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Record W2547035332 · doi:10.1177/2047487316677115

Seeking ‘meta guidelines’ for lipids

2016· letter· en· W2547035332 on OpenAlexaff
William Dafoe, Robert A. Hegele

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2016
Typeletter
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsWestern UniversityUniversity of Alberta
Fundersnot available
KeywordsMedicineMEDLINEIntensive care medicine

Abstract

fetched live from OpenAlex

‘Medicine is a science of uncertainty and an art of probability.’ Sir William Osler ‘Metacognition’ is defined as the ‘awareness and understanding of one’s own thought processes’.1 Perhaps we all need to develop such a ‘meta approach’ to deal with the ongoing release of new national and international lipid guidelines. Guidelines represent an important touchstone for the management of individual patients, for secondary prevention and for population strategies, usually for primary prevention. Why are clinical practice guidelines important? Both patients and practitioners presume that guidelines crystallize the outcome of a sound scientific process that utilized standardized protocols to grade and evaluate evidence, typically randomized controlled trials (RCTs) and meta-analyses of these. Guidelines can influence standards of care and inform regulatory policies. Financial reimbursement may depend on achieving prespecified guideline-defined targets. Once they become the de facto standard of care, guidelines are used in peer review assessments and malpractice claims. Mehta et al.2 reported a strong ‘dose-response’ association between adherence to guidelines and performance measures and outcomes. But why do different recommendations seem to arise from various guidelines, especially as the same evidence supposedly forms the basis for each? The problem intensifies as clinicians feel inundated with different guidelines, especially when some present seemingly conflicting messages. It is beyond the scope of this editorial to compare and contrast all the different guidelines; however, the process of guideline development and the terms of reference are important determinants in the final recommendations. In an attempt to standardize guideline development, the US Institute of Medicine (IOM)3 suggested the following guiding principles: (1) establish transparency; (2) manage conflict of interest; (3) describe guideline development group composition; (4) clarify clinical practice guidelines/systematic review intersection; (5) establish evidence foundations for rating the strength of recommendations; (6) articulate recommendations; (7) define external review; and (8) establish a process for updating. The American College of Cardiology/American Heart Association (ACC/AHA) 2013 guidelines4 articulated such guiding principles, and groups such as a Mayo Clinic task force were able to offer constructive criticisms.5 Some of these criticisms included the concern that medical truth is more than the sum of only randomized trials (i.e. ‘scientific proof’), and excluding non-statin lipid-lowering therapies since there was no proof (‘the lack of evidence of benefit does not equal evidence of no benefit’). An expert writing committee of the ACC/AHA, in reaction to these concerns,6 recommended measurement of an initial and follow-up lipid panel to gauge desired treatment efficacy, with the option that non-statin drugs ‘may be considered’ to reduce low-density lipoprotein (LDL) cholesterol. One of us (RAH) was a contributing author for the recent Canadian Cardiovascular Society (CCS) lipid guidelines,7 and can verify that this process endeavoured to adhere to all IOM principles. A somewhat novel feature of the 2016 CCS guidelines was utilizing population, intervention, comparator, outcomes (PICO) questions posed by the primary panel. The PICO format helps clinicians determine whether the recommendations apply to patients in their practice. A sample PICO question is: ‘Among adults in whom screening is recommended is non-fasting lipid determination equivalent to fasting lipid determination for risk assessment?’ The guidelines discuss and evaluate the evidence and provide recommendations in the context of this question and answer format. The process involved review of articles published since the previous 2013 CCS guidelines, an initial face-to-face meeting of the primary writing panel, regular teleconferences and frequent emails between subcommittee members. Each recommendation required ratification by a 2/3 blinded and anonymous vote, and 90% of them were accepted. The previous 2013 CCS lipid guidelines were highly cited, indicating that there is considerable interest and need in the medical community for such guidance. The guidelines did not create the need. Are there any ‘downsides’ to guidelines? The general perspective is that guidelines aid or ‘guide’ the treatment of lipid patients, with decisions for individual patients left to the physician’s discretion. However, for certain legal proceedings, guidelines may not merely provide guiding principles, but can serve as legal precedents known as ‘soft laws’.8 The process of guideline development is complex and time consuming, and revisions of guidelines may take years. While recent trials such as IMPROVE-IT,9 which showed the incremental efficacy of combining ezetimibe with a statin were taken into account for the 2016 CCS guidelines, the other recommendations may need to be updated as RCTs with newer agents, such as PCSK9 inhibitors are reported. Another potential disadvantage of relying on mandated standards is that practitioners may stop thinking critically about underlying physiological principles or the applicability to the individual patient. Many guidelines recommend the use of a risk engine to stratify patients into categories in order to guide intensity of treatment. However, there may be limitations to such a ‘one size fits all’ approach. For instance, Thanassoulis et al.10 proposed an ‘individualized benefit approach’ based on predicted absolute risk reduction over 10 years (ARR10) of 2.3% or greater (number needed to treat (NNT) <44) derived from RCT data. They applied this ‘individual benefit approach’ versus the traditional 10-year risk for a large American cohort from the National Health and Nutrition Examination Survey (NHANES). They calculated that an additional 9.5 million ‘low-risk’ individuals (<7.5% 10-year risk) with an ARR10 greater than 2.3% could benefit from statins. A similar perspective was introduced by Soran et al.,11 who proposed that the NNT, the pre-treatment LDL cholesterol and achievable LDL cholesterol with statins be used. They argue that this approach should be used in the establishment of formal lipid guidelines. In this issue of the European Journal of Preventive Cardiology, Soran et al.12 now apply their overall approach using a NNT of less than 50 as the trigger for treatment. They demonstrate that for primary prevention, when pre-treatment LDL cholesterol is greater than 3.2 mmol/L, it is more effective to treat to a target of 1.8 mmol/L rather than to administer a fixed dosage of atorvastatin 20 mg/day (Figure 1). Note that for pre-treatment LDL cholesterol of 2 mmol/L and 10-year risk greater than 20% (i.e. high risk), the treatment goal must be lower than the ‘usual’ goal of 1.8 mmol/L to arrive at an appropriate NNT. Robinson and Ray13 argue that this demonstrates the weakness of specific targets in that a ‘high-risk patient may be at goal and not receive appropriate risk reduction therapy’. For secondary prevention, a contrasting perspective emerges. Treating with atorvastatin 80 mg/day will be appropriate, either as a fixed dose or targeting LDL cholesterol to less than 1.8 mmol/L when pre-treatment LDL cholesterol is about 3 mmol/L. However, when pre-treatment LDL cholesterol is less than 2 mmol/L and 5 year cardiovascular disease risk is less than 30%, a post-treatment goal of 1.8 mmol/L yields an unacceptably high NNT (>50): atorvastatin 80 mg/day is still required in this situation, despite the fact that the patient is already at or close to ‘target’ (Figure 2). In this scenario, the clinician must not be lulled into a false sense of security and should still treat assertively, even if the resulting LDL cholesterol straddles 1 mmol/L. Number need to treat (NNT) to prevent one cardiovascular disease (CVD) event in 10 years with low-density lipoprotein (LDL) targets (T) and without LDL cholesterol targets (NoT) for primary prevention. As derived from Soran et al., Table 1.12 Number need to treat (NNT) to prevent one cardiovascular disease (CVD) event in 5 years with low-density lipoprotein (LDL) cholesterol targets (T) and without LDL cholesterol targets (NoT) for secondary prevention. As derived from Soran et al., Table 2.12 Traditional risk calculations are influenced largely by age, and thus, the importance of increased LDL cholesterol in younger or low-risk individuals may not be appreciated. It is suggested that utilizing these other approaches (e.g. NNT, individual benefit) could be considered for future guidelines to determine eligibility for statins. Robinson and Ray13 suggest that LDL cholesterol ‘thresholds’ rather than ‘targets’ may be preferable. If independent analyses can replicate some of these concepts, it is possible that future guidelines may be directed by concepts grounded in NNT considerations, and by more individualized approaches to the use of targets versus the use of evidence-based dosages in a fixed manner. Most guidelines are ‘flawed’ at any point in time; they evolve as the evidence base evolves. However, the most useful guidelines help practitioners to keep their ‘eye on the ball’ by helping their patients treat risk and improve outcomes in a manner that is both acceptable and clinically impactful. Lipid-lowering agents are effective, and with newer options already available, their judicious use requires the support of scientific lipid guidelines. We believe that the next iteration of lipid guidelines might begin to incorporate such concepts as the above described ARR thresholds or NNT.14 The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The author(s) received no financial support for the research, authorship, and/or publication of this article.

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)0.041
metaresearch head score (Gemma)0.264
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.097
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.264
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0040.004
Science and technology studies0.0040.005
Scholarly communication0.0090.007
Open science0.0040.006
Research integrity0.0970.067
Insufficient payload (model declined to judge)0.0090.010

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 head0.101
GPT teacher head0.339
Teacher spread0.238 · 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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Citations1
Published2016
Admission routes1
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