Bibliographic record
Abstract
This editorial refers to ‘Cost-effectiveness of the coronary sinus Reducer and its impact on the healthcare burden of refractory angina patients’, by G. Gallone et al., doi:10.1093/ehjqcco/qcz027. Patients with ongoing angina despite optimal medical management who have limited available revascularization options are a challenging population.1–3 Although mortality rates appear to have improved for these ‘no-option’ refractory angina patients,4 they frequently are left with disabling symptoms and have limited treatment options leading to high healthcare utilization rates. The conduct of clinical trials to test new therapeutic options for these patients is challenging given the limited number with truly ‘no-option’ disabling refractory angina patients who meet stringent inclusion criteria.5,6 Clinicians make personalized decisions not only about the care of their patients but also play a role in institutional decisions regarding cost-effectiveness. Whether patient- or institution-oriented, these decisions encompass the risk and benefits of the proposed innovations but also healthcare utilization. The economic aspects of refractory angina are not well described even though the number of patients appears to be growing as mortality rates decline and the population ages.1–5 When studying innovative therapies for chronic conditions, such as refractory angina, economic analyses may be as important as outcomes analyses given the potential cost implications are considerable vis-à-vis the expected health benefits. In Canada for instance, the annualized cost of angina-related disability from a societal perspective including direct, indirect, and system costs were conservatively estimated at $19 209 per patient.7 Gallone et al.8 present a much-needed cost-effectiveness analysis of the coronary sinus reducer in Belgium, The Netherlands, and Italy. Their analysis accounted for angina-driven hospitalizations and emergency department (ED) admissions, outpatient visits, and coronary angiography or percutaneous coronary intervention in patients with refractory angina. The modelling approaches of decision analysis enabled investigators to minimize the analytical consequences of variable length of follow-up and to normalize costs after national tariffs associated to the specific Diagnosis Related Group. As acknowledged by the authors, the lack of a control group—as opposed to a pre- vs. post-implantation comparison within the same patient—and the retrospective, incomplete data collection, remain significant limitations of the analysis.8 Decision analytic approach rely on assumptions that, if flawed, might not yield an accurate picture. The investigators made a considerable effort to verify their assumptions and to run sensitivity analyses. Regardless of the analytic assumptions made, the results proposed by investigators need to be interpreted in the context of the phenomenon being studied: angina. Angina may be the most challenging cardiovascular outcome to study. Any angina study, randomized trial, or economic analyses must take into account a number of important considerations inherent to angina, namely the fluctuating nature of angina, the regression to the mean, the Hawthorne effect, and the placebo effect (Figure 1). Chronic stable angina fluctuates both in frequency and intensity and patients typically report bad and good days despite no obvious signs of disease progression (Figure 1A). This cyclic nature of angina contributes to the variability seen in patient-reported outcomes and disease-related quality of life and greatly complicates the task of demonstrating the efficacy of a novel therapy. The regression to the mean bias (Figure 1B) stems from the tendency of patients to seek medical attention (and to accept participation in a dedicated research project) when their symptoms are most prominent. Regression to the mean reflects the likelihood that angina will naturally return to its baseline status regardless of the proposed treatment. Regression to the mean may also be explained by the capacity of patients to self-manage2 and adapt to their new ischaemic threshold.3 When unaccounted for, regression to the mean can be falsely interpreted as a positive therapeutic effect of the treatment being investigated. The Hawthorne effect refers to the alteration of behaviour (including the reporting of symptoms) by the study participants due to their awareness of being observed (Figure 1C). The Hawthorne effect is inherent to any clinical situation where ground-breaking interventions are proposed by enthusiastic physicians to desperate patients. In such case, patients are more likely to report the expected favourable outcomes. Finally, the placebo effect (Figure 1D) is well-known to occur with angina. In the COSIRA trial, for instance, 42% of the participants assigned to the sham intervention control arm improved by at least one Canadian Cardiac Society angina class.9 Importantly, the placebo effect associated with angina may diminish over time. The inherent biases and confounders of refractory angina. These include: (A) the fluctuating nature of angina; (B) the regression to the mean; (C) the Hawthorne effect; (D) the placebo effect and (E) the therapeutic effect. All of these factors create noise that may mask the real therapeutic effect (Figure 1E). The extent to which the authors made their assumptions transparent on these factors is not clear but would have added depth to the analysis. There is a growing body of evidence to suggest that the coronary sinus reducer is not only safe but effective in reducing angina and improving quality of life.10 The present analysis now suggests that the use of this device may also be cost-effective. These findings are even more impressive considering that patients with recent acute coronary syndrome or coronary revascularization were excluded from the cohort (which arguably drove the costs down in the standard-of-care period). This study illustrates the challenges and complexity of cost-effectiveness analysis. Being aware of all the limitations and biases inherent to the study of refractory angina, this article is a call for additional research, including the completion of a properly designed pivotal prospective controlled trial in patients with limited therapeutic options that includes a cost-effectiveness analysis. Conflict of interest: T.H. reports a scientific advisory contract for COSIRA-2 outside the submitted work. E.M.J. has no conflict of interest to declare. The opinions expressed in this article are not necessarily those of the Editors of the European Heart Journal – Quality of Care and Clinical Outcomes or of the European Society of Cardiology.
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 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 | 0.011 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.041 | 0.053 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".