Running title
Hierarchy of evidence in endometriosis.
Abstract
Without an animal model and a non-invasive diagnosis, the
pathophysiology of endometriosis is unclear and information is limited
to symptomatic women. Lesions are biochemically variable. Medical
therapy cannot be blinded and extensive surgery combines low numbers
with variable difficulty and surgical skills. Experience is spread among
specialists in imaging, medical therapy, infertility, pain and surgery.
Besides the recent changes in interpreting statistical analyses, the
limited good-quality evidence increases the importance of clinical
experience. Therefore trial design, analysis and judgment of results
should be done by experts in the different disciplines of endometriosis,
before being translated into guidelines.
Introduction
Medicine started with observations, experience, and trial and error,
initially hampered by conflicting beliefs such as Greek mythology.
Later, statistical analysis was needed to interpret and evaluate the
variability of observations and to help understand mechanisms. For more
than a century classical statistical methods used significance levels,
power and P-values. However, the interpretation of the widely used
P-values has created confusion (1). P-values were introduced 100 years
ago by R Fisher to grasp in one value the probability that an observed
effect, can be attributed to chance (null hypothesis), taking into
account its distribution and power (2). A P-value thus measures the
extremeness of a result given the null hypothesis but not that the
hypothesis is true given the observed results. Judgement of the validity
of a hypothesis requires Bayesian reasoning, needing a prior probability
and the Bayesian factor indicating the improvement of that probability
(3). Instead of being an argument to accept a hypothesis, a P value of
0.05 or 0.01 only increases a prior probability of 50% to 71% or 89%,
the initial hypothesis being wrong in 29% or 11% respectively (1).
That the probability of a hypothesis depends on much more than P-values
(4) led to the suggestion that many research findings are wrong and
often ‘an accurate measure of the prevailing bias’ (5). The precision of
P-values (6), the poor reproducibility (7) and the misuse in medicine
resulted in 2016 in a warning by the American statistical association
that P-values ‘do not measure the probability that the studied
hypothesis is true or the probability that the data were produced by
random chance alone and that they are not a good measure of evidence
regarding a model or hypothesis’ (8). Examples illustrating the
difference between P-values and probability are: the high P-value that
measles produces a rash, cannot be used to predict the probability of
measles in those with a rash; similarly, a 60% probability of rain is
different from a non-significant P-value describing the odds of rain.
However, today, statistical reporting is changing slowly for most
gynecological journals (9) except JMIG and BJOG (10, 11).
Evidence-based medicine (EBM) intended to integrate research data
corrected for biases into clinical medicine and was initiated when
calculators permitted more complex analyses such as meta-analysis. In
the 1990ies, EBM embraced P-values for evidence resulting in a pyramid
of evidence (12) with the RCT (13), and later their meta-analysis (14)
and systematic reviews on top. However, the integration of results in
clinical medicine and guidelines proved to be difficult (15). Besides
that it is unclear how clinical experience is used to translate results
into grades of evidence (15), this difficulty could be seen as the
unconscious conflict between the inappropriate use of P-values as
‘evidence’, and clinical medicine using a rather Bayesian approach.
An EBM approach to endometriosis needs specific considerations. Without
an adequate animal model and the ongoing debate about pathophysiology,
it remains unclear whether endometriosis is one or several diseases.
Without an adequate non-invasive diagnosis, epidemiology is poorly
understood. RCTs are difficult to organise when medical therapy cannot
be blinded since recognised by the patient (e.g. when affecting
menstruation) and extensive surgery is too variable for the available
numbers.
Therefore some difficulties with EBM will be reviewed to discuss EBM of
endometriosis from a clinical perspective.
Evidence Based Medicine.
‘Evidence-based medicine is the conscientious, explicit, and judicious
use of current best evidence in making decisions about the care of
individual patients’ (16). As reviewed elegantly (12) evidence needs to
be obtained by credible processes taking into account the quality and
the totality of the evidence. However, the resulting hierarchy of
evidence was often poorly reproducible and many systematic reviews could
be considered a review of evidence. Therefore grades of evidence were
introduced to judge all available evidence including observational
series and case reports.
EBM can claim many successes in changing clinical therapy by formalising
RCTs, reporting, meta-analyses and systematic reviews, in the prevention
of selection and observation biases to obtain accurate data (the water
of a river cannot rise above its source (17)), and in a rigorous
clinical interpretation. EBM, initiated in the 1990s, embraced
traditional statistical analyses and P-values to judge the efficacy of
treatments, and the accuracy of diagnostic tests and their clinical
interpretation to formulate guidelines and recommendations. However, the
integration of EBM into clinical medicine has been difficult (12).
Without reviewing the risks of misuse of P-values and the more recent
Bayesian (18) approach to judge the validity of hypotheses, we need to
realise that Bayesian inference has not yet been fully incorporated in
EBM, although more similar to clinical decision making for diagnosis and
therapy.
Clinical considerations of traditional analysis of
treatment and diagnosis
Rarely considered is that traditional statistical analyses require a
homogeneous population and that the analysis is not suited to detect
smaller subgroups with different behaviour (19) or to handle rare
events, that would need prohibitively large groups. Although well known
that small, clinically irrelevant differences can reach ‘significance’
since P-values improve with the square root of the number of
observations, this still creates a publication bias that is difficult to
deal with (20, 21).
Diagnostic tests are used to estimate the probability that a patient has
or does not have a disease, which are the positive (PPV) or negative
predictive values (NPV). However, the accuracy of prediction decreases
sharply when the prevalence of the diseases is low, especially below 5
or 1%. A Bayesian approach (18) permits the exact calculation of this
relationship (PPV=sensitivity*prevalence/(sensitivity*prevalence +
(1-specificity)(1-prevalence)). Clinically we knew this since a test
with 99% sensitivity and 99% specificity for a disease with a 1%
prevalence, has a predictive value of only 50% since the numbers of
false positives as true positives are equal. Unfortunately, prevalences
are not always well known and can be variable e.g. because of a referral
bias. Therefore, predictive values of diagnostic tests for rare diseases
are better in tertiary referral centres (22) with a higher prevalence.
The influence of prevalences on PPV’s is still poorly integrated into
the clinical interpretation of diagnostic tests. More difficult is the
estimation of the combined diagnostic accuracy of several tests (23).
The difficulty was illustrated in a recent Cochrane review, suggesting
to use tests sequentially, using first a test with high sensitivity, and
subsequently using another test to re-test the negative group (24). The
added value and the combined accuracy of tests can be calculated with a
Bayesian approach as demonstrated for endometriosis (25, 26), but this
is overall rarely used.
Clinical judgment, experience and artificial
intelligence
Difficult to standardise is the experience-based clinical judgement when
types of data cannot be compared. It is not clear how to balance data
such as the efficacy of treatment with severity and incidence of side
effects. Moreover, these are not always independent variables, such as
when both are surgeon dependent. A historic example is that
chloramphenicol, an excellent antibiotic, was no longer used after many
years because of the 1/10.000 risk of aplastic anaemia (27). It is not
clear how to judge drugs when the absence of blinding and placebo
effects need to be balanced. Also, the judgement of imprecision,
inconsistency, publication bias and external validity, remains
difficult. The same holds for the grades of evidence (15, 28), defined
as the expectation that the conclusions will or will not be modified by
further research, and for meta-analyses, requiring inclusion and
exclusion criteria judging the quality of RCTs (29) up to becoming
misleading (30). The value of clinical judgement is also illustrated by
the conclusion that evaluation of biases in diagnostic accuracy by
Quadas tools (31, 32) is not much superior to clinical judgment.
Clinical experience integrates knowledge with experience in the entire
population, including heredity, age, antecedents, rare events and
multimorbidity. This also includes the many conclusions based on common
sense or previous experience without trial evidence. Trials are not
performed when results have no clinical consequences, or when
practically difficult to perform because of low prevalence as in
multimorbidity or when practically impossible to perform. An example is
the choice of suture material, which is based on tensile strength and
resorption rate but without trial evidence, since suture or knot
complications are rare. Many individual and local preferences were
implemented following rare events, accidents or near accidents. Since
these events are often forgotten years later it is suggested to be
prudent when changing habits because of lack of evidence.
Clinical experience is complex (33) and some aspects are difficult to
quantify such as the skill of a surgeon and rare but severe
complications not reflected in RCTs. A clinical diagnosis considers a
series of potential diagnoses, ranging from likely to rare, taking into
account age, antecedents, symptoms, clinical exams, blood tests and
imaging. The integration of all results into a PPV for each diagnosis
including the risk of mistakes, is a complex experience-based,
artificial intelligence like (34) process. Clinical experience also
precedes RCTs (Fig 1) either when performed to confirm observations, or
not performed when the superiority of a treatment or an intervention
seems repetitively observed, without exceptions, or when the expected
effect is so little that the result will be clinically irrelevant.
Emotional intelligence is rarely considered since even more difficult to
judge. The interaction of the clinician and the patient through body
language and expectations influences diagnosis and therapy, and similar
data can be interpreted differently by clinicians with comparable
experience. Therefore the attitude of patients is reflected, although
not explicitly, in clinical judgment.
Conclusion
These considerations explain the ongoing discussions on the hierarchy of
evidence(35), procedural aspects such as financial bias in funding (36)
and drug research (37), and the epistemological discussion to
distinguish justified belief from opinions (38). Also, medicolegal
aspects influence clinical judgment as evidenced by the recent
introduction of NUTS (Number of Unnecessary Tests to avoid one Suit)
statistics (39). Clinical experience and judgment somehow integrate the
entire population with rare cases and multimorbidity with case reports
and the vast literature of descriptive observations, influencing our
judgement.
EBM and Endometriosis
Endometriosis is poorly
understood
Endometriosis is a frequent disease causing pain and infertility and is
the most frequent reason for surgery in women (40). Given the likely
association with adenomyosis and bleeding disorders (41), endometriosis
can be considered for almost any complaint in gynaecology. Without an
animal model permitting experiments and without a non-invasive
diagnosis, the pathophysiology, the natural history and the epidemiology
(42) are poorly understood, and the data on endometriosis is scanty in
adolescence and limited to symptomatic women. The consequent difficulty
to handle the epidemiology of endometriosis is illustrated by the recent
suggestion to redefine endometriosis as ‘symptomatic’, eliminating those
who did not undergo a laparoscopy (43). Even for cystic ovarian
endometriosis the accuracy of imaging seldom exceeds 90% while it is
difficult to exclude ovarian cancer, especially in older women (44).
Good-quality data are
limited
A laparoscopy is performed only in women with pain or infertility, and
it is difficult to ascertain in individual women that endometriosis and
pain or infertility are causally related since only half of the
superficial lesions are painful (45) and considering the many other
causes of pelvic pain or infertility. Medical therapy has an important
placebo effect (46), but cannot be blinded since the patient recognizes
active therapy, especially when affecting menstruation. It is unclear if
women with ‘proven endometriosis’ as a criterion to be included in
trials, still have endometriosis since they underwent laparoscopy with
surgical destruction of endometriosis in most women. This variable
judgment of a trial is illustrated by the not-blinded ENDOCAN trial (47)
showing improvement in fertility following surgery, resulting in a
Cochrane meta-analysis which was subsequently withdrawn (48). In cystic
ovarian endometriosis the results of surgery, ovarian damage and
recurrence rates are surgeon dependent (49). Deep endometriosis is
highly variable and surgery is technically difficult and complication
prone. Because of the variable skill of the surgeon, with low numbers of
interventions, RCTs are not realistic, and if performed nevertheless,
unexpected results risk being criticised as occurred recently in the
LACC trial demonstrating higher recurrence rates after laparoscopic
surgery for cervical cancer (50).
Clinical judgment varies with subspecialties
The clinical judgment of the evidence is further complicated by
different sub-specialists. Clinical experiences are bound to vary
between radiologists performing MRI, gynaecologists specialising in
ultrasonography or endocrinology or medical therapy or surgery, and
abdominal surgeons with little expertise in other aspects of
endometriosis management. An additional difficulty is the degree of
commercialisation and industrialisation (51), especially in Infertility
and medical therapy.
Clinical judgment varies with our perception of
pathophysiology
Management should be based on evidence, but the clinical judgement might
vary with understanding pathophysiology. The implantation theory (52,
53) defined endometriosis as ‘endometrial glands and stroma outside the
uterus’ and thus as one disease, which became clinically considered
progressive and recurrent. According to the genetic-epigenetic (G-E),
theory endometriosis starts developing after a cumulative series of
cellular incidents (54). Endometriosis lesions thus are clonal and
individually different which is consistent with the variable aromatase
activity and progesterone resistance (55), and the variable response to
medical therapy (56, 57). If lesions are different, traditional
statistical analysis is inadequate (19). The risk of G-E incidents
increases by oxidative stress of retrograde menstruation or the
peritoneal microbiome. Therefore susceptible women have an increased
risk after puberty, and the remaining group will have a progressively
lower risk (58). Age thus becomes an important factor in epidemiology.
Pelvic endometriosis lesions grow in the peritoneal cavity which is
endocrinologically and immunologically a specific microenvironment, but
the growth of endometrial lesions is self-limiting (58) probably as a
consequence of fibrosis and inflammation secondary to the immunologic
reaction. This is consistent with the clinical observation that most
deep endometriosis lesions that are followed clinically since symptoms
were insufficient for surgery, do not grow. Viewed as a G-E driven
disease, recurrences might become preventable by decreasing oxidative
stress when decreasing retrograde menstruation. This is consistent with
the lower recurrence rate of cystic ovarian endometriosis when taking
oral contraception. Although not demonstrated yet, we might consider
prevention by preventing ascending infections, or by changing the
peritoneal microbiome by food intake and exercise (59). This is
consistent with the observations that the risk of developing
endometriosis seems lower when taking food rich in antioxidant as
omega3, Vit E, Vit C and citrus (60, 61). It is too early to fully
understand the effect of vitamins on inflammation and immune response in
endometriosis (62). New concepts of pathophysiology should be considered
for future trials. This could apply more specifically to endometriosis
in adolescence, to the prevention of endometriosis, and to interpret
results of endometriosis if heterogeneous and more than one disease.
Non-biomedical health
systems
A growing number of reports document the management of endometriosis
with complementary therapies (63-65), acupuncture, food intake(66) and
exercise (59), and more recently traditional Chinese medicine(67). These
reports are difficult to interpret since indications and results of
treatment poorly fit EBM standards. However, indirect and circumstantial
evidence is too strong to be ignored altogether.
We are at the crossroads of understanding the role of food intake and
exercise on the peritoneal (68) and the intestinal microbiome. Both
might influence endometriosis onset and growth either directly or
through immunology and oxidative stress.
Conclusion
In conclusion, (Fig 1) high-quality evidence is limited and the clinical
judgment varies with experience, which is different for each
subdiscipline involved. Without questioning the importance of the rules
to grade evidence, recognise bias and understand statistical analysis,
either numerical or Bayesian, the differences in judgment by
subdisciplines need to be addressed. It seems logical that the ranking
of evidence for diagnosis, medical therapy and surgery should be
performed separately by different subspecialists. Although the lines
between disciplines are not rigid, the prior hypotheses to be tested
must be formulated by clinicians with experience.
Surgery for severe deep endometriosis needs specific comments. Data are
limited to observational series with referral biases and differences in
technique. However, the surgeons with extensive experience (e.g. more
than 5-10 years of experience and more than 200 interventions) are a
small group, who know each other’s surgery and who meet and discuss
several times a year and progressively adapt their surgery (69).
Therefore the elements on which this group agrees because of a similar
experience not changing over time, constitute rather solid evidence.
Hopefully, statisticians will help to formalise these experience-based
observations of this group into evidence. Similarly, it seems important
to register when and why opinions/experiences are different.
Discussion
The principles of EBM (16) are clear, but the ranking of the evidence is
struggling with a poorly defined clinical judgment and experience, which
for endometriosis moreover varies between subspecialists. It thereover
seems logical to match judgment and expertise to judge evidence and to
involve thereafter all stakeholders in the translation into clinical
recommendations. It should also be realised that interpretation and
judgment vary with the understanding of pathophysiology. Whether
endometriosis is seen as one or several G-E different diseases will help
to understand that some 50% of typical lesions are not painful and that
response to medical therapy is absent or inadequate in 10% to 40%
respectively (56, 70). Future trials should reflect this and it seems
logical that at least superficial, cystic ovarian and deep endometriosis
are evaluated and reported separately being clinically different
entities (71).
Clinicians appreciate the achievements of EBM but were educated and did
grow up with significances and P-values, and thus risk having misused
their limited value to confirm a hypothesis (1). It was refreshing to
realise the importance of the prior hypotheses (18) and to understand
that clinical medicine has a Bayesian approach. Seeing a woman of that
age, with these antecedents and these symptoms, results in many
differential diagnoses which are refined into a workable probability by
additional exams and tests to result in a treatment considering the
consequences of mistakes and complications. Clinical medicine is also
highly multivariate, with independent and dependent variables.
Unfortunately, the gap between statistical inference and clinical
understanding seems to be widening as illustrated in a recent report
describing a new diagnostic test using a ‘penalized regression model and
machine learning with random forest’ (72). This risks not being readily
understood by most clinicians.
The quality and ranking of evidence need to be re-evaluated for medical
therapy of endometriosis. The judgement is moreover bound to change if
peritoneal fluid concentrations and progesterone resistance are taken
into account (70, 73) and if endometriosis lesions are no longer
considered a homogeneous group (19, 74) as illustrated by the
biochemical heterogeneity (55) and by some deep lesions that continue to
grow during medical therapy (56) or after menopause (75). Besides
adjusting statistical inference, many aspects need to be defined such as
“adequate” pain relief to continue treatment, or placebo effect
without blinding, or ‘women with proven endometriosis’ after laparoscopy
with surgical treatment. Nevertheless, we think that the clinical
treatment of superficial endometriosis could be summarised as follows.
Women with proven or suspected endometriosis and pain deserve a trial
with medical therapy. However, the eventual growth of lesions during
therapy should be monitored and if pain relief is inadequate, other
options should be considered.
The quality and completeness of surgery for endometriosis are poorly
defined. The severity of the disease and the surgery are variable, with
cystic and deep endometriosis being technically difficult and
complication prone causing oocyte damage, sexual problems and bladder,
ureteral and bowel complications (76). Randomisation is irrealistic and
can be unethical when surgeons are not equally trained in the techniques
to be compared. However, It is suggested that the technique, results and
complications can be judged by the small group of deep endometriosis
surgeons with a large experience over a longer period. They know each
other and understand the surgery each of them is doing, and the aspects
they agree upon are probably high ranking evidence. This is not
contradicted by the decision of doing a bowel resection or a
conservative excision or a discoid excision being based to a large
extent on personal preferences (77) since results and complications vary
with surgical skills and experience. To convert this ‘consensus opinion
of experts’ into evidence is a methodological and statistical challenge
for the future.
In conclusion, an EBM approach to endometriosis has specific challenges.
The diagnosis is limited to those undergoing laparoscopy and this
clinical decision is based on a variable mixture of clinical exams and
symptoms and imaging. The accuracies of imaging as ultrasound or MRI are
well described (78, 79), but the predictive values vary with the locally
variable prevalences, and their importance in clinical decision making
varies from little (58, 76) to very much (80). Not only the indication
for laparoscopy is variable also the recognition of endometriosis (81)
as demonstrated for subtle lesions and observed for deep and appendiceal
endometriosis. Medical therapy needs re-appraisal and for extensive
surgery, the judgement of surgeons with experience needs validation. The
complexity will need better integration of statistical analysis and
inference to understand which exams and therapies improve outcomes (82).
Acknowledgements.
We do thank Manu Lesaffre and Geert Page, Belgium and Lone Hummelsjoy,
United kingdom for fruitful discussions.
Funding
No funding
Disclosure of interests
None of the authors has anything to disclose
Contribution of authors
All authors have discussed endometriosis diagnosis and especially
surgery during several yearly meetings and live surgeries. These
discussions moreover resulted in a series of joint publications on
endometriosis. PK’s interest in statistics initiated this manuscript
with the help of ML author of “Bayesian biostatistics”. The text was
subsequently edited and discussed by all authors.
Ethics
Ethical approval was not necessary
List of legends
Fig 1. Evidence in medicine starts with observations and trials, which
have less risk of bias. Traditional statistics test the Null hypotheses
resulting in P-values. Bayesian analysis is better suited to judge the
hypothesis. Clinical experience is important for each aspect and judges
the risk of bias provides the prior information to perform trials,
orient analysis and evaluate external validity and grades of evidence.
Considering the importance of clinical experience, the variability of
experience by the sub-disciplines in endometriosis needs to be formally
addressed to understand diagnosis and therapy.
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