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Record W2915301701 · doi:10.1002/ejhf.1426

Individualizing Surgical Revascularization in Patients with Ischaemic Heart Failure — A Further Dive into Stiches

2019· letter· en· W2915301701 on OpenAlexaboutno aff
Søren Lund Kristensen

Bibliographic record

VenueEuropean Journal of Heart Failure · 2019
Typeletter
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsnot available
FundersUniversity of GlasgowBritish Heart Foundation
KeywordsMedicineEjection fractionHeart failureCardiologyInternal medicineRevascularizationCanadian Cardiovascular SocietyCoronary artery diseaseAnginaIschemic cardiomyopathyClinical endpointRandomized controlled trialMedical therapyUnstable anginaMyocardial infarction

Abstract

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This article refers to 'Burden of medical co-morbidities and benefit from surgical revascularization in patients with ischaemic cardiomyopathy' by A.P. Ambrosy et al., published in this issue on pages 373–381. In the Surgical Treatment for Ischemic Heart Failure (STICH) trial, 1212 patients with heart failure and reduced ejection fraction (HFrEF) and significant coronary artery disease were randomized to coronary artery bypass grafting (CABG) plus medical therapy or medical therapy alone, and followed for a median of 4.7 years.1 The most important inclusion criteria were ejection fraction ≤ 35%, an expected remaining lifetime of > 3 years, absence of severe angina (Canadian Cardiovascular Society angina class III–IV) and left main coronary artery stenosis. While STICH did not meet its primary endpoint of reduction in all-cause death in those randomized to CABG, the extended study (STICHES) did and reported an absolute risk reduction of 7% for all-cause death (P = 0.02) and 9% for cardiovascular death (P = 0.006) at a median follow-up of 9.8 years.2 In this issue of the Journal, Ambrosy and colleagues examined whether the benefits of CABG were consistent across a spectrum of co-morbidity.3 To do so, they used the Charlson co-morbidity index (CCI), which was developed to quantify the prognostic importance of co-morbidities in medical patients.4 Based on a combination of age and information on 16 medical conditions, the CCI has been previously shown to predict 10-year mortality. The authors categorized patients enrolled in STICH into a group with mild-moderate co-morbidity (CCI score 1–4, 29% of patients) and those with severe co-morbidity (CCI score ≥ 5, 71% of patients). What did they find? They noted that the benefit of CABG plus medical therapy vs. medical therapy alone was retained in both CCI subgroups; 10-year rates of all-cause death by CABG and non-CABG randomization groups were 46.3% vs. 53.0% and 65.2% vs. 73.5% in the mild-moderate, and severe co-morbidity group, respectively. This corresponded to relative and absolute risk reductions of 12.6% and 6.7%, respectively, in the mild-moderate co-morbidity CCI score group and 11.3% and 8.3%, respectively, in the severe co-morbidity CCI score group, with no significant interaction between the effect of treatment and baseline CCI score. Findings were similar for cardiovascular death whereas the relative risk reduction was substantially smaller for the group with severe co-morbidity when looking at a composite endpoint of all-cause death or cardiovascular hospitalization. From the cumulative event curves, it is evident that patients with mild-moderate co-morbidity had similar mortality in either randomization group in the first 2 years, whereas in those with severe co-morbidity, there was an initial excess in mortality in the CABG group, with the survival curves crossing and benefits emerging only after around 2 years. Of course, the authors took a rather simple approach of dichotomizing patients into mild-moderate or severe co-morbidity and probably had limited power to detect meaningful differences across smaller CCI score subgroups. That said, a closer inspection of the spline curves at 1, 5 and 10 years of follow-up did suggest a potential lack of treatment benefit (in terms of risk for all-cause death for those randomized to CABG) in patients with a baseline CCI score ≥ 8. This high-risk subgroup represented 6% of the population. Of interest, a prior analysis of STICH demonstrated an increased perioperative mortality (and a signal towards lesser benefit from CABG) in patients with 6-min walking distance < 300 m, or a physical ability score ≤ 55,5 whereas an analysis of treatment effect according to age indicated that the effect of CABG on all-cause death weakened in patients aged 65–70 years and older, much like what was seen in the recent Danish Study to Assess the Efficacy of ICDs in Patients With Non-Ischemic Systolic Heart Failure on Mortality (DANISH) trial.6, 7 For cardioverter-defibrillator implantation, an increasing burden of co-morbidity has been associated with similar rates of appropriate therapy and higher mortality in an observational study.8 These collective observations also seem to point towards the same message. Namely, that one should expect a diminished effect on all-cause death of any cardiovascular intervention in the presence of considerable baseline co-morbidity (or advanced age). These findings, in turn, make considerable sense given the increased competing risk of non-cardiovascular death in all such scenarios. It is also worth noticing that the authors used a modified version of the CCI score as they did not have information on all conditions included in the score. While some conditions were omitted due to lacking information (AIDS, connective tissue disease, peptic ulcer disease, liver disease), others were substituted by proxies (active smoking instead of chronic obstructive pulmonary disease, depression instead of dementia). Further important caveats to the interpretation of the present analyses is the fact that almost every patient had at least two points from prior myocardial infarction and HFrEF. For this reason alone, hardly any patients had a mild co-morbidity score (1–2 points). Secondly, and perhaps most importantly, the per-protocol exclusion of patients with an investigator-estimated substantial operative mortality risk or estimated survival for non-cardiac disease of < 3 years will have introduced a 'healthy' selection bias. An alternative non-surgical revascularization strategy, potentially particularly relevant in patients deemed at high perioperative risk, is percutaneous coronary intervention (PCI). There is no single substantive randomized trial, although a meta-analysis of small observational studies has suggested a benefit from PCI compared to medical therapy, but not as large as seen with CABG.9 These analyses are, however, very likely to be confounded by indication, in terms of healthier patient selection. A prospective multicentre, open-label trial, comparing PCI to medical therapy in patients with ischaemic HFrEF is ongoing with more than 400 patients recruited as per June 2018.10 This trial, the REVascularization for Ischaemic VEntricular Dysfunction (REVIVED-BCIS2), notably has myocardial viability as one of its inclusion criteria, although this did not seem to influence prognosis or treatment effect in STICH.11 The results presented by Ambrosy et al.3 are important and, combined with prior post-hoc analyses, they add valuable information to the primary findings from STICH and STICHES. Taken together, these various analyses clarify that recommendation of CABG to a patient with ischaemic HFrEF should not be unduly influenced by a specific co-morbidity or even the number of co-morbidities with the possible exception of patients with very severe co-morbidity (CCI score ≥ 8). More important considerations appear to be the severity of coronary artery disease, age, symptoms, performance status and an individual estimation of remaining life expectancy (Figure 1). Whether PCI is a reasonable alternative to CABG, and, in some patients an additional therapeutic opportunity when CABG is contraindicated due to high periprocedural risk, remains to be determined in future trials, including the ongoing REVIVED-BCIS2.10 The author wish to acknowledge the input and valuable help in discussion of the subject from Drs. Naveed Sattar, and John McMurray, BHF Cardiovascular Research Centre University of Glasgow, Glasgow, United Kingdom. Conflict of interest: none declared.

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)0.010
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.005
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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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 designNot applicable
Domainnot available
GenreCommentary

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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Citations2
Published2019
Admission routes1
Has abstractyes

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