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Text Content

NEUROLOGY ONLINE JOURNAL CLUB

Online summaries of Journal Club discussions of the Dept. of Neurology at Queens
Hospital, Romford, UK
Skip to content
 * About this Website
   * The Queens Hospital Neurology Online Journal Club
   * A site for Neurologists, Trainees and General Readers
   * Primers for General Readers
   * Scope of Topics Covered
   * Why Online?
   * Negative Reviews and Comments
   * Site Author
 * Disease Categories
   * Epilepsy
   * Infectious Diseases
   * Inflammatory/ Auto-Immune Diseases
   * Intensive Care Neurology
   * Migraine
   * Myasthenia
   * Myopathy
   * Parkinson’s Disease
   * Stroke
   * Vertigo
 * Primer Posts for General Readers
 * Primer on Statistics for Non-Statisticians
   * Standard Distribution and Standard Error
   * Determining the Standard Error of the Mean
   * Estimating the True Mean from a Sample Mean – “Significant Differences”
   * One-Tailed versus Two-Tailed Probability
   * Confidence Intervals
   * No Evidence for a Significant Difference versus Evidence for no Significant
     Difference
   * Determining the False Negative Error
   * Power Calculations for One Sample
   * Statistical Tests on Normally Distributed Data comparing Two Samples
   * Standard Errors and Estimation
   * Power Calculations for Two Samples
   * Comparing a Sample Proportion with an Expected Proportion
   * Comparing Two Sample Proportions
   * Student’s t-Test for comparing Two Independent Samples
   * Paired t-Test for Dependent Samples
   * Determining and Comparing Times to a Discrete Event: the Kaplan-Meier
     Survival Plot
   * Comparing Variables of Different Types
   * Summary of Statistical Tests
   * Designing a Research Study
 * Online Apps

← Older posts



INHALED LEVODOPA

Posted on March 24, 2023 by dulcetware


INTRODUCTION

Levodopa was the first main, and arguably still the best, treatment for
Parkinson’s disease. Being similar in structure to tyrosine, it is absorbed
through the gut by an amino acid transporter – its half life in the blood is
short, namely 0.7 to 1.4 hours. This has advantages in terms of avoiding
peripheral dopaminergic effects and dopa decarboxylase inhibitors further reduce
this latter effect.

The fact that the clinical action of levodopa is far longer than its half life
is presumably due to presynaptic storage and control of release as dopamine and
so it is no wonder that, as the disease progresses and there is increasing loss
of the remaining presynaptic terminals, PD control from oral levodopa becomes
more brittle and the clinician resorts to frequent dosing, longer acting
agonists, delayed release preparations and even continuous intrajejunal
delivery.

This review article, Profile of inhaled levodopa and its potential in the
treatment of Parkinson’s disease, describes a further new delivery method,
namely inhaled levodopa. This avoids the variability of gastric emptying and
competing with the amino acids of food for absorption.

CVT301 inhaled levodopa is a complex of molecules in a large low density porous
particle that can aerosolise and avoid phagocytic destruction in the lungs. The
particles are readily absorbed through the alveolar membranes. The Arcus inhaler
works by delivering the particles in dry powder form and requires the patient
only to breathe in rather than also to activate a pump.


PHASE I STUDY

In a Phase I study, following safety trials in animals, plasma levodopa
concentrations rose within 10 minutes of inhalation (they also took oral
carbidopa) by healthy volunteers.




PHASE II STUDY

Ini this study, patients with Parkinson’s disease and significant off periods
had a median T max (time to max concentration ) of 15 minutes vs 66 minutes for
oral, and clinical improvement (finger tapping) within 5 minutes and lasting
90-100 minutes.

Side effect of cough occurred in 25% which often settled after initial dose and
was described as mild. There was less dyskinesia giving 50 mg than for oral 100
mg – but the study did not quantify the relative benefits of finger tapping
tasks, so perhaps the oral was simply a bigger dose with more benefit and more
side effects.

There were no differences in lung function between oral and inhaled groups after
4 weeks.




PHASE III STUDY


THERE WAS A 12 WEEK DOUBLE BLIND, PLACEBO CONTROLLED STUDY IN 339 PATIENTS WITH
MOTOR FLUCTUATIONS RANDOMISED TO PLACEBO, 35 MG DOSE AND 50 MG DOSE. CHRONIC
LUNG DISEASE WAS A CONTRAINDICATION.

Mean change on motor UPDRS at 12 weeks from pre treatment versus 30 minutes
after treatment was -9.83 versus -5.91 for placebo (p = 0.009). There was no
reduction in off time according to patient diaries; 85% of subjects completed
the study. There were side effects of cough (14.5% versus1.9% placebo), URTI
(6.1% v 2.7%), nausea (5.3% v 2.7%), sputum discolouration (5.3% v 0%), and
dyskinesia (3.5% v 0%). Only two subjects discontinued due to cough. No
statistically significant differences in FEV1 or diffusion capacity of lungs
were found (but is this evidence for no effect on the lungs rather than no
evidence for effect?).


LONG-TERM STUDIES

In a longer term open label study of 408 subjects, the treatment arm used a mean
of 2.3 doses a day. After dosings at four weeks into the study, they experienced
a mean UPDRS III change of -5.7 at 10 min, -12.0 at 20 min, -15.5 at 30 min and
-16.1 at 60 min at 4 weeks and at 60 weeks the equivalent changes were -5.0,
-11.5, -15.3, -14.8.



In another study, FEV1 and diffusion of carbon monoxide were not statistically
different (!). Dyskinesia was worse; 5.5% versus 3.1% in the group with standard
of care. Two of I think 100 subjects discontinued due to cough.

In a study of other subjects having morning off, subjects getting 50 mg inhaled
levodopa came on in 25 mins vs 35 on placebo, and an on event was 35% more
likely than after placebo.

The C max of the higher strength (50 mg) is 500 ng/ml while 100 mg oral achieves
700-1000 ng/ml. As the authors comment, there are no head to head comparisons of
inhaled versus oral.

The authors conclude that inhaled levodopa is an option for rescue therapy.


JOURNAL CLUB DISCUSSION

The data demonstrate only biological efficacy, and it does seem fast acting.
There are not data on the quantitative effect versus oral levodopa or even the
speed of action versus oral levodopa, dispersible levodopa or subcutaneous
apomorphine. There appears to be no carbidopa or benserazide, so patients are
going to be needing to take oral levodopa at around the same time or at least
have had a decent amount fairly recently.

On a google search in March 2023, the cost is $1223 for 60 capsules of what is
stated to be 45 mg but I think this is equivalent to 35 mg in the study. This
might seem about a 1 month supply but no – one dose is two capsules. Patients
had an average of 2 doses a day and the company specifies a maximum of 5 doses a
day or ten capsules which is $204 a day!

In comparison, oral levodopa is $13 for 90 100 mg tablets. The 100 mg appears to
be up to double the strength of the higher strength inhaled, which we presume is
2 capsules. So the cost of inhaled levodopa is over 500x greater than standard
treatment. It does seem almost provocative not to attempt a comparison with
standard care so much cheaper. We can surmise that perhaps inhaled might work
about 15 minutes more quickly. The magnitude of benefit is demonstrated
biologically but there is no comparison with oral. Measurements were made when
the patients were rendered off to maximise the benefit rather than in a real
world scenario dealing with unpredictable sudden off. Would the plasma
concentrations as little as half that of 100 mg of oral really rescue such
patients?

Regarding safety there are one year data. No worsened lung function was
demonstrated but this does not mean there is evidence of no side effects unless
the study was powered and the type B error quantified. We have many examples of
preliminary data not showing the dangers revealed subsequently on post-
marketing surveillance.


Posted in Parkinson's Disease, Uncategorized | Tagged inhaled levodopa, L-DOPA,
Parkinson Disease | Leave a comment


REVOLUTION IN ACUTE ISCHAEMIC STROKE CARE: A PRACTICAL GUIDE TO
MECHANICAL THROMBECTOMY

Posted on March 24, 2023 by dulcetware


INTRODUCTION

Stroke is the most common cause of disability in Western Countries, and its
lifetime risk is up to 25%. While managing acute stroke patients in hyperacute
stroke units is regarded as having benefits for short and long term outcome,
specific therapeutic options are limited. The first major option for treatment
of ischaemic stroke was intravenous thrombolysis, paralleling its previous
development in acute myocardial infarction. However, while use in the latter
indication was widespread in the 1990’s, it has only been widely for stroke in
the last ten years. This is probably because of the narrower therapeutic window
and the more severe consequences of haemorrhagic complications in the brain. In
addition, its benefits are actually relatively modest. In randomised clinical
trials on use within three hours (bearing in mind that in the first hour a
stroke often spontaneously recovers – termed a TIA), using NICE data (June 2007)
15% of patients have an improved outcome, but with a brain haemorrhage risk of
7% greater, and a major haemorrhage rate actually considerably worsening the
stroke (>= 4 points on the National Institute of Health Stroke Severity Scale)
of 1.7% . When delivered between 3 and 4.5 hours after stroke onset the benefits
were not even clearly significant.

So it is not surprising that there has been a move, just like in cardiology a
decade or two earlier, away from intravenous thrombolysis and towards direct
intra-arterial catheter treatment. This article discusses this new treatment and
the ramifications for delivery of such a service.

The paper, Revolution in acute ischaemic stroke care: a practical guide to
mechanical thrombectomy, summarises recent evidence in favour of this treatment
and the infrastructure required to manage patients in this way.


THE PROCEDURE

While the first such devices were approved for use in 2004, technical
developments and the improved expertise that comes with experience show in
recent studies published since 2010 on new generation devices that they yield
major improvements in outcome. The HERMES collaboration meta-analysis revealed
that 46% of patients had a good outcome with functional independence (grades 0-2
on the Modified Rankin scale) compared with 26.5% on best medical treatment.
Most of the patients in both groups received intravenous thrombolysis, since in
most study protocols patients had iv thrombolysis before going on to have
thrombectomy an hour or so later. Mortality and the risk of brain haemorrhage
did not differ between the two groups. The benefit seemed still to be present in
patients over 80, and still present when patients did not receive iv
thrombolysis, though the numbers in this case were smaller. While the window for
thrombectomy was within 6 hours, there may still be improved outcomes up to 7.3
hours after symptom onset, but in generation faster intervention leads to
greater benefit.

The procedure involves a number of variations depending on the Neuroradiologist
and the particular nature of the thrombus. It may be done under general
anaesthesia or local anaesthesia and sedation with anaesthetic support. A large
gauge catheter is directed to the internal carotid via a femoral puncture, and
an intermediate catheter inside it is directed to the Circle of Willis. Then a
microcatheter inside that serves as a guide wire to the actual clot. The
microcatheter is then removed and a stent retriever is placed within the clot,
and pulled back to draw the clot to the intermediate catheter. Suction is
applied to this catheter to remove the clot entirely. Some techniques involve
directly removing the clot by suction on the intermediate catheter. A balloon
may be located on the distal end of the clot to prevent forward movement. When
removing the clot reveals a tight lumen, there is the further option to perform
angioplasty or stenting to open the vessel. The same can apply to a more
proximal carotid stenosis occurring in tandem with the more distal thrombus.


COMPLICATIONS

The main complications are technical, including vessel perforation (1.6%), other
symptomatic intrcranial haemorrhage (3-9%), subarachnoid haemorrhage (0.6 – 5%),
arterial dissection (0.6 to 3.9%), or emboli distally (1-9%). In addition ,
there can be vasospasm or issues related to the puncture site. While the total
incidence is 15%, not always is there any actual clinical adverse consequence.

 * While the time window for thrombectomy is wider than for intravenous
   treatment, there are other selection criteria that are more strict.
 * There should be a documented anterior circulation large vessel occlusion of
   the middle cerebral or carotid artery. (There is only limited evidence for
   efficacy in basilar occlusion.)
 * There should be good collateral cerebral circulation.
 * There should be relatively normal extracranial arterial anatomy from the
   technical viewpoint of passing the catheter.
 * There should be significant clinical deficit at the time of treatment, but
   this parallels the criteria for intravenous treatment and a large vessel
   occlusion with minimal clinical deficit nevertheless incurs a significant
   risk of clinical deterioration.
 * There should be a lack of extensive early ischaemic change on CT (according
   to ASPECTS score a threshold of 5). The role of more advanced imaging , eg CT
   perfusion to establish salvageable brain, is yet to be clarified.
 * Consideration should be given to pre-stroke functional status and the
   potential of benefit.
 * Patients should have had iv thrombolysis within 4.5 hours of symptom onset.


Posted in Uncategorized | Leave a comment


TESTING FOR COVID-19 INFECTION

Posted on November 22, 2021 by dulcetware

Accurate testing for SARS-CoV-2 infection, by which we mean testing with few
false positives as well as few false negatives, is important not only for
clinical management of individual cases but for epidemiological case tracing,
limiting spread of infection and informing public health strategies. In the
latter situations, tests that are rapid, cheap and easy to perform are
particularly desirable.

Two main forms of testing for SARS-CoV-2 infection are in use.

 * Antigen testing, which includes the self administered immunochromatographic
   lateral flow test (LFT), detects viral coat material and is developed by
   raising a specific antibody against the antigen target. It therefore measures
   active production of the viral protein that constitutes the antigen.
   Developing such a test requires knowledge of how the viral behaves in the
   host and is reliant upon generating a sensitive and specific antibody to be
   used for the test.
 * Molecular tests, such as polymerase chain reaction (PCR), loop-mediated
   isothermal amplification (LAMP) and clustered regularly interspaced short
   palindromic repeats (CRISPR) tests amplify viral RNA material. These tests
   are potentially specific for different variant mutations of SARS-CoV-2. The
   gold standard test is considered to be lab based reverse transcription viral
   PCR (RTPCR). Rapid testing, such as the LAMP test, skips some of the
   time-consuming stages of formal PCR and therefore is useful for screening and
   epidemiology.

Many different studies have reported on the performance of different brands of
rapid antigen and molecular test. This article discusses a Cochrane review of
diagnostic test accuracy that collected data from 64 studies that investigated
16 different antigen tests and five different rapid molecular tests. In all
there were 24087 samples. Of these, 7415 were found on the subsequent gold
standard RTPCR test to be positive. No study actually included clinical
diagnosis of infection as a standard or criterion of infection.

Antigen tests had a sensitivity in symptomatic cases of 72% (95% CI 63.7 to
79%). The sensitivity was higher in the first week of infection (78.3%) than in
the second week (50%). The overall specificity was 99.6% (in both symptomatic
and asymptomatic – obviously if symptomatic one wonders what they actually had
if we are considering false positives.

Analysing a different way, the positive predictive value (PPV) in comparison was
only 84-90% using the best sensitivity brands at 5% population prevalence and at
0.5% prevalence in asymptomatic people the PPV was only 11-28%!

Molecular tests had an average sensitivity of 73% (66.8 to 78.4%) and
specificity was 97.2 to 99.7% depending on brand. In most trials the samples
were collected in labs rather than in real life conditions. There are no data
about symptoms.

For reference, WHO considers >80% sensitivity and >97% specificity an
appropriate test as a replacement for a lab test.

The authors note that in low prevalence settings the dramatically reduced PPV
value means that confirmatory tests may be required and that data are required
for the settings in which tests are intended to be used as well as on user
dependence (i.e self testing)



Journal Club Conclusions

Why the huge difference between positive predictive value and specificity?

Specificity = 1 minus false positive rate (FPR)

FPR = false positives/all negative cases (i.e. true negatives and false
positives)

So high specificity means false positives is low compared to true negatives

PPV = true positives/all positive results (i.e. true positive and false
positives

So high PPV means false positives low compared to true positives

The difference between specificity and PPV is essentially that sensitivity is in
relation to the total number of actual cases while PPV is in relation to the
total number of positive test results. PPV could be much worse than specificity
if the true negatives much more common that true positives. This would happen if
infection rates are very low in the population.

Is it disingenuous to quote specificity based on trials where infection rates
were 31% when in the real world the infection rates are perhaps two orders of
magnitude lower than that? If someone wanted to argue that they were being
denied going to work, going to school or going on holiday based on a test where
the predictive value was only 11-28%, would they have a good case? Is it
worthless as a screening tool? If the policy is to go on to a molecular test if
the result is positive, is this also invalid if the PPV of the molecular test is
similarly low?

Clearly, the use of tests should be tailored to the information that can be
provided. In an outbreak of high prevalence, one wants a sensitive test to pick
up people who might have infection after contact tracing. One could have a
specific screen and lab test only those negative to make completely sure. The
priority is not to miss positive cases.

If medicolegally one wants to prove workers were not source of a case or
outbreak, when the prevalence of infection is low, one may as well go straight
to lab testing as there are too many false positives in such a situation.

In a situation where someone wants to question a positive result, it is not
clear that rapid molecular testing is superior to antigen testing, and a lab
based PCR may again be necessary. as specificity not clearly higher than some
molecular tests.

There are also biological as well as statistical issues. For example, antigen
tests may theoretically have false positives if the nature of the generated
antibody to a viral coat antigen is not clear. If the trial was done in the
summer time with no winter flu or coronavirus common colds in a setting where
one in three subjects have COVID and none has any other type of infection, is
the generated antibody really shown to be specific for SARS-CoV-2? The same may
apply to demographic factors, such as expecting the test to have the same
performance in children or in care homes as in a healthy adult test population.

On the other hand, a molecular test may correlate better with actual infectivity
than a bit of residual RNA and therefore be more biologically useful for
epidemiological control.

Finally, in extremely high prevalence trial populations where there is no actual
clinical corroboration, the absolute reliance upon lab PCR as a gold standard
may be of concern.









Posted in Infectious Diseases | Tagged Covid-19, Diagnosis, epidemiology, SARS |
Leave a comment


DISTINGUISHING ENCEPHALITIS FROM ENCEPHALOPATHY

Posted on December 11, 2020 by dulcetware

Encephalitis may be defined as infection or inflammation of the brain substance,
resulting typically in disturbed sensorium and perhaps seizures or focal
neurological deficits and sometimes pyrexia. Encephalopathy in the other hand
represents disturbed sensorium not due to an infective or inflammatory cerebral
process and its causes range from toxins and drugs to metabolic upset, non
cerebral sepsis, cerebral hypoperfusion and post-ictal states.

Distinguishing the two is important because considerable morbidity and mortality
is associated with delayed treatment with appropriate antiviral, antibiotic or
immune therapy for encephalitis and with delayed treatment of the various causes
of other causes of encephalopathy.

The paper presented, “To what extent can clinical characteristics be used to
distinguish encephalitis from encephalopathy of other causes? Results from a
prospective observational study” by Else Quist-Paulsen et al., attempts to use
clinical and rapidly available investigatory findings to distinguish the two
conditions by a prospective observational study on 136 patients.

They identified candidate patients on the basis they had a lumbar puncture, and
then excluded those with no evidence of encephalopathy. Their criteria for
encephalitis were:

 * Pyrexia
 * Encephalopathy > 24 hours with no other cause identified and 2 of:

 * CSF WCC >=5 x 106/l
 * New onset seizures
 * New onset focal neurological findings
 * CT/MRI consistent with encephalitis
 * EEG consistent with encephalitis

The gold standard by which to gauge their test would surely be a definitive
diagnosis but, as is commonly the case in clinically suspected encephalitis,
such a diagnosis was only made in 10 of 19 patients. In some of the patients
with non-encephalitis encephalopathy, the diagnosis was also vague, e.g.
“aseptic meningitis” (which could be encephalitis), “epilepsy” (which could be
autoimmune encephalitis), “headache/migraine”, “unspecified disorientation or
coma”.

Subsequent analysis of specific features in the two groups then becomes somewhat
difficult because the criteria themselves become the gold standard and because
some specific features were in themselves their criteria. Interestingly,
systemic features of infection such as raised blood white cells or CRP, argued
against encephalitis because general sepsis was a common cause of
encephalopathy. Nausea and personality change were more common in their
encephalitis group.

They used ROC curves to look at the predictive value of these specific features
and their combinations, but these were again based against their “testing
variable”, their criteria, not on some objective gold standard. It would have
been better to look at them only in the 10 diagnosed cases rather than all 19,
but then the total number of cases would be even lower.

The diversity of diagnosis of their cases was interesting, especially that Lyme
disease and TB were as common as VZV and more common than HSV. Only one of their
cases had NMDA receptor antibodies, but we do not know that all the patients had
this test and a full battery of other autoimmune antibody tests. Many might have
been put in the encephalopathy with seizures category. Since encephalitis can be
associated with meningism, some “aseptic meningitis” patients might have been
viral but with negative testing, or even autoimmune with a migrainous headache
and stiff neck.

The group felt that the study was very worthwhile but a more clear guide as to
which cases of encephalitis warranted antimicrobial therapy  or immune therapy
would be the clear goal. This would require clarity on the gold standard
diagnosis and many more patients.

The Journal Club discussion on which this post is based was presented by Dr Aram
Aslanyan, Specialist Registrar in Neurology at Queens Hospital, Romford, Essex.

Posted in Infectious Diseases, Inflammatory/ Auto-Immune Diseases | Tagged
encephalitis, encephalopthy | Leave a comment


MAKING A DIFFERENTIAL DIAGNOSIS USING A CLINICAL DATABASE

Posted on November 20, 2020 by dulcetware


BACKGROUND

A great deal of time is spent in medicine reading and writing case reports.
Essentially, clinical features are listed and a diagnosis made. Excluding those
cases that point to a novel means of treatment, a case report is often
noteworthy simply because the diagnosis is rare, or because the clinical
features were most un-likely to be associated with the diagnosis. This hardly
seems a reliable method of archiving medical knowledge.

Much less time is spent on attempting a method of diagnosis that is more
systematic than the recalling of case reports. One can see that if one did wish
to move medical diagnosis into the information age, natural instinct would be to
use an internet search engine to enter a list of clinical features and see what
disease diagnoses were associated with these terms. Unfortunately, internet
search engines concern themselves only with the popularity of search terms and
because of the dominance of case reports such practice may be likely to throw up
the least likely cause of those features, or that which is most “titillating” to
those who most perform internet searches.

There have been attempts to provide a more balanced means of linking clinical
features with diseases and hence making clinical diagnoses. Rare disease with a
large number of different clinical features are least easily diagnosed by
clinical experience or key investigations, and so the focus of these attempts
has been on rare genetic diseases using ever-expanding databases such as
Orphanet, Online Mendelian inheritance in Man (OMIM) and the London
Dysmorphology Database and the Pictures of Standard Syndromes and Undiagnosed
Malformations (POSSUM).

One method of searching for clinical features on these databases is simple text
matching. A way of quantifying the match is the feature vector method, which
calculates the mathematical overlap between the Query (the clinical features of
the case) and the Disease (the clinical features of the disease). A vector of
the query is calculated with dimensions for each feature and a value of 1 if
present and 0 if absent. The same is done for the disease. The dot product of
the two vectors is the strength of the match (a 1 for both query and disease
will sway the two vectors in a common direction, and a 0 for both will leave
their relationship unchanged, while a 0 and a 1 will make one move away from the
other).

A potentially better quantification of matching is to take into account the
different specificities of different clinical features. If a clinical feature is
present in only a few diseases, its annotation (the linkage of a clinical
feature to a disease) is more specific for that disease (in database terms this
is called the information content (IC)) and so that linkage should have more
weighting. The IC is simply the negative log of the frequency of the annotation.
For example, AV block is a term that annotates 3 diseases in the 4813-disease
OMIM database. The frequency is 3/4813. Loge of this is -7.38 and minus loge of
this is 7.38. A much more general term will have many annotations and a much
lower negative log, tending towards zero. The ICs of all the clinical features
of the query can be summed or otherwise combined to provide an overall match.

The authors of the presented paper have described a further refinement of this
method. This is called the Ontology Similarity Search (OSS). Instead of simply
matching the text of terms, they fit clinical features into a standardised
language within an ontological framework. This means that the features are
related to one another in a hierarchy, with more general terms higher in the
hierarchy and more specific subcategories of those general terms lower in the
hierarchy. While “parent” terms obviously have many “child” terms, child terms
can also belong to multiple parent terms. For example, optic atrophy could be a
child of demyelinating disease and also a child of visual disturbance. Their
ontology is called the Human Phenotype Ontology (HPO) and has around 9000 terms.

The advantage of using the ontology is that if a clinical feature of a case does
not fit the clinical features of the disease, but shares a parent term with one
of the features of the disease, instead of scoring a zero match, this scores as
a match but less so than if the match was with the specific terms. The method
specifically find the most informative common ancestor of the two different
clinical features, and uses the IC of that term. Being a more general term, it
will be a feature of more diseases and so have a lower IC. (In the database,
ancestor terms are implicitly annotated when child terms are annotated.) The
overall strength of match is the average of all the ICs – there will always be a
IC for each feature, even if it is just that they are both a feature of “any
disease”, which of course has an IC of zero and would bring down the average.


SUMMARY OF THE PAPER

The presented paper, Clinical Diagnostics in Human Genetics with Semantic
Similarity Searches in Ontologies by Köhler et al. (Am J Hum Genet. 2009 Oct 9;
85(4): 457–464), describes a further refinement of the method using a
statistical treatment. For a given disease, if random clinical features from the
HPO were selected one would expect a lower OSS score than for a patient who
actually had the disease. If the OSS for random features were repeated many
times, a distribution would be created and so one could then look at the real
patient OSS and determine a p-value on this distribution. If the real OSS was
higher than 95% of the random OSS scores, the p-value would be lower than 0.05
and indicate a likely match. Furthermore, if the same features were compared
with different diseases and their random OSS distributions, a ranking of the
likelihood of diseases could be determined by ranking the corresponding
p-values. They call this the OSS –PV.



Since they considered it too onerous to enter, within the framework of the terms
of the HPO, the clinical features of real patients with known diseases, they
used simulated patients. This was done for 44 diseases, where they created a
“patient” having a disease with a selection of the clinical features of the
disease weighted by how commonly those features were found in that disease. For
each disease 100 patients were created, so if from the clinical literature a
feature is found in 1% of cases with the disease, 1 of the 100 simulated
patients would have that feature.

They added “noise” to the process by adding to the patients some random features
that were not part of the disease, and “imprecision” to the process by replacing
some features with their HPO parent terms.

Then they looked at the rank position of the true disease among all the 5000 or
so database diseases found by the different methods. The closer the rank
position to the true position (first!), the better the method performed.



Unsurprisingly, the performance of the feature vector method, as shown by box
plots of rankings for all 44 diseases tested, was found to suffer when imprecise
terms were used, because that was the point of using the ontological system. The
OSS-PV method more modestly outperformed the raw OSS method when noise and
imprecision were added.

As the authors point out, the OSS method potentially suffers from the fact that
it only matches query terms with disease terms. If a disease also had many terms
that did not match the query terms, surely the overall match would be less
specific. This can be taken into account by performing a symmetrical similarity
search, where the OSS is the average of the matches of the query to the disease
and the matches of the disease to the query. However, they did not use this
method in their presented data, only stating that when they used it the
symmetrical OSS-PV still significantly outperformed the feature-vector method.
They do not state that it still outperforms the symmetrical raw OSS.

Another point raised by the paper is that if one finds on a disease search that
no disease fits the features with a p-value less than 0.05, exploration could be
made of other clinical features, or child features of the entered clinical
features that would have a higher information content and provide a more
significant match. Going back and looking for a specific feature, or performing
a specific investigation, would be an example of this.


JOURNAL DISCUSSION

As described in the introduction, any attempt to quantify and rationalise
differential diagnosis should be lauded and this paper clearly describes
progressive refinements of this process. It is almost negligent to have all the
data available on thousands of diseases and not to use them because the unaided
human mind simply cannot store so much information.

However, a number of further refinements and limitations present themselves.

First, the matching of terms is still semantic rather that systematic. While a
knowledge-based approach, it nevertheless does not rely on understanding of
disease pathophysiologies and pathognomonic features. Some clinical features
that share a close parent may in fact best distinguish diseases rather than be
considered loosely positively associated features. This may apply particularly
in neurology where there is a more systematic approach. For example, upper
motoneurone lesion and lower motoneurone lesion may be considered together and
share a common parent in “motor neurone lesion”, but apart from the case of
motoneurone disease, they split the differential diagnosis more than upper
motoneurone lesion and no motor lesion at all. They are semantically similar but
nosologically opposite. Horizontal supranuclear gaze palsy and vertical
supranuclear gaze palsy may share a strong information content parent, but may
be the feature that best separates Gaucher disease from Nieman Pick disease.



This leads to the second point. The frequency, or sensitivity, of a clinical
feature in a disease is not considered, although ironically considered when
creating the simulated patients with the 44 tested diseases. In large part this
reflects the lack of clinical data in the databases themselves. It is
regrettable that case reports are not combined into case series which contain
information on the frequencies of occurrence of clinical features, or when there
are case series, these data are not actually collected systematically. If a
clinical feature occurs in 1% of cases of one disease and 100% of cases of
another disease, clearly the annotation of the feature for the second disease
should be considered far stronger than for the first. Instead, because there are
no such data, they are given equal weight; the weighting only considers whether
or not the feature is also found in a number of other diseases, not how commonly
it is found in those diseases.

There is no consideration of how common the disease is in the first place. While
restricting themselves to rare and genetic diseases by definition, there can be
a frustrating tendency for searches to throw up the least likely diagnosis. It
is often the case in practice that the clinician does not know in advance that
the patient has a rare genetic disease, and a diagnostic tool should be most
useful to those with least intimate knowledge of the database. Thus, when
entering the features dystonia, spastic hemiparesis and spastic dysarthria in a
case of cerebral palsy, it comes as a surprise when the top diagnosis is cleft
palate-lateral synechia syndrome.

Finally, the methods assume that clinical features are independent. In fact,
many clinical features are strongly interdependent; they especially occur
together. The association of the second feature is not really very additionally
informative if the first is present. This problem would be common to most forms
of differential diagnosis calculators, including those using Baysian methods,
and could only be solved if there were data on the interdependence of clinical
features in different diseases; currently it is hard to find even raw frequency
data for most diseases.

The point that the authors raise about using their App to find features that
would be more specific in making a diagnosis is an interesting one, and opens a
new approach to diagnosis and refinement of the process of often expensive and
sometimes risk-associated investigation. One could imagine the improvements in
medical care that would arise from use of an App that gave a differential
diagnosis based on initial clinical information and then showed the relative
power of different investigations in narrowing that differential.

A further use of these methods would be in creating diagnostic criteria. While
clinical practice is rightly focused on the most likely diagnosis in a patient,
clinical research is focused on a group of patients where the diagnosis is
certain, i.e. specificity at the expense of sensitivity. Currently, diagnostic
criteria seem to be set largely by “workshops” – gatherings of the great and the
good usually in an exotic location who draw up a list of features, create two
categories of importance and then decide how many features are required for a
“definite diagnosis”. Using a quantified method such as that described in this
paper for every study patient and including only patients where the diagnosis
reaches a threshold p-value score would seem to be a far more reliable method.

The paper on which this journal club article is based was presented by Dr John
McAuley, Consultant and Honorary Senior Lecturer in Neurology at Queens
Hospital, Romford.

Posted in Genetics | Tagged differential diagnosis, Human Phenotype Ontology,
OMIM, Orphanet | Leave a comment


CORONAVIRUS

Posted on March 6, 2020 by dulcetware

Coronavirus is obviously not a neurological disease, apart from an isolated case
report of encephalitis associated with the condition, which is to be expected
very rarely in association with viral infections, but because it is so topical
this paper Clinical Characterisics of Coronavirus Disease 2019 in China,
published in haste in the New England Journal of Medicine on 3rd March, 2020,
was nevertheless presented.


BACKGROUND

A novel enveloped RNA virus of coronavirus type, similar to SARS coronavirus,
was first identified as causing viral pneumonia in early December 2019 and named
as Covid-19 disease. It is believed to have first been transmitted through
livestock in a large market in Wuhan, Hubei province. It is thought in general
that such viruses are endemic in wildlife, such as in bats, and mutate to become
transmissible to other animals and to humans.

As of Friday 6th March, there were 100,645 confirmed cases worldwide, and 3411
deaths linked to the virus. There were 55,753 cases who had recovered. In Hubei
province, for the first day since the outbreak no new cases had been reported.

The details are unclear, but the fact that the UK government is said to be
moving from a containment to a delay phase suggests that at least some UK cases
have been identified that appear to have had no contact with potential suffers
in China, Iran, Italy or other hotspots, nor with other UK individuals known to
have the disease.


JOURNAL CLUB ARTICLE

The paper discussed is an early report focusing on numbers affected, initial
outcomes and clinical presentation. It was approved by the Chinese authorities.

Data were sourced from records of laboratory confirmed cases using assay of
nasal and pharyngeal swabs between 11th December 2019 and 29th January 2020.
Certain hospitals were sampled, so by no means were data collected from all
cases.  In all, 14.2% of all known hospitalised cases were included in the
study. It is not clear how widespread was the screening of the population by
these laboratory tests; all the patients in this study were hospitalised.

26% of these cases had not had contact with Wuhan residents, indicating
widespread serial human to human transmission.

Clinical information is as follows:

 * Incubation period (presumably from ascertaining likely time of exposure was
   median 4 days (2 to 7 days interquartile).
 * Fever in only 44% on admission, but developed later.
 * Cough in 68%.
 * Viraemic symptoms occurred in some patients, but upper respiratory tract
   symptoms, lymphadenopathy and rash were very rare.
 * CT chest abnormalities were very common (86%) in both mild and severe cases.
 * Lymphopaenia was common (83%).
 * Only 1% of cases were under 15 years old.

Of these hospitalised cases, 926 were considered mild and 173 severe. The main
factors predicting this were advanced age and comorbid disease (especially
coronary heart disease, diabetes, COPD and hypertension), also breathlessness at
38% versus 15% (unsurprisingly as this would be a criterion for severity).
Similarly, inflammatory markers and markers of multi-organ involvement were
associated with more severe disease. The main complicating feature of severe
cases was acute respiratory distress syndrome, occurring in 16%.

The outcomes were 25% risk in severe cases of intensive care admission,
mechanical ventilation or death (8%). Only 0.1% of cases categorised as
non-severe died. The overall death rate was 1.4%. The national statistics at the
time had a death rate of 3.2%.

By the data cut-off point, 95% of mild cases and 89% of severe cases were still
hospitalised; the median lengths of hospital stay were 11 and 13 days
respectively. Perhaps mild cases were hospitalised for purposes of isolation.


JOURNAL CLUB DISCUSSION

The paper reports likely ascertainment bias from milder cases not being tested.
Nevertheless, the scale of the morbidity and mortality of the disease is not
underestimated. Ascertainment bias becomes more relevant if one expects a
pandemic and most of the population to become exposed. By these means the
population risk can be inferred.



The paper also reports the fact that many patients were still in hospital, and
perhaps very unwell, by the study’s end point. In the study, the number of cases
requiring intensive care treatment is three times the death rate. Perhaps the
death rate of already infected cases may climb. On the other hand, ARDS, the
major serious complication of coronavirus infection, has a mortality of around
40%, and since 16% had this condition and 8% died, perhaps few more would be
expected to die.

There does appear to be an opportunity for more information to be gleaned from
these data or similar studies. The large number of cases could be randomised to
have treatments not clear to be effective, such as oseltamivir, steroids and
intravenous immunoglobulin. Less than half of cases had these treatments, but
nevertheless appreciable numbers. It would have been helpful to know the death
rates for patients who did or did not have these treatments rather than only the
end point rates, as in reality some of these treatments might be most relevant
when patients have already reached the ITU admission end point.

A follow up study would give better indicators of important epidemiological
issues such as ultimate death rates and morbidity, the possibility of
reinfection versus lasting immunity and any signs that more recently infected
cases, where transmission has been via several human hosts, have any milder
disease than those directly exposed to the transmitting animals.

A population based study that tested all individuals in high risk areas would
determine the likely proportion of individuals who have been infected but not
become very symptomatic.

Worldwide, we would also want to know how ambient temperature and sunlight
levels affect transmissibility.

One suspects that epidemiologists in charge of advising governments have more
information than is released to the public, and various advanced tools to model
infection spread, but from the recent explosion of cases in Italy and now
elsewhere, where talk is of delay rather than containment, there is little
confidence that the slowing up of cases in China is going to be replicated
worldwide.

From the death rates reported in Italy, there appears to be no clear evidence
that the disease is becoming milder, but from the delay of many days from
exposure to developing critical illness, perhaps it is too early to tell.

The lack of cases in hot or southern hemisphere countries would suggest a
seasonal effect of the virus, and some reassurance to northern hemisphere
countries approaching Spring. But in Australia there were already 40 cases
confirmed by 4th  March and at least three cases had had no recent foreign
travel and no traceable contact.

It seems that one scenario for the UK is that the infection eventually
replicates that of Hubei province, which has a similar population to the UK and
had around 11,000 cases with few new cases to come, and with around a 1-3%
mortality rate, mainly in the elderly and infirm for whom ‘flu’ is also a
significant source of mortality. With around 20% of cases classed as severe,
this would require an extra 2000 of some form of high dependency inpatient beds
for several days and spread over only a month or two.

However, we do not have an explanation for the slowing of new infection rates in
China. It could be that most of the local population has already been exposed
and most were resistant to severe symptoms, or it could be that containment
measures have been very effective. If the latter is the explanation and is in
reality only delaying inevitable spread through the population, or if
containment is not replicated to the same degree in Western countries and if
there is no seasonal dip in transmission, one could imagine hundreds of
thousands of cases in the UK spread over the next year. And with a current
mortality rate seemingly up to 3% this is unlikely to drop when there are
insufficient hospital resources to manage such numbers.

The paper on which this journal club article is based was presented by Dr Bina
Patel, Specialist Registrar in Neurology at Queens Hospital, Romford.

Posted in Infectious Diseases | Tagged China, Covid-19, UK | Leave a comment


ANTICONVULSANT MEDICATIONS FOR STATUS EPILEPTICUS

Posted on December 9, 2019 by dulcetware

Status epilepticus is a medical emergency with significant morbidity and
mortality and, in circumstances where benzodiazepines alone have failed to
terminate seizures, has traditionally been treated with anticonvulsants such as
phenytoin or phenobarbitone. Other intravenously administered antiepileptics
have also been found to be effective.

There is a lack of comparative data on different anticonvulsants and this
blinded prospective study “Randomised Trial of Three Anticonvulsant Medications
for Status Epilepticus” by Kapur et al. (2019) compares three options:
fosphenytoin (a pro drug of phenytoin which is more expensive but more soluble
and can be given intravenously faster with fewer extravasation problems and can
also be given intramuscularly), valproate and levetiracetam.


STUDY DETAILS

Patients in the study had to be over 2 years of age, and had to have convulsive
status (persistent or recurrent convulsions) for at least 5 minutes, and then
more convulsions between 5-30 minutes after an adequate dose of benzodiazepine
(5 minutes to have allowed the benzodiazepines to work and less than 30 minutes,
after which point another dose of benzodiazepines could have been tried
instead). Patients were randomised by stratifying for age.

Patients with major trauma or anoxia, etc., were excluded, as were pregnant
women (give levetiracetam and consider magnesium).

The doses of the intravenous anticonvulsants levetiracetam (60 mg/kg) and
valproate (40 mg/kg) seemed very high.

The primary successful outcome was absence of clinical seizure activity and
improved responsiveness at 60 min after infusion start.

Analysis was based on assuming equal prior probability of success for the three
treatments, then using the binomial probability of positive or negative outcome
to calculate the posterior probabilities. An iterative method was then used from
these three separate probabilities to calculate the probability that a given
treatment was better than the other two, or worse than the other two.

The sample size was set on the basis of correctly identifying with 90%
probability a difference when one treatment was 15% better than the other two
(65% response for the best and 50% response for the other two).

A total of 400 patients were enrolled. The intention to treat population was
only 384 because some patients were enrolled more than once. Nearly a third of
patients were then excluded because treatment did not follow the protocol, e.g.
not status epilepticus such as functional seizures, did not receive the correct
amount of benzodiazepine or anticonvulsant or wrong timing with respect to
benzodiazepine.

Half the patients were unblinded to avoid suboptimal management.

In the per-protocol population, 47% of patients responded to each of the three
treatments, with probability of most effective treatment distributed as follows:
levetiracetam (0.34), fosphenytoin (0.35), valproate (0.31). There was also an
“adjudicated population” outcome, which was perhaps based on an adjudicator
clinician looking retrospectively at the notes, whether following the protocol
or having had previous treatment or not, and deciding if the treatment worked.
Although the data were similar, it did seem that levetiracetam may have been
worse (0.51 versus 0.29 and 0.2) and clearly 0.51 is 31% worse than 0.2
(valproate), which is more than their threshold of meaningful difference of 15%
for best treatment.

Secondary outcomes included requirement for admission to ICU (87% for
levetiracetam and only 71% for valproate).

Regarding safety, there were 4.7% deaths in the levetiracetam group and 1.6% in
the valproate group, with fosphenytoin in the middle. Hypotension, a known issue
with phenytoin was 3.2% in the fosphenytion group to a life-threatening degree
and only 0.7% for levetiractem and 1.6% for valproate. Cardiac arrhythmia only
occurred in one patient. Acute respiratory depression occurred in 12.8 % with
fosphenytoin and 8% with levetiracetam and valproate. None of these differences
reached significance.

The conclusion was that there was no difference between the drugs.


JOURNAL CLUB DISCUSSION

The study was welcome as it was on an important practical topic. The group
wondered about the high doses used, and whether our own guidelines should
reflect these doses. The trial was powered for the primary efficacy outcome and
then stopped. However it was always going to be as likely that any differences
between the drugs wold lie in their side effects as in their efficacy and it is
a shame that the powering did not reflect this so that what may have been real
differences in respiratory depression or hypotension never reached significance.



The vagaries of statistics are illustrated by the per-protocol efficacies, which
seem identical, and the adjudicator population efficacies, where there was
actually a 31% greater chance of levetiracetam being the worst drug compared to
valproate.

Negative study results always make us turn to how the study was powered: were
there no differences seen because there are no differences, or because too few
patients were studied (i.e. too low power)? When powering a study, a judgement
must always be made on what level of difference would be considered meaningful,
otherwise if accepting any difference as being meaningful it would require an
infinite population to prove there is no difference. They chose a meaningful 15%
difference for one drug being better than the other two, but if they had chosen
one drug worse than the other two, the 31% difference in the adjudicator
population would have been more than their set level. There should have been
more explanation of their adjudicator population, and perhaps more explanation
of the advantage of using Baysian probabilities in addition to a simple
comparison of means and standard errors of success rates.

In real practice, there should perhaps be tailoring of treatment to the patient.
If a patient is already on therapeutic levels of phenytoin, is more of the same
going to be the best choice? If a patient is a female of child bearing
potential, is valproate the best choice when the patient often ends up on the
oral equivalent of the status treatment they received. On reviewing the data in
this study and knowing that the levetiracetam dose was very high, valproate
might shade the other two choices, especially in men.

The Journal Club on which this article is based was presented by Dr Katie
Yoganathan, SpR in Neurology at Queens Hospital, Romford.

Posted in Epilepsy, Intensive Care Neurology | Tagged Epilepsy, fosphenytoin,
levetiracetam, phenytoin, Statistics, status epilepticus, valproate | Leave a
comment


GALCANEZUMAB IN CHRONIC MIGRAINE

Posted on November 8, 2019 by dulcetware

Migraine is one of the most common neurological conditions, and chronic migraine
is a condition that, while less common than episodic migraine, is nevertheless a
major cause of loss of quality of life in otherwise well individuals.

Once analgesia headache has been effectively treated, and tension type headache
excluded, chronic migraine is treated with migraine preventative medications,
often very effectively. However there are a proportion of patients who remain
resistant to single or combination preventative treatments.

A novel target for migraine treatment is the calcitonin gene related peptide
CGRP receptor on the smooth muscle of blood vessels in the head. CGRP is
released from trigeminal ganglion efferents to the blood vessels to cause potent
vasodilation as part of the trigeminovascular response (analogous to the “triple
response” of pain, redness and swelling of skin inflammation). Blocking this may
therefore block this response. Monoclonal antibodies raised against the
receptor, or against CGRP itself, have been explored as migraine treatments.

This study describes a double blind trial on galcanesumab, one such monoclonal
antibody targeting CGRP. The paper does not discuss the relative hypothetical or
actual benefits versus other monoclonal Ab migraine therapies already marketed
or in development.


STUDY DESIGN

Around 270 patients were given each of two doses of galcanezumab by monthly
subcutaneous injection, and 560 were given normal saline placebo. To be enrolled
on the study, patients had to have 15+ headache days per month, at least 8 of
which had to be migraine days. They needed at least 1 headache free day per
month. If a patient failed >3 other preventatives, they were excluded. Before
the study, patients had to stop all their existing migraine preventatives except
propranolol or topiramate at least 30 days before study start.

Migraine days were defined as >30 minutes of migraine or probable migraine
according to ICHD-3 beta criteria (even though the duration criterion of the
latter is 4+ hours). If a patient thought it was a migraine and it did not
satisfy the criteria but responded to a triptan, that also counted as a migraine
day.

Over 90% of patients completed the study. Only 15% of patients were on
topiramate or propranolol (not specified if this was the same proportion in the
three treatment groups).

The primary outcome measure was migraine days per month. At the start of
treatment, this was around 19 days. Placebo reduced this by 2.7 days per month,
low dose galcanezumab by 4.8 days and high dose by 4.6 days. Therefore, compared
to placebo, the drug on average reduced migraine by 2 days per month. There were
only about 2 extra non migraine headache days per month on average.



There were many secondary measures. Of note, 4.5% of placebo patients had a 75%
reduction in migraine days, and 7% of low dose and 8.8% of high dose patients,
while 0.5% of placebo patients had a 100% response, and 0.7% of low dose and 1.3
% of high dose patients (not significantly different).

There was no overall quality of life measure, but there was a migraine related
quality of life measure that showed significantly more improvement, about 25%
more improvement than placebo. There was a patient global disease severity 7
point scale, where there was a 0.6 point improvement from placebo, and 0.8 for
low dose and 0.9 for high dose, only the latter reaching significance.



The side effect profiles were similar between placebo and drug, notably common
in both groups! However, there were no concerning side effects, nor indeed any
characteristic enough to tend to unblind the patients or investigators.


OPINION

The Journal Club thought it was strange that the study would exclude the very
patients in whom the drug would mainly be used, namely those who had failed >3
conventional treatments. The focus was clearly on maximising benefit as measured
by the study. By the same token, patients had to stop any preventatives before
the study, even if they were partially beneficial, apart from topiramate and
propranolol.

It was furthermore strange that only 15% of the recruited patients were on the
two most common treatments for chronic migraine. Had they only been tried on the
others, or had they had side effects? In real practice, there are usually at
least some marginal benefits from preventatives and patients often remain on
them.

It is therefore possible that many patients were treatment naïve as far as
preventatives were concerned. This makes the 2 fewer migraine days per month vs
placebo (from an initial 19 days per month) an all the more modest magnitude of
benefit.

It is difficult to reconcile the cost of the drug with the fact that patients on
average will still have 15 migraine days a month. Most patients would not
consider this a treatment success, and certainly not such that a patient would
happily be discharged from specialist care. In terms of patients having a 75%+
reduction in migraine days, generally the minimum level of meaningful benefit in
a pain study, the excess over placebo was only 3-4% of patients.

The lack of a general quality of life measure means that cost benefit analysis
cannot be performed. The quality of life measure used was specific for migraine
and likely to show much larger differences; a cured migraine sufferer might have
a near 0% to 100% swing on this scale, but another individual considering the
range from death to total disability to perfect health might assign curing
migraine only a swing from 90% to 100%.

A major aspect of migraine care is what happens when treatment is stopped.
Patients do not want lifelong medication, let alone lifelong monthly injections.
Fortunately we find that after six months of treatment, traditional
preventatives can often be withdrawn. Although the study mentioned that there
was an open label period and then a wash out period, we do not know any of these
results; presumably they are to be held back for another publication. Is there
rebound migraine on treatment withdrawal? Any funding body would want to know if
the patients would likely need the treatment for 3-6 months or for many years.

As a final point, it was queried whether the definition of migraine is
sufficiently specific; perhaps this limits the observed benefit in this and
similar studies. Some headaches recorded as migraine may be tension type
headache and therefore not responsive to specific anti-migraine treatment. The
table below shows the relevant criteria.

ICHD-3 Headache Diagnostic Criteria

Probable Migraine Probable Tension Type Headache Definite Tension Type headache
2+ of: 2+ of: All of: 4-72 hours duration 30 min to 7 days duration 30 min to 7
days duration 2+ of:



Unilateral,

Pulsing,

Moderate+ severity,

Avoid routine physical activity

2+ of:



Bilateral

Pressing or tightening

Moderate- severity

Not aggravated by routine activity

2+ of:



Bilateral

Pressing or tightening

Moderate- severity

Not aggravated by routine activity

Nausea or



Photo plus phonophobia

No nausea



Not both phono and photophobia

No nausea



Not both phono and photophobia



 

A headache is diagnosed as a migraine if fits probable migraine and is not a
better fit with another headache diagnosis, which presumably means definite
rather than probable tension type headache. The severities and durations overlap
so they cannot distinguish. One of photophobia or phonophobia overlaps. So a
unilateral, pressing headache with avoidance of routine activity with no nausea
no photophobia and no phonophobia  is classified as migraine as long as it lasts
4 hours, but it seemed that some of the migraine days were half an hour of
headache. Also a headache not satisfying these criteria is a migraine if there
is a response to triptans, but we have seen the large placebo response already
from the main data. In general practice a tension type headache might be
unilateral, and might interfere with routine activity if at the more severe end
of the scale; certainly a neck ache or jaw (including temporalis muscle) ache
from which a tension headache may arise may have these features.

The paper on which this Journal Club article is based was presented by Dr
Piriyankan Ananthavarathan, Specialist Registrar in Neurology at Barking,
Havering and Redbridge University Hospitals Trust.

Posted in Migraine | Tagged chronic, Migraine, monoclonal antibody | Leave a
comment


DISEASE MODIFYING THERAPIES IN MULTIPLE SCLEROSIS: BACKGROUND FOR
GENERAL READERS

Posted on September 19, 2019 by dulcetware

Multiple sclerosis (MS) is a presumed autoimmune condition of demyelination and
often inflammation of the central nervous system. Its evolution is very
variable; some patients suffer episodes lasting weeks to months with complete or
near complete recovery in between, and the periods between episodes may span
months to decades (relapsing remitting MS). Other patients accumulate
progressive disability as a result of or between episodes (secondary progressive
MS). Still other patients, around 10% in total, do not suffer episodes but
instead undergo a gradually progressive course with variable rapidity, but
usually noticeable over the course of months to years (primary progressive MS).
Patients with MS can evolve from one category to another; some in fact at a
certain point remain clinically stable indefinitely.

For many decades, its immune basis has prompted trials of various
immunomodulatory agents to try and reverse or at least arrest the progression of
multiple sclerosis. Some have been shown not to work, e.g corticosteroids,
immunoglobulin. Some work but have largely been overtaken by newer, more
expensive, therapies. For example, azathioprine is a traditional commonly used
immunosuppressant and in a Cochrane review was found to reduce relapses by
around 20% each year for three years of therapy, and to reduce disease
progression in secondary progressive disease by 44% (though with wide confidence
intervals of 7-64%). There were the expected side effects but no increased risk
of malignancy. However it remains possible that there could be a cumulative risk
of malignancy for treatment durations above ten years. In the 1990s,
beta-interferon became widely used but was never compared directly with
azathioprine. With the 21st century came the introduction of “biological
therapies”, typically monoclonal antibodies against specific immune system
antigen targets. There has also been a reintroduction of non-biological
therapies originally used to treat haematological malignancy or to prevent organ
transplant rejection.

These new therapies, called disease modifying therapies, as opposed to
symptomatic treatments or short courses of steroids for relapses, are now
conceptually, though not biochemically or mechanistically, divided into two
groups: those better tolerated or with fewer risks of causing malignancy or
infections but less effective, and those with more risk of cancer and serious
infection, including reactivation of the JC virus to cause fatal progressive
multifocal leukoencephalopathy, but with greater efficacy.

The former group includes beta-interferons, glatirimer acetate and fingolimod.
Fingolimod is an agent derived, like ciclosporin, from fungal toxins that
parasitise insects and has the convenience of oral administration, but is now
not routinely recommended because of severe relapses on withdrawal, and cardiac
and infection risks.  The latter group includes the biological agents
natalizumab (which targets a cell adhesion molecule on lymphocytes), rituximab
and ocrelizumab (which target CD20 to deplete B-cells) and alemtuzimab (which
targets CD52 expressed on more mature B and T cells) and the oral non-biological
anti-tumour agent cladribine which blocks deoxycytidine kinase and thus
interferes with DNA synthesis. Another  non biological oral agent, dimethyl
fumarate, acts as an immunomodulatory rather than immunosuppressive agent and
sits somewhere between the two groups, having oral administration convenience
and better efficacy than the first group, but also possessing the increased PML
and Fanconi renal syndrome risk of the second group.

Recent studies indicate that higher strength DMTs may slow disability
progression in secondary progressive MS, as well as reduce the number of
relapses. There have also been trials in primary progressive MS but these, most
notably using rituximab, were not clearly positive. For a study looking at
ocrelizumab on primary progressive MS, see the accompanying Journal Club review.

 


COST OF DISEASE MODIFYING THERAPIES

The disease modifying therapies are extremely expensive and, given MS is
unfortunately not a rare disease, have a significant impact upon the health
economy.

For example, in relation to the accompanying paper review of ocrelizumab for
primary progressive MS, this drug is not really expensive compared to similar
medications, having a list price of £4790 per 300 mg vial, with four infusions a
year. There are many further costs associated with imaging, screening,
monitoring and admission for infusions.

Normally, cost effectiveness is justified at around £35,000 per Quality of Life
Adjusted Year (QUALY). This means the cost would be justified at £35,000 a year
if each year it gave patients 100% quality of life who would otherwise die or
have zero quality of life. Clearly ocrelizumab does not do that; it appears to
preserve at least 0.5 or 1 out of 10 on a disability scale in 6% of patients on
an ongoing basis, giving a quality of life per patient benefit of very roughly
0.6% and a QUALY estimate of over £3 million. Of course, there are other
considerations such as wider health economy costs of disability, the fact that
some patients might have been prevented from deteriorating by more than 1 point
on the EDSS, and the potential costs of monitoring for and treating cancer and
PML complications in a relatively young patient population even after treatment
is stopped. Note that there was actually no significant difference in this study
in the SF 36, with both groups remaining surprisingly little changed after about
2 years, which probably fits with the 0.6% mean improvement figure calculated
above.



If the NHS, or the health economies of other countries, do not consider a
tighter subset of primary progressive patients who might respond better, it is
difficult to balance this with other medical, or indeed social care, conditions
that require resourcing.

Posted in Inflammatory/ Auto-Immune Diseases, Primer Posts for General Readers |
Tagged Disease Modifying Therapy, Multiple Sclerosis | 1 Comment


OCRELIZUMAB VERSUS PLACEBO IN PRIMARY PROGRESSIVE MULTIPLE SCLEROSIS

Posted on September 19, 2019 by dulcetware

Recent studies indicate that higher strength disease modifying therapies (DMTs)
may slow disability progression in secondary progressive multiple sclerosis
(MS), as well as reduce the number of relapses. There have also been trials in
primary progressive MS but these, most notably using rituximab, were not clearly
positive. For a more general review, please see the post Disease modifying
therapies in multiple sclerosis.

The study being reviewed in this post, by Montalban et al., 2019 is on
rituximab’s sister compound, ocrelizumab, and targets younger patients with more
active disease, which seemed to be a subgroup that might have responded to
rituximab.





STUDY DESIGN

There were 732 patients randomly assigned to ocrelizumab or placebo in a 2:1
ratio. Inclusion criteria were a diagnosis of primary progressive MS according
to established criteria and age 18 to 55 years. Their disability had to range
from moderate disability but still no walking impairment to impaired walking but
able to walk 20m, perhaps with crutches (EDSS 3.0 to 6.5). The disease duration
had to be within 10-15 years. They should never have had any relapses.

Pairs of ocrelizumab or placebo infusions were given every 24 weeks for at least
five courses. The main end point was the % of patients with disability
progression, defined as at least 1 point on the EDSS scale sustained for 12
weeks, or 0.5 points at the more disabled end of the scale.

Only if this primary end point was reached would the study be continued to test
secondary end points such as 24 week sustained disability progression, timed
walk at week 120, change in volume of MRI brain lesions, and change in quality
of life on the SF36 score.





RESULTS

Patients had a mean disease duration of around 6 years, and 3% more patients
having ocrelizumab had gadolinium enhancing lesions on MRI (27% versus 24%).

39.3% of placebo patients had increased disability sustained for a period of 12
weeks, and only 32.9% of ocrelizumab patients (p=0.03, relative risk reduction
24%). This was similar when confirming sustained disability over 24 weeks.

On the timed walk, there was a mean 39% slower performance after 120 weeks in
patients on ocrelizumab and 55% slower in patients on placebo (p=0.04). There
was no difference in quality of life (SF36 – physical component; a 0.7 out of
100 deterioration on ocrelizumab and 1.1 out of 100 on placebo).

There were three potentially relevant deaths in the ocrelizumab group (out of
486 patients), two from pneumonia and one from cancer, and none in the placebo
group, but the overall rate of serious infections was not really different.
Cancer rate was 2.3 % versus 0.8%, but obviously this would have to be monitored
over further decades. Even during one year of open label extension there were
two further cancers in the ocrelizumab group. The overall rate of neoplasms to
date is 0.4% per 100 patient years, double the baseline rate, but this reflects
a short time in a large number of patients.

In summary, a modest reduction in disability was seen on ocrelizumab, namely
preserving against 0.5 to 1 point loss on the EDSS scale in 6 % of patients.

 


OPINION

We focused mainly on the figure (see below) where it seems that ocrelizumab
stopped about 5% of patients deteriorating in the first 12 to 24 weeks, from
about 9% down to 4%, and then this difference was maintained throughout until
the end of the trial where about 60% of patients still had not deteriorated. The
plateau at 3-4 years is probably because of the end of the trial (see below),
not a stable MS population.





The journal club were surprised at the focus on a 12 week primary end point.
Patients would have progressed from zero to 3-6 out of 10 on the EDSS scale over
a mean period of 6 years, yet they were measuring progression of 0.5 to 1 point
over just three months. This is because there was some confusion over the phrase
in the paper describing the primary end point as “percentage of patients with
disability progression confirmed at 12 weeks”, and then in the results
“percentage of patients with 12-week confirmed disability progression (primary
end point) was 32.9% with ocrelizumab versus 39.3% with placebo.” It might seem
that the primary end point was recorded at 12 weeks following treatment
initiation. In fact the primary end point was recorded at the end of the study
stopped after over 2 years when a prior defined proportion of patients had
deteriorated. It means that over 2+ years, 32.9% of patients had a deterioration
that was sustained over at least 12 weeks, i.e. not a relapse.

On the graph, it shows the numbers of patients remaining without disability at
different times, starting at 487 and dropping to 462 at 12 weeks for
ocrelizumab, which is 5.1% of patients and 244 to 232 for placebo which is 4.9%.
Then at 24 weeks, this was 7.6% versus 13.1%. Some of the dropouts might be due
to stopping from tolerability, but this was a small amount, possibly accounting
from the small numbers of drop-outs between assessments every 12 weeks. For a 12
week confirmed disability progression, clearly there will be a lag in
identifying patients whose increase in disability is sustained for 12 weeks. It
seems that the time points do not add this 12 weeks because there is a first
jump at 12 weeks in both groups. However, these numbers drop down to zero, not
to the 60% of patients that appear not to have dropped out. This is likely to be
because of patients dropping out because they started the study later and the
study was terminated for them before 216 weeks. Nevertheless, factors such as
drop outs due to tolerability and end of study probably explain the difference
between the figures in the results and the plateau levels on the graphs.

What is interesting is that the difference between ocrelizumab and placebo
diverged very early on the graph, and not really further over 2 years. While the
12-week sustained disability was designed to eliminate the possibility that the
study is scoring relapses in previously primary progressive disease, or some
other temporary factor such as injury from a fall or intercurrent infection,
there is nevertheless a suspicion that ocrelizumab was mainly working well on a
small subset with more active disease. The extra 3% with gadolinium enhanced
lesions – a proportional difference of about 12% – unfortunately suggests a
potential issue with randomisation; this might precisely be the group who could
respond better.

It is noteworthy therefore that in its most recent NICE appraisal, the criteria
for considering ocrelizumab are not those in this study, but a subset of primary
progressive patients with enhancing disease on MRI imaging.

The journal club article described in this post was kindly presented by Dr Bina
Patel, Specialist Registrar in Neurology.

Posted in Inflammatory/ Auto-Immune Diseases | Tagged Autoimmune, Disease
Modifying Therapy, Multiple Sclerosis, Neuroprotection, Primary Progressive | 1
Comment
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