Statins for influenza and pneumonia

Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

The objective of this review is to assess the efficacy and safety of statins in people with influenza and pneumonia. Comparisons will be made between people who have received statin therapy with those who have not received any statin therapy. This review will explore the following questions.

1. Do statins reduce mortality from influenza and pneumonia?

2. Do statins reduce morbidity and severity of influenza and pneumonia?

3. How safe is it to administer statins to people with influenza and pneumonia?

Background

Description of the condition

Influenza is a common but potentially serious respiratory illness caused by the influenza virus. In annual influenza epidemics, 5% to 15% of the population are affected with upper respiratory tract infections. Although most cases are mild, these epidemics still cause severe illness annually in 35 million people and up to 500,000 deaths worldwide (WHO 2003). Common clinical features of influenza are non‐specific and include fever, cough, sore throat, runny or stuffy nose, myalgia, headache, malaise and fatigue (Monto 2000). The World Health Organization (WHO) declared the first influenza pandemic of the 21st century on 11 June 2009. To date there have been about 18,000 deaths reported worldwide from the H1N1 pandemic, but this number is an under‐representation as many deaths are not laboratory‐confirmed or not recognised as influenza‐related (or both) (WHO 2009a).

Pneumonia is an acute inflammation of the lungs which clinically presents with cough, sputum production, fever, chills, fatigue, shortness of breath, night sweats and pleuritic chest pain. Other symptoms of pneumonia may include loss of appetite, skin discolouration, nausea, vomiting, mood swings, joint pains or muscle aches (Hoare 2006). In children under five years of age pneumonia is the major cause of death. It is estimated that 1.8 million child deaths in 2007 were caused by pneumonia (WHO 2009b). The annual incidence of community‐acquired pneumonia (CAP) is 34 to 40 cases per 1000 children in Europe and North America (Ostapchuk 2004). In the United Kingdom the annual incidence is six cases for every 1000 people for the 18 to 39 age group and 75 cases for every 1000 people for those aged over 75 years. About 20% to 40% of those who contract pneumonia require hospital admission, of which 5% to 10% need critical care unit admission (Hoare 2006). It is estimated that there are three to four million episodes of pneumonia in the US every year, accounting for about 500,000 hospitalizations annually (Plouffe 1996).

In Australia, statistical data on influenza and pneumonia mortality are published together as influenza may be complicated by pneumonia, especially in the elderly. Influenza and pneumonia have substantial health impacts, being the underlying cause of death for 2623 Australians in 2007 (ABS 2007). Together with influenza, pneumonia is the seventh leading cause of death in people aged 65 and older in the US (Miniño 2007), and more than 900,000 cases of CAP occur each year among this older American population (Jackson 2004).

Description of the intervention

Hydroxymethyl glutaryl coenzyme A reductase inhibitors, or statins, are widely used to prevent and treat cardiovascular disease. Interest in their potential to decrease morbidity and mortality from infection has recently grown (Dublin 2009).

How the intervention might work

Statins are often used in patients with coronary heart disease to lower cholesterol levels, thus reducing their mortality and morbidity (Brugts 2009; Wei 2005). Apart from lowering cholesterol levels, laboratory and clinical studies have also shown that statins have anti‐inflammatory and immunomodulatory effects. Several clinical studies suggest that statins decrease mortality in patients with bacterial sepsis (Almog 2007; Fedson 2006; Kruger 2006). A recent systematic review and meta‐analysis suggested that statin use may be associated with a beneficial effect in treating and preventing different infections (Tleyjeh 2009).

Studies have shown that the fatal outcome in influenza and pneumonia is largely a manifestation of excessive release of inflammatory cytokines, as embodied in the “cytokine storm” concept of disease. Cytokines play an important role in host defence mechanisms for people with influenza and pneumonia and may be crucial to the development of septic shock or acute respiratory distress syndrome. Recent studies suggest that statins have anti‐inflammatory and immunomodulatory effects that might control the host immune response to conditions like influenza virus infection and pneumonia. The immunomodulatory and anti‐inflammatory effects are the results of a complex pathway of actions that includes improved endothelial function, modulation of inflammatory responses, maintenance of plaque stability and prevention of thrombus formation (Furberg 1999). 

Why it is important to do this review

Currently there is great interest in exploring inexpensive generic medications that target the host immune response to mitigate the effects of influenza (Fedson 2008). Statins have great potential since recent studies involving both human and animal models suggest that they have anti‐inflammatory and immunomodulatory effects that might control the host immune response to conditions like severe influenza and pneumonia through prevention of excess cytokine release. Several clinical and epidemiological studies have indicated that statins can reduce influenza and pneumonia‐related hospitalizations, intensive care unit (ICU) admissions and mortality rates (Fedson 2009). A recent study presented at the Infectious Diseases Society of America (IDSA) demonstrated a significant reduction in mortality in 1019 treated individuals of a total 3921 with laboratory‐confirmed influenza (Vandermeer 2009). So far no systematic review on the use of statins specifically in the treatment of influenza and pneumonia has been conducted; therefore we will carry out a systematic review to examine the role of statins in the management of influenza and pneumonia by exploring the strengths and weaknesses of available trials.

Objectives

The objective of this review is to assess the efficacy and safety of statins in people with influenza and pneumonia. Comparisons will be made between people who have received statin therapy with those who have not received any statin therapy. This review will explore the following questions.

1. Do statins reduce mortality from influenza and pneumonia?

2. Do statins reduce morbidity and severity of influenza and pneumonia?

3. How safe is it to administer statins to people with influenza and pneumonia?

Methods

Criteria for considering studies for this review

Types of studies

Randomised controlled trials (RCTs), cohort studies and case‐control studies that explore the effects of statins on the management of influenza and pneumonia.

To date, only one RCT has been published on the use of statins in pneumonia and a few other RCTs are ongoing. Most of the published data on the proposed study are from large cohort studies. We understand The Cochrane Collaboration's recommendations on systematic reviews but on rare occasions Cochrane Reviews have included observational studies. Considering the scarcity of RCTs on our topic, we will consider including cohort and case‐control studies in our review.

Types of participants

We will include participants of any age and sex.

Types of interventions

All interventions where statins (or HMG‐CoA reductase inhibitors) were administered by oral or parenteral route in participants with influenza and pneumonia. For comparisons, we will consider placebo or control medication or no intervention, or comparing doses or schedules of statins.

Types of outcome measures

We will consider the outcomes in three groups of participants.

1. Those with influenza‐like illness (ILI): defined as a set of typical signs and symptoms (fever plus at least one respiratory sign and/or symptom).

2. Those with laboratory‐confirmed influenza: defined as a set of typical signs and symptoms (fever plus at least one respiratory sign and/or symptom) backed up by polymerase chain reaction (PCR), immuno‐fluorescence, serological confirmation or isolation of influenza virus, while influenza‐like‐illness (ILI) will be defined as typical clinical features of influenza without laboratory confirmation.  

3. Those with pneumonia: usually diagnosed by a medical history, a physical examination and a chest X‐ray. Occasionally sputum cultures assist in the diagnosis. We will rely on case definitions applied in individual studies. We will take diagnostic dilemmas occurring in different settings and groups of participants (for example, hospital‐acquired pneumonia or immunocompromised participants) into consideration.

Primary outcomes  Mortality.

Primary outcomes

1. Mortality.

Secondary outcomes

1. Hospitalisation.

2. Intensive care unit (ICU) admission.

3. Proportion of participants requiring either ventilatory or inotropic support.

4. Duration of ICU stay.

5. Symptom severity.

6. Time to resolution of symptoms (both clinical and diagnostic).

7. Adverse events or side effects after treatment administration (including renal failure, rhabdomyolysis, interstitial lung disease etc.).

Search methods for identification of studies

Electronic searches

We will search the Cochrane Central Register of Controlled Clinical Trials (CENTRAL) (The Cochrane Library, latest issue), which contains the Cochrane Acute Respiratory Infections (ARI) Group’s Specialised Register, OVID MEDLINE (1975 to present), OVID EMBASE (1975 to present), Thomson Scientific Web of Science (1975 to present) and Elsevier Scopus (inception to present).

We will use the following search strategy to search MEDLINE and CENTRAL. We will combine the search strategy with the Cochrane Highly Sensitive Search Strategy for identifying randomized trials in MEDLINE (Lefebvre 2009). We will adapt the search strategy for other databases. There will be no language or publication restrictions.

MEDLINE (Ovid)

1 exp Influenza, Human/ 2 exp Influenzavirus A/ 3 exp Influenzavirus B/ 4 Influenzavirus C/ 5 (influenz* or flu).tw. 6 exp Pneumonia/ 7 pneumon*.tw. 8 (hap or cap).tw. 9 exp Respiratory Tract Infections/ 10 acute respiratory infection*.tw. 11 respiratory tract infection*.tw. 12 or/1‐11 13 exp Hydroxymethylglutaryl‐CoA Reductase Inhibitors/ 14 hmg‐coa reductase inhibitor*.tw. 15 statin*.tw. 16 (lovastatin or meglutol or pravastatin or simvastatin or atorvastatin or fluvastatin or rosuvastatin ).tw,nm. 17 or/13‐16 18 12 and 17

Searching other resources

We will search for ongoing trials in:

1. ClinicalTrials.gov (http://www.clinicaltrials.gov);

2. Australian New Zealand Clinical Trial Registry (http://www.anzctr.org.au/);

3. Chinese Clinical Trial Registry (http://www.chictr.org);

4. Current Controlled Trials (http://www.controlledtrials.com);

5. WHO ICTRP Search Portal (http://www.who.int/ictrp/network/en/index.html);

6. The National Research Register (www.updatesoftware.com/National/nrr‐frame.html);

7. The ISRCTN Register (http://isrctn.org);

8. The Uppsala Monitoring Centre (http://www.umc‐products.com/); and

9. Medicines and Healthcare products Regulatory Agency (http://www.mhra.gov.uk/index.htm).

We will search grey literature (for example, conference proceedings, unpublished data such as theses, letters and commentaries) by contacting relevant authorities and authors of published papers.

Data collection and analysis

Three review authors (GK, HR, MC) will independently undertake the selection of studies.

Selection of studies

Two review authors (GK, MC) will independently screen search results by title and abstract to identify studies which might be relevant to this topic. We will include studies that have explored the effects of statins on the management of influenza and pneumonia. After title and abstract searches we will obtain the full text of the identified papers. Two review authors (GK, HR and/or MC) will select relevant studies by reading the full text according to the above mentioned criteria and we will resolve disagreements by discussion.

Data extraction and management

We will extract data using a standardized study record form in accordance with Chapter 22 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2009), which will include the following data: general information (for example, review identification, authors, contact details, etc.), search strategies, inclusion and exclusion criteria, methodological quality assessment, participants, interventions, outcomes and results. Two review authors (GK, HR/MC) will independently extract the methodological details and data from publications. The third review author (HR or MC) will arbitrate if disagreements occur.

Assessment of risk of bias in included studies

Three review authors (GK, HR and/or MC) will perform methodological quality assessments using the criteria described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2009).

We will assess methodological quality of each RCT in terms of sequence generation, allocation concealment, blinding of participants, personnel and outcome, outcome reporting and other sources of bias. We will follow the criteria for judging risk of bias in the ‘Risk of bias’ assessment tool as outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2009) (Appendix 1). This assessment will be incorporated into the interpretation of results by performing sensitivity analyses, excluding studies of the lowest quality.

We will use the Newcastle‐Ottawa scale (NOS 2010) for assessing the quality of non‐randomised and observational studies (Appendix 2).

Measures of treatment effect

We expect to encounter both dichotomous and continuous data. We will analyze dichotomous outcome data by calculating the risk difference (RD) and risk ratio (RR). We will express continuous data as mean differences (MD). We will express the overall results with 95% confidence interval (95% CI). We will calculate the number needed to treat to benefit (NNTB) or the number needed to treat to harm (NNTH) if there are similarities between different trials.

Unit of analysis issues

We will carry out analysis separately for influenza and pneumonia and combined. We will analyze between the groups and within the groups. We will analyze and present the observational studies separately from the RCTs.

Dealing with missing data

We will take the following steps for any missing data.

1. Whenever possible we will contact the original investigators to request missing data.

2. We will perform sensitivity analyses to assess how sensitive results are to reasonable changes in the assumptions that are made.

3. In the Discussion section we will address the potential impact of missing data on the findings of the review, including commenting on any analysis regarding intention‐to‐treat (ITT).

Assessment of heterogeneity

We will assess clinical and methodological heterogeneity before pooling. We will carry out assessment for statistical heterogeneity using the Chi² test with significance set at P < 0.1 if data are similar enough. We will estimate the total variation across studies using the I² statistic. An I² statistic less than 40% is considered as not having important heterogeneity, 30% to 60% as moderate heterogeneity, 50% to 90% as substantial heterogeneity and 75% to 100% as considerable heterogeneity (Higgins 2009). We will perform subgroup analyses to determine the possible reason(s) if there is evidence of statistical heterogeneity. We will conduct sensitivity analysis to explore whether the heterogeneity is a result of low quality trials or not. We will exclude the lowest quality trials if heterogeneity is suspected.

Assessment of reporting biases

We will assess publication bias by funnel plots (Egger 1997). We will also report any possibility of residual confounding and healthy‐user bias between statin use and the outcomes.

Data synthesis

We will carry out meta‐analyses using the Review Manager 5 software program (RevMan 2008) if data are sufficiently similar (low level of heterogeneity). We will use both fixed‐effect and random‐effects models for pooled data analysis. We will present one model (and also assume that there is no important statistical heterogeneity) if both models give identical results. We will select the most conservative option if there are varying results in different methods .

Subgroup analysis and investigation of heterogeneity

We will perform subgroup analysis for the following groups.

1. Elderly participants (older than 65 years of age).

2. Participants with chronic medical conditions.

3. Previous statin‐users (participants continuing) versus participants starting stains (new users).

4. Participants with influenza versus pneumonia.

5. Hospitalised patients versus outpatients.

6. Severely versus non‐severe patients (e.g. those admitted to the ICU versus others).

Sensitivity analysis

We will perform sensitivity analyses to test the robustness of the evidence as follows if a sufficient number of trials are found.

1. Exclusion of studies with inadequate concealment of allocation.

2. Exclusion of studies in which the outcome evaluation was not blinded.

3. Comparing the difference of pooling analysis results by using a fixed‐effect model and a random‐effects model. Robust evidence should not be transposed by changing the effect model.

Appendices

Appendix 1. 'Risk of bias' table (for RCTs)

Entry	Judgement (Yes/No/Unclear)	Description (low risk/high risk/unclear risk of bias)
Adequate sequence generation?		Quote: Comments:
Allocation concealment?		Quote: Comments:
Blinding? (Patient‐reported outcomes)		Quote: Comments:
Blinding? (Mortality)		Quote: Comments:
Incomplete outcome data addressed? (Short‐term outcomes (2 to 6 weeks))		Quote: Comments:
Incomplete outcome data addressed? (Longer‐term outcomes (> 6 weeks))		Quote: Comments:
Free of selective reporting?		Quote: Comments:
Free of other bias?		Quote: Comments:

Appendix 2. Newcastle‐Ottawa quality assessment scale (case‐control studies)

Note: A study can be awarded a maximum of one star for each numbered item within the Selection and Exposure categories. A maximum of two stars can be given for Comparability.

Selection

1) Is the case definition adequate?

a) yes, with independent validation *

b) yes, e.g. record linkage or based on self‐reports

c) no description

2) Representativeness of the cases

a) consecutive or obviously representative series of cases *

b) potential for selection biases or not stated

3) Selection of controls

a) community controls *

b) hospital controls

c) no description

4) Definition of controls

a) no history of disease (endpoint) *

b) no description of source

Comparability

1) Comparability of cases and controls on the basis of the design or analysis

a) study controls for _______________  (Select the most important factor) *

b) study controls for any additional factor * (This criteria could be modified to indicate specific control for a second important factor)

Exposure

1) Ascertainment of exposure

a) secure record (e.g. surgical records) *

b) structured interview where blind to case/control status *

c) interview not blinded to case/control status

d) written self‐report or medical record only

e) no description

2) Same method of ascertainment for cases and controls

a) yes *

b) no

3) Non‐response rate

a) same rate for both groups *

b) non‐respondents described

c) rate different and no designation

Newcastle‐Ottawa quality assessment scale (cohort studies)

Note: A study can be awarded a maximum of one star for each numbered item within the Selection and Outcome categories. A maximum of two stars can be given for Comparability

Selection

1) Representativeness of the exposed cohort

a) truly representative of the average _______________ (describe) in the community *

b) somewhat representative of the average ______________ in the community *

c) selected group of users, e.g. nurses, volunteers

d) no description of the derivation of the cohort

2) Selection of the non‐exposed cohort

a) drawn from the same community as the exposed cohort *

b) drawn from a different source

c) no description of the derivation of the non‐exposed cohort   

3) Ascertainment of exposure

a) secure record (e.g. surgical records) *

b) structured interview *

c) written self‐report

d) no description

4) Demonstration that outcome of interest was not present at start of study

a) yes *

b) no

Comparability

1) Comparability of cohorts on the basis of the design or analysis

a) study controls for _____________ (select the most important factor) *

b) study controls for any additional factor * (This criteria could be modified to indicate specific control for a second important factor)          

Outcome

1) Assessment of outcome

a) independent blind assessment *

b) record linkage *

c) self‐report

d) no description

2) Was follow up long enough for outcomes to occur

a) yes (select an adequate follow‐up period for outcome of interest) *

b) no

3) Adequacy of follow up of cohorts

a) complete follow up ‐ all subjects accounted for *

b) subjects lost to follow up unlikely to introduce bias ‐ small number lost ‐ > ____ % (select an adequate %) follow up, or description provided of those lost) *

c) follow up rate < ____% (select an adequate %) and no description of those lost

d) no statement

What's new

Date	Event	Description
9 January 2017	Amended	Protocol withdrawn

Contributions of authors

All the review authors contributed equally.

Sources of support

Internal sources

• All the authors are employed at the National Centre for Immunisation Research and Surveillance, The Children's Hospital at Westmead, Australia.

External sources

• No sources of support supplied

Declarations of interest

Robert Booy has received financial support from vaccine producers, including CSL, Sanofi, GSK, Roche and Wyeth, to attend/present at scientific meetings; if fees were offered, these were placed in a university research account. Harunor Rashid received consultation fees from Novartis Vaccines.

Notes

This protocol was withdrawn from the Cochrane Library, issue 1, 2017, as the authors were unable to complete the review.

Withdrawn from publication for reasons stated in the review