). Agreement from a panel of 18 experts in the field of NCC was collected through a 7-point Likert scale with consensus defined a-priori by ≥ 80% selection of a 6 ("agree") or 7 ("strongly agree"). The endpoint of the study was defined as the completion of three separate rounds of DELPHI consensus. DELPHI statements that had met consensus would not be included in subsequent rounds of DELPHI consensus. The authors refined DELPHI statements that did not reach consensus with the guidance of de-identified expert comments for subsequent rounds of DELPHI. All DELPHI statements that reached consensus by the end of three rounds of DELPHI consensus would go on to be used to inform the construction of the digital twin model. RESULTS After the completion of three rounds of DELPHI, 93 (77.5%) statements reached consensus, 11 (9.2%) statements were excluded, and 16 (13.3%) statements did not reach a consensus of the original 120 DELPHI statements. CONCLUSION This descriptive study demonstrates the use of the DELPHI process to generate consensus among experts and establish a set of rules for the development of a digital twin model for use in the neurologic ICU. Compared to associative models of AI, which develop rules based on finding associations in datasets, digital twin AI created by the DELPHI process are easily interpretable models based on a current understanding of underlying physiology.
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Affiliation(s)
- Johnny Dang
- Department of Neurology, Cleveland Clinic, Cleveland, USA
| | - Amos Lal
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, USA.
| | | | - Laure Flurin
- Infectious Diseases Research Laboratory, Mayo Clinic, Rochester, USA
- Department of Critical Care, University Hospital of Guadeloupe, Guadeloupe, France
| | - John Litell
- Abbott Northwestern Emergency Critical Care, Minneapolis, USA
| | - Ognjen Gajic
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, USA
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Dang J, Lal A, Flurin L, James A, Gajic O, Rabinstein AA. Predictive modeling in neurocritical care using causal artificial intelligence. World J Crit Care Med 2021; 10:112-119. [PMID: 34316446 PMCID: PMC8291004 DOI: 10.5492/wjccm.v10.i4.112] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/17/2021] [Accepted: 07/02/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.
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Affiliation(s)
- Johnny Dang
- Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Laure Flurin
- Division of Clinical Microbiology, Mayo Clinic, Rochester, MN 55905, United States
| | - Amy James
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Ognjen Gajic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Alejandro A Rabinstein
- Department of Medicine, Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN 55905, United States
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Liu X, zhou Y, Zongrun W. Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big data. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2020. [DOI: 10.1016/j.ijinfomgt.2019.05.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Shao H, Yang S, Stoecker C, Fonseca V, Hong D, Shi L. Addressing Regional Differences in Diabetes Progression: Global Calibration for Diabetes Simulation Model. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2019; 22:1402-1409. [PMID: 31806197 PMCID: PMC9115837 DOI: 10.1016/j.jval.2019.08.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 07/25/2019] [Accepted: 08/08/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVES To develop a practical solution for modeling diabetes progression and account for the variations in risks of diabetes complications in different regions of the world, which is critical for model-based evaluations on the value of diabetes intervention across populations from different regions globally. METHODS A literature search was conducted to identify eligible clinical trials to support calibration. The Building, Relating, Assessing, and Validating Outcomes (BRAVO) model was employed to simulate diabetes complications using the baseline characteristics of each clinical trial cohort. We utilized regression methods to estimate regional variations across the United States, Europe, Asia, and other regions (eg, Latin America, Africa) in 6 outcomes: myocardial infarction (MI), congestive heart failure (CHF), stroke, angina, revascularization, and mortality. RESULTS Regional variations were detected in 4 outcomes. Compared with other regions, individuals from the United States had higher risks of MI (hazard ratio [HR] 1.64; 95% confidence interval [CI]1.41-1.91) and revascularization (HR 3.6; 95% CI 2.94-4.41). Individuals from Europe had a lower risk of stroke (HR 0.61; 95% CI 0.46-0.81), and individuals from other regions outside of the United States, Europe, and Asia had a lower risk of CHF (HR 0.18; 95% CI 0.06-0.58). Finally, the simulated outcomes were regressed on observed outcomes using an ordinary least squares model, with an intercept (0.026), slope (1.005), and R-squared value (0.789) indicating good prediction accuracy. CONCLUSION Recalibrating the BRAVO model's diabetes risk engine to account for regional differences shows improved prediction accuracy when the model is applied to multi-region populations commonly recruited for clinical trials.
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Affiliation(s)
- Hui Shao
- College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Shuang Yang
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Charles Stoecker
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Vivian Fonseca
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Dongzhe Hong
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Lizheng Shi
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA.
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Abstract
PURPOSE OF REVIEW A patient's prognosis and risk of adverse drug effects are important considerations for individualizing care of older patients with diabetes. This review summarizes the evidence for risk assessment and proposes approaches for clinicians in the context of current clinical guidelines. RECENT FINDINGS Diabetes guidelines vary in their recommendations for how life expectancy should be estimated and used to inform the selection of glycemic targets. Readily available prognostic tools may improve estimation of life expectancy but require validation among patients with diabetes. Treatment decisions based on prognosis are difficult for clinicians to communicate and for patients to understand. Determining hypoglycemia risk involves assessing major risk factors; models to synthesize these factors have been developed. Applying risk assessment to individualize diabetes care is complex and currently relies heavily on clinician judgment. More research is need to validate structured approaches to risk assessment and determine how to incorporate them into patient-centered diabetes care.
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Affiliation(s)
- Scott J Pilla
- Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Welch Center for Prevention, Epidemiology & Clinical Research, Baltimore, MD, USA.
| | - Nancy L Schoenborn
- Department of Medicine, Division of Geriatric Medicine and Gerontology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nisa M Maruthur
- Department of Medicine, Division of General Internal Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology & Clinical Research, Baltimore, MD, USA
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elbert S Huang
- Division of General Internal Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
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Jiang L, Wu G, Fang P, Xu Z, Tang Z. Development of clinical risk models for diabetic cardiovascular autonomic neuropathy in a Chinese population using logistic regression analysis. TRADITIONAL MEDICINE AND MODERN MEDICINE 2018. [DOI: 10.1142/s2575900018500076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background: We developed clinical risk models for predicting diabetic cardiovascular autonomic neuropathy (DCAN) in Chinese diabetic patients. Methods: A Chinese cohort of 455 diabetic participants underwent a short heart rate variability (HRV) test which was recruited between 2011 and 2013. Clinical risk models were developed that included independent and significant risk factors by using multiple variable stepwise regressions. These clinical risk models were tested in another independent cohort of Chinese individuals. Results: The clinical risk models included age, fasting plasma glucose, 2-h plasma blood glucose, triglycerides, resting HRs, and duration of diabetes mellitus. The area under the receiver-operating characteristic (ROC) curve of the study group was 0.794. In the model with the continuous variables, the area under the ROC curve was 0.810. A cutoff score of 12.54 which produced the optimal sensitivity (68.20%) and specificity (76.80%) and identified the percentage (35.77%) of the population that required subsequent testing. Conclusions: The clinical risk models showed high sensitivity and specificity for the prediction of DCAN in Chinese diabetic patients.
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Affiliation(s)
- Lin Jiang
- Health Management Center, Huashan Hospital, Fudan University, Shanghai, P. R. China
| | - Genlong Wu
- Qingpu Hospital of Traditional Chinese Medicine, Shanghai, P. R. China
| | - Ping Fang
- Department of Endocrinology and Metabolism, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Zhensheng Xu
- Health Management Center, Huashan Hospital, Fudan University, Shanghai, P. R. China
| | - Zihui Tang
- Department of Endocrinology and Metabolism, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, P. R. China
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Folse HJ, Mukherjee J, Sheehan JJ, Ward AJ, Pelkey RL, Dinh TA, Qin L, Kim J. Delays in treatment intensification with oral antidiabetic drugs and risk of microvascular and macrovascular events in patients with poor glycaemic control: An individual patient simulation study. Diabetes Obes Metab 2017; 19:1006-1013. [PMID: 28211604 DOI: 10.1111/dom.12913] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 02/02/2017] [Accepted: 02/14/2017] [Indexed: 11/30/2022]
Abstract
AIMS To use the Archimedes model to estimate the consequences of delays in oral antidiabetic drug (OAD) treatment intensification on glycaemic control and long-term outcomes at 5 and 20 years. MATERIALS AND METHODS Using real-world data, we modelled a cohort of hypothetical patients with glycated haemoglobin (HbA1c) ≥8%, on metformin, with no history of insulin use. The cohort included 3 strata based on the number of OADs taken at baseline. The first add-on in the intensification sequence was a sulphonylurea, next was a dipeptidyl peptidase-4 inhibitor, and last, a thiazolidinedione. The scenarios included either no delay or delay, based on observed and extrapolated times to intensification. RESULTS At 1 year, HbA1c was 6.8% for patients intensifying without delay, and 8.2% for those delaying intensification. For no delay vs delay, risks of major adverse cardiac events, myocardial infarction, heart failure and amputations were reduced by 18.0%, 25.0%, 13.7%, and 20.4%, respectively, at 5 years; severe hypoglycaemia risk, however, increased to 19% for the no delay scenario vs 12.5% for delay. At 20 years, the results showed similar trends to those at 5 years. CONCLUSIONS Timing of intensification of OAD therapy according to guideline recommendations led to greater reductions in HbA1c and lower risks of complications, but higher risks of hypoglycaemia than delaying intensification. These results highlight the potential impact of timely treatment intensification on long-term outcomes.
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Affiliation(s)
| | | | - John J Sheehan
- AstraZeneca Pharmaceuticals, Fort Washington, Pennsylvania
| | | | | | | | - Lei Qin
- AstraZeneca, One MedImmune Way, Gaithersburg, Maryland
| | - Jennifer Kim
- AstraZeneca, One MedImmune Way, Gaithersburg, Maryland
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Masconi KL, Echouffo-Tcheugui JB, Matsha TE, Erasmus RT, Kengne AP. Predictive modeling for incident and prevalent diabetes risk evaluation. Expert Rev Endocrinol Metab 2015; 10:277-284. [PMID: 30298773 DOI: 10.1586/17446651.2015.1015989] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
With half of individuals with diabetes undiagnosed worldwide and a projected 55% increase of the population with diabetes by 2035, the identification of undiagnosed and high-risk individuals is imperative. Multivariable diabetes risk prediction models have gained popularity during the past two decades. These have been shown to predict incident or prevalent diabetes through a simple and affordable risk scoring system accurately. Their development requires cohort or cross-sectional type studies with a variable combination, number and definition of included risk factors, with their performance chiefly measured by discrimination and calibration. Models can be used in clinical and public health settings. However, the impact of their use on outcomes in real-world settings needs to be evaluated before widespread implementation.
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Affiliation(s)
- Katya L Masconi
- a 1 Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa
- b 2 Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Justin Basile Echouffo-Tcheugui
- c 3 Hubert Department of Public Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- d 4 Department of Medicine, MedStar Health System, Baltimore, MD, USA
| | - Tandi E Matsha
- e 5 Department of Biomedical Technology, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
| | - Rajiv T Erasmus
- a 1 Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa
| | - Andre Pascal Kengne
- b 2 Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa
- f 6 Department of Medicine, University of Cape Town, Cape Town, South Africa
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Maglio PP, Sepulveda MJ, Mabry PL. Mainstreaming modeling and simulation to accelerate public health innovation. Am J Public Health 2014; 104:1181-6. [PMID: 24832426 PMCID: PMC4056212 DOI: 10.2105/ajph.2014.301873] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/26/2013] [Indexed: 11/04/2022]
Abstract
Dynamic modeling and simulation are systems science tools that examine behaviors and outcomes resulting from interactions among multiple system components over time. Although there are excellent examples of their application, they have not been adopted as mainstream tools in population health planning and policymaking. Impediments to their use include the legacy and ease of use of statistical approaches that produce estimates with confidence intervals, the difficulty of multidisciplinary collaboration for modeling and simulation, systems scientists' inability to communicate effectively the added value of the tools, and low funding for population health systems science. Proposed remedies include aggregation of diverse data sets, systems science training for public health and other health professionals, changing research incentives toward collaboration, and increased funding for population health systems science projects.
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Affiliation(s)
- Paul P Maglio
- Paul P. Maglio is with the School of Engineering, University of California, Merced, and IBM Research, Almaden, CA. Martin-J. Sepulveda is with Health Systems and Policy Research, IBM Research, Yorktown, NY. At the time of the study, Patricia L. Mabry was with the Office of Behavioral and Social Sciences Research, National Institutes of Health, Bethesda, MD and is now with the Office of Disease Prevention, National Institutes of Health, Rockville, MD. Patricia L. Mabry is also a guest editor for this theme issue
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Wilson KJ, Brown HS, Bastida E. Cost-effectiveness of a community-based weight control intervention targeting a low-socioeconomic-status Mexican-origin population. Health Promot Pract 2014; 16:101-8. [PMID: 24893680 DOI: 10.1177/1524839914537274] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION The objective of our study was to evaluate the cost-effectiveness of a community-based intervention designed to improve physical activity levels and dietary intake and to reduce diabetes risk in a largely Hispanic population residing along the U.S.-Mexico border. METHOD We forecasted disease outcomes, quality-adjusted life-years (QALYs) gained, and lifetime costs associated with actual and projected attainment of 2% and 5% weight loss taking a societal cost perspective. We extrapolated changes in beverage calorie consumption between baseline and 6-month follow-up to attain projected weight loss measures. Outcomes were projected 5, 10, and 20 years into the future and discounted at a 3.0% rate. RESULTS The incremental cost-effectiveness ratio was $57,430 and $61,893, respectively, per QALY gained when compared with usual care for the 2% and 5% weight loss scenarios. The intervention was particularly cost-effective for morbidly obese participants. Cost-effectiveness improves when using 3-year weight loss projections based on changes in sugar-sweetened beverage caloric consumption to $49,478 and $24,092 for the 2% and 5% weight loss scenarios. CONCLUSIONS This analysis demonstrates that a culturally sensitive community-based weight loss and maintenance intervention can be cost-effective even when healthy weight individuals participate.
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Affiliation(s)
| | - H Shelton Brown
- University of Texas Health Science Center Houston, Austin, TX, USA
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Sussman J, Vijan S, Hayward R. Using benefit-based tailored treatment to improve the use of antihypertensive medications. Circulation 2013; 128:2309-17. [PMID: 24190955 DOI: 10.1161/circulationaha.113.002290] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Current guidelines for prescribing antihypertensive medications focus on reaching specific blood pressure targets. We sought to determine whether antihypertensive medications could be used more effectively by a treatment strategy based on tailored estimates of cardiovascular disease events prevented. METHODS AND RESULTS We developed a nationally representative sample of American adults aged 30 to 85 years with no history of myocardial infarction, stroke, or severe congestive heart failure using the National Health and Nutrition Examination Survey III. We then created a simulation model to estimate the effects of 5 years of treatment with treat-to-target (treatment to specific blood pressure goals) and benefit-based tailored treatment (treatment based on estimated cardiovascular disease event reduction) approaches to antihypertensive medication management. All effect size estimates were derived directly from meta-analyses of randomized trials. We found that 55% of the overall population of 176 million Americans would be treated identically under the 2 treatment approaches. Benefit-based tailored treatment would prevent 900 000 more cardiovascular disease events and save 2.8 million more quality-adjusted life-years, despite using 6% fewer medications over 5 years. In the 45% of the population treated differently by the strategies, benefit-based tailored treatment would save 159 quality-adjusted life-years per 1000 treated versus 74 quality-adjusted life-years per 1000 treated by the treat-to-target approach. The findings were robust to sensitivity analyses. CONCLUSIONS We found that benefit-based tailored treatment was both more effective and required less antihypertensive medication than current guidelines based on treating to specific blood pressure goals.
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Affiliation(s)
- Jeremy Sussman
- Division of General Internal Medicine, University of Michigan (J.S., S.V., R.H.), and the Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI (J.S., S.V., R.H.)
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Abstract
Objective To determine if IndiGO individualized clinical guidelines could be implemented in routine practice and assess their effects on care and care experience. Methods Matched comparison observational design. IndiGO individualized guidelines, based on a biomathematical simulation model, were used in shared decision-making. Physicians and patients viewed risk estimates and tailored recommendations in a dynamic user interface and discussed them for 5–10 min. Outcome measures were prescribing and dispensing of IndiGO-recommended medications, changes in physiological markers and predicted 5-year risk of heart attack and stroke, and physician and patient perceptions. Results 489 patients using IndiGO were 4.9 times more likely to receive a statin prescription than were matched usual care controls (p=0.015). No effect was observed on prescribing of antihypertensive medications, but IndiGO-using patients were more likely to pick up at least one dispensing (p<0.05). No significant changes were observed in blood pressure or serum lipid levels. Predicted risk of heart attack or stroke decreased 1.6% among patients using IndiGO versus 1.0% among matched controls (p<0.01). Physician and patient experiences were positive to neutral. Limitations We could not assess the separate effects of individualized guidelines, user interface, and physician–patient discussions. Patient selection could have influenced results. The measure of risk reduction was not independent of the individualized guidelines. Conclusions IndiGO individualized clinical guidelines were successfully implemented in primary care and were associated with increases in the use of cardioprotective medications and reduction in the predicted risk of adverse events, suggesting that a larger trial could be warranted.
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Affiliation(s)
- Jim Bellows
- Kaiser Permanente, Care Management Institute, Oakland, California, USA
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Pagán JA, Carlson EK. Assessing long-term health and cost outcomes of patient-centered medical homes serving adults with poor diabetes control. J Prim Care Community Health 2013; 4:281-5. [PMID: 23799676 DOI: 10.1177/2150131913489885] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The patient-centered medical home (PCMH) is an integrated primary care delivery model particularly suited for patients with poor diabetes control. Although PCMH models targeting adults with diabetes have shown some early success, little is known about the long-term benefits of medical homes in terms of health and cost outcomes. The performance of a PCMH model in adults with poor diabetes control was assessed using simulated controlled trial data obtained from the Archimedes model of disease progression and health care utilization. Using the Cardio-Metabolic Risk data set, we compared health and cost outcomes over a 20-year period between adults with poor diabetes control (HbA1c >9%) receiving standard care and these same adults receiving care under a PCMH model with a 49% HbA1c intervention improvement rate at a per-beneficiary per-month care management cost of $20 per month. The results suggest that the PCMH model has the potential to not only reduce the proportion of the population with bilateral blindness, foot amputations, and myocardial infarctions-and the mortality rate-but it can also do so in a cost-effective manner ($7898 per quality-adjusted life year). The PCMH model is cost saving for the population 50 to 64 years old and it is particularly cost-effective for men ($883 per quality-adjusted life year). Moreover, these effects are relatively large for adults 30 to 49 years old (lower bilateral blindness and death rates), women (lower foot amputation and death rates), and men (lower bilateral blindness and myocardial infarction rates). The PCMH model has potential long-term benefits to both patients with poor diabetes control as well as health care systems and providers willing to invest in this health care delivery approach.
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Affiliation(s)
- José A Pagán
- University of North Texas Health Science Center, Fort Worth, TX, USA
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Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB. Model Transparency and Validation. Med Decis Making 2012; 32:733-43. [DOI: 10.1177/0272989x12454579] [Citation(s) in RCA: 342] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well it reproduces reality). This report describes recommendations for achieving transparency and validation, developed by a task force appointed by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM). Recommendations were developed iteratively by the authors. A nontechnical description should be made available to anyone—including model type and intended applications; funding sources; structure; inputs, outputs, other components that determine function, and their relationships; data sources; validation methods and results; and limitations. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing same problem), external validity (comparing model results to real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this paper contains a number of recommendations that were iterated among the authors, as well as the wider modeling task force jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.
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Affiliation(s)
- David M. Eddy
- Archimedes, Inc., San Francisco, CA (DE)
- School of Social and Community Medicine, University of Bristol, Bristol, UK (WH)
- United BioSource Corporation and McGill University, Montreal, Canada (JJC)
- University of Cincinnati, College of Medicine and Cincinnati Veterans Affairs Medical Center, Cincinnati, OH (JT)
- Center for Health Policy/Center for Primary Care and Outcomes Research, Stanford, CA (KM)
| | - William Hollingworth
- Archimedes, Inc., San Francisco, CA (DE)
- School of Social and Community Medicine, University of Bristol, Bristol, UK (WH)
- United BioSource Corporation and McGill University, Montreal, Canada (JJC)
- University of Cincinnati, College of Medicine and Cincinnati Veterans Affairs Medical Center, Cincinnati, OH (JT)
- Center for Health Policy/Center for Primary Care and Outcomes Research, Stanford, CA (KM)
| | - J. Jaime Caro
- Archimedes, Inc., San Francisco, CA (DE)
- School of Social and Community Medicine, University of Bristol, Bristol, UK (WH)
- United BioSource Corporation and McGill University, Montreal, Canada (JJC)
- University of Cincinnati, College of Medicine and Cincinnati Veterans Affairs Medical Center, Cincinnati, OH (JT)
- Center for Health Policy/Center for Primary Care and Outcomes Research, Stanford, CA (KM)
| | - Joel Tsevat
- Archimedes, Inc., San Francisco, CA (DE)
- School of Social and Community Medicine, University of Bristol, Bristol, UK (WH)
- United BioSource Corporation and McGill University, Montreal, Canada (JJC)
- University of Cincinnati, College of Medicine and Cincinnati Veterans Affairs Medical Center, Cincinnati, OH (JT)
- Center for Health Policy/Center for Primary Care and Outcomes Research, Stanford, CA (KM)
| | - Kathryn M. McDonald
- Archimedes, Inc., San Francisco, CA (DE)
- School of Social and Community Medicine, University of Bristol, Bristol, UK (WH)
- United BioSource Corporation and McGill University, Montreal, Canada (JJC)
- University of Cincinnati, College of Medicine and Cincinnati Veterans Affairs Medical Center, Cincinnati, OH (JT)
- Center for Health Policy/Center for Primary Care and Outcomes Research, Stanford, CA (KM)
| | - John B. Wong
- Archimedes, Inc., San Francisco, CA (DE)
- School of Social and Community Medicine, University of Bristol, Bristol, UK (WH)
- United BioSource Corporation and McGill University, Montreal, Canada (JJC)
- University of Cincinnati, College of Medicine and Cincinnati Veterans Affairs Medical Center, Cincinnati, OH (JT)
- Center for Health Policy/Center for Primary Care and Outcomes Research, Stanford, CA (KM)
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Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--7. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2012; 15:843-50. [PMID: 22999134 DOI: 10.1016/j.jval.2012.04.012] [Citation(s) in RCA: 295] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2012] [Accepted: 04/05/2012] [Indexed: 05/05/2023]
Abstract
Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well the model reproduces reality). This report describes recommendations for achieving transparency and validation developed by a taskforce appointed by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making. Recommendations were developed iteratively by the authors. A nontechnical description--including model type, intended applications, funding sources, structure, intended uses, inputs, outputs, other components that determine function, and their relationships, data sources, validation methods, results, and limitations--should be made available to anyone. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing the same problem), external validity (comparing model results with real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this article contains a number of recommendations that were iterated among the authors, as well as among the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.
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Morrissey JP, Lich KH, Price RA, Mandelblatt J. Computational modeling and multilevel cancer control interventions. J Natl Cancer Inst Monogr 2012; 2012:56-66. [PMID: 22623597 DOI: 10.1093/jncimonographs/lgs014] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This chapter presents an overview of computational modeling as a tool for multilevel cancer care and intervention research. Model-based analyses have been conducted at various "beneath the skin" or biological scales as well as at various "above the skin" or socioecological levels of cancer care delivery. We review the basic elements of computational modeling and illustrate its applications in four cancer control intervention areas: tobacco use, colorectal cancer screening, cervical cancer screening, and racial disparities in access to breast cancer care. Most of these models have examined cancer processes and outcomes at only one or two levels. We suggest ways these models can be expanded to consider interactions involving three or more levels. Looking forward, a number of methodological, structural, and communication barriers must be overcome to create useful computational models of multilevel cancer interventions and population health.
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Affiliation(s)
- Joseph P Morrissey
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Rm 126, 725 Martin Luther King Jr Blvd, Chapel Hill, NC 27599-7590, USA.
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Rhodes ET, Prosser LA, Hoerger TJ, Lieu T, Ludwig DS, Laffel LM. Estimated morbidity and mortality in adolescents and young adults diagnosed with Type 2 diabetes mellitus. Diabet Med 2012; 29:453-63. [PMID: 22150528 DOI: 10.1111/j.1464-5491.2011.03542.x] [Citation(s) in RCA: 111] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AIMS To estimate remaining life expectancy (RLE), quality-adjusted life expectancy (QALE), causes of death and lifetime cumulative incidence of microvascular/macrovascular complications of diabetes for youths diagnosed with Type 2 diabetes. METHODS A Markov-like computer model simulated the life course for a hypothetical cohort of adolescents/young adults in the USA, aged 15-24 years, newly diagnosed with Type 2 diabetes following either conventional or intensive treatment based on the UK Prospective Diabetes Study. Outcomes included RLE, discounted QALE in quality-adjusted life years (QALYs), cumulative incidence of microvascular/macrovascular complications and causes of death. RESULTS Compared with a mean RLE of 58.6 years for a 20-year-old in the USA without diabetes, conventional treatment produced an average RLE of 43.09 years and 22.44 discounted QALYs. Intensive treatment afforded an incremental 0.98 years and 0.44 discounted QALYs. Intensive treatment led to lower lifetime cumulative incidence of all microvascular complications and lower mortality from microvascular complications (e.g. end-stage renal disease (ESRD) death 19.4% vs. 25.2%). Approximately 5% with both treatments had ESRD within 25 years. Lifetime cumulative incidence of coronary heart disease (CHD) increased with longer RLE and greater severity of CHD risk factors. Incorporating disutility (loss in health-related quality of life) of intensive treatment resulted in net loss of QALYs. CONCLUSIONS Adolescents/young adults with Type 2 diabetes lose approximately 15 years from average RLE and may experience severe, chronic complications of Type 2 diabetes by their 40s. The net clinical benefit of intensive treatment may be sensitive to preferences for treatment. A comprehensive management plan that includes early and aggressive control of cardiovascular risk factors is likely needed to reduce lifetime risk of CHD.
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Affiliation(s)
- E T Rhodes
- Division of Endocrinology, Children's Hospital Boston, Boston, MA 02115, USA.
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Brown HS, Wilson KJ, Pagán JA, Arcari CM, Martinez M, Smith K, Reininger B. Cost-effectiveness analysis of a community health worker intervention for low-income Hispanic adults with diabetes. Prev Chronic Dis 2012; 9:E140. [PMID: 22916995 PMCID: PMC3475531 DOI: 10.5888/pcd9.120074] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION The objective of our study was to estimate the long-term cost-effectiveness of a lifestyle modification program led by community health workers (CHWs) for low-income Hispanic adults with type 2 diabetes. METHODS We forecasted disease outcomes, quality-adjusted life years (QALYs) gained, and lifetime costs associated with attaining different hemoglobin A1c (A1c) levels. Outcomes were projected 20 years into the future and discounted at a 3.0% rate. Sensitivity analyses were conducted to assess the extent to which our results were dependent on assumptions related to program effectiveness, projected years, discount rates, and costs. RESULTS The incremental cost-effectiveness ratio of the intervention ranged from $10,995 to $33,319 per QALY gained when compared with usual care. The intervention was particularly cost-effective for adults with high glycemic levels (A1c > 9%). The results are robust to changes in multiple parameters. CONCLUSION The CHW program was cost-effective. This study adds to the evidence that culturally sensitive lifestyle modification programs to control diabetes can be a cost-effective way to improve health among Hispanics with diabetes, particularly among those with high A1c levels.
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Affiliation(s)
- H Shelton Brown
- University of Texas Health Science Center School of Public Health, Austin, TX 78701, USA
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Abstract
OBJECTIVE To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. DESIGN Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. DATA SOURCES Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. DATA EXTRACTION Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. RESULTS 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as "simple" or "easily implemented," although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. CONCLUSION Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk "hotspots" for targeted public health interventions.
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Affiliation(s)
- Douglas Noble
- Centre for Primary Care and Public Health, Barts and the London School of Medicine and Dentistry, London E1 2AT, UK.
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Abdul-Ghani MA, Abdul-Ghani T, Stern MP, Karavic J, Tuomi T, Bo I, Defronzo RA, Groop L. Two-step approach for the prediction of future type 2 diabetes risk. Diabetes Care 2011; 34:2108-12. [PMID: 21788628 PMCID: PMC3161295 DOI: 10.2337/dc10-2201] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2010] [Accepted: 06/20/2011] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To develop a model for the prediction of type 2 diabetes mellitus (T2DM) risk on the basis of a multivariate logistic model and 1-h plasma glucose concentration (1-h PG). RESEARCH DESIGN AND METHODS The model was developed in a cohort of 1,562 nondiabetic subjects from the San Antonio Heart Study (SAHS) and validated in 2,395 nondiabetic subjects in the Botnia Study. A risk score on the basis of anthropometric parameters, plasma glucose and lipid profile, and blood pressure was computed for each subject. Subjects with a risk score above a certain cut point were considered to represent high-risk individuals, and their 1-h PG concentration during the oral glucose tolerance test was used to further refine their future T2DM risk. RESULTS We used the San Antonio Diabetes Prediction Model (SADPM) to generate the initial risk score. A risk-score value of 0.065 was found to be an optimal cut point for initial screening and selection of high-risk individuals. A 1-h PG concentration >140 mg/dL in high-risk individuals (whose risk score was >0.065) was the optimal cut point for identification of subjects at increased risk. The two cut points had 77.8, 77.4, and 44.8% (for the SAHS) and 75.8, 71.6, and 11.9% (for the Botnia Study) sensitivity, specificity, and positive predictive value, respectively, in the SAHS and Botnia Study. CONCLUSIONS A two-step model, based on the combination of the SADPM and 1-h PG, is a useful tool for the identification of high-risk Mexican-American and Caucasian individuals.
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Vigneault CB, Asch WS, Dahl NK, Bia MJ. Should Living Kidney Donor Candidates with Impaired Fasting Glucose Donate? Clin J Am Soc Nephrol 2011; 6:2054-9. [DOI: 10.2215/cjn.03370411] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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A proposal to speed translation of healthcare research into practice: dramatic change is needed. Am J Prev Med 2011; 40:637-44. [PMID: 21565657 DOI: 10.1016/j.amepre.2011.02.023] [Citation(s) in RCA: 238] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2010] [Revised: 01/11/2011] [Accepted: 02/03/2011] [Indexed: 11/24/2022]
Abstract
Efficacy trials have generated interventions to improve health behaviors and biomarkers. However, these efforts have had limited impact on practice and policy. It is suggested that key methodologic and contextual issues have contributed to this state of affairs. Current research paradigms generally have not provided the answers needed for more probable and more rapid translation. A major shift is proposed to produce research with more rapid clinical, public health, and policy impact.
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Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev 2011; 33:46-62. [PMID: 21622851 PMCID: PMC3132807 DOI: 10.1093/epirev/mxq019] [Citation(s) in RCA: 191] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Trials have demonstrated the preventability of type 2 diabetes through lifestyle modifications or drugs in people with impaired glucose tolerance. However, alternative ways of identifying people at risk of developing diabetes are required. Multivariate risk scores have been developed for this purpose. This article examines the evidence for performance of diabetes risk scores in adults by 1) systematically reviewing the literature on available scores and 2) their validation in external populations; and 3) exploring methodological issues surrounding the development, validation, and comparison of risk scores. Risk scores show overall good discriminatory ability in populations for whom they were developed. However, discriminatory performance is more heterogeneous and generally weaker in external populations, which suggests that risk scores may need to be validated within the population in which they are intended to be used. Whether risk scores enable accurate estimation of absolute risk remains unknown; thus, care is needed when using scores to communicate absolute diabetes risk to individuals. Several risk scores predict diabetes risk based on routine noninvasive measures or on data from questionnaires. Biochemical measures, in particular fasting plasma glucose, can improve prediction of such models. On the other hand, usefulness of genetic profiling currently appears limited.
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Affiliation(s)
- Brian Buijsse
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
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Abstract
BACKGROUND A small number of risk scores for the risk of developing diabetes have been produced but none has yet been widely used in clinical practice in the UK. The aim of this study is to independently evaluate the performance of QDSCORE(®) for predicting the 10-year risk of developing diagnosed Type 2 diabetes in a large independent UK cohort of patients from general practice. METHODS A prospective cohort study of 2.4 million patients (13.6 million person years) aged between 25 and 79 years from 364 practices from the UK contributing to The Health Improvement Network (THIN) database between 1 January 1993 and 20 June 2008. RESULTS QDSCORE(®) showed good performance data when evaluated on a large external data set. The score is well calibrated with reasonable agreement between observed and predicted outcomes. There is a slight underestimation of risk in both men and women aged 60 years and above, although the magnitude of underestimation is small. The ability of the score to differentiate between those who develop diabetes and those who do not is good, with values for the area under the receiver operating characteristic curve exceeding 0.8 for both men and women. Performance data in this external validation are consistent with those reported in the development and internal validation of the risk score. CONCLUSIONS QDSCORE(®) has shown to be a useful tool to predict the 10-year risk of developing Type 2 diabetes in the UK.
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Affiliation(s)
- G S Collins
- Centre for Statistics in Medicine, University of Oxford, Oxford UK.
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Sussman JB, Vijan S, Choi H, Hayward RA. Individual and population benefits of daily aspirin therapy: a proposal for personalizing national guidelines. Circ Cardiovasc Qual Outcomes 2011; 4:268-75. [PMID: 21487091 DOI: 10.1161/circoutcomes.110.959239] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Clinical practice guidelines that help clinicians and patients to understand the magnitude of expected individual risks and benefits would help with patient-centered decision-making and prioritization of care. We assessed the net benefit from taking daily aspirin to estimate the individual and public health implications of a more individualized decision-making approach. METHODS AND RESULTS We used data from the National Health and Nutrition Examination Survey representing all US persons aged 30 to 85 years with no history of myocardial infarction and applied a Markov model based on randomized evidence and published literature to estimate lifetime effects of aspirin treatment in quality-adjusted life years (QALYs). We found that treatment benefit varies greatly by an individual's cardiovascular disease (CVD) risk. Almost all adults have fewer major clinical events on aspirin, but for most, events prevented would be so rare that even a very small distaste for aspirin use would make treatment inappropriate. With minimal dislike of aspirin use (disutility, 0.005 QALY per year), only those with a 10-year cardiac event risk >6.1% would have a net benefit. A disutility of 0.01 QALY moves this benefit cut point to 10.6%. Multiple factors altered the absolute benefit of aspirin, but the strong relationship between CVD risk and magnitude of benefit was robust. CONCLUSIONS The benefits of aspirin therapy depend substantially on an individual's risk of CVD and adverse treatment effects. Understanding who benefits from aspirin use and how much can help clinicians and patients to develop a more patient-centered approach to preventive therapy.
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Affiliation(s)
- Jeremy B Sussman
- Department of Veterans Affairs, VA Health Service Research and Development Center of Excellence, VA Ann Arbor Healthcare System, Ann Arbor, MI 48109, USA.
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Kahn R. Guidelines: we'll always need them, we sometimes dislike them, and we have to make them better. Diabetologia 2010; 53:2280-4. [PMID: 20734022 DOI: 10.1007/s00125-010-1885-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2010] [Accepted: 07/28/2010] [Indexed: 10/19/2022]
Affiliation(s)
- R Kahn
- Department of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA.
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Using mechanistic models to simulate comparative effectiveness trials of therapy and to estimate long-term outcomes in HIV care. Med Care 2010; 48:S90-5. [PMID: 20473184 DOI: 10.1097/mlr.0b013e3181e2b744] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND In HIV care, it is difficult to decide when to initiate therapy, which drugs to use for initial treatment, and which drugs to use if drug resistance develops. With hundreds of possible drug regimens available and variable patterns of drug resistance, randomized controlled trials cannot answer all HIV treatment decisions. Mechanistic models of HIV infection can be used to conduct virtual therapeutic trials with the goal of predicting outcomes, some of which are long-term and may not fall within the time frame of a typical therapeutic trial. METHODS We used a previously developed and validated model of HIV infection to replicate 2 arms of an HIV initial treatment trial (ACTG A5142) and predict long-term outcomes. The model incorporated data about biologic processes involved in the development of drug resistance. RESULTS The model reproduced the proportion that developed AIDS (0.04 and 0.05 for the efavirenz arm and lopinavir arms, respectively, vs. 0.04 and 0.06 for the trial), the development of virologic failure (0.27 and 0.33 for the Efavirenz arm and lopinavir arms, respectively, vs. 0.24 and 0.37 for the trial), and drug resistance. The hazard ratio for the time to treatment failure, a combination of resistance and other causes (0.96 for the model vs. 0.75 for the trial; 95% confidence interval, 0.57-0.98), and changes in CD4 cell count, were less accurate. The model estimated longer-term life expectancy, quality-adjusted life expectancy, and HIV-related deaths. CONCLUSIONS Mechanistic models of HIV infections have the potential to be useful in comparative effectiveness research.
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Affiliation(s)
- Guy Rutten
- University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, Netherlands.
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Association of a diabetes risk score with risk of myocardial infarction, stroke, specific types of cancer, and mortality: a prospective study in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Eur J Epidemiol 2009; 24:281-8. [DOI: 10.1007/s10654-009-9338-7] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2008] [Accepted: 03/23/2009] [Indexed: 01/01/2023]
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Current literature in diabetes. Diabetes Metab Res Rev 2009; 25:i-x. [PMID: 19219862 DOI: 10.1002/dmrr.918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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