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Bravata DM, Myers LJ, Homoya B, Miech EJ, Rattray NA, Perkins AJ, Zhang Y, Ferguson J, Myers J, Cheatham AJ, Murphy L, Giacherio B, Kumar M, Cheng E, Levine DA, Sico JJ, Ward MJ, Damush TM. The protocol-guided rapid evaluation of veterans experiencing new transient neurological symptoms (PREVENT) quality improvement program: rationale and methods. BMC Neurol 2019; 19:294. [PMID: 31747879 PMCID: PMC6865042 DOI: 10.1186/s12883-019-1517-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 10/28/2019] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Transient ischemic attack (TIA) patients are at high risk of recurrent vascular events; timely management can reduce that risk by 70%. The Protocol-guided Rapid Evaluation of Veterans Experiencing New Transient Neurological Symptoms (PREVENT) developed, implemented, and evaluated a TIA quality improvement (QI) intervention aligned with Learning Healthcare System principles. METHODS This stepped-wedge trial developed, implemented and evaluated a provider-facing, multi-component intervention to improve TIA care at six facilities. The unit of analysis was the medical center. The intervention was developed based on benchmarking data, staff interviews, literature, and electronic quality measures and included: performance data, clinical protocols, professional education, electronic health record tools, and QI support. The effectiveness outcome was the without-fail rate: the proportion of patients who receive all processes of care for which they are eligible among seven processes. The implementation outcomes were the number of implementation activities completed and final team organization level. The intervention effects on the without-fail rate were analyzed using generalized mixed-effects models with multilevel hierarchical random effects. Mixed methods were used to assess implementation, user satisfaction, and sustainability. DISCUSSION PREVENT advanced three aspects of a Learning Healthcare System. Learning from Data: teams examined and interacted with their performance data to explore hypotheses, plan QI activities, and evaluate change over time. Learning from Each Other: Teams participated in monthly virtual collaborative calls. Sharing Best Practices: Teams shared tools and best practices. The approach used to design and implement PREVENT may be generalizable to other clinical conditions where time-sensitive care spans clinical settings and medical disciplines. TRIAL REGISTRATION clinicaltrials.gov: NCT02769338 [May 11, 2016].
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Affiliation(s)
- D M Bravata
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA.
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA.
- Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA.
- Regenstrief Institute, Indianapolis, IN, USA.
| | - L J Myers
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA
- Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Regenstrief Institute, Indianapolis, IN, USA
| | - B Homoya
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA
| | - E J Miech
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA
- Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Regenstrief Institute, Indianapolis, IN, USA
| | - N A Rattray
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA
- Regenstrief Institute, Indianapolis, IN, USA
| | - A J Perkins
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Y Zhang
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA
| | - J Ferguson
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA
| | - J Myers
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA
| | - A J Cheatham
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA
| | - L Murphy
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA
| | - B Giacherio
- Office of Healthcare Transformation (OHT), Veterans Health Administration (VHA), Washington, DC, USA
| | - M Kumar
- Office of Healthcare Transformation (OHT), Veterans Health Administration (VHA), Washington, DC, USA
| | - E Cheng
- Department of Neurology, VA Greater Los Angeles Healthcare System, California, Los Angeles, USA
- Department of Neurology, David Geffen School of Medicine, University of California at Los Angeles, California, Los Angeles, USA
| | - D A Levine
- Department of Internal Medicine and Neurology and Institute for Health Policy and Innovation, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - J J Sico
- Clinical Epidemiology Research Center and Neurology Service, VA Connecticut Healthcare System, West Haven, CT, USA
- Departments of Internal Medicine and Neurology and Center for Neuroepidemiology and Clinical Neurological Research, Yale School of Medicine, New Haven, CT, USA
| | - M J Ward
- VA Tennessee Valley Healthcare System, Nashville, TN, USA
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - T M Damush
- Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Precision Monitoring to Transform Care (PRISM) Quality Enhancement Research Initiative (QUERI), Indianapolis, USA
- VA HSR&D Center for Health Information and Communication (CHIC), Richard L. Roudebush VA Medical Center, HSR&D Mail Code 11H, 1481 West 10th Street, Indianapolis, IN, 46202, USA
- Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Regenstrief Institute, Indianapolis, IN, USA
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Trivedi V, Zinchuk A, Qin L, Bravata DM, Strohl KP, Selim BJ, Yaggi HK. 0574 A Composite Model of Commonly Derived Polysomnographic Variables Predicts Risk of Cardiovascular Outcomes Better than the Apnea Hypopnea Index Alone. Sleep 2018. [DOI: 10.1093/sleep/zsy061.573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- V Trivedi
- Yale New Haven Hospital, New Haven, CT
| | - A Zinchuk
- Yale New Haven Hospital, New Haven, CT
| | - L Qin
- Yale New Haven Hospital, New Haven, CT
| | - D M Bravata
- Indiana University Center for Health Services, Indianapolis, IN
| | - K P Strohl
- Case Western Reserve University, Cleveland, OH
| | | | - H K Yaggi
- Yale New Haven Hospital, New Haven, CT
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Lenet AS, Stahl SM, Guenther D, Ferguson J, Lightner N, Aichinger J, Miech EJ, Bravata DM. 0524 THE INDIANA TELEMONITORING TO OPTIMIZE USE OF CPAP AT HOME PROGRAM. Sleep 2017. [DOI: 10.1093/sleepj/zsx050.523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Schmid AA, Ofner S, Shorr RI, Williams LS, Bravata DM. Bleeding risk, physical functioning and non-use of anticoagulation among patients with stroke and atrial fibrillation. QJM 2015; 108:189-96. [PMID: 25174049 DOI: 10.1093/qjmed/hcu176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is common among people with stroke. Anticoagulation medications can be used to manage the deleterious impact of AF after stroke, however, may not be prescribed due to concerns about post-stroke falls and decreased functioning. Thus, the purpose of this study was to identify, among people with stroke and AF, predictors of anticoagulation prescription at hospital discharge. METHODS This is a secondary analysis of a retrospective cohort study of data retrieved via medical records, including National Institutes of Health Stroke Scale score, Functional Independence Measure (FIM) motor score (motor or physical function), ambulation on second day of hospitalization, Morse Falls Scale (fall risk) and HAS-BLED score (Hypertension; Abnormal renal and liver function; Stroke; Bleeding; Labile INRs; Elderly >65; and Drugs or alcohol). Data analyses included bivariate comparisons between people with and without anticoagulation at discharge. Logistic-regression modeling was used to assess predictors of discharge anticoagulation. RESULTS There were 334 subjects included in the analyses, whose average age was 75 years old. Anticoagulation was prescribed at discharge for 235 (70%) of patients. In the adjusted regression analyses, only the FIM motor score (adjusted OR = 1.015, 95% CI 1.001-1.028) and the HAS-BLED score (adjusted OR = 0.36, 95% CI 0.22-0.58) were significantly associated with anticoagulation prescription at discharge. CONCLUSION It appears that in this sample, post-stroke anticoagulation decisions appear to be made based on clinical factors associated with bleed risk and motor deficits or physical functioning. However, opportunities may exist for improving clinician documentation of specific reasoning for non-anticoagulation prescription.
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Affiliation(s)
- A A Schmid
- From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Department of Epidemiology, University of Florida, Gainesville, FL, Roudebush Veterans Administration (VA) Medical Center, Health Services Research and Development (HSR&D) Center for Health Information and Communication, Indianapolis, IN, Indiana University Center for Aging Research, Indianapolis, IN, Regenstrief Institute, Inc., Indianapolis, IN, Department of Neurology, Indiana University, School of Medicine and Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, USA From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Department of Epidemiology, University of Florida, Gainesville, FL, Roudebush Veterans Administration (VA) Medical Center, Health Services Research and Development (HSR&D) Center for Health Information and Communication, Indianapolis, IN, Indiana University Center for Aging Research, Indianapolis, IN, Regenstrief Institute, Inc., Indianapolis, IN, Department of Neurology, Indiana University, School of Medicine and Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, USA
| | - S Ofner
- From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Department of Epidemiology, University of Florida, Gainesville, FL, Roudebush Veterans Administration (VA) Medical Center, Health Services Research and Development (HSR&D) Center for Health Information and Communication, Indianapolis, IN, Indiana University Center for Aging Research, Indianapolis, IN, Regenstrief Institute, Inc., Indianapolis, IN, Department of Neurology, Indiana University, School of Medicine and Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, USA
| | - R I Shorr
- From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Department of Epidemiology, University of Florida, Gainesville, FL, Roudebush Veterans Administration (VA) Medical Center, Health Services Research and Development (HSR&D) Center for Health Information and Communication, Indianapolis, IN, Indiana University Center for Aging Research, Indianapolis, IN, Regenstrief Institute, Inc., Indianapolis, IN, Department of Neurology, Indiana University, School of Medicine and Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, USA From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Department of Epidemiology, University of Florida, Gainesville, FL, Roudebush Veterans Administration (VA) Medical Center, Health Services Research and Development (HSR&D) Center for Health Information and Communication, Indianapolis, IN, Indiana University Center for Aging Research, Indianapolis, IN, Regenstrief Institute, Inc., Indianapolis, IN, Department of Neurology, Indiana University, School of Medicine and Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, USA
| | - L S Williams
- From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Department of Epidemiology, University of Florida, Gainesville, FL, Roudebush Veterans Administration (VA) Medical Center, Health Services Research and Development (HSR&D) Center for Health Information and Communication, Indianapolis, IN, Indiana University Center for Aging Research, Indianapolis, IN, Regenstrief Institute, Inc., Indianapolis, IN, Department of Neurology, Indiana University, School of Medicine and Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, USA From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Department of Epidemiology, University of Florida, Gainesville, FL, Roudebush Veterans Administration (VA) Medical Center, Health Services Research and Development (HSR&D) Center for Health Information and Communication, Indianapolis, IN, Indiana University Center for Aging Research, Indianapolis, IN, Regenstrief Institute, Inc., Indianapolis, IN, Department of Neurology, Indiana University, School of Medicine and Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, USA From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Depar
| | - D M Bravata
- From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Department of Epidemiology, University of Florida, Gainesville, FL, Roudebush Veterans Administration (VA) Medical Center, Health Services Research and Development (HSR&D) Center for Health Information and Communication, Indianapolis, IN, Indiana University Center for Aging Research, Indianapolis, IN, Regenstrief Institute, Inc., Indianapolis, IN, Department of Neurology, Indiana University, School of Medicine and Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, USA From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Department of Epidemiology, University of Florida, Gainesville, FL, Roudebush Veterans Administration (VA) Medical Center, Health Services Research and Development (HSR&D) Center for Health Information and Communication, Indianapolis, IN, Indiana University Center for Aging Research, Indianapolis, IN, Regenstrief Institute, Inc., Indianapolis, IN, Department of Neurology, Indiana University, School of Medicine and Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, USA From the Department of Occupational Therapy, College of Health and Human Sciences, Colorado State University, Fort Collins, CO, VA HSR&D Stroke Quality Enhancement Research Initiative (QUERI), Indianapolis, IN, Department of Biostatistics, Indiana University, Indianapolis, IN, GRECC (182), Malcom Randall VAMC, Gainesville, FL, Depar
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Reeves MJ, Myers LJ, Williams LS, Phipps MS, Bravata DM. Do-not-resuscitate orders, quality of care, and outcomes in veterans with acute ischemic stroke. Neurology 2012; 79:1990-6. [DOI: 10.1212/wnl.0b013e3182735ced] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Abstract
Cortisol increases have been associated with psychological and physiological stress; however, cortisol dynamics after weight loss (bariatric) surgery have not been defined. Obese participants not using exogenous glucocorticoids were eligible to participate. Female participants (n=24) provided salivary cortisol samples at bedtime, upon awakening the following morning, and 30 min after awakening before, and at 6 or 12 months after bariatric surgery. The Medical Outcomes Study Short Form-12 version 2 questionnaire regarding health-related quality of life was also completed. Preoperatively, mean body mass index was 45.1±8.1 kg/m2. Mean late night (1.8±1.1 nmol/l), awakening (10.7±7.4 nmol/l), and after-awakening (11.5±7.9 nmol/l) salivary cortisol values were within normal ranges. The cortisol awakening response (mean 21.1±79.7%, median 13.7%) was at the low end of normal. Preoperatively, participants had lower mental and physical health-related quality of life scores than US adult norms (p<0.001). Salivary cortisol was not correlated with measures of health-related quality of life. Mean BMI decreased over time (p<0.001) and participants experienced improved physical and mental health-related quality of life (p≤0.011). Postoperative late night salivary cortisol was not different from preoperative values. Awakening and after-awakening cortisol levels were higher than preoperative values (15.3±7.7 nmol/l, p=0.013; 17.5±10.2 nmol/l, p=0.005; respectively), but the cortisol awakening response was not changed (mean 26.7±66.2%; median 7.8%). Morning salivary cortisol increased at long-term follow-up after bariatric surgery. Although self-evaluated mental and physical health improved after surgery, the cortisol awakening response is at the low end of normal, which may indicate continued physiological stress.
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Affiliation(s)
- A R Valentine
- Stanford University School of Medicine, Stanford, CA, USA.
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Kernan WN, Viscoli CM, Demarco D, Mendes B, Shrauger K, Schindler JL, McVeety JC, Sicklick A, Moalli D, Greco P, Bravata DM, Eisen S, Resor L, Sena K, Story D, Brass LM, Furie KL, Gutmann L, Hinnau E, Gorman M, Lovejoy AM, Inzucchi SE, Young LH, Horwitz RI. Boosting enrollment in neurology trials with Local Identification and Outreach Networks (LIONs). Neurology 2009; 72:1345-51. [PMID: 19365056 DOI: 10.1212/wnl.0b013e3181a0fda3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE Our purpose was to develop a geographically localized, multi-institution strategy for improving enrolment in a trial of secondary stroke prevention. METHODS We invited 11 Connecticut hospitals to participate in a project named the Local Identification and Outreach Network (LION). Each hospital provided the names of patients with stroke or TIA, identified from electronic admission or discharge logs, to researchers at a central coordinating center. After obtaining permission from personal physicians, researchers contacted each patient to describe the study, screen for eligibility, and set up a home visit for consent. Researchers traveled throughout the state to enroll and follow participants. Outside the LION, investigators identified trial participants using conventional recruitment strategies. We compared recruitment success for the LION and other sites using data from January 1, 2005, through June 30, 2007. RESULTS The average monthly randomization rate from the LION was 4.0 participants, compared with 0.46 at 104 other Insulin Resistance Intervention after Stroke (IRIS) sites. The LION randomized on average 1.52/1,000 beds/month, compared with 0.76/1,000 beds/month at other IRIS sites (p = 0.03). The average cost to randomize and follow one participant was $8,697 for the LION, compared with $7,198 for other sites. CONCLUSION A geographically based network of institutions, served by a central coordinating center, randomized substantially more patients per month compared with sites outside of the network. The high enrollment rate was a result of surveillance at multiple institutions and greater productivity at each institution. Although the cost per patient was higher for the network, compared with nonnetwork sites, cost savings could result from more rapid completion of research.
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Affiliation(s)
- W N Kernan
- Departments of Internal Medicine, Yale School of Medicine, New Haven, CT 06519, USA.
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Sims TL, Garber AM, Miller DE, Mahlow PT, Bravata DM, Goldstein MK. Multimedia quality of life assessment: advances with FLAIR. AMIA Annu Symp Proc 2005; 2005:694-8. [PMID: 16779129 PMCID: PMC1560717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Assessing impact of functional dependency on quality of life (QOL) among older adults can provide an in-depth understanding of health preferences. Utilities as a measure of preferences are necessary in conducting cost-effectiveness evaluations of healthcare interventions designed to improve overall QOL. We describe further development of a multimedia utility elicitation instrument that is highly portable and easily accessible. An earlier version, FLAIR1, introduced features designed for older adult, computer inexperienced users. FLAIR2 includes modifications such as migration to a web-based platform, consistency checks, audio/visual updates, and more response methods. As compared with FLAIR1, more FLAIR2 respondents (n=318) preferred using the computer and found the computer program to be enjoyable, easy to use, and easily understood. There were also fewer inconsistencies among FLAIR2 respondents. FLAIR2 enhancements have increased portability, minimized invariance and inconsistency, and produced a more user friendly design.
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Affiliation(s)
- T L Sims
- Center for Primary Care and Outcomes Research, Stanford University School of Medicine, Stanford, CA, USA
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Bravata DM, McDonald KM, Owens DK, Wilhelm ER, Brandeau ML, Zaric GS, Holty JEC, Liu H, Sundaram V. Regionalization of bioterrorism preparedness and response. Evid Rep Technol Assess (Summ) 2004:1-7. [PMID: 15133889 PMCID: PMC4781155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
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Abstract
BACKGROUND Hyperglycaemia is common among patients with acute ischaemic stroke, and may be due to the physiological stress of the acute stroke event or reflect underlying diabetes mellitus. The under-diagnosis of diabetes in the general population, combined with the association of diabetes and stroke, suggests a rationale for screening for diabetes among hyperglycaemic stroke patients. AIM To determine how often clinicians screen for diabetes among hyperglycaemic stroke patients without a prior diagnosis of diabetes. DESIGN Retrospective medical record review. METHODS We reviewed the records of acute ischaemic stroke patients admitted at any of ten Connecticut hospitals from May 1996 through December 1998. RESULTS We identified 90 acute stroke patients with no prior history of diabetes. The prevalence of hyperglycaemia varied from 31% down to 6%, depending on the maximum glucose cut-off used to define hyperglycaemia: from > or = 140 mg/dl (7.8 mmol/l) to > or = 200 mg/dl (11.1 mmol/l). Only one of the hyperglycaemic patients (1/90, 1%) had any evidence that a clinician screened or planned to screen for undiagnosed diabetes: one patient had a haemoglobin A1c measured during the hospitalization, none received oral glucose tolerance testing while hospitalized, and no discharge summary included a plan to screen for diabetes as an out-patient. DISCUSSION Hyperglycaemic stroke patients without a previous diagnosis of diabetes are not routinely screened for diabetes. This situation represents an opportunity, currently unused, to identify an important and modifiable condition.
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Affiliation(s)
- D M Bravata
- Clinical Epidemiology Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA.
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Kernan WN, Inzucchi SE, Viscoli CM, Brass LM, Bravata DM, Shulman GI, McVeety JC, Horwitz RI. Impaired insulin sensitivity among nondiabetic patients with a recent TIA or ischemic stroke. Neurology 2003; 60:1447-51. [PMID: 12743229 DOI: 10.1212/01.wnl.0000063318.66140.a3] [Citation(s) in RCA: 82] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVES To determine the prevalence of impaired insulin sensitivity among nondiabetic patients with a recent TIA or nondisabling ischemic stroke. METHODS Eligible subjects were nondiabetic men and women over age 45 years who were hospitalized with a TIA or ischemic stroke. To measure insulin sensitivity, subjects underwent an oral glucose tolerance test between 2 and 6 months after their event. Impaired insulin sensitivity was defined by a value of < or =2.5 on the Composite Insulin Sensitivity Index derived from insulin and glucose values during the test. RESULTS Between July 2000 and June 2001, we identified 177 eligible patients, among whom 105 declined to participate and 72 enrolled. The median age of participants was 71 years and 46 (64%) were men. The baseline event was stroke for 57 subjects (79%). A history of myocardial infarction (MI) was reported by 14 subjects (19%), and 16 (22%) were obese (body mass index > 30). Fasting glucose was normal (<110 mg/dL) for 58 (80%) participants and impaired (110 to 125 mg/dL) for 14 (20%). Among 72 participants, the median insulin sensitivity index value was 2.6 (range 0.9 to 10.2). The prevalence of impaired insulin sensitivity was 36 of 72 (50%, 95% CI 38% to 62%). Impaired insulin sensitivity was more prevalent among younger patients and patients with obesity, lacunar stroke etiology, and disability (Rankin grade >1). CONCLUSION Impaired insulin sensitivity is highly prevalent among nondiabetic patients with a recent TIA or nondisabling ischemic stroke. This finding has important therapeutic implications if treatment to improve insulin sensitivity is shown to reduce risk for subsequent stroke and heart disease.
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Affiliation(s)
- W N Kernan
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520-8025, USA.
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Abstract
BACKGROUND AND PURPOSE Resistance to insulin-mediated glucose uptake by peripheral tissues is a cardinal defect in type 2 diabetes mellitus. Insulin resistance is also common among nondiabetic individuals, and may be an important risk factor for stroke in both populations. The authors review the definition, epidemiology, and treatment of insulin resistance. METHODS The authors searched Medline (1977-2001) and reviewed bibliographies to identify pertinent English-language publications. RESULTS Insulin resistance is present in most patients with type 2 diabetes. It is also common among elderly persons, certain ethnic groups, and persons with hypertension, obesity, physical deconditioning, and vascular disease. The principal pathophysiologic defect is impaired intracellular signaling in muscle tissue leading to defective glycogen synthesis. Insulin resistance is associated with numerous metabolic, hematologic, and cellular events that promote atherosclerosis and coagulation. The association between insulin resistance and risk for stroke has been examined in four case-control studies and five prospective observational cohort studies. Six of the nine studies are methodologically sound and provide evidence that insulin resistance is associated with risk for stroke. CONCLUSION Insulin resistance may be a prevalent risk factor for stroke. New drugs can safely reduce insulin resistance and may have a role in stroke prevention.
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Affiliation(s)
- W N Kernan
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA.
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Bravata DM, McDonald K, Owens DK, Buckeridge D, Haberland C, Rydzak C, Schleinitz M, Smith WM, Szeto H, Wilkening D, Musen M, Duncan BW, Nouri B, Dangiolo MB, Liu H, Shofer S, Graham J, Davies S. Bioterrorism preparedness and response: use of information technologies and decision support systems. Evid Rep Technol Assess (Summ) 2002:1-8. [PMID: 12154489 PMCID: PMC4781373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
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14
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Chen SC, Bravata DM, Weil E, Olkin I. A comparison of dermatologists' and primary care physicians' accuracy in diagnosing melanoma: a systematic review. Arch Dermatol 2001; 137:1627-34. [PMID: 11735713 DOI: 10.1001/archderm.137.12.1627] [Citation(s) in RCA: 66] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
OBJECTIVE To compare the accuracy of dermatologists and primary care physicians (PCPs) in identifying pigmented lesions suggestive of melanoma and making the appropriate management decision to perform a biopsy or to refer the patient to a specialist. DATA SOURCES Studies published between January 1966 and October 1999 in the MEDLINE, EMBASE, and CancerLit databases; reference lists of identified studies; abstracts from recent conference proceedings; and direct contact with investigators. Medical subject headings included melanoma, diagnosis, screening, primary care, family practitioner, general practitioner, internal medicine, dermatologist, and skin specialist. Articles were restricted to those involving human subjects. STUDY SELECTION Studies that presented sufficient data to determine the sensitivity and specificity of dermatologists' or PCPs' ability to correctly diagnose lesions suggestive of melanoma and to perform biopsies on or refer patients with such lesions. DATA EXTRACTION Two reviewers independently abstracted data regarding the sensitivity and specificity of the dermatologists and PCPs for diagnostic and biopsy or referral accuracy. Disagreements were resolved by discussion. The quality of the studies was also evaluated. DATA SYNTHESIS Thirty-two studies met inclusion criteria; 10 were prospective studies. For diagnostic accuracy, sensitivity was 0.81 to 1.00 for dermatologists and 0.42 to 1.00 for PCPs. None of the studies reported specificity for dermatologists; one reported specificity for PCPs (0.98). For biopsy or referral accuracy, sensitivity ranged from 0.82 to 1.00 for dermatologists and 0.70 to 0.88 for PCPs; specificity, 0.70 to 0.89 for dermatologists and 0.70 to 0.87 for PCPs. Receiver operating characteristic curves for biopsy or referral ability were inconclusive. CONCLUSIONS The published data are inadequate to demonstrate differences in dermatologists' and PCPs' diagnostic and biopsy or referral accuracy of lesions suggestive of melanoma. We offer study design suggestions for future studies.
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Affiliation(s)
- S C Chen
- Department of Dermatology, Emory Center for Outcomes Research, Emory University School of Medicine, Atlanta, GA, USA.
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15
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Abstract
1. In general, the health-related quality of life (QOL) of transplant recipients is impaired before and improves after liver transplantation. 2. Transplant recipients report largest gains in those aspects of QOL most affected by physical health and smaller improvements in areas affected by psychological functioning. 3. No utility-based measures have been used with liver transplant recipients; therefore, the necessary QOL weights for use in cost-effectiveness evaluations of liver transplantation are lacking. 4. Pretransplantation, the percentage of candidates with alcoholic liver disease (ALD) who work is less than that of candidates without ALD. However, there is no difference in rates of employment posttransplantation. 5. No study has described types of wages and benefits associated with jobs before or after transplantation or the extent to which health insurance benefits associated with employment motivate changes in work status. 6. Many studies in this field used non-validated heterogeneous instruments to measure QOL and employment, thus limiting the opportunity to combine results across studies and compare outcomes for liver transplant recipients with other patient populations.
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Affiliation(s)
- D M Bravata
- Department of Medicine, Center for Primary Care and Outcomes Research, Stanford University School of Medicine, Stanford, CA, USA
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16
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Abstract
The simple pooling of data is often used to provide an overall summary of subgroup data or data from a number of related studies. In simple pooling, data are combined without being weighted. Therefore, the analysis is performed as if the data were derived from a single sample. This kind of analysis ignores characteristics of the subgroups or individual studies being pooled and can yield spurious or counterintuitive results. In meta-analysis, data from subgroups or individual studies are weighted first, then combined, thereby avoiding some of the problems of simple pooling. The purpose of this article is to describe how simple pooling differs from meta-analysis, provide a detailed analysis of why simple pooling can be a poor procedure, and show that combining by meta-analytic methods avoids such problems.
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Affiliation(s)
- D M Bravata
- VA Palo Alto Health Care System and Stanford University
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Bravata DM, Olkin I, Barnato AE, Keeffe EB, Owens DK. Employment and alcohol use after liver transplantation for alcoholic and nonalcoholic liver disease: a systematic review. Liver Transpl 2001; 7:191-203. [PMID: 11244159 DOI: 10.1053/jlts.2001.22326] [Citation(s) in RCA: 71] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The purpose of the study is to evaluate patterns of employment and alcohol use among liver transplant recipients with alcoholic (ALD) and nonalcoholic liver disease (non-ALD). MEDLINE, EMBASE, and bibliographic searches identified 5,505 potentially relevant articles published between January 1966 and October 1998. Eighty-two studies reporting data on 5,020 transplant recipients met our inclusion criteria. Pre-orthotopic liver transplantation (OLT), 29% of transplant recipients with ALD and 59% of those with non-ALD worked versus 33% and 80% at 3 years for transplant recipients with ALD and non-ALD, respectively (P <.00001 for each interval). We found no difference in the proportion of transplant recipients with ALD and non-ALD reporting early alcohol use post-OLT: 4% versus 5% at 6 months and 17% versus 16% at 12 months. However, among post-OLT drinkers, transplant recipients with non-ALD were more likely to drink moderately and those with ALD to drink excessively. At 7 years post-OLT, 32% of the patients with ALD reported using alcohol. The odds ratio for alcohol use among patients who maintained abstinence for fewer than 6 months pre-OLT versus those who maintained abstinence for greater than 6 months was 7.8 (95% confidence interval, 4.0 to 15.3). Before OLT and at long-term follow-up, substantially more transplant recipients with non-ALD than ALD were employed. The proportions of transplant recipients with ALD and non-ALD reporting alcohol use did not differ, although those with ALD tended to consume greater quantities.
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Affiliation(s)
- D M Bravata
- Department of Veterans Affairs Health Care System, Palo Alto, CA, USA.
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Owens DK, Bravata DM. Computer-based decision support: wishing on a star? Eff Clin Pract 2001; 4:34-8. [PMID: 11234184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
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Bravata DM, Olkin I, Barnato AE, Keeffe EB, Owens DK. Health-related quality of life after liver transplantation: a meta-analysis. Liver Transpl Surg 1999; 5:318-31. [PMID: 10388505 DOI: 10.1002/lt.500050404] [Citation(s) in RCA: 160] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The goal of this study is to assess health-related quality of life (HRQL) after orthotopic liver transplantation (OLT). Structured MEDLINE and Embase literature searches identified 5473 potentially relevant articles. Thirty-two additional references were collected from the bibliographies. Of the 5505 identified articles, 49 studies reporting data on 3576 transplant recipients met our inclusion criteria, which were an assessment of quality of life (QOL) in adult patients reported as either pretransplantation and posttransplantation data or with a comparison group and written in English. We combined posttransplantation QOL scores from 15 studies that reported data from the same QOL scales to assess the magnitude of the effect of OLT on QOL scales. We also performed a sign test on the 49 studies to evaluate the direction (positive or negative) of the effect of transplantation on QOL. Transplantation resulted in an improvement of 32% in Karnofsky scores, 11% in Sickness Impact Profile scores, and 20% to 50% in the domains of the Nottingham Health Profile. The sign test showed significant improvement in posttransplantation physical health (P <.0004), sexual functioning (P <.008), daily activities (P <.02), general HRQL (P <.02), and social functioning (P <.05), but not psychological health (P <.08). In general, the HRQL of the 3576 patients was impaired pretransplantation and improved posttransplantation. Transplant recipients reported large gains in those aspects of QOL most affected by physical health and smaller improvements in areas affected by psychological functioning.
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Affiliation(s)
- D M Bravata
- Department of Veterans Affairs Health Care System, Palo Alto, CA 94304, USA
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