QUARTERLY FOCUS ISSUE: HEART FAILURE: VIEWPOINT
What Is the Strength of Evidence for Heart Failure Disease-Management Programs?
Alexander M. Clark, PhD*,*,
Lori A. Savard, BSc* and
David R. Thompson, PhD
* University of Alberta, Edmonton, Alberta, Canada
Department of Health Sciences and Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
Manuscript received March 1, 2009;
revised manuscript received April 6, 2009,
accepted April 14, 2009.
* Reprint requests and correspondence: Dr. Alexander M. Clark, University of Alberta, Level 3, Clinical Sciences Building, Edmonton, Alberta T6G 2G3, Canada (Email: alex.clark{at}ualberta.ca).
 |
Abstract
|
|---|
Heart failure (HF) disease-management programs are increasingly common. However, some large and recent trials of programs have not reported positive findings. There have also been parallel recent advances in reporting standards and theory around complex nonpharmacological interventions. These developments compel reconsideration in this Viewpoint of how research into HF-management programs should be evaluated, the quality, specificity, and usefulness of this evidence, and the recommendations for future research. Addressing the main determinants of intervention effectiveness by using the PICO (Patient, Intervention, Comparison, and Outcome) approach and the recent CONSORT (Consolidated Standards of Reporting Trials) statement on nonpharmacological trials, we will argue that in both current trials and meta-analyses, interventions and comparisons are not sufficiently well described; that complex programs have been excessively oversimplified; and that potentially salient differences in programs, populations, and settings are not incorporated into analyses. In preference to more general meta-analyses of programs, adequate descriptions are first needed of populations, interventions, comparisons, and outcomes in past and future trials. This could be achieved via a systematic survey of study authors based on the CONSORT statement. These more detailed data on studies should be incorporated into future meta-analyses of comparable trials and used with other techniques such as patient-based outcomes data and meta-regression. Although trials and meta-analyses continue to have potential to generate useful evidence, a more specific evidence base is needed to support the development of effective programs for different populations and settings.
Key Words: chronic • health promotion • self care • disease management • CHF
|
Abbreviations and Acronyms
| | AHA = American Heart Association | | CONSORT = Consolidated Standards of Reporting Trials | | HF = heart failure | | PICO = Patient, Intervention, Comparison, Outcome |
|
The development of disease-management programs for the large and vulnerable population with heart failure (HF) brought important multidisciplinary support to patients. However, a number of recent trials (1–6) have found no or limited benefits from programs. In the context of new CONSORT (Consolidated Standards of Reporting Trials) reporting requirements (7) and theoretical advances (8–10), it is timely to address how current research into HF-management programs should be evaluated; the quality, specificity, and usefulness of current evidence; and useful directions for future research.
|
How Should Program Trials Be Evaluated?
|
|---|
What are fair criteria with which to evaluate the quality of existing program trials? There is a strong case for drawing on the reporting requirements of the CONSORT statement (7) for nonpharmacological trials (Table 1). Published trials of programs predate these new reporting requirements but rather than introducing novel principles, the new CONSORT statement (7) codifies well-established principles of design and appraisal. As expressed in the acronym PICO (11), the effectiveness of any intervention is determined by the characteristics of the intervention (I) and the population receiving it (P). In trials, apparent effectiveness is also influenced by what the intervention is compared with (C), what outcomes are measured, and the quality and timing of these measurements (O) (11). As the 4 main determinants of treatment effectiveness, design weakness or vague reporting in any one of these aspects extensively impacts study quality.
View this table:
[in this window]
[in a new window]
|
Table 1 Reporting Standards for Intervention Components From CONSORT and Modified CONSORT for Nonpharmacological Treatments
|
|
The CONSORT statement (7) also recognizes that nonpharmacological trials are different than pharmacological trials. Recent theoretical work acknowledges the inherent complexity of health services interventions, for example, in systems (10) and contextual effects (12). These advances are evident in trial development frameworks (8,9), policy (13), and evaluation (14). For example, the American Heart Association (AHA) developed a taxonomy of HF-management programs that classified programs in structure and design around at least 8 parameters, each in at least 4 different ways (15).
To improve knowledge translation, there is a growing realization that decision and policymakers need specific and context-responsive evidence (16). Knowledge is needed not only of whether there is a general likelihood that an entire genre of programs will work in any setting but the size of likely benefit in a particular setting from programs with specified components, delivery mechanisms, and personnel (10). Hence, rather than threatening the future existence of HF-management programs, considering evidence in the light of recent advances addresses essential elements of research design, theory, and knowledge translation.
|
Evidence From Current Trials
|
|---|
What is the quality of existing evidence from program trials? There are many complex issues to address in the design of a randomized trial (17), but both CONSORT (7) and PICO suggest some fundamental aspects require attention.
What interventions are studied?.
Interventions should be described comprehensively in terms of content, components, providers, and standardization procedures (7) because these dimensions could influence treatment effects (7,8,10). Concerns have been raised that, in current trials, programs are poorly described (18–22), and elements are seldom justified (15,23). There is a tendency to categorize programs based on a single or small number of macro characteristics (such as the main intervention setting or provider), although many other characteristics may be important (15,19,23). Wide diversity in trials exists around these potentially influential characteristics, including follow-up period, drug therapy optimization, intervention content, and mode(s) of provision (20,22). Other unknown or previously undocumented dimensions of interventions and their settings may also influence outcomes (8,10,24).
To what are interventions compared?.
Both PICO and CONSORT require that the care that comparison groups receive should be described comprehensively (7). Systematic review has identified that trials of programs currently do not detail sufficiently what "usual" or "routine" care comparison groups received (20,22,25,26). This lack of detail creates bias because pharmacological care for HF varies widely over place, time, and sector (27,28). Prescription rates of key pharmacotherapies have differed historically and are influenced by geographic location (28), the availability of specialists (29,30), and the aggressiveness of management (31,32). These differences are crucial because any apparent effect (or lack thereof) in a trial could be attributed equally to variations in usual care as to the intervention (7).
What is measured and how?.
More research is needed into the long-term effects of programs. Some trials with long-term follow-up show sustained benefits (33,34), but others do not (35). Most trials follow patients up for 9 to 12 months (27,36), but this duration may be insufficient to demonstrate impact on mortality (37), or patients may be readmitted before receiving the full effect of a program (1).
What should be concluded from current trials? Taking account of the trends in design and reporting described, there remains uncertainty regarding the direction and size of short- and long-term benefits likely to arise from different types of programs in different populations and settings. This is not to say that such benefits do not exist. However, to conclude that all or any types of programs will be as effective in all settings assumes a similarity or irrelevance of population, intervention, comparison, and context that is not substantiated by current data or theory.
|
Building a Better Trial Evidence Base
|
|---|
More randomized trials of well-described interventions with longer-term follow-up periods are needed. To evaluate the effects of different types of programs in particular populations and settings, precise details should be provided of care for both the intervention and comparison groups, how the intervention was standardized across settings and personnel, and the degree and assessment of adherence of the providers of the intervention to the protocol (7). To identify the multiple dimensions of a complex intervention and the ways it is intended to improve outcomes, future trials should be developed by the use of frameworks designed to support the design and evaluation of complex interventions (8,9). The CONSORT standards (7) and AHA taxonomy (15) should be used to incorporate reporting standards into design, data collection, and other trial documentation. Adjunct qualitative studies should be used to better understand "what works for whom, when and why" (19,24) via exploration of the mechanisms of effect of interventions and the moderating effects of population and context (8,10,19).
|
Evidence From Current Meta-Analyses
|
|---|
Trials of programs form complex, diverse, and often poorly described evidence, but this has not deterred frequent meta-analysis (20,22,26,36–46).
Populations: sample composition, size, and study weighting.
Reflecting the trials themselves, the sample size in the majority of systematic reviews is comparatively small (22,42), with total sample sizes rarely reaching 5,000 (36,37) and most being around 2,000 (22,37–39,42,44,46). Size is likely to be related to the degree of focus of the review, but smaller sample size can lead to false conclusions due to random error (47). Also, because few reviews include >15 trials (22,42,46,48), pooled estimates in meta-analysis are then heavily influenced by a small number of large single trials (Table 2) (49).
No existing reviews pool data on outcomes by sex or age, and many do not identify the sex of the population that pooling is based on (22,37,38,40,42,44). This reflects incomplete reporting in the trials, but treatment and outcomes do vary by sex (50,51). This omission also prevents subanalysis to determine whether variation occurs in programs (52), an important step when studies have diverse populations (53).
Interventions: comparisons and heterogeneity.
In metaanalysis, a lack of comprehensive descriptions of interventions and usual care makes it problematic to decide if and when trial findings should be pooled or to pinpoint the source of variations (54) because there must be sufficient similarity between trials for data to be pooled (55). Those (20,22,25,26) undertaking meta-analysis of programs have acknowledged a shortage of information about interventions in trial reports. However, few (22) have fully recognized the constraining implications this has on the ability to pool data.
That said, when viewed as complex, differences between health services interventions abound (56). Heterogeneity resulting from these differences is detectable not only via statistical testing but also by examining the characteristics of populations, interventions, and outcomes (57). Current reviews focus overwhelmingly on statistical heterogeneity with notable exceptions (22,40). Clinical and methodological heterogeneity arising from differences in population, programs, and methods remain comparatively ignored (58). This is unfortunate because exploring sources of clinical and methodological heterogeneity is arguably more important than testing for statistical heterogeneity (57,58).
Reflecting the diversity of trials included in reviews, many meta-analyses (20,26,36–38,40,41,44–46) report high statistical heterogeneity. Rather than merely commenting on the existence of statistical heterogeneity, it is more important to take it into account in the Methods and Conclusions sections (57,58) and explain or investigate why differences occur (56). This is hampered by the lack of comprehensive and detailed descriptions of trials and control groups. Even with strong evidence of statistical heterogeneity, some reviews (41,59) nevertheless pool data to produce summative estimates and make conclusions thereon.
Measurements: duration and follow-up.
Duration of intervention and length of follow-up are not consistently reported in reviews and few reviews report data in both areas (36,37,40,45,46), which again raises the possibility that studies with short-term follow-up will not identify actual changes in mortality and morbidity.
How should future programs be evaluated?.
Given the complex nature of programs and the need for specific evidence, how should programs be evaluated in the future by the use of systematic review? Meta-analysis has had success in allowing the benefits of cardiovascular therapies to emerge from inconsistent results from trials and reviews (60). However, when used simplistically, inappropriately or with a biased sample of studies, meta-analysis can be a misplaced attempt to create certainty where none such can exist (61).
New trial findings of programs should not be incorporated de facto into additional general meta-analyses of programs to calculate amended overall effect sizes (62). Meta-analysis is dependent on comprehensive and accurate information on past trials. There is an urgent need for a comprehensive and systematic survey of the authors of existing trials, drawing on CONSORT (7) and the AHA taxonomy (15), around the key population, intervention, comparison, and outcomes characteristics missing from existing published trials. Although most published trials of health services interventions do not describe interventions adequately (63), trial authors often record many details on interventions beyond those included in publications (63). This would support future meta-analyses by adding important and rigorous descriptive detail of past trials.
Meta-analyses can be too occupied with seeking "headline" summative effect sizes (61,64) by pooling data from incomparable trials to increase sample size and the likelihood of a significant effect (61,64). This assumes similar effect sizes in different types of programs, populations, and settings (24); does not explain inconsistent trial results (24); and provides findings that are too general to be useful for practice and policy (12). Recognizing the complexity of programs (15), it is inappropriate to focus new meta-analyses on whether programs en masse work or do not work for all populations and settings (19). As has occurred in drug-eluting stents, strenuous efforts are needed to specify benefits and costs of programs of different types in different settings and subpopulations (64), including women, different health systems, patients >80 years of age, and patients in rural settings. It should be a priority to perform meta-analyses based on patient-based data rather than published results; this may allow more rapid understanding of the effects of comparable programs on different subpopulations (65).
Meta-analysis of any complex health services intervention faces the challenge of determining at which point a trial becomes sufficiently incomparable to pool with other studies. The multitude of known and unknown differences in a complex intervention means that differences and heterogeneity are inevitable (66). Findings should only be pooled in future meta-analyses when programs share similar features that are likely to have an impact on outcomes. This accepts the inevitability of heterogeneity but accepts this providing it occurs around features that are unlikely to be associated with the treatment effects or program costs (66).
To inform this, more evidence is needed regarding what program characteristics are most likely to determine program effects. A number of existing reviews have attempted to do this in relation to effectiveness (22,26,27), but these attempts are constrained by inadequate reporting in trials. The potential for bias arising from the effects of a small number of trials could also be addressed in pooling by weighting trials by quality (49). With improved data on existing trials, knowledge of which characteristics are most influential should accrue. Meta-regression offers a promising means to identify which characteristics of programs predict better outcomes (27) and could inform subanalyses and sensitivity analyses.
|
Footnotes
|
|---|
Dr. Clark is the recipient of career award support from the Alberta Heritage Foundation for Medical Research and the Canadian Institutes for Health Research.
|
References
|
|---|
1. Jaarsma T, van der Wal M, Lesman-Leegte I, et al. Effect of moderate or intensive disease management program on outcome in patients with heart failure coordinating study evaluating outcomes of advising and counseling in heart failure (COACH) Arch Intern Med 2008;168:316-324.[Abstract/Free Full Text]2. Nucifora G, Albanese M, De Biaggio P, et al. Lack of improvement of clinical outcomes by a low-cost, hospital-based heart failure management programme J Cardiovasc Med 2006;7:614-622.[Web of Science] 3. Smith B, Forkner EZ, Krasuski R, et al. Disease management produces limited quality-of-life improvements in patients with congestive heart failure: evidence from a randomized trial in community-dwelling patientsB Am J Manage Care 2005;11:701-703.[Web of Science][Medline] 4. Nguyen V, Ducharme A, White M, et al. Lack of long-term benefits of a 6-month heart failure disease management program J Card Fail 2006;13:287-293.[CrossRef] 5. Holland R, Brooksby I, Lenaghan E, et al. Effectiveness of visits from community pharmacists for patients with heart failure: HeartMed randomised controlled trial BMJ 2007;334:1098.[Abstract/Free Full Text] 6. Ledwidge M, Ryan E, O'Loughlin C, et al. Heart failure care in a hospital unit: a comparison of standard 3-month and extended 6-month programs Eur J Heart Fail 2005;16:385-391. 7. Boutron I, Moher M, Altman DG, Schulz KF, Ravaud P, CONSORT Group Extending the CONSORT statement to randomized trials of nonpharmacologic treatment: explanation and elaboration Ann Intern Med 2008;148:295-309.[Abstract/Free Full Text] 8. Medical Research Council Developing and Evaluating Complex InterventionsLondon: MRC; 2008. 9. Craig P, Dieppe P, MacItyre S, Mitchie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council Guidance BMJ 2008;337:979-983.[CrossRef] 10. Campbell N, Murray E, Darbyshire J, et al. Designing and evaluating complex interventions to improve health care BMJ 2007;334:455-459.[Free Full Text] 11. Guyatt G, Rennie D, Meade M, Cook D. User's guide to the medical literatureNew York, NY: American Medical Association; 2008. 12. Pawson R. Evidence-Based Policy: A Realist PerspectiveLondon: Sage; 2006. 13. Singh D. How Can Chronic Disease Managament Programmes Operate Across Care Settings and Providers?Cophenhagen: World Health Organization; 2008. 14. Shiell A, Hawe P, Gold L. Complex interventions or complex systems?. Implications for health economic evaluation. BMJ 2008;336:1281-1283.[Free Full Text] 15. Krumholz H, Currie P, Riegel B, et al. A taxonomy for disease management: a scientific statement from the American Heart Assocaition Disease Management Taxononmy Writing Group Circulation 2006;114:1432-1445.[Abstract/Free Full Text] 16. Glasgow RE, Emmons KM. How can we increase the translation of research into practice: types of evidence needed Ann Rev Publ Health 2007;28:413-433.[CrossRef][Web of Science][Medline] 17. Guyatt G, Straus S, Meade MO, et al. Therapy (randomized trials)In: Guyatt G, Rennie D, Meade MO, Cook DJ, editors. Users' Guides to the Medical Literature: A Manual for Evidence based Clinical Practice. 2nd edition. New York, NY: McGraw-Hill Professional; 2008. pp. 67-86. 18. Nallamothu BK, Hayward RA. Beyond the randomized clinical trial: the role of effectiveness studies in evaluating cardiovascular therapies Circulation 2008;118:1294-1303.[Free Full Text] 19. Clark AM, Thompson DR. The future of heart failure disease management programs Lancet 2008;372:784-786.[CrossRef][Web of Science][Medline] 20. Gohler A, Januzzi J, Worrell SS, et al. A systematic meta-analysis of the efficacy and heterogeneity of disease management programs in congestive heart failure J Card Fail 2006;12:554-567.[CrossRef][Web of Science][Medline] 21. Herbert PL, Sisk JE. Challenges facing nurse-led disease management for heart failure Disease Manage Health Outcomes 2008;16:1-6.[CrossRef] 22. Taylor S, Bestall J, Cotter S, et al. Clinical service organization for heart failure Cochrane Database of Syst Rev 2005;2CD002752. 23. Ekman I, Swedberg K. Home-based management of patients with chronic heart failure—focus on content not just form! Eur Heart J 2002;23:1323-1325.[Free Full Text] 24. Pawson R, Tilley N. Realistic EvaluationLondon: Sage; 1997. 25. Roccaforte R, Demers C, Baldassarrre F, Teo K, Yusuf S. Effectiveness of comprehensive disease management programmes in improving outcomes in heart failure patients. A meta-analysis. Eur J Heart Fail 2005;7:1133-1144.[Abstract/Free Full Text] 26. Koshman S, Charrois T, Simpson S, McAlister F, Tsuyuki R. Pharmacist care of patients with heart failure: a systematic review of randomized trials Arch Intern Med 2008;168:687-694.[Abstract/Free Full Text] 27. Yu DSF, Thompson DR, Lee DTF. Disease management programmes for older people with heart failure: crucial characteristics which improve post-discharge outcomes Eur Heart J 2006:596-612. 28. Chin M, Wang J, Zhang J. Utilization and dosing of angio-tensin-converting enzymes inhibitors for heart failure. Effect of physician specialty and patient characteristics. J Gen Intern Med 1997;12:563-566.[CrossRef][Web of Science][Medline] 29. Remme W. Filling the gap between guidelines and clinical practice in heart failure treatment: still a far cry from reality Eur J Heart Fail 2007;9:1143-1145.[Free Full Text] 30. Groote PIR, Assyag P, Clerson P, Ducardonnet A, Galinier M, et al. Is the gap between guidelines and clinical practice in heart failure treatment being filled?. Insights from the IMPACT RECO survey. Eur J Heart Fail 2007;9:1205-1211.[Abstract/Free Full Text] 31. Patel J, Fotis M. Comparison of treatment of patients with heart failure by cardiologists versus noncardiologists Am J Health-System Pharmacy 2005;62:168-172.[Abstract/Free Full Text] 32. Rutten F, Gribbee D, Hoes A. Differences between general practitioners and cardiologists in diagnosis and management of heart failure: a survey in every-day Eur J Heart Fail 2003;5:337-344.[Abstract/Free Full Text] 33. Inglis SC, Pearson S, Treen S, Gallasch T, Horowitz JD, Stewart S. Extending the horizon in chronic heart failure: effects of multidisciplinary, home-based intervention relative to usual care Circulation 2006;114:2466-2473.[Abstract/Free Full Text] 34. Del Sindaco D, Pulignano G, Minardi G, et al. Two-year outcome of a prospective, controlled study of a disease management programme for elderly patients with heart failure J Cardiovasc Med 2007;8:324-329.[Web of Science] 35. Murray M, Young J, Hoke S, et al. Pharmacist intervention to improve medication adherence in heart failure: a randomized trial Ann Intern Med 2007;146:714-725.[Abstract/Free Full Text] 36. Whellan DJ, Hasselblad V, Peterson E, O'Conner C, Schulman K. Metaanalysis and review of heart failure disease management randomized controlled clinical trials Am Heart J 2005;149:722-729.[CrossRef][Web of Science][Medline] 37. McAlister FA, Stewart S, Ferrua S, McMurray JJ. Multi-disciplinary strategies for the management of heart failure patients at high risk of admission: a systematic review of randomized trials J Am Coll Cardiol 2004;44:810-819.[Abstract/Free Full Text] 38. Gonseth J, Guallar-Castillion P, Banegas J, Rodriguez-Artelajo F. The effectiveness of disease management programmes in reducing hospital re-admissoin in older patients with heart failure: a systematic review of published papers Eur Heart J 2004;25:1570-1595.[Abstract/Free Full Text] 39. Gwadry-Sridhar FH, Flintoft V, Lee DS, Lee H, Guyatt GH. A systematic review and meta-analysis of studies comparing readmission rates and mortality rates in patients with heart failure Arch Intern Med 2004;164:2315-2320.[Abstract/Free Full Text] 40. McAlister FA, Lawson FME, Teo KK, Armstrong PW. A systematic review of randomized trials of disease management programs in heart failure Am J Med 2001;110:378-384.[CrossRef][Web of Science][Medline] 41. Holland R, Battersby J, Harvey I, Lenaghan E, Smith J, Hay L. Systematic review of multidisciplinary interventions in heart failure Heart 2005;91:899-906.[Abstract/Free Full Text] 42. Clark R, Inglis S, McAlister F, Cleland J, Stewart S. Telemonitoring or structured telephone support programmes for patients with chronic heart failure: systematic review and meta-analysis BMJ 2007;334:942.[Abstract/Free Full Text] 43. Jovicic A, Holroyd-Leduc JM, Straus SE. Effects of self-management intervention on health outcomes of patients with heart failure: a systematic review of randomized controlled trials BMC Cardiovasc Disord 2006;6:43.[CrossRef][Medline] 44. Kim Y, Soeken KL. A meta-analysis of the effect of hospital-based case management on hospital length of stay and readmission Nurs Res 2005;54:255-264.[Web of Science][Medline] 45. Roccaforte R, Demers C, Baldassarre F, Teo KK, Yusuf S. Effectiveness of comprehensive disease management programmes in improving clinical outcomes in heart failure patients: a meta-analysis Eur J Heart Fail 2005;7:1133-1144.[Abstract/Free Full Text] 46. Phillips CO, Wright SM, Kern DE, Singa RM, Shepperd S, Rubin HR. Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta-analysis JAMA 2004;291:1358-1367.[Abstract/Free Full Text] 47. Brok J, Thorlund K, Wetterslev J, Gluud C. Apparently conclusive meta-analyses may be inconclusive—trial sequential analysis adjustment of random error risk due to repetitive testing of accumulating data in apparently conclusive neonatal meta-analyses Int J Epidemiol 2009;38:287-298.[Abstract/Free Full Text] 48. Gustafsson F, Arnold J. Heart failure clinics and outpatient management: review of the evidence and call for quality assurance Eur Heart J 2004;25:1596-1604.[Abstract/Free Full Text] 49. Detsky AS, Naylor CD, O'Rourke K, McGeer AJ, L'Abbé KA. Incorporating variations in the quality of individual randomized trials into meta-analysis J Clin Epidemiol 1992;45:255-265.[CrossRef][Web of Science][Medline] 50. Tu K, Gong Y, Austin P, Jaakimanian L, Tu J, Canadian Cardiovascular Outcomes Research Team An overview of the types of physicians treating acute cardiac conditions in Canada Can J Cardiol 2004;20:282-291.[Web of Science][Medline] 51. Roger V, Weston S, Redfield M, et al. Trends in heart failure incidence and survival in a community-based population JAMA 2004;292:344-350.[Abstract/Free Full Text] 52. Hahn S, Williamson PR, Hutton JL, Garner P, Flynn EV. Assessing the potential for bias in meta-analysis due to selective reporting of subgroup analyses within studies Stat Med 2000;19:3325-3336.[CrossRef][Web of Science][Medline] 53. Xu H, Platt RW, Luo ZC, Wei S, Fraser WD. Exploring heterogeneity in meta-analyses: needs, resources and challenges Paediatr Perinat Epidemiol 2008;22:18-28.[CrossRef][Web of Science][Medline] 54. Lau J, Ioannidis JPA, Schmid CH. Summing up the evidence: one answer is not always enough Lancet 1998;351:123-127.[CrossRef][Web of Science][Medline] 55. Ioannidis JPA, Patsopoulos NA, Evangelou E. Uncertainty in heterogeneity estimates in meta-analyses BMJ 2007;335:914-916.[Free Full Text] 56. Higgins JPT. Heterogeneity in meta-analysis should be expected and appropriately quantified Int J Epidemiol 2008;37:1158-1160.[Free Full Text] 57. Higgins J, Thompson S, Deeks J, Altman D. Measuring inconsistency in meta-analysis BMJ 2003;327:557-560.[Free Full Text] 58. Fletcher J. What is heterogeneity and is it important? BMJ 2007;334:94-96.[Free Full Text] 59. Phillips CO, Singa RM, Rubin HR, Jaarsma T. Complexity of program and clinical outcomes of heart failure disease management incorporating specialist nurse-led heart failure clinics. A meta-regression analysis. Eur J Heart Fail 2005;7:333-341.[Abstract/Free Full Text] 60. Egger M, Davey Smith DG, O'Rourke K. Rationale, potentials, and promise of systematic revewIn: Egger M, Davey Smith DG, Altman DG, editors. Systematic Reviews in Health Care: Meta Analysis in Context. London: BMJ Books; 2001. pp. 3-19. 61. Feinstein AR. Meta-analysis: Statistical alchemy for the 21st century J Clin Epidemiol 1995;48:71-79.[CrossRef][Web of Science][Medline] 62. Bagshaw S, McAlister FA, Manns B, Ghali W. Acetylcysteine in the prevention of constrast induced nephropathy Ann Intern Med 2006;166:161-166.[CrossRef] 63. Glasziou P, Meats E, Heneghan C, Shepperd S. What is missing from descriptions of treatment in trials and reviews? BMJ 2008;336:1472-1474.[Free Full Text] 64. Schulze R, Holling H, Grobmann H, Jutting A, Brocke M. Differences in the results of two meta-analytical approachesIn: Schulze R, Holling H, Bohning D, editors. Meta-Analysis: New Developments and Applications in Medical and Social Sciences. Cambridge, MA: Hogrefe & Huber; 2003. pp. 21-40. 65. Koch A, Rohmel J. Meta-analysis of randomized clinical trials in the evaluation of medical treatments: a partly regularatory perspectiveIn: Schulze R, Holling H, Bohning D, editors. Meta-Analysis: New Developments and Application in Medical and Social Sciences. Cambridge, MA: Hogrefe & Huber; 2003. pp. 99-112. 66. Mutt GE. Will it work in Munster?. Meta-analysis and the empirical generalization of causal relationships. In: Schulze R, Holling H, Bohning D, editors. Meta-Analysis: New Developments and Application in Medical and Social Sciences. Cambridge, MA: Hogrefe & Huber; 2003. pp. 113-140.
Related Articles
-
DIAL Trial Not Included in a Review of Health Failure Management Programs
- Daniel Ferrante, Hugo Grancelli, Alejandro Macchia, Sergio Varini, Daniel Nul, and Hernan Doval
J. Am. Coll. Cardiol. 2010 56: 159-160.
[Full Text]
[PDF]
-
Inside This Issue
J. Am. Coll. Cardiol. 2009 54: A26.
[Full Text]
[PDF]
This article has been cited by other articles:
|
|
|
|
 
K. Hebert, H. C. Quevedo, I. Gogichaishvili, N. Nozadze, E. Sagirashvili, P. Trahan, N. Kipshidze, and L. M. Arcement
Feasibility of a Heart Failure Disease Management Program in Eastern Europe: Tbilisi, Georgia
Circ Heart Fail,
November 1, 2011;
4(6):
763 - 769.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
R. D. Kociol, J. R. Horton, G. C. Fonarow, E. M. Reyes, L. K. Shaw, C. M. O'Connor, G. M. Felker, and A. F. Hernandez
Admission, Discharge, or Change in B-Type Natriuretic Peptide and Long-Term Outcomes: Data From Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure (OPTIMIZE-HF) Linked to Medicare Claims
Circ Heart Fail,
September 1, 2011;
4(5):
628 - 636.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Stewart, M. J. Carrington, T. Marwick, P. M. Davidson, P. Macdonald, J. Horowitz, H. Krum, P. J. Newton, C. Reid, P. A. Scuffham, et al.
The WHICH? trial: rationale and design of a pragmatic randomized, multicentre comparison of home- vs. clinic-based management of chronic heart failure patients
Eur J Heart Fail,
August 1, 2011;
13(8):
909 - 916.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. F. Ski and D. R. Thompson
Cardiovascular Nursing Research: Challenges and Opportunities
European Journal of Cardiovascular Nursing,
March 1, 2011;
10(1):
1 - 2.
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. Y. H. Lip, H.-F. Tse, and A. J. Stewart Coats
Disease management programmes
ODR Cardiology,
January 1, 2011;
1(1):
med-9780199568093-div1-00 - med-9780199568093-div1-00.
[Full Text]
|
|
|
|
|
|
|
A. M. Clark and D. R. Thompson
What heart failure programme works best? Wrong question, wrong assumptions
Eur J Heart Fail,
December 1, 2010;
12(12):
1271 - 1273.
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. Ferrante, S. Varini, A. Macchia, S. Soifer, R. Badra, D. Nul, H. Grancelli, H. Doval, and on behalf of the GESICA Investigators
Long-Term Results After a Telephone Intervention in Chronic Heart Failure: DIAL (Randomized Trial of Phone Intervention in Chronic Heart Failure) Follow-Up
J. Am. Coll. Cardiol.,
July 27, 2010;
56(5):
372 - 378.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. A. Konstam and V. Konstam
Heart Failure Disease Management: A Sustainable Energy Source for the Health Care Engine
J. Am. Coll. Cardiol.,
July 27, 2010;
56(5):
379 - 381.
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. Ferrante, H. Grancelli, A. Macchia, S. Varini, D. Nul, and H. Doval
DIAL Trial Not Included in a Review of Health Failure Management Programs
J. Am. Coll. Cardiol.,
July 6, 2010;
56(2):
159 - 160.
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. M. Clark, L. Savard, and D. R. Thompson
Reply.
J. Am. Coll. Cardiol.,
July 6, 2010;
56(2):
160 - 160.
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. R. Thompson
Systematic review finds no difference between home-based and centre-based cardiac rehabilitation in terms of effect on mortality, morbidity and modifiable risk factors in patients with CHD
Evid. Based Med.,
April 1, 2010;
15(2):
38 - 38.
[Full Text]
[PDF]
|
|
|
|
Top
Abstract
How Should Program Trials...