Do self-management interventions in COPD patients work and which patients benefit most? An individual patient data meta-analysis
Bibliographic record
Abstract
Nini H Jonkman,1 Heleen Westland,1 Jaap CA Trappenburg,1 Rolf HH Groenwold,2 Erik WMA Bischoff,3 Jean Bourbeau,4 Christine E Bucknall,5 David Coultas,6 Tanja W Effing,7 Michael J Epton,8 Frode Gallefoss,9 Judith Garcia-Aymerich,10–12 Suzanne M Lloyd,13 Evelyn M Monninkhof,2 Huong Q Nguyen,14 Job van der Palen,15,16 Kathryn L Rice,17 Maria Sedeno,4 Stephanie JC Taylor,18 Thierry Troosters,19 Nicholas A Zwar,20 Arno W Hoes,2 Marieke J Schuurmans1 1Department of Rehabilitation, Nursing Science and Sports, 2Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, 3Department of Primary and Community Care, Radboud University Medical Center, Nijmegen, the Netherlands; 4Respiratory Epidemiology and Clinical Research Unit, Department of Medicine, McGill University Health Center, McGill University, Montreal, QC, Canada; 5Department of Respiratory Medicine, Glasgow Royal Infirmary, Glasgow, UK; 6Veterans Administration Portland Health Care System and Oregon Health & Science University, Portland, OR, USA; 7Department of Respiratory Medicine, Repatriation General Hospital, Adelaide, SA, Australia; 8Canterbury District Health Board, Respiratory Services, Christchurch Hospital, Christchurch, New Zealand; 9Department of Pulmonary Medicine, Sorlandet Hospital, Kristiansand, Norway; 10Centre for Research in Environmental Epidemiology CREAL, 11Pompeu Fabra University, 12CIBER Epidemiología y Salud Pública CIBERESP, Barcelona, Spain; 13Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK; 14Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA; 15Department of Research Methodology, Measurement and Data Analysis, University of Twente, 16Department of Clinical Epidemiology, Medisch Spectrum Twente, Enschede, the Netherlands; 17Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Minneapolis Veterans Affairs Health Care Service and University of Minnesota, Minneapolis, MN, USA; 18Centre for Primary Care and Public Health, Queen Mary University of London, London, UK; 19Department of Rehabilitation Sciences, Catholic University of Leuven, Leuven, Belgium; 20School of Public Health and Community Medicine, UNSW Australia, Sydney, NSW, Australia Background: Self-management interventions are considered effective in patients with COPD, but trials have shown inconsistent results and it is unknown which patients benefit most. This study aimed to summarize the evidence on effectiveness of self-management interventions and identify subgroups of COPD patients who benefit most.Methods: Randomized trials of self-management interventions between 1985 and 2013 were identified through a systematic literature search. Individual patient data of selected studies were requested from principal investigators and analyzed in an individual patient data meta-analysis using generalized mixed effects models.Results: Fourteen trials representing 3,282 patients were included. Self-management interventions improved health-related quality of life at 12 months (standardized mean difference 0.08, 95% confidence interval [CI] 0.00–0.16) and time to first respiratory-related hospitalization (hazard ratio 0.79, 95% CI 0.66–0.94) and all-cause hospitalization (hazard ratio 0.80, 95% CI 0.69–0.90), but had no effect on mortality. Prespecified subgroup analyses showed that interventions were more effective in males (6-month COPD-related hospitalization: interaction P=0.006), patients with severe lung function (6-month all-cause hospitalization: interaction P=0.016), moderate self-efficacy (12-month COPD-related hospitalization: interaction P=0.036), and high body mass index (6-month COPD-related hospitalization: interaction P=0.028 and 6-month mortality: interaction P=0.026). In none of these subgroups, a consistent effect was shown on all relevant outcomes.Conclusion: Self-management interventions exert positive effects in patients with COPD on respiratory-related and all-cause hospitalizations and modest effects on 12-month health-related quality of life, supporting the implementation of self-management strategies in clinical practice. Benefits seem similar across the subgroups studied and limiting self-management interventions to specific patient subgroups cannot be recommended. Keywords: chronic obstructive pulmonary disease, individual patient data meta-analysis, self-management, subgroup analysis
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.040 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".