1. Journal Club FLUshing Out the Evidence for Neuraminidase Inhibitors in the Treatment of InFLUenza
Manish Khullar BSc.(Pharm)
Sukhjinder Sidhu BSc.(Pharm)
Interior Health Pharmacy Residents
Infectious Disease Rotation
May 1, 2014

2. 2014

3. The Controversy

4. Influenza Signs/symptoms Complications Fever Sore throat Rhinitis
Nonproductive cough
Myalgias
Malaise
Complications
Secondary infections (bacterial pneumonia, otitis media)
Hospitalizations
Death

5. How does Oseltamivir Work?
influenza virus spreads by airborne droplets and binds to a cell in the upper respiratory tract and gets taken up into the cell by endocytosis
viral RNA is released and is used to produce new viral components
To be released from the cell, neuraminidase cleaves receptor and the replicated virus is released for continued viral replication.
neuraminidase inhibitor prevents this cleaving step, halting viral replication. This is why neuraminidase inhibitors are more effective when taken within 48 hrs of symptom onset

6. Our PICO Design Meta-analysis Population
Hospitalized patients with confirmed or suspected exposure to influenza
Intervention
Oseltamivir 75 mg PO bid x 5 days
Comparator
Placebo
Outcome
Reducing mortality
Reducing morbidity (i.e. pneumonia)
Reducing length of hospital stay
Minimizing adverse events

7. Cochrane PICO D Meta-analysis P
Hospitalized patients with confirmed or suspected exposure to natural occurring influenza I
Oseltamivir
20 RCTs
11 treatment (adults)
4 treatment (children)
5 prophylaxis
F/U 6-42 days
Zanamivir
26 RCTs
14 treatment (adults)
2 treatment (children)
10 prophylaxis
F/U 5-35 days C
Placebo O
Many outcomes for prophylaxis, treatment, on-treatment and off-treatment
Not mortality
They really didn’t have a focused clinical question so right off the bat its hard to figure out what it is they’re asking and what it is they are wanting to know  Tx vs Px, adults vs children; and 2 different drugs with a whole whack load of outcomes

8. Title Title does not state it’s a meta-analysis or systematic review
Authors are Cochrane Collaboration Group
Can imply it’s at least a systematic review

9. Introduction Rationale
Influenza antivirals are commonly used and stockpiled
Previous reviews have risk of reporting bias
On list of WHO essential drugs
Objectives
potential benefits and harms of NIs for influenza in all age groups… all clinical study reports of published and unpublished R, PC trials and regulatory comments
What benefits? What harms?
All age groups – very broad category; severe vs. mild; outpatients vs. inpatients

10. Methods Eligibility Criteria Studies
NIs RCTs for prophylaxis, post-exposure prophylaxis and treatment of influenza
Published and unpublished trials
Manufacturer-funded and non-manufacturer funded clinical trials
No specific length of follow-up considered
Exclusion criteria not identified
Described what Primary and Secondary outcomes they wanted to answer with the meta-analysis, however they did not use these outcomes as eligibility study
Should they have included observational data?
people most at risk of severe outcomes (older people, comorbidities etc) are not included in RCTs; this study looks at previous healthy people and doesn’t include observational studies that have shown protective benefits against severe outcomes
But observational studies are weaker than RCT data….but addresses key points in at risk and seriously ill/hospitalized patients
*cant compare apples and oranges so its good that they didn’t do that in this case but is this information important?
- it is important and it makes sense they keep it like with like but in terms of generalizability? NO because we use this in very sick patients so not ethical and standard of care is if you’re very sick and its during flu season and suspect influenza you will be put on Tamiflu
THOUGHTS?

11. Methods Eligibility Criteria Participants
Previously health children and adults
Exposed to naturally occurring influenza with or without symptoms
Excluded people with illnesses with more significant effects on the immune system (i.e. malignancy or HIV infection)
All age groups – very broad category; severe vs. mild; outpatients vs. inpatients

12. Methods Information Sources Electronic databases
CENTRAL, MEDLINE, MEDLINE (Ovid), EMBASE, PubMed (NOT MEDLINE), DARE, NHSEED, HEED
January 2010 – July 2013
Clinical study reports
Extensive searches conducted
Regulatory information searches
Trial registries; manufacturer submissions to regulators; drug product info sheet; previous published reviews; Health Technoology Assessment documents; public and manufacturer reigsters
Regulatory – FDA, EMEA, Roche, Japanese regulator (PMDA, SBA)
They looked at multiple sources and lots of grey literature and were actually quite proud of themselves that they did this. This is important to keep in mind, especially later on when we get into their sensitivity analyses

13. Methods Search Strategy
Just one example; another provided was from EMBASE.com
Unaware of limits used therefore would be hard to reproduce

14. Methods Study Selection 2 authors reviewed title & abstracts
4 authors independently read all data
definitely include; definitely exclude; need more information
2 more authors reviewed for inclusion in Stage 1

15. Methods Study Selection
Stage 1  assessing the reliability and completeness of trial data
authors discussed face-to-face each trial with comments and other information from regulatory sources
decision to whether move trial to stage 2 via consensus
Stage 2 satisfied following criteria:
completeness – CONSORT-specified methods & specified results
internal consistency
external consistency

16. Methods Data Collection Process
Utilized a modified CONSORT statement-based extraction template
2 authors each searched oseltamivir and zanamivir trials
Disagreements were resolved amongst each other in oseltamivir group and by a 3rd author in the zanamivir group
For clinical study reports, complete list of trials were sent to manufacturers asking them to check accuracy and completeness of their list

17. Methods Risk of Bias in Individual Studies
Used Cochrane “Risk of bias” tool
Review author judged risk of bias
Bias assessed at outcome and study level
Studies included had high risk of bias
Some outcomes from studies were poorly documented/collected
They were more conservative in their approach to Bias’s – if unknown, classified study as high risk of bias
They didn’t use any JADAD scales and overall this review seemed to look at more quantity than quality

18. Methods Synthesis of Results Chi2: test for heterogeneity
I2: level of statistical heterogeneity
Threshold for significance unknown
Tau2: estimate of between-study variance
Combining data using random-effects approach
Chi – tells you if heterogeneity is present or not (p < 0.1); assesses whether observed differences are compatible with chance alone
I2 – tells you magnitude of heterogeneity; represents the percentage of total variation across studies due to heterogeneity
0-25% - not important
25-50% - be careful
> 50% - heterogeneity
Tau2 - estimated standard deviation of underlying effects across studies; > 1 = presence of substantial statistical heterogeneity
WHAT SHOULD THEIR THRESHOLD BE?

19. Methods Risk of Bias Across Studies Included unpublished trials
-They assessed that although they are RCTs they question the methodology so why not just analyze the only high quality studies?
-They did assess this but didn’t do anything about it because they included these studies still which makes this interesting

20. Methods Additional analyses
Subgroup analysis to investigate high estimates of heterogeneity
Meta-regression to investigate pneumonia heterogeneity
Sensitivity analysis
Fixed-effect method of Mantel and Haenszel to supplement primary analyses using random-effects method
Peto’s method used when sparse data and borderline sensitivity
Sensitivity -Another type of analysis that would be interesting to see and be of use is one involving study design quality (ie. high vs low quality) so see how the results would differ but unfortunately they didn’t do this.
2) Subgroup Analysis of severely ill (would be good to look at severely ill patients because in clinical practice the main people who get tamiflu are those with severe illness)
WHAT ANALYSES WOULD BE BENEFICIAL?

21. Results Study Selection 208 studies identified form various sources
123 studies excluded
19 studies awaiting classification
66 studies for which clinical study reports requested
53 studies met eligibility
5 trials excluded due to incompleteness
2 trials excluded as didn’t fit inclusion criteria
46 trials included
20 oseltamivir
26 zanamivir
123 trials excluded from entering stage 1 due to PK studies, active comparator, compared higher vs. lower dose schedules, on-going trials; 19 awaiting assessment

22. Results Risk of Bias within Studies
Presented selection, attrition, reporting, performance and detection bias
To address issue of reporting bias, they ignored published trial reports if clinical study reports and regulatory information were available
Random sequence generation missing in many trials
Blinding may have been affected in many trials
Placebos may have contained active substances
Others – data on participants were not reported in oseltamivir trials; data on effects of rescue/relief meds were incomplete in clinical study reports of oseltamivir and not reported separately in zanamivir trials
High risk of bias in studies included – affecting validity of included studies; however they were conservative with labeling “high risk of bias” – pg 17
- Placebo pills – zanamivir – lactose powder, which can cause bronchospasm; oseltamivir – dehydrochloric acid/Ca-PO4 dehydrate, which can cause GI symptoms

23. Results No analysis conducted on mortality outcome
Discussed deaths in the oseltamivir and zanamivir arms, but no statistical analyses completed
THOUGHTS?

24. Time to First Alleviation of Symptoms in Adult Treatment (ITT Population) Oseltamivir vs. Placebo
Orient to forest plot
Outcome
Result is reported as a mean difference
Using random effects model to pool results from 11 trials vs. fixed effects model – will discuss this in relation to the results on the next slide
If diamond falls left of the line = outcome in favor of oseltamivir; if it falls right of the line = outcome in favor of placebo
Given evaluating mean difference, line of no difference is 0 (i.e. if confidence interval crosses/touches line of no difference = not statistically significant)
Lets take a closer look at the result

25. Time to First Alleviation of Symptoms in Adult Treatment (ITT Population) Oseltamivir vs. Placebo
In adult treatment, oseltamivir reduced the time to first alleviation of symptoms by 16.8 hrs = representing a 10% reduction from 7 to 6.3 days
MAKE SURE YOU SAY: AT THE END OF THE DAY DO WE CARE about this clinically?
Discuss heterogeneity analysis and use of random effects model
3. Random Effects model
-more conservative test that makes it more difficult to make overall estimate precise
-problem with this model, is it assumes distribution of effects across different studies and follows a normal distribution which may not be the case and may make it difficult to show overall statistical effect
*when see middle of the road heterogeneity (ie 30-50%) then switching to random effects model will show you as it could make the diamond wider and move it to the right to make it more conservative.
Clinically significant reduction?
When to use random vs. fixed effects?

26. Complication: Pneumonia Oseltamivir vs. Placebo
Orient to forest plot
Outcome
Result is reported as a risk ratio
Using random effects model to pool results vs. fixed effects model
Given evaluating risk ratio, line of no difference is now 1
2 separate analyses completed – 1. Trials with non-specific diagnosis of pneumonia 2. Trials with specific diagnosis of pneumonia
Will focus on those trials with a specific diagnosis of pneumonia

27. Complication: Pneumonia Oseltamivir vs. Placebo
NNT = 100
Oseltamivir reduced the risk of pneumonia by 31% vs. placebo in those with a diagnosis of pneumonia (5 trials), however this is not a statistical difference given diamond crosses line of non-significance
Once again looking at the heterogeneity tests …. not appropriate to use random effects model
For interest sake, they combined the trials with a reliable diagnosis of pneumonia and those without and found a RRR of 45% with a NNT of 100 favoring oseltamivir
This is a perfect case of outcome level bias because they noticed that diagnosis of pneumonia was weak in a number of trials yet they included them in the analysis. So why did they include it?

28. Nausea in Adult Treatment (On-Treatment) Oseltamivir vs. Placebo
Orient to forest plot
Outcome
Result is reported as a risk ratio
Using random effects model to pool results
Line of no difference is 1
11 trials included

29. Nausea in Adult Treatment (On-Treatment) Oseltamivir vs. Placebo
NNH = 28
Oseltamivir was associated with more nausea vs. placebo (NNH 28), which is statistically significant given diamond doesn’t cross line of non-significance
Use of random effects in this analysis appropriate given I2
-more conservative test that makes it more difficult to make overall estimate precise
-problem with this model, is it assumes distribution of effects across different studies and follows a normal distribution which may not be the case and may make it difficult to show overall statistical effect

30. Vomiting in Adult Treatment (On-Treatment) Oseltamivir vs. Placebo
Orient to forest plot
Outcome
Result is reported as a risk ratio
Using random effects model to pool results
Line of no difference is 1
11 trials included

31. Vomiting in Adult Treatment (On-Treatment) Oseltamivir vs. Placebo
NNH = 22
Oseltamivir was associated with more vomiting vs. placebo (NNH 22), which is statistically significant given diamond doesn’t cross line of non-significance
Use of random effects in this analysis was not appropriate given I2

32. Results Risk of Bias Across Studies
No funnel plot or Egger’s test done to rule out publication bias
Made every effort to minimize publication bias by including non-published trials
No discussion on bias across studies, only bias within studies
Outcome bias – attrition bias from incomplete outcome data; detection bias (blinding of outcome assessment)  unable to ID all data for all outcomes in all oseltamivir trials and 8 of zanamivir trials;
Study bias – selection bias from random sequence generation; reporting bias; other bias (active placebos); performance bias (blinding of participants)
This brings us back again, if these trials had a lot of bias, why include them or why not do a sensitivity analysis afterwards looking at the higher vs lower quality of studies.
ASK EVERYONE: SHOULD WE BE WORRIED ABOUT PUBLICATION BIAS IN THIS STUDY?
SHOULD WE BE WORRIED ABOUT PUBLICATION BIAS?

33. Results Additional Analysis Subgroup analysis
Time to first alleviation of symptoms in adults by infection status
Pneumonia (diagnosis vs. non-diagnosis)
Time to first alleviation.. done only in zanamivir trials – therefore we did not discuss
Pneumonia subgroup analysis was presented in outcome data
Other subgroup analyses that would have been beneficial = severity of illness, hospitalized vs. non-hospitalized for example

34. Discussion Summary of Evidence
NIs have small, non-specific effects on reducing time to alleviation of influenza-like illness symptoms in adults
Treatment trials… do not settle the question of whether complications of influenza, such as pneumonia are reduced
Use of oseltamivir increases the risk of adverse events such as nausea, vomiting…

35. Discussion Conclusions
… appears to be no evidence for patients, clinicians or policy-makers to use these drugs (NIs) to prevent serious outcomes
Implications for future research
More effective preventative measures
Early identification of complications
Page 41 – discuss in context to Kaiser 2003 and Hernan and Lipsitch

36. Limitations Did not formally assess mortality outcome
Effect of complications was based on unclear and potentially unreliable definitions
Included many trials with high risk of bias
Affects validity of the results
Authors do not summarize results in the context of observational study results that are driving standard of care in influenza treatment
A generalized conclusion is made without taking severity of illness into account
Based upon these trials and high risk of bias – it is difficult to conclude effects on a broad range of populations; would have been beneficial to do a sensitivity analysis to conclude results of the higher quality RCTs

37. Our Conclusions Population studied was broad
Not generalizable to specific groups
Doesn’t address the question of whether to give NIs in high risk patients
More reviews needed to draw definite conclusions about mortality and reductions in complications
More reviews needed in severe influenza (i.e. critical care patients)
Standard of care is to give Tamiflu to very sick patients… how will you ever get ethical approval to put sick patients on placebo  observational studies and M-A, may open up RCT potential
or a subgroup analysis of the more severe patients

38. Your Conclusions?
Cochrane
IDSA