Between the Lines. Finding the Truth in Medical Literature [Book Review]

19 07 2013

In the 1970s a study was conducted among 60 physicians and physicians-in-training. They had to solve a simple problem:

“If a test to detect a disease whose prevalence is 1/1000 has a false positive rate of 5 %, what is the chance that a person found to have a positive result actually has the disease, assuming that you know nothing about the person’s symptoms or signs?” 

Half of the “medical experts” thought the answer was 95%.
Only a small proportion, 18%, of the doctors arrived at the right answer of 2%.

If you are a medical expert who comes the same faulty conclusion -or need a refresher how to arrive at the right answer- you might benefit from the book written by Marya Zilberberg: “Between the Lines. Finding the Truth in Medical Literature”.

The same is true for a patient whose doctor thinks he/she is among the 95% to benefit form such a test…
Or for journalists who translate medical news to the public…
Or for peer reviewers or editors who have to assess biomedical papers…

In other words, this book is useful for everyone who wants to be able to read “between the lines”. For everyone who needs to examine medical literature critically from time to time and doesn’t want to rely solely on the interpretation of others.

I hope that I didn’t scare you off with the abovementioned example. Between the Lines surely is NOT a complicated epidemiology textbook, nor a dull studybook where you have to struggle through a lot of definitions, difficult tables and statistic formulas and where each chapter is followed by a set of review questions that test what you learned.

This example is presented half way the book, at the end of Part I. By then you have enough tools to solve the question yourself. But even if you don’t feel like doing the exact calculation at that moment, you have a solid basis to understand the bottomline: the (enormous) 93% gap (95% vs 2% of the people with a positive test are considered truly positive) serves as the pool for overdiagnosis and overtreatment.

In the previous chapters of Part I (“Context”), you have learned about the scientific methods in clinical research, uncertainty as the only certain feature of science, the importance of denominators, outcomes that matter and outcomes that don’t, Bayesian probability, evidence hierarchies, heterogeneous treatment effects (does the evidence apply to this particular patient?) and all kinds of biases.

Most reviewers prefer part I of the book. Personally I find part II (“Evaluation”) as interesting.

Part II deals with the study question, and study design, pros and cons of observational and interventional studies, validity, hypothesis testing and statistics.

Perhaps part II  is somewhat less narrative. Furthermore, it deals with tougher topics like statistics. But I find it very valuable for being able to critically appraise a study. I have never seen a better description of “ODDs”: somehow ODDs it is better to grasp if you substitute “treatment A” and “treatment B” for “horse A” and “horse B”, and substitute “death” for “loss of a race”.
I knew the basic differences between cohort studies, case control studies and so on, but I kind of never realized before that ODDs Ratio is the only measure of association available in a case-control study and that case control studies cannot estimate incidence or prevalence (as shown in a nice overview in table 4).

Unlike many other books about “the art of reading of medical articles”, “study designs” or “Evidence Based Medicine”, Marya’s book is easy to read. It is written at a conversational tone and statements are illustrated by means of current, appealing examples, like the overestimation of risk of death from the H1N1 virus, breast cancer screening and hormone replacement therapy.

Although I had printed this book in a wrong order (page 136 next to 13 etc), I was able to read (and understand) 1/3 of the book (the more difficult part II) during a 2 hour car trip….

Because this book is comprehensive, yet accessible, I recommend it highly to everyone, including fellow librarians.

Marya even mentions medical librarians as a separate target audience:

Medical librarians may find this book particularly helpful: Being at the forefront of evidence dissemination, they can lead the charge of separating credible science from rubbish.

(thanks Marya!)

In addition, this book may be indirectly useful to librarians as it may help to choose appropriate methodological filters and search terms for certain EBM-questions. In case of etiology questions words like “cohort”, “case-control”, “odds”, “risk” and “regression” might help to find the “right” studies.

By the way Marya Ziberberg @murzee at Twitter and she writes at her blog Healthcare etc.

p.s. 1 I want to apologize to Marya for writing this review more than a year after the book was published. For personal reasons I found little time to read and blog. Luckily the book lost none of its topicality.

p.s. 2 patients who are not very familiar with critical reading of medical papers might benefit from reading “your medical mind” first [1]. 

bwtn the lines

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No, Google Scholar Shouldn’t be Used Alone for Systematic Review Searching

9 07 2013

Several papers have addressed the usefulness of Google Scholar as a source for systematic review searching. Unfortunately the quality of those papers is often well below the mark.

In 2010 I already [1]  (in the words of Isla Kuhn [2]) “robustly rebutted” the Anders’ paper PubMed versus Google Scholar for Retrieving Evidence” [3] at this blog.

But earlier this year another controversial paper was published [4]:

“Is the coverage of google scholar enough to be used alone for systematic reviews?

It is one of the highly accessed papers of BMC Medical Informatics and Decision Making and has been welcomed in (for instance) the Twittosphere.

Researchers seem  to blindly accept the conclusions of the paper:

But don’t rush  and assume you can now forget about PubMed, MEDLINE, Cochrane and EMBASE for your systematic review search and just do a simple Google Scholar (GS) search instead.

You might  throw the baby out with the bath water….

… As has been immediately recognized by many librarians, either at their blogs (see blogs of Dean Giustini [5], Patricia Anderson [6] and Isla Kuhn [1]) or as direct comments to the paper (by Tuulevi OvaskaMichelle Fiander and Alison Weightman [7].

In their paper, Jean-François Gehanno et al examined whether GS was able to retrieve all the 738 original studies included in 29 Cochrane and JAMA systematic reviews.

And YES! GS had a coverage of 100%!

WOW!

All those fools at the Cochrane who do exhaustive searches in multiple databases using controlled vocabulary and a lot of synonyms when a simple search in GS could have sufficed…

But it is a logical fallacy to conclude from their findings that GS alone will suffice for SR-searching.

Firstly, as Tuulevi [7] rightly points out :

“Of course GS will find what you already know exists”

Or in the words of one of the official reviewers [8]:

What the authors show is only that if one knows what studies should be identified, then one can go to GS, search for them one by one, and find out that they are indexed. But, if a researcher already knows the studies that should be included in a systematic review, why bother to also check whether those studies are indexed in GS?

Right!

Secondly, it is also the precision that counts.

As Dean explains at his blog a 100% recall with a precision of 0,1% (and it can be worse!) means that in order to find 36 relevant papers you have to go through  ~36,700 items.

Dean:

Are the authors suggesting that researchers consider a precision level of 0.1% acceptable for the SR? Who has time to sift through that amount of information?

It is like searching for needles in a haystack.  Correction: It is like searching for particular hay stalks in a hay stack. It is very difficult to find them if they are hidden among other hay stalks. Suppose the hay stalks were all labeled (title), and I would have a powerful haystalk magnet (“title search”)  it would be a piece of cake to retrieve them. This is what we call “known item search”. But would you even consider going through the haystack and check the stalks one by one? Because that is what we have to do if we use Google Scholar as a one stop search tool for systematic reviews.

Another main point of criticism is that the authors have a grave and worrisome lack of understanding of the systematic review methodology [6] and don’t grasp the importance of the search interface and knowledge of indexing which are both integral to searching for systematic reviews.[7]

One wonders why the paper even passed the peer review, as one of the two reviewers (Miguel Garcia-Perez [8]) already smashed the paper to pieces.

The authors’ method is inadequate and their conclusion is not logically connected to their results. No revision (major, minor, or discretionary) will save this work. (…)

Miguel’s well funded criticism was not well addressed by the authors [9]. Apparently the editors didn’t see through and relied on the second peer reviewer [10], who merely said it was a “great job” etcetera, but that recall should not be written with a capital R.
(and that was about the only revision the authors made)

Perhaps it needs another paper to convince Gehanno et al and the uncritical readers of their manuscript.

Such a paper might just have been published [11]. It is written by Dean Giustini and Maged Kamel Boulos and is entitled:

Google Scholar is not enough to be used alone for systematic reviews

It is a simple and straightforward paper, but it makes its points clearly.

Giustini and Kamel Boulos looked for a recent SR in their own area of expertise (Chou et al [12]), that included a comparable number of references as that of Gehanno et al. Next they test GS’ ability to locate these references.

Although most papers cited by Chou et al. (n=476/506;  ~95%) were ultimately found in GS, numerous iterative searches were required to find the references and each citation had to be managed once at a time. Thus GS was not able to locate all references found by Chou et al. and the whole exercise was rather cumbersome.

As expected, trying to find the papers by a “real-life” GS search was almost impossible. Because due to its rudimentary structure, GS did not understand the expert search strings and was unable to translate them. Thus using Chou et al.’s original search strategy and keywords yielded unmanageable results of approximately >750,000 items.

Giustini and Kamel Boulos note that GS’ ability to search into the full-text of papers combined with its PageRank’s algorithm can be useful.

On the other hand GS’ changing content, unknown updating practices and poor reliability make it an inappropriate sole choice for systematic reviewers:

As searchers, we were often uncertain that results found one day in GS had not changed a day later and trying to replicate searches with date delimiters in GS did not help. Papers found today in GS did not mean they would be there tomorrow.

But most importantly, not all known items could be found and the search process and selection are too cumbersome.

Thus shall we now for once and for all conclude that GS is NOT sufficient to be used alone for SR searching?

We don’t need another bad paper addressing this.

But I would really welcome a well performed paper looking at the additional value of a GS in SR-searching. For I am sure that GS may be valuable for some questions and some topics in some respects. We have to find out which.

References

  1. PubMed versus Google Scholar for Retrieving Evidence 2010/06 (laikaspoetnik.wordpress.com)
  2. Google scholar for systematic reviews…. hmmmm  2013/01 (ilk21.wordpress.com)
  3. Anders M.E. & Evans D.P. (2010) Comparison of PubMed and Google Scholar literature searches, Respiratory care, May;55(5):578-83  PMID:
  4. Gehanno J.F., Rollin L. & Darmoni S. (2013). Is the coverage of Google Scholar enough to be used alone for systematic reviews., BMC medical informatics and decision making, 13:7  PMID:  (open access)
  5. Is Google scholar enough for SR searching? No. 2013/01 (blogs.ubc.ca/dean)
  6. What’s Wrong With Google Scholar for “Systematic” Review 2013/01 (etechlib.wordpress.com)
  7. Comments at Gehanno’s paper (www.biomedcentral.com)
  8. Official Reviewer’s report of Gehanno’s paper [1]: Miguel Garcia-Perez, 2012/09
  9. Authors response to comments  (www.biomedcentral.com)
  10. Official Reviewer’s report of Gehanno’s paper [2]: Henrik von Wehrden, 2012/10
  11. Giustini D. & Kamel Boulos M.N. (2013). Google Scholar is not enough to be used alone for systematic reviews, Online Journal of Public Health Informatics, 5 (2) DOI:
  12. Chou W.Y.S., Prestin A., Lyons C. & Wen K.Y. (2013). Web 2.0 for Health Promotion: Reviewing the Current Evidence, American Journal of Public Health, 103 (1) e9-e18. DOI:





Friday Foolery #51 Statistically Funny

1 06 2012

Epidemiologists, people working in the EBM field and, above all, statisticians are said to have no sense of humor.*

Hilda Bastian is a clear exception to this rule.

I met Hilda a few years ago at a Cochrane colloquium. At that time she was working as a consumer advocate in Australia. Nowadays she is editor and curator of PubMed Health. According to her Twitter Bio (she tweets as @hildabast) she is (still) “Interested in effective communication as well as effective health care”. She also writes important articles, like “Seventy-Five Trials and Eleven Systematic Reviews a Day: How Will We Ever Keep Up? (PLOS 2010), reviewed at this blog.

Today I learned she also has a great creative talent in cartoon drawing, in the field of …  yeah… EBM, epidemiology & statistics.

Below is one of her cartoons, which fits in well with a recent post in the BMJ by Ray Moynihan, retweeted by Hilda: Preventing overdiagnosis: how to stop harming the healthy. In her post she refers to another article: Overdiagnosis in cancer (JNCI 2010), saying:

“Finding and aggressively treating non-symptomatic disease that would never have made people sick, inventing new conditions and re-defining the thresholds for old ones: will there be anyone healthy left at all?”

I invite you to go and visit Hilda’s blog Statistically funny (Commenting on the science of unbiased health research with cartoons) and to enjoy her cartoons, that are often inspired by recent publications in the field.

* My post #NotSoFunny #16: ridiculing RCTs and EBM even led David Rind to sigh that “EBM folks are not necessarily known for their great senses of humor”. (so I’m no exception to the rule ;)





The Scatter of Medical Research and What to do About it.

18 05 2012

ResearchBlogging.orgPaul Glasziou, GP and professor in Evidence Based Medicine, co-authored a new article in the BMJ [1]. Similar to another paper [2] I discussed before [3] this paper deals with the difficulty for clinicians of staying up-to-date with the literature. But where the previous paper [2,3] highlighted the mere increase in number of research articles over time, the current paper looks at the scatter of randomized clinical trials (RCTs) and systematic reviews (SR’s) accross different journals cited in one year (2009) in PubMed.

Hofmann et al analyzed 7 specialties and 9 sub-specialties, that are considered the leading contributions to the burden of disease in high income countries.

They followed a relative straightforward method for identifying the publications. Each search string consisted of a MeSH term (controlled  term) to identify the selected disease or disorders, a publication type [pt] to identify the type of study, and the year of publication. For example, the search strategy for randomized trials in cardiology was: “heart diseases”[MeSH] AND randomized controlled trial[pt] AND 2009[dp]. (when searching “heart diseases” as a MeSH, narrower terms are also searched.) Meta-analysis[pt] was used to identify systematic reviews.

Using this approach Hofmann et al found 14 343 RCTs and 3214 SR’s published in 2009 in the field of the selected (sub)specialties. There was a clear scatter across journals, but this scatter varied considerably among specialties:

“Otolaryngology had the least scatter (363 trials across 167 journals) and neurology the most (2770 trials across 896 journals). In only three subspecialties (lung cancer, chronic obstructive pulmonary disease, hearing loss) were 10 or fewer journals needed to locate 50% of trials. The scatter was less for systematic reviews: hearing loss had the least scatter (10 reviews across nine journals) and cancer the most (670 reviews across 279 journals). For some specialties and subspecialties the papers were concentrated in specialty journals; whereas for others, few of the top 10 journals were a specialty journal for that area.
Generally, little overlap occurred between the top 10 journals publishing trials and those publishing systematic reviews. The number of journals required to find all trials or reviews was highly correlated (r=0.97) with the number of papers for each specialty/ subspecialty.”

Previous work already suggested that this scatter of research has a long tail. Half of the publications is in a minority of papers, whereas the remaining articles are scattered among many journals (see Fig below).

Click to enlarge en see legends at BMJ 2012;344:e3223 [CC]

The good news is that SRs are less scattered and that general journals appear more often in the top 10 journals publishing SRs. Indeed for 6 of the 7 specialties and 4 of the 9 subspecialties, the Cochrane Database of Systematic Reviews had published the highest number of systematic reviews, publishing between 6% and 18% of all the systematic reviews published in each area in 2009. The bad news is that even keeping up to date with SRs seems a huge, if not impossible, challenge.

In other words, it is not sufficient for clinicians to rely on personal subscriptions to a few journals in their specialty (which is common practice). Hoffmann et al suggest several solutions to help clinicians cope with the increasing volume and scatter of research publications.

  • a central library of systematic reviews (but apparently the Cochrane Library fails to fulfill such a role according to the authors, because many reviews are out of date and are perceived as less clinically relevant)
  • registry of planned and completed systematic reviews, such as prospero. (this makes it easier to locate SRs and reduces bias)
  • Synthesis of Evidence and synopses, like the ACP-Jounal Club which summarizes the best evidence in internal medicine
  • Specialised databases that collate and critically appraise randomized trials and systematic reviews, like www.pedro.org.au for physical therapy. In my personal experience, however, this database is often out of date and not comprehensive
  • Journal scanning services like EvidenceUpdates from mcmaster.ca), which scans over 120 journals, filters articles on the basis of quality, has practising clinicians rate them for relevance and newsworthiness, and makes them available as email alerts and in a searchable database. I use this service too, but besides that not all specialties are covered, the rating of evidence may not always be objective (see previous post [4])
  • The use of social media tools to alert clinicians to important new research.

Most of these solutions are (long) existing solutions that do not or only partly help to solve the information overload.

I was surprised that the authors didn’t propose the use of personalized alerts. PubMed’s My NCBI feature allows to create automatic email alerts on a topic and to subscribe to electronic tables of contents (which could include ACP journal Club). Suppose that a physician browses 10 journals roughly covering 25% of the trials. He/she does not need to read all the other journals from cover to cover to avoid missing one potentially relevant trial. Instead it is far more efficient to perform a topic search to filter relevant studies from journals that seldom publish trials on the topic of interest. One could even use the search of Hoffmann et al to achieve this.* Although in reality, most clinical researchers will have narrower fields of interest than all studies about endocrinology and neurology.

At our library we are working at creating deduplicated, easy to read, alerts that collate table of contents of certain journals with topic (and author) searches in PubMed, EMBASE and other databases. There are existing tools that do the same.

Another way to reduce the individual work (reading) load is to organize journals clubs or even better organize regular CATs (critical appraised topics). In the Netherlands, CATS are a compulsory item for residents. A few doctors do the work for many. Usually they choose topics that are clinically relevant (or for which the evidence is unclear).

The authors shortly mention that their search strategy might have missed  missed some eligible papers and included some that are not truly RCTs or SRs, because they relied on PubMed’s publication type to retrieve RCTs and SRs. For systematic reviews this may be a greater problem than recognized, for the authors have used meta-analyses[pt] to identify systematic reviews. Unfortunately PubMed has no publication type for systematic reviews, but it may be clear that there are many more systematic reviews that meta-analyses. Possibly systematical reviews might even have a different scatter pattern than meta-analyses (i.e. the latter might be preferentially included in core journals).

Furthermore not all meta-analyses and systematic reviews are reviews of RCTs (thus it is not completely fair to compare MAs with RCTs only). On the other hand it is a (not discussed) omission of this study, that only interventions are considered. Nowadays physicians have many other questions than those related to therapy, like questions about prognosis, harm and diagnosis.

I did a little imperfect search just to see whether use of other search terms than meta-analyses[pt] would have any influence on the outcome. I search for (1) meta-analyses [pt] and (2) systematic review [tiab] (title and abstract) of papers about endocrine diseases. Then I subtracted 1 from 2 (to analyse the systematic reviews not indexed as meta-analysis[pt])

Thus:

(ENDOCRINE DISEASES[MESH] AND SYSTEMATIC REVIEW[TIAB] AND 2009[DP]) NOT META-ANALYSIS[PT]

I analyzed the top 10/11 journals publishing these study types.

This little experiment suggests that:

  1. the precise scatter might differ per search: apparently the systematic review[tiab] search yielded different top 10/11 journals (for this sample) than the meta-analysis[pt] search. (partially because Cochrane systematic reviews apparently don’t mention systematic reviews in title and abstract?).
  2. the authors underestimate the numbers of Systematic Reviews: simply searching for systematic review[tiab] already found appr. 50% additional systematic reviews compared to meta-analysis[pt] alone
  3. As expected (by me at last), many of the SR’s en MA’s were NOT dealing with interventions, i.e. see the first 5 hits (out of 108 and 236 respectively).
  4. Together these findings indicate that the true information overload is far greater than shown by Hoffmann et al (not all systematic reviews are found, of all available search designs only RCTs are searched).
  5. On the other hand this indirectly shows that SRs are a better way to keep up-to-date than suggested: SRs  also summarize non-interventional research (the ratio SRs of RCTs: individual RCTs is much lower than suggested)
  6. It also means that the role of the Cochrane Systematic reviews to aggregate RCTs is underestimated by the published graphs (the MA[pt] section is diluted with non-RCT- systematic reviews, thus the proportion of the Cochrane SRs in the interventional MAs becomes larger)

Well anyway, these imperfections do not contradict the main point of this paper: that trials are scattered across hundreds of general and specialty journals and that “systematic reviews” (or meta-analyses really) do reduce the extent of scatter, but are still widely scattered and mostly in different journals to those of randomized trials.

Indeed, personal subscriptions to journals seem insufficient for keeping up to date.
Besides supplementing subscription by  methods such as journal scanning services, I would recommend the use of personalized alerts from PubMed and several prefiltered sources including an EBM search machine like TRIP (www.tripdatabase.com/).

*but I would broaden it to find all aggregate evidence, including ACP, Clinical Evidence, syntheses and synopses, not only meta-analyses.

**I do appreciate that one of the co-authors is a medical librarian: Sarah Thorning.

References

  1. Hoffmann, Tammy, Erueti, Chrissy, Thorning, Sarah, & Glasziou, Paul (2012). The scatter of research: cross sectional comparison of randomised trials and systematic reviews across specialties BMJ, 344 : 10.1136/bmj.e3223
  2. Bastian, H., Glasziou, P., & Chalmers, I. (2010). Seventy-Five Trials and Eleven Systematic Reviews a Day: How Will We Ever Keep Up? PLoS Medicine, 7 (9) DOI: 10.1371/journal.pmed.1000326
  3. How will we ever keep up with 75 trials and 11 systematic reviews a day (laikaspoetnik.wordpress.com)
  4. Experience versus Evidence [1]. Opioid Therapy for Rheumatoid Arthritis Pain. (laikaspoetnik.wordpress.com)




Can Guidelines Harm Patients?

2 05 2012

ResearchBlogging.orgRecently I saw an intriguing “personal view” in the BMJ written by Grant Hutchison entitled: “Can Guidelines Harm Patients Too?” Hutchison is a consultant anesthetist with -as he calls it- chronic guideline fatigue syndrome. Hutchison underwent an acute exacerbation of his “condition” with the arrival of another set of guidelines in his email inbox. Hutchison:

On reviewing the level of evidence provided for the various recommendations being offered, I was struck by the fact that no relevant clinical trials had been carried out in the population of interest. Eleven out of 25 of the recommendations made were supported only by the lowest levels of published evidence (case reports and case series, or inference from studies not directly applicable to the relevant population). A further seven out of 25 were derived only from the expert opinion of members of the guidelines committee, in the absence of any guidance to be gleaned from the published literature.

Hutchison’s personal experience is supported by evidence from two articles [2,3].

One paper published in the JAMA 2009 [2] concludes that ACC/AHA (American College of Cardiology and the American Heart Association) clinical practice guidelines are largely developed from lower levels of evidence or expert opinion and that the proportion of recommendations for which there is no conclusive evidence is growing. Only 314 recommendations of 2711 (median, 11%) are classified as level of evidence A , thus recommendation based on evidence from multiple randomized trials or meta-analyses.  The majority of recommendations (1246/2711; median, 48%) are level of evidence C, thus based  on expert opinion, case studies, or standards of care. Strikingly only 245 of 1305 class I recommendations are based on the highest level A evidence (median, 19%).

Another paper, published in Ann Intern Med 2011 [3], reaches similar conclusions analyzing the Infectious Diseases Society of America (IDSA) Practice Guidelines. Of the 4218 individual recommendations found, only 14% were supported by the strongest (level I) quality of evidence; more than half were based on level III evidence only. Like the ACC/AHH guidelines only a small part (23%) of the strongest IDSA recommendations, were based on level I evidence (in this case ≥1 randomized controlled trial, see below). And, here too, the new recommendations were mostly based on level II and III evidence.

Although there is little to argue about Hutchison’s observations, I do not agree with his conclusions.

In his view guidelines are equivalent to a bullet pointed list or flow diagram, allowing busy practitioners to move on from practice based on mere anecdote and opinion. It therefore seems contradictory that half of the EBM-guidelines are based on little more than anecdote (case series, extrapolation from other populations) and opinion. He then argues that guidelines, like other therapeutic interventions, should be considered in terms of balance between benefit and risk and that the risk  associated with the dissemination of poorly founded guidelines must also be considered. One of those risks is that doctors will just tend to adhere to the guidelines, and may even change their own (adequate) practice  in the absence of any scientific evidence against it. If a patient is harmed despite punctilious adherence to the guideline-rules,  “it is easy to be seduced into assuming that the bad outcome was therefore unavoidable”. But perhaps harm was done by following the guideline….

First of all, overall evidence shows that adherence to guidelines can improve patient outcome and provide more cost effective care (Naveed Mustfa in a comment refers to [4]).

Hutchinson’s piece is opinion-based and rather driven by (understandable) gut feelings and implicit assumptions, that also surround EBM in general.

  1. First there is the assumption that guidelines are a fixed set of rules, like a protocol, and that there is no room for preferences (both of the doctor and the patient), interpretations and experience. In the same way as EBM is often degraded to “cookbook medicine”, EBM guidelines are turned into mere bullet pointed lists made by a bunch of experts that just want to impose their opinions as truth.
  2. The second assumption (shared by many) is that evidence based medicine is synonymous with “randomized controlled trials”. In analogy, only those EBM guideline recommendations “count” that are based on RCT’s or meta-analyses.

Before I continue, I would strongly advice all readers (and certainly all EBM and guideline-skeptics) to read this excellent and clearly written BJM-editorial by David Sackett et al. that deals with misconceptions, myths and prejudices surrounding EBM : Evidence based medicine: what it is and what it isn’t [5].

Sackett et al define EBM as “the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients” [5]. Sackett emphasizes that “Good doctors use both individual clinical expertise and the best available external evidence, and neither alone is enough. Without clinical expertise, practice risks becoming tyrannised by evidence, for even excellent external evidence may be inapplicable to or inappropriate for an individual patient. Without current best evidence, practice risks becoming rapidly out of date, to the detriment of patients.”

Guidelines are meant to give recommendations based on the best available evidence. Guidelines should not be a set of rules, set in stone. Ideally, guidelines have gathered evidence in a transparent way and make it easier for the clinicians to grasp the evidence for a certain procedure in a certain situation … and to see the gaps.

Contrary to what many people think, EBM is not restricted to randomized trials and meta-analyses. It involves tracking down the best external evidence there is. As I explained in #NotSoFunny #16 – Ridiculing RCTs & EBM, evidence is not an all-or-nothing thing: RCT’s (if well performed) are the most robust, but if not available we have to rely on “lower” evidence (from cohort to case-control to case series or expert opinion even).
On the other hand RCT’s are often not even suitable to answer questions in other domains than therapy (etiology/harm, prognosis, diagnosis): per definition the level of evidence for these kind of questions inevitably will be low*. Also, for some interventions RCT’s are not appropriate, feasible or too costly to perform (cesarean vs vaginal birth; experimental therapies, rare diseases, see also [3]).

It is also good to realize that guidance, based on numerous randomized controlled trials is probably not or limited applicable to groups of patients who are seldom included in a RCT: the cognitively impaired, the patient with multiple comorbidities [6], the old patient [6], children and (often) women.

Finally not all RCTs are created equal (various forms of bias; surrogate outcomes; small sample sizes, short follow-up), and thus should not all represent the same high level of evidence.*

Thus in my opinion, low levels of evidence are not per definition problematic. Even if they are the basis for strong recommendations. As long as it is clear how the recommendations were reached and as long as these are well underpinned (by whatever evidence or motivation). One could see the exposed gaps in evidence as a positive thing as it may highlight the need for clinical research in certain fields.

There is one BIG BUT: my assumption is that guidelines are “just” recommendations based on exhaustive and objective reviews of existing evidence. No more, no less. This means that the clinician must have the freedom to deviate from the recommendations, based on his own expertise and/or the situation and/or the patient’s preferences. The more, when the evidence on which these strong recommendations are based is ‘scant’. Sackett already warned for the possible hijacking of EBM by purchasers and managers (and may I add health insurances and governmental agencies) to cut the costs of health care and to impose “rules”.

I therefore think it is odd that the ACC/AHA guidelines prescribe that Class I recommendations SHOULD be performed/administered even if they are based on level C recommendations (see Figure).

I also find it odd that different guidelines have a different nomenclature. The ACC/AHA have Class I, IIa, IIb and III recommendations and level A, B, C evidence where level A evidence represents sufficient evidence from multiple randomized trials and meta-analyses, whereas the strength of recommendations in the IDSA guidelines includes levels A through C (OR D/E recommendations against use) and quality of evidence ranges from level I through III , where I indicates evidence from (just) 1 properly randomized controlled trial. As explained in [3] this system was introduced to evaluate the effectiveness of preventive health care interventions in Canada (for which RCTs are apt).

Finally, guidelines and guideline makers should probably be more open for input/feedback from people who apply these guidelines.

————————————————

*the new GRADE (Grading of Recommendations Assessment, Development, and Evaluation) scoring system taking into account good quality observational studies as well may offer a potential solution.

Another possibly relevant post at this blog: The Best Study Design for … Dummies

Taken from a summary of an ACC/AHA guideline at http://guideline.gov/
Click to enlarge.

References

  1. Hutchison, G. (2012). Guidelines can harm patients too BMJ, 344 (apr18 1) DOI: 10.1136/bmj.e2685
  2. Tricoci P, Allen JM, Kramer JM, Califf RM, & Smith SC Jr (2009). Scientific evidence underlying the ACC/AHA clinical practice guidelines. JAMA : the journal of the American Medical Association, 301 (8), 831-41 PMID: 19244190
  3. Lee, D., & Vielemeyer, O. (2011). Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines Archives of Internal Medicine, 171 (1), 18-22 DOI: 10.1001/archinternmed.2010.482
  4. Menéndez R, Reyes S, Martínez R, de la Cuadra P, Manuel Vallés J, & Vallterra J (2007). Economic evaluation of adherence to treatment guidelines in nonintensive care pneumonia. The European respiratory journal : official journal of the European Society for Clinical Respiratory Physiology, 29 (4), 751-6 PMID: 17005580
  5. Sackett, D., Rosenberg, W., Gray, J., Haynes, R., & Richardson, W. (1996). Evidence based medicine: what it is and what it isn’t BMJ, 312 (7023), 71-72 DOI: 10.1136/bmj.312.7023.71
  6. Aylett, V. (2010). Do geriatricians need guidelines? BMJ, 341 (sep29 3) DOI: 10.1136/bmj.c5340




Experience versus Evidence [1]. Opioid Therapy for Rheumatoid Arthritis Pain.

5 12 2011

ResearchBlogging.orgRheumatoid arthritis (RA) is a chronic auto-immune disease, which causes inflammation of the joints that eventually leads to progressive joint destruction and deformity. Patients have swollen, stiff and painful joints.  The main aim of treatment is to reduce swelling  and inflammation, to alleviate pain and stiffness and to maintain normal joint function. While there is no cure, it is important to properly manage pain.

The mainstays of therapy in RA are disease-modifying anti-rheumatic drugs (DMARDs) and non-steroidal anti-inflammatory drugs (NSAIDs). These drugs primarily target inflammation. However, since inflammation is not the only factor that causes pain in RA, patients may not be (fully) responsive to treatment with these medications.
Opioids are another class of pain-relieving substance (analgesics). They are frequently used in RA, but their role in chronic cancer pain, including RA, is not firmly established.

A recent Cochrane Systematic Review [1] assessed the beneficial and harmful effects of opioids in RA.

Eleven studies (672 participants) were included in the review.

Four studies only assessed the efficacy of  single doses of different analgesics, often given on consecutive days. In each study opioids reduced pain (a bit) more than placebo. There were no differences in effectiveness between the opioids.

Seven studies between 1-6 weeks in duration assessed 6 different oral opioids either alone or combined with non-opioid analgesics.
The only strong opioid investigated was controlled-release morphine sulphate, in a single study with 20 participants.
Six studies compared an opioid (often combined with an non-opioid analgesic) to placebo. Opioids were slightly better than placebo in improving patient reported global impression of clinical change (PGIC)  (3 studies, 324 participants: relative risk (RR) 1.44, 95% CI 1.03 to 2.03), but did not lower the  number of withdrawals due to inadequate analgesia in 4 studies.
Notably none of the 11 studies reported the primary and probably more clinical relevant outcome “proportion of participants reporting ≥ 30% pain relief”.

On the other hand adverse events (most commonly nausea, vomiting, dizziness and constipation) were more frequent in patients receiving opioids compared to placebo (4 studies, 371 participants: odds ratio 3.90, 95% CI 2.31 to 6.56). Withdrawal due to adverse events was  non-significantly higher in the opioid-treated group.

Comparing opioids to other analgesics instead of placebos seems more relevant. Among the 11 studies, only 1 study compared an opioid (codeine with paracetamol) to an NSAID (diclofenac). This study found no difference in efficacy or safety between the two treatments.

The 11 included studies were very heterogeneous (i.e. different opioid studied, with or without concurrent use of non-opioid analgesics, different outcomes measured) and the risk of bias was generally high. Furthermore, most studies were published before 2000 (less optimal treatment of RA).

The authors therefore conclude:

In light of this, the quantitative findings of this review must be interpreted with great caution. At best, there is weak evidence in favour of the efficacy of opioids for the treatment of pain in patients with RA but, as no study was longer than six weeks in duration, no reliable conclusions can be drawn regarding the efficacy or safety of opioids in the longer term.

This was the evidence, now the opinion.

I found this Cochrane Review via an EvidenceUpdates email alert from the BMJ Group and McMaster PLUS.

EvidenceUpdate alerts are meant to “provide you with access to current best evidence from research, tailored to your own health care interests, to support evidence-based clinical decisions. (…) All citations are pre-rated for quality by research staff, then rated for clinical relevance and interest by at least 3 members of a worldwide panel of practicing physicians”

I usually don’t care about the rating, because it is mostly 5-6 on a scale of 7. This was also true for the current SR.

There is a more detailed rating available (when clicking the link, free registration required). Usually, the newsworthiness of SR’s scores relatively low. (because it summarizes ‘old’ studies?). Personally I would think that the relevance and newsworthiness would be higher for the special interest group, pain.

But the comment of the first of the 3 clinical raters was most revealing:

He/she comments:

As a Palliative care physician and general internist, I have had excellent results using low potency opiates for RA and OA pain. The palliative care literature is significantly more supportive of this approach vs. the Cochrane review.

Thus personal experience wins from evidence?* How did this palliative care physician assess effectiveness? Just give a single dose of an opiate? How did he rate the effectiveness of the opioids? Did he/she compare it to placebo or NSAID (did he compare it at all?), did he/she measure adverse effects?

And what is “The palliative care literature”  the commenter is referring to? Apparently not this Cochrane Review. Apparently not the 11 controlled trials included in the Cochrane review. Apparently not the several other Cochrane reviews on use of opioids for non-chronic cancer pain, and not the guidelines, syntheses and synopsis I found via the TRIP-database. All conclude that using opioids to treat non-cancer chronic pain is supported by very limited evidence, that adverse effects are common and that long-term use may lead to opioid addiction.

I’m sorry to note that although the alerting service is great as an alert, such personal ratings are not very helpful for interpreting and *true* rating of the evidence.

I would rather prefer a truly objective, structured critical appraisal like this one on a similar topic by DARE (“Opioids for chronic noncancer pain: a meta-analysis of effectiveness and side effects”)  and/or an objective piece that puts the new data into clinical perspective.

*Just to be clear, the own expertise and opinions of experts are also important in decision making. Rightly, Sackett [2] emphasized that good doctors use both individual clinical expertise and the best available external evidence. However, that doesn’t mean that one personal opinion and/or preference replaces all the existing evidence.

References 

  1. Whittle SL, Richards BL, Husni E, & Buchbinder R (2011). Opioid therapy for treating rheumatoid arthritis pain. Cochrane database of systematic reviews (Online), 11 PMID: 22071805
  2. Sackett DL, Rosenberg WM, Gray JA, Haynes RB, & Richardson WS (1996). Evidence based medicine: what it is and what it isn’t. BMJ (Clinical research ed.), 312 (7023), 71-2 PMID: 8555924
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