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The impact of non-pharmacological therapies on cardiovascular outcomes in patients with refractory angina: a systematic review and meta-analysis of randomized controlled trials

2021· review· en· W4200546724 on OpenAlexaboutno aff
Sarah Verhemel, David A. Jones, Deshan Weeraman, Jessry Veerapen, Andreas Baumbach, Akanksha Mathur

Bibliographic record

VenueEuropean Heart Journal · 2021
Typereview
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRandomized controlled trialAnginaRevascularizationInternal medicineClinical trialCanadian Cardiovascular SocietyIntensive care medicineMaceMeta-analysisPercutaneous coronary interventionMyocardial infarctionPhysical therapy

Abstract

fetched live from OpenAlex

Abstract Despite advances in revascularization techniques and optimal medical management, refractory angina (RFA) represents an essential group of patients where progress has stalled, and in which therapeutic approaches remain uncertain. Numerous randomized control trials have reported clinical outcomes on a variety of treatments but to date no direct outcome comparison has been made. Our aim is to investigate and compare the outcomes of these different non-pharmacological technologies in RFA, centring on major adverse cardiac events and all-cause mortality. We performed a systematic review and meta-analysis of randomized controlled trials using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A comprehensive search was performed of PubMed, EMBASE (Excerpta Medica database),Cochrane, ClinicalTrials.gov, Google Scholar databases of randomized controlled trials, and scientific session abstracts. Studies were deemed eligible if they met the following criteria: (1) full-length publications in peer-reviewed journals; (2) evaluated non-pharmacological therapies use in patients with no further revascularization options while on optimal medical treatment; (3) patients had ongoing angina, Canadian Cardiovascular Society class II–IV; and (4) included a placebo/control arm. We calculated risk ratios for all-cause mortality, combined MACE events. We assessed heterogeneity using χ2 and I2 tests. We analysed 3292 citations with 51 randomized control trials testing 9 therapies including angiogenic proteins, stem-cell therapy, lipoprotein apheresis, coronary sinus reducer, spinal cord stimulator, percutaneous laser revascularization, shock-wave therapy, transmyocardial laser revascularization and enhanced external counter pulsation all meeting the inclusion criteria (table 1). Our analysis identified stem cell therapy as the only therapy with a reduction in all-cause mortality (Odds ratio, 0.45; CI, 0.21–1.00) (figure 1). A corresponding reduction in major adverse cardiac events (MACE) was also seen with stem cell therapy (OR 0.48: CI 0.30–0.75) alongside patients who received angiogenic proteins (OR 0.72: CI 0.55–0.93) and cardiac shockwave therapy (OR, 0.21: CI 0.10–0.46) Improvements in secondary measures of angina symptoms or frequency were seen with stem cell therapy, angiogenic proteins, coronary sinus reducer, spinal cord stimulator, shock-wave therapy, transmyocardial laser revascularization and enhanced external counterpulsation. This is the largest meta-analysis comparing outcomes of novel technologies used in refractory angina. This suggests that stem cell therapy is the only non-pharmacological therapy for RFA associated with a reduction in mortality, MACE and anginal symptoms. We propose further larger randomized control trials, to support these findings. Funding Acknowledgement Type of funding sources: None. Table 1. Randomized control trials and outcomesFigure 1. All-cause mortality forest plot

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.054
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0260.046
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.120
GPT teacher head0.410
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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