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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5537_Библиотеки_им_академика_М_И_Перельмана.pdf
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Evidence-based Medicine Primer
Sarah Kharkhanechi*, Amir Abadir†, Abhinav Sharma‡,
Andrew Burke
‡
, George Farag‡, Thanu Nadarajah‡and
Anne Holbrook
‡,§
Introduction
Clinicians who specialize in hospital medicine face numerous information
challenges in the course of every working day; there are likely to be at
least two important but unanswered clinical questions per patient seen per
day. Most clinicians are experienced information gatherers, but many find
it challenging to sort high quality “wheat” from the lower quality “chaff”
of literature found. We have two choices — either we must limit ourselves
to information sources already critically appraised, of which at least one
outstanding example exists (MacPlus, available via http://plus.mcmaster.ca/macplusfs), or learn how to critically appraise relevant literature
ourselves. Fortunately, a systematic and well-tested approach to the latter
is available. This chapter describes such an approach to four key types of
questions that are routinely encountered: Diagnosis, therapy, prognosis
and economics/resource utilization. Each is presented using an example
relevant to hospital medicine practice.
15
2
Chapter
*Department of Radiology, McMaster University;
†
Department of Medicine, University of Toronto, Canada;
‡
Department of Medicine, McMaster University;
§
Division of Clinical Pharmacology & Therapeutics, McMaster University, Canada
Diagnosis
Assessing the value of a diagnostic test is a crucial part of every clinician’s practice. It is important to consider the limits and applicability of
these tests and understand their role in clarifying the diagnosis.
Clinical Scenario
Mrs. Green is a healthy 45-year-old woman who presents to your clinic
with a swollen left calf. There are no significant risk factors for venous
thromboembolism (VTE). Are there any tests that can aid you in ruling
out a deep venous thrombosis (DVT)?
Diagnosis Study
A study evaluating the use of a latex agglutination quantitative D-Dimer
assay in outpatient clinics of four hospitals retrospectively examined 595
unselected patients with a follow-up period of three months.
1
The sensitivities, specificities, predictive values, and likelihood ratios of the assay
were calculated for all patients using objective testing and eventual clinical diagnosis as the gold standard.
Diagnosis Criteria
2–3
a) Availability: Is this test available in your institution? Do you have a
well-trained person to perform the test? Are you sufficiently knowledgeable or do you have an expert available to interpret the results? Can
the test be completed and reported in the necessary timeframe? If your
answer is NO to any of the above practical questions, then there is likely
not much point in ordering the test no matter how valid it may be.
b) Validity: This term refers to the accuracy and the ability of the test to
rule in or rule out the diagnosis in question, and thus help you make
your clinical decision.
c) Conclusiveness: How much do the results of this test help you in making
a diagnosis? Will you need another test to confirm the diagnosis? This
depends on the answers to the questions presented below in Table 1.
16
S. Kharkhanechi et al.
17
Evidence-based Medicine Primer
Table 1. Criteria for Evaluating a Study on a Diagnostic Test
Was there truly uncertainty about the A diagnostic test is useful if it is able to
diagnosis in the sample used in distinguish between disorders when they
the study? have overlapping clinical presentations. For
example, pulmonary embolism is difficult
to diagnose clinically due to variability in
presentation and overlap of its symptoms
with other diseases.
Was the test being evaluated compared Many diseases do not have a high quality
to a gold (reference) standard? Was the diagnostic standard (gold standard) test.
gold standard applied to ALL of However, to avoid bias, the new test and
the patients? the diagnostic standard test should be
performed on everyone in the study.
Were the results of the gold standard Blinding of the test interpreters is crucial to
interpreted without knowledge of the ensure no bias is introduced in the analysis
new test result and vice versa? of the results.
What is the “sensitivity” (% patients A highly sensitive test helps to rule out the
with condition who have positive condition (SNout — sensitive rules out).
results — true positives) of the test? Sensitivity = true positive/(true positive +
false negative).
What is the “specificity” (% patients A highly specific test helps to rule in the
who do not have the condition who condition (SPin — Specific rules in).
have negative results — true negatives) Specificity = true negative/(true negative+
of the test? false positive).
What is the “likelihood ratio” (LR) The probability of getting this test result in
associated with the test? those with the condition divided by those
without the condition. Positive likelihood
ratio (LR+) = Sensitivity/(1− Specificity),
while negative likelihood ratio (LR-) =
(1− Sensitivity/Specificity). A test with a
LR+ more than 10 or a LR- less than
0.1 will provide a considerable change
from pre-test to post-test probability.
Have the results of the diagnostic Results of retrospective studies determining
accuracy study been confirmed in a Sn, Sp, and LR should be confirmed with a
prospective management study? prospective management study where the
results of the diagnostic test are
incorporated into patient management
decisions.
d) Applicability: Generally, diagnostic tests are only worth considering
if they will change your management decisions. In addition to good
diagnostic properties, a test must also be safe to use and provide
acceptable cost-effectiveness.
Discussion
The study mentioned above included patients with suspected VTE presenting to an outpatient hospital setting.
1
Patients were categorized according to their pretest probability of VTE as “low,” “intermediate” or “high.”
All patients underwent further testing, for example with compression ultrasound or V/Q scan, except for a portion of patients who had both a low
pretest probability and a negative SimpliRED D-Dimer. Patients with no
objective evidence of VTE were followed for three months. This differential application of the “gold standard” is a minor methodologic flaw in the
study, but is a well-validated clinical approach to VTE. The sensitivity and
specificity of the latex agglutination quantitative D-dimer test using a cutoff point of 0.5 µg FEU/mL, were found to be 96 and 45%, respectively.
The PPV and NPV of the test were 29 and 98%, with LR+ and LR− at 1.4
and 0.09, respectively. These results indicate that for patients with low or
moderate pre-test probability of VTE, a negative quantitative latex DDimer assay can rule out VTE due to its high sensitivity and good negative
likelihood ratio. However, the test is not very helpful when it is positive. A
subsequent prospective cohort management study confirmed that a negative D-dimer result safely eliminated the need for further testing in patients
with low or moderate pre-test probability of DVT.
4
Mrs. Green, meanwhile, has a low pre-test probability of having a
DVT given her history, so a quantitative latex D-Dimer was conducted
and its result was negative. She was reassured and sent home.
Prognosis
An estimate of prognosis is essential to guide treatment and inform
patients about clinically relevant outcomes.
18
S. Kharkhanechi et al.
Clinical Scenario
Mr. Bing, 65 years of age, is brought to the ER with right-sided facial
droop and mild hemiparesis. His symptoms resolve within 60 min of
onset. After blood tests and a CT scan, you explain that he has likely suffered a transient ischemic attack (TIA). He asks you what his chances are
of having a major debilitating stroke.
Prognosis Study
A recent trial looked at the ABCD2 score for determining risk of recurrent
TIA, minor stroke or major stroke after presentation with a TIA.
5
This was
a prospective sub-study of all consecutive individuals with identified TIA
or stroke in the region of Oxfordshire, England. A consecutive sample of
500 patients diagnosed with a TIA were identified and followed-up in
person at 30 days.
Prognosis Criteria
6
19
Evidence-based Medicine Primer
Table 2. Criteria for Evaluating a Study on Prognosis
a) Are the results valid (worth reading)?
Does the population in the study include a An inception cohort (everyone entered
spectrum of patients with the disease, and at the same stage of their disease,
were they entered in the study at a similar preferably early) has the potential to
stage of disease? provide the best prognosis information.
Was the duration of follow-up sufficient to The study’s validity may be compromised
capture development of the outcome? if there are a large number of patients lost
Was follow-up complete? to follow-up relative to those who have
suffered an event or if the follow-up
period is too short.
Were the outcomes of interest defined and There should be a clear definition of
determined in an objective and unbiased outcomes of interest before study
manner? commences. Detailed adjudication rules
may be required for subjective outcomes
such as disability.
(Continued )
Discussion
The ABCD2 score (age, blood pressure, clinical findings, duration of
symptoms, diabetes) is a tool developed to predict individual risk of
stroke at seven days and thus meant to affect the triaging of patients with
TIA symptoms.
5
The ABCD2 score in these patients presenting initially
with TIA, was highly predictive of the 7-day risk of major stroke —
12.8% in patients with ABCD2scores of 5 or more versus 1.3% in patients
with a score 4 or less (P < 0.001).
Going back to Mr. Bing, he scores 5 on the ABCD2 scoring system
(age ≥ 60 = 1 point, unilateral weakness = 2 and duration of symptoms
≥ 60 min = 2), giving a 7-day risk of stroke of 12.8%. This ABCD2 score
would suggest that the patient should be counseled on the high risk of
20
S. Kharkhanechi et al.
Table 2. (Continued )
Were there adjustments for other Prognostic characteristics, including
important prognostic factors? treatments provided, other than those
being studied, will need to be considered.
b) What are the results?
How large was the likelihood of the The quantitative results from studies of
outcome event in a specified period of prognosis are the number of outcome
time? events that occur over time.
How precise was the estimate of likelihood? The precision of this outcome can be
evaluated based on the confidence interval
(CI). The smaller the CI, the greater the
precision.
c) How can I apply the results to my
patient care?
Were the study patients similar to Consider whether your patient would meet
my patient? the inclusion and exclusion criteria of the
study.
Will the results lead directly to selecting Useful prognosis markers will help guide
or avoiding therapy? treatment selection.
Are the results useful for reassuring or Giving patients an estimate of outcomes
counseling patients? will allow them to make informed
decisions regarding treatment options.
early major stroke and should potentially be hospitalized to facilitate
further diagnostic testing and to optimize preventive treatment quickly.
Therapy
As the number of therapeutic options increase, clinicians must be able to
identify treatments that will minimize patient morbidity and mortality
without undue harm of the treatment itself.
Clinical Scenario
Mrs. Geller is a 65-year-old woman with hypertension who presents to the
ER with a non-ST elevation myocardial infarction (NSTEMI). Her troponin T is 0.8 µg/L and her EKG shows inferior ST depression. She has
already received 160 mg of aspirin, 25 mg of metoprolol, 5000 units of
subcutaneous heparin and 40 mg of atorvastatin. The Emergency physician asks you to decide about clopidogrel.
Therapy Trial
The CURE trial was a large, randomized, placebo-controlled trial that
studied the effect of clopidogrel added to aspirin versus placebo plus
aspirin, on the composite outcome of cardiovascular (CV) death, non-fatal
myocardial infarction (MI) or stroke in patients with acute coronary
syndromes without ST-segment elevation.
7
Therapy Criteria
8
21
Evidence-based Medicine Primer
Table 3A. Criteria for Evaluating a Study on Therapy
a) Are the results valid (worth reading)?
Were the patients randomized? Randomization is the key to minimizing
bias — it balances factors that predict
outcome, both those known and
unknown, between the groups.
(Continued )
22
S. Kharkhanechi et al.
Table 3A. (Continued )
Was randomization allocation concealed? Concealment ensures that those enrolling
patients in the study cannot influence the
arm to which patients are allocated (and
thus bias the results).
Were all participants blinded? Patients, caregivers, data collectors,
adjudicators, and data analysts should be
blinded to group allocation throughout
the study.
How complete was the follow-up to the Ideally, every patient’s study outcome
end of the study? would be known. If many patients drop
out and are lost to follow-up, the study
results may be unreliable even with
imputation techniques.
Was intention to treat analysis utilized? Intention to treat analysis (participant
outcomes are assigned to the group that
the participant was randomized, no
matter whether they stayed in that group
or not) is the most conservative method
of analyzing a typical randomized trial
(non-inferiority trials are different, but
are beyond the discussion in this chapter)
Did all of the groups receive similar Similar co-intervention helps ensure that
co-interventions? the treatment versus control comparison
is the only cause of any difference in
outcome.
b) What are the results of the study?
How large was the treatment effect? The treatment effect is determined by a
number of variables, including the
absolute risk, relative risk, relative risk
reduction, number needed to treat, and
number needed to harm. These are
defined below in Table 3B.
How precise was the estimate of the The precision of the effect of a treatment
treatment effect? is based on the confidence interval. A
95% confidence interval tells you how
widely the true result might vary from
the actual result found — the smaller the
confidence interval, the greater the
precision of the treatment.
(Continued )
23
Evidence-based Medicine Primer
Table 3A. (Continued )
c) Can I apply the results of this study
to my patient?
Is there reasonable similarity between my Consider whether your patient would
patient and the study patients? meet the inclusion and exclusion criteria
of the study. If not, consider whether
there is a compelling reason why your
patient would not be similar.
Were all patient-important outcomes Ensure that the investigators have chosen
considered? outcomes that would be considered
important by patients. Also ensure that
all important adverse effects of the
intervention have been documented.
Is treating my patient worth the potential The NNT must be weighed against the
harms and costs? adverse effects (see NNH below) as well
as the cost of treatment. (see Economics)
Table 3B. Definitions of Terms to Measure Treatment Effect
Treatment Effect Calculation
Control event rate (CER) Number of events in control (placebo, for
example) arm/Total patients in control arm
Experimental event rate (EER) Number of events treatment arm/Total patients
in treatment arm
Absolute risk reduction (ARR): The CER — EER
decrease in risk of a experimental
treatment in relation to a control
treatment.
Relative risk (RR): Probability of the EER/CER. Relative risk reduction
event occurring in the treated group (RRR) = 1-RR
versus the non-treated group.
Number needed to treat (NNT): The 1/ARR. The same calculation can be
number of people needed to be treated applied to harmful outcomes; this is
in order to prevent one bad outcome. termed number needed to harm (NNH).
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