Most attempts to transform exciting findings from laboratories into clinical treatments are unsuccessful. One reason for this may be the failure to consider all of the laboratory work that has been performed before deciding to test a treatment on patients for the first time. In particular, negative findings (that suggest that a potential new treatment is ineffective) may be overlooked.

Stem cells may help to treat life-threatening infections, but this has not been tested in human patients. However, the effectiveness of stem cell treatments has been tested in animals that act as models of human infection.

Before deciding to begin a clinical trial of stem cell therapy for life-threatening infections, Lalu et al. performed an exhaustive search to find all the studies in which stem cells were used to treat animal models of infection. Combining the results of all of these studies using particular analysis techniques revealed that stem cell therapy increased the survival of these animals overall. These positive effects were seen over a range of different experimental conditions (for example, when treating the animals with different doses of stem cells, or giving the doses at different times).

Lalu et al. also identified some limitations with most of the laboratory studies that had tested stem cell therapy for infections. Many of the studies used animal models that may not be the best representations of humans with severe infection. In addition, many of the scientists did not report that they had used methods (such as randomization) that would generate the most confidence in their results. Despite these limitations, there was a lot of consistency in the reported results.

Overall, the results support the decision to proceed to a clinical trial that tests the effectiveness of stem cells for treating human infections. More generally, Lalu et al.’s analysis demonstrates a way of considering all laboratory evidence before deciding to proceed to a first clinical trial in humans. This may help researchers to identify promising therapies to further develop, and also to identify potential failures before they are tested in patients.

The decision to initiate an early phase clinical trial requires careful evaluation of the benefits and risks of a novel intervention. However, for first-in-human studies for which there is no prior clinical experience, the assessment of potential therapeutic efficacy must rely solely on the preclinical investigations. Although regulatory guidance exists for the conduct of preclinical evaluation of novel therapies (U.S. Department of Health and Human services, 2013), there is little guidance to help stakeholders summarize and assess the benefit and risks of novel therapies prior to first-in-human studies. As a result, the evidence from individual preclinical studies is often summarized and described in a non-systematic and potentially biased manner (Food and Drug Administration, 2015). Here, we present an approach to transparently evaluate preclinical evidence of a therapy prior to its potential clinical translation. Our exemplar is mesenchymal stem cell (MSC) therapy for sepsis.

A selective narrative summary of preclinical evidence has significant limitations because the methods used to identify studies are neither comprehensive nor transparent (Sena et al., 2014). This is of particular concern given that studies replicating high profile experiments fail in up to 50–90% of attempts (Begley and Ellis, 2012; Scott et al., 2008; Steward et al., 2012) and significant publication bias results in a skewed representation of effects (Sena et al., 2010). Further, fewer than 5% of high impact preclinical reports are clinically translated (Contopoulos-Ioannidis et al., 2003) and only 11% of clinically tested agents receive licensing (Kola and Landis, 2004). Thus trialists have based predictions of clinical success of novel therapies on flawed data and an inappropriately highly selected and positive preclinical evidence base (Grankvist and Kimmelman, 2016).

Systematic reviews and meta-analyses have become very popular because they can overcome many of these challenges by promoting the transparent evaluation of therapies. Systematic reviews are guided by a protocol with explicit methods to identify, synthesize (which may include meta-analysis), and appraise all investigations pertinent to a particular research question. Similarly, meta-analysis enables pooling of effect sizes across studies and increases statistical power by reducing standard error around the average effect size, providing a more precise estimate of an overall treatment effect (Sena et al., 2014; Cohn and Becker, 2003). Systematic reviews and meta-analyses have long been regarded as essential tools to summarize and evaluate clinical research (Higgins and Green, 2009) and have become a requisite component of grant applications for clinical trials (Canadian Institutes of Health Research, 2016); however, the application of these tools to preclinical studies has been limited.

Preclinical systematic reviews may help predict the magnitude and direction of novel therapeutic effects in high stakes first-in-human trials. For example, preclinical systematic reviews of stroke (Horn et al., 2001) and heart failure (Lee et al., 2003) therapies demonstrated that the resulting negative clinical trials could have been predicted had available preclinical evidence been analyzed in a rigorous manner. Thus, thousands of patients may have avoided exposure to potential risk without any benefit (Kalra et al., 2002; Shuaib et al., 2007). Similarly, previous preclinical systematic reviews have demonstrated that failure to report threats to methodological quality (i.e. internal validity, risk of bias) and construct validity (i.e. extent a model corresponds to the human condition it is intended to represent [Henderson et al., 2013]) influence treatment effect sizes (Crossley et al., 2008; Hirst et al., 2014; Macleod et al., 2008, 2015; Rooke et al., 2011). Unlike this ‘retrospective’ approach that has been described in previous studies, a prospective application of preclinical systematic review methodology may help delineate the limits of a therapy prior to first-in-human application.

Our preclinical systematic review was conducted prior to the initiation of a Phase 1/2 clinical trial of immunomodulatory cell therapy (mesenchymal stromal cells, mesenchymal stem cells [MSCs], “adult stem cells”) for septic shock (NCT02421484). The specific question addressed was: In preclinical in-vivo animal models of sepsis, what is the effect of MSC administration (compared to control treatment) on death? Septic shock is the result of an overwhelming systemic infection; it is one of the most common and acutely devastating health problems in the intensive care unit with a 90-day mortality rate of approximately 20–30% despite modern therapy (Peake et al., 2014; Mouncey et al., 2015; Stevenson et al., 2014). It is caused by a maladaptive mismatch between host inflammatory response and pathogenic stimuli which leads to organ failure and death. MSCs are ubiquitous cells (da Silva Meirelles et al., 2006) that support tissue repair and are mobilized under inflammatory conditions (Hannoush et al., 2011; Rochefort et al., 2006). Exogenously administered MSCs represent an especially attractive therapeutic for sepsis because they have antibacterial and organ protective effects, in addition to their immune modulatory functions (Walter et al., 2014).

We quantitatively summarized the results of all preclinical studies of MSC therapy for in vivo animal models of sepsis to predict effect size and establish an ethical basis for exposing high-risk patients to this novel therapy. This is the first systematic evaluation of a novel biologic therapy prior to initiating a first-in-human trial. We believe our approach serves as a roadmap to transparently evaluate a preclinical therapy prior to its potential clinical translation. This study has been written in an explicatory manner so that other preclinical and translational researchers not familiar with systematic review methodology may replicate our approach. Readers wishing to replicate our approach for their research agendas are directed to the methods section where explanations are provided in greater depth, and encouraged to contact the authors for further guidance.

Preclinical studies provide necessary justification to conduct a first-in-human clinical trial. Thus, a systematic review approach offers an attractive method to comprehensively synthesize the totality of available evidence. Our systematic review demonstrates that MSC therapy reduces the odds of death in preclinical animal sepsis models. This effect is maintained over a range of time periods (less than two days, between two to four days, and longer than four days). These early outcome windows capture the majority of deaths that occur in these acute models. Moreover, the effect sizes are robustly maintained (replicated) over a variety of experimental conditions, varying models, and differing MSC immunologic compatibility (e.g. allogeneic vs. syngeneic).

It has been suggested that individual study findings have low probability of being ‘true’ (Ioannidis, 2005), however by aggregating results of similar experiments the positive predictive value of a finding dramatically increases (Moonesinghe et al., 2007). Thus, the findings of this systematic review helped support our decision to initiate a Phase 1/2 trial to evaluate the safety of MSC therapy in human patients with septic shock (NCT02421484). We believe our approach of systematically reviewing preclinical evidence is widely applicable for researchers considering first-in-human studies. Although our synthesis suggests MSC treatment of sepsis may be beneficial these results are tempered by the presence of potential threats to validity.

Our preclinical systematic review evaluated internal, external, and construct validity of the data. Methodological weaknesses (i.e. poor internal validity) in clinical trials are associated with an exaggeration of the treatment effect. Similarly, in preclinical studies, failure to address selection bias (through methods such as randomization and allocation concealment) and detection bias (through blinded outcome assessment) results in significantly increased effect sizes (Crossley et al., 2008; Hirst et al., 2014; Rooke et al., 2011). The significance of selection and detection bias has been acknowledged by The National Institutes of Health’s recently issued guidelines for reporting preclinical research. These guidelines have specifically proposed randomization, blinding, and sample size calculations as key methodological information that must be described in preclinical reports (National Institutes of Health, 2015; Landis et al., 2012). In our review, none of the included studies reported randomization or allocation concealment in a manner that could be considered at low risk of bias. Similarly, no studies reported appropriate a priori defined sample sizes. Most of these items were judged as ‘unclear’ in our risk of bias evaluation due to the convention to judge unreported items as ‘unclear’ rather than ‘high risk’. We speculate that many of these ‘unclear’ items were not performed (i.e. they were ‘high risk’) due to a general lack of training of basic scientists in methods to reduce risk of bias (Landis et al., 2012; Collins and Tabak, 2014). This lack of reporting precluded an evaluation of their efforts and points to the need to improve the methodology used in preclinical investigations.

To address external validity (i.e. generalizability) we performed a number of subgroup analyses. Overall, subgroup analyses suggested that MSC effects appeared to be robust over a number of varying experimental conditions and across a number of different laboratories. Results of specific subgroups (e.g. autologous cells, multiple doses, intraperitoneal administration, and adipose tissue source) should be interpreted cautiously as few studies were included in these groups, and the results of one study with differing results (Chang et al., 2012) may have skewed data. The ability of one study to heavily influence overall effect estimates is a short-coming of meta-analyses that include few studies. As such, these subgroup analyses should be treated as exploratory.

Despite the large effect sizes noted, one must bear in mind the potential effect of publication bias (i.e. bias due to the publication of only positive studies). Our funnel plot demonstrated a highly asymmetrical pattern and our trim and fill analysis indicated that a number of unpublished negative studies may exist. This is in keeping with previous analyses of preclinical stroke data that suggested up to one in six animal studies in that field were unreported and unpublished. (Sena et al., 2010) Our inability to analyze these potential studies may have led to an overstatement of effect size.

To evaluate the potential clinical applicability of these results, we examined the construct validity of included studies. This was determined using recommendations that had been developed to improve the clinical generalizability of preclinical sepsis studies (Lamontagne et al., 2010). Animal sepsis models may not be representative of human sepsis because of the timing and severity of sepsis induction, the dose and timing of the treatment in relation to sepsis induction, the use of small/young animals without comorbid illnesses, and lack of administration of standard of care co-interventions such as fluids and antibiotics during the study period. How well animal models of sepsis mimic the pathophysiology of human sepsis has also been a contentious issue (Dyson and Singer, 2009; Osuchowski et al., 2014; Seok et al., 2013). Only two studies incorporated a majority of elements addressing construct validity, thus the effect of construct validity on MSC therapy of sepsis remains to be determined.

There are a number of other issues of note that may impact the translation of MSC therapy to the clinical setting. First, although we did not formally evaluate characterization of cell products, this was variably reported in the included studies. Differences in the quality of cell therapeutics may have accounted for some of the heterogeneity of results observed. Second, dosing of cell products was not equivalent between species, even after adjusting for total cells given. Equivalence dosing of drugs between species is a complex issue and the FDA has endorsed conversion based on body surface area, rather than a dose per weight basis. (Reagan-Shaw et al., 2008) Applying this guidance, 1 million cells in a mouse may be equivalent to 0.5 million in a rat; similarly, this dose in a human would be roughly equivalent to 3 million cells/kg. These equivalencies should be interpreted cautiously given the differences between typical drug therapies and the cellular therapy evaluated in our review. Third, the severity of disease in these animal models at the time of MSC administration is unclear. Based on our experience with endotoxemia and cecal-ligation and puncture models, at 1 hr after disease induction some symptoms may be apparent and after 6 hr most animals have both biochemical and physiological evidence of inflammation and organ-dysfunction. Thus, we performed a subgroup analysis based on timing of administration <1 hr, >1–6 hr, >6 hr as a rough correlate to early and more delayed (intermediate and late) administration of cells in an attempt to simulate the delays in treatment that may be seen in humans who present with severe infection. Of note, no study administered cells at a late time point. A clearer reporting of disease severity at time of cell administration may allow a more precise analysis of when these cells are more (or less) efficacious. A fourth issue is the lack of transparent reporting of risk of bias elements that minimize the ability to evaluate threats to validity in our systematic review. We would suggest that general poor understanding of these core methodological issues may underlie their incomplete reporting. In order to increase the robustness and interpretation of future preclinical systematic review results we submit that authors of primary studies and journal editors should ensure adherence to published reporting guidelines for pre-clinical research studies (National Institutes of Health , 2015; Kilkenny et al., 2010). These guidelines not only detail items relating to risk of bias (e.g. randomization and blinding) but also touch on issues that are very important when primary studies are included in systematic reviews (e.g. differentiating between biological and technical replicates, providing exact n numbers).

The strengths of our systematic review are in the transparent and thorough literature search and an attempt to examine potential for translation by evaluating threats to validity. To date, three clinical trials have been initiated following a systematic review and meta-analysis of animal data (McCann et al., 2014; van der Worp et al., 2007); all have repurposed currently used interventions for neurological conditions and are currently recruiting (NCT01833312, NCT01910259, ISRCTN83290762). To the best of our knowledge ours is the first preclinical systematic review that has evaluated a novel biological therapeutic in preparation for a high risk first-in-human clinical trial.

The limitations of our review should be noted. First, we restricted our search to unmodified MSCs since our group was only considering a clinical trial of unmodified cells for sepsis. Although modified cells may be of clinical interest, there are a number of additional regulatory, ethical, and safety issues which significantly increase the complexity of clinical trials using these cells; this is an issue that members of our group have experienced first-hand (NCT00936819). Other limitations of our review relate to the potential methodological limitations of the included studies. None of the included studies were considered low risk of bias across all domains, and their construct validity was highly variable. It is unclear what the influence of these methodological limitations might be in this particular study due to our inability to perform meaningful subgroup analyses. Our evaluation of the methodological aspects of included studies also relied on what the authors reported, and this may have been incomplete in cases. We would suggest however, that similar to other fields, the failure to address threats to internal validity likely contributes to an exaggerated effect size.

Despite the stated limitations of this review, the consistency of the results across the included studies and the large effect size suggest that MSCs reduce the odds of death in preclinical models of sepsis. Moreover, there are a number of studies that have demonstrated biological mechanisms that may underlie the benefits of MSCs in sepsis, including antibacterial, anti-inflammatory, and trophic effects (Spees et al., 2016). These mechanisms do not require engraftment and have been demonstrated to work over thousands of molecular pathways that include improved cellular energetics and activation of macrophages (dos Santos et al., 2012). Given the results of our review along with this biological plausibility, our group gained the support of regulatory agencies, ethics boards, and other stakeholders to proceed to a first-in-human clinical trial. Nonetheless, our efforts to translate this therapy into a clinical trial were tempered by the limitations of the preclinical studies performed to date. If this support was not provided, alternative methods to address efficacy of MSC therapy for sepsis could include conducting a low risk of bias ‘confirmatory’ preclinical study that was informed by the results of this systematic review (Kleikers et al., 2015), or performing a multicenter preclinical study (Llovera et al., 2015). Ultimately, ongoing and future clinical evaluations will determine whether the therapeutic effects of MSCs will translate to the human patient population.

The methods section has been written completely and transparently for researchers unfamiliar with systematic review and meta-analysis methodology. We would encourage readers wishing to replicate our approach for their own research agendas to refer to available resources (Sena et al., 2014; Higgins and Green, (2009)) and/or our group for further guidance.

1. 1. Begley CG
   2. Ellis LM

   (2012) Drug development: Raise standards for preclinical cancer research

   Nature 483:531–533.

   https://doi.org/10.1038/483531a

   • PubMed
   • Google Scholar
2. 1. Bi L-K
   2. Tang B
   3. Zhu B
   4. Xie C-L
   5. Li S
   6. Lin T-X
   7. Hunag J
   8. Zhang W
   9. Zhu J-Y

   (2010) Systemic delivery of IL-10 by bone marrow derived stromal cells has therapeutic benefits in sepsis therapy*

   Progress in Biochemistry and Biophysics 37:678–685.

   https://doi.org/10.3724/SP.J.1206.2010.00107

   • Google Scholar
3. Website

   1. Canadian Institutes of Health Research

   (2016) RCT Evaluation Criteria and Headings

   http://www.cihr-irsc.gc.ca/e/39187.html
4. 1. Chang CL
   2. Leu S
   3. Sung HC
   4. Zhen YY
   5. Cho CL
   6. Chen A
   7. Tsai TH
   8. Chung SY
   9. Chai HT
   10. Sun CK
   11. Yen CH
   12. Yip HK

   (2012) Impact of apoptotic adipose-derived mesenchymal stem cells on attenuating organ damage and reducing mortality in rat sepsis syndrome induced by cecal puncture and ligation

   Journal of Translational Medicine 10:244.

   https://doi.org/10.1186/1479-5876-10-244

   • PubMed
   • Google Scholar
5. 1. Chao YH
   2. Wu HP
   3. Wu KH
   4. Tsai YG
   5. Peng CT
   6. Lin KC
   7. Chao WR
   8. Lee MS
   9. Fu YC
   10. Fu Y-C

   (2014) An increase in CD3+CD4+CD25+ regulatory T cells after administration of umbilical cord-derived mesenchymal stem cells during sepsis

   PLoS One 9:e110338.

   https://doi.org/10.1371/journal.pone.0110338

   • PubMed
   • Google Scholar
6. 1. Cohn LD
   2. Becker BJ

   (2003) How meta-analysis increases statistical power

   Psychological Methods 8:243–253.

   https://doi.org/10.1037/1082-989X.8.3.243

   • PubMed
   • Google Scholar
7. 1. Collins FS
   2. Tabak LA

   (2014) Policy: Nih plans to enhance reproducibility

   Nature 505:612–613.

   https://doi.org/10.1038/505612a

   • Google Scholar
8. 1. Contopoulos-Ioannidis DG
   2. Ntzani E
   3. Ioannidis JP

   (2003) Translation of highly promising basic science research into clinical applications

   The American Journal of Medicine 114:477–484.

   https://doi.org/10.1016/S0002-9343(03)00013-5

   • PubMed
   • Google Scholar
9. 1. Crossley NA
   2. Sena E
   3. Goehler J
   4. Horn J
   5. van der Worp B
   6. Bath PM
   7. Macleod M
   8. Dirnagl U

   (2008) Empirical evidence of bias in the design of experimental stroke studies: a metaepidemiologic approach

   Stroke 39:929–934.

   https://doi.org/10.1161/STROKEAHA.107.498725

   • PubMed
   • Google Scholar
10. 1. da Silva Meirelles L
    2. Chagastelles PC
    3. Nardi NB

    (2006) Mesenchymal stem cells reside in virtually all post-natal organs and tissues

    Journal of Cell Science 119:2204–2213.

    https://doi.org/10.1242/jcs.02932

    • PubMed
    • Google Scholar
11. 1. de Vries RB
    2. Hooijmans CR
    3. Tillema A
    4. Leenaars M
    5. Ritskes-Hoitinga M

    (2014) Updated version of the Embase search filter for animal studies

    Laboratory Animals 48:88.

    https://doi.org/10.1177/0023677213494374

    • PubMed
    • Google Scholar
12. 1. Dominici M
    2. Le Blanc K
    3. Mueller I
    4. Slaper-Cortenbach I
    5. Marini F
    6. Krause D
    7. Deans R
    8. Keating A
    9. Prockop D
    10. Horwitz E

    (2006) Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for Cellular Therapy position statement

    Cytotherapy 8:315–317.

    https://doi.org/10.1080/14653240600855905

    • PubMed
    • Google Scholar
13. 1. dos Santos CC
    2. Murthy S
    3. Hu P
    4. Shan Y
    5. Haitsma JJ
    6. Mei SH
    7. Stewart DJ
    8. Liles WC

    (2012) Network analysis of transcriptional responses induced by mesenchymal stem cell treatment of experimental sepsis

    The American Journal of Pathology 181:1681–1692.

    https://doi.org/10.1016/j.ajpath.2012.08.009

    • PubMed
    • Google Scholar
14. 1. Dyson A
    2. Singer M

    (2009) Animal models of sepsis: why does preclinical efficacy fail to translate to the clinical setting?

    Critical Care Medicine 37:S30–37.

    https://doi.org/10.1097/CCM.0b013e3181922bd3

    • PubMed
    • Google Scholar
15. Website

    1. Food and Drug Administration

    (2015) Department of Health and Human Services, Drugs for huamn use: Investigational New Drug Application

    https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfCFR/CFRSearch.cfm?fr=312.23
16. 1. Gonzalez-Rey E
    2. Anderson P
    3. Gonzalez MA
    4. Rico L
    5. Buscher D
    6. Delgado M

    (2009) Human adult stem cells derived from adipose tissue protect against experimental colitis and sepsis

    Gut 58:929–939.

    https://doi.org/10.1136/gut.2008.168534

    • Google Scholar
17. 1. Grankvist H
    2. Kimmelman J

    (2016) How do researchers decide early clinical trials?

    Medicine, Health Care and Philosophy 19:191–198.

    https://doi.org/10.1007/s11019-016-9685-6

    • PubMed
    • Google Scholar
18. 1. Hall SR
    2. Tsoyi K
    3. Ith B
    4. Padera RF
    5. Lederer JA
    6. Wang Z
    7. Liu X
    8. Perrella MA

    (2013) Mesenchymal stromal cells improve survival during sepsis in the absence of heme oxygenase-1: the importance of neutrophils

    Stem Cells 31:397–407.

    https://doi.org/10.1002/stem.1270

    • PubMed
    • Google Scholar
19. 1. Hannoush EJ
    2. Sifri ZC
    3. Elhassan IO
    4. Mohr AM
    5. Alzate WD
    6. Offin M
    7. Livingston DH

    (2011) Impact of enhanced mobilization of bone marrow derived cells to site of injury

    The Journal of Trauma 71:283–291.

    https://doi.org/10.1097/TA.0b013e318222f380

    • PubMed
    • Google Scholar
20. 1. Henderson VC
    2. Kimmelman J
    3. Fergusson D
    4. Grimshaw JM
    5. Hackam DG

    (2013) Threats to validity in the design and conduct of preclinical efficacy studies: a systematic review of guidelines for in vivo animal experiments

    PLoS Medicine 10:e1001489.

    https://doi.org/10.1371/journal.pmed.1001489

    • PubMed
    • Google Scholar
21. 1. Higgins JPT
    2. Green S

    (2009)

    Cochrane Handbook for Systematic Reviews of Interventions

    Cochrane Handbook for Systematic Reviews of Interventions, ed. 5.0.2.

    • Google Scholar
22. 1. Hirst JA
    2. Howick J
    3. Aronson JK
    4. Roberts N
    5. Perera R
    6. Koshiaris C
    7. Heneghan C

    (2014) The need for randomization in animal trials: an overview of systematic reviews

    PLoS One 9:e98856.

    https://doi.org/10.1371/journal.pone.0098856

    • PubMed
    • Google Scholar
23. 1. Hooijmans CR
    2. Rovers MM
    3. de Vries RB
    4. Leenaars M
    5. Ritskes-Hoitinga M
    6. Langendam MW

    (2014) SYRCLE's risk of bias tool for animal studies

    BMC Medical Research Methodology 14:43.

    https://doi.org/10.1186/1471-2288-14-43

    • PubMed
    • Google Scholar
24. 1. Hooijmans CR
    2. Tillema A
    3. Leenaars M
    4. Ritskes-Hoitinga M

    (2010) Enhancing search efficiency by means of a search filter for finding all studies on animal experimentation in PubMed

    Laboratory Animals 44:170–175.

    https://doi.org/10.1258/la.2010.009117

    • PubMed
    • Google Scholar
25. 1. Horn J
    2. de Haan RJ
    3. Vermeulen M
    4. Luiten PG
    5. Limburg M

    (2001) Nimodipine in animal model experiments of focal cerebral ischemia: a systematic review

    Stroke 32:2433–2438.

    https://doi.org/10.1161/hs1001.096009

    • PubMed
    • Google Scholar
26. 1. Ioannidis JP

    (2005) Why most published research findings are false

    PLoS Medicine 2:e124.

    https://doi.org/10.1371/journal.pmed.0020124

    • PubMed
    • Google Scholar
27. 1. Kalra PR
    2. Moon JC
    3. Coats AJ

    (2002) Do results of the ENABLE (endothelin antagonist bosentan for lowering cardiac events in heart failure) study spell the end for non-selective endothelin antagonism in heart failure?

    International Journal of Cardiology 85:195.

    https://doi.org/10.1016/S0167-5273(02)00182-1

    • PubMed
    • Google Scholar
28. 1. Kilkenny C
    2. Browne WJ
    3. Cuthill IC
    4. Emerson M
    5. Altman DG

    (2010) Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research

    PLoS Biology 8:e1000412.

    https://doi.org/10.1371/journal.pbio.1000412

    • PubMed
    • Google Scholar
29. 1. Kim H
    2. Darwish I
    3. Monroy MF
    4. Prockop DJ
    5. Liles WC
    6. Kain KC

    (2014) Mesenchymal stromal (stem) cells suppress pro-inflammatory cytokine production but fail to improve survival in experimental staphylococcal toxic shock syndrome

    BMC Immunology 15:1.

    https://doi.org/10.1186/1471-2172-15-1

    • PubMed
    • Google Scholar
30. 1. Kimmelman J
    2. Henderson V

    (2016) Assessing risk/benefit for trials using preclinical evidence: a proposal

    Journal of Medical Ethics 42:50–53.

    https://doi.org/10.1136/medethics-2015-102882

    • Google Scholar
31. 1. Kleikers PW
    2. Hooijmans C
    3. Göb E
    4. Langhauser F
    5. Rewell SS
    6. Radermacher K
    7. Ritskes-Hoitinga M
    8. Howells DW
    9. Kleinschnitz C
    10. Schmidt HH

    (2015) A combined pre-clinical meta-analysis and randomized confirmatory trial approach to improve data validity for therapeutic target validation

    Scientific Reports 5:13428.

    https://doi.org/10.1038/srep13428

    • PubMed
    • Google Scholar
32. 1. Kola I
    2. Landis J

    (2004) Can the pharmaceutical industry reduce attrition rates?

    Nature Reviews Drug Discovery 3:711–716.

    https://doi.org/10.1038/nrd1470

    • PubMed
    • Google Scholar
33. 1. Krasnodembskaya A
    2. Samarani G
    3. Song Y
    4. Zhuo H
    5. Su X
    6. Lee J-W
    7. Gupta N
    8. Petrini M
    9. Matthay MA

    (2012) Human mesenchymal stem cells reduce mortality and bacteremia in gram-negative sepsis in mice in part by enhancing the phagocytic activity of blood monocytes

    AJP: Lung Cellular and Molecular Physiology 302:L1003–L1013.

    https://doi.org/10.1152/ajplung.00180.2011

    • Google Scholar
34. 1. Lamontagne F
    2. Briel M
    3. Duffett M
    4. Fox-Robichaud A
    5. Cook DJ
    6. Guyatt G
    7. Lesur O
    8. Meade MO

    (2010) Systematic review of reviews including animal studies addressing therapeutic interventions for sepsis

    Critical Care Medicine 38:2401–2408.

    https://doi.org/10.1097/CCM.0b013e3181fa0468

    • PubMed
    • Google Scholar
35. 1. Landis SC
    2. Amara SG
    3. Asadullah K
    4. Austin CP
    5. Blumenstein R
    6. Bradley EW
    7. Crystal RG
    8. Darnell RB
    9. Ferrante RJ
    10. Fillit H
    11. Finkelstein R
    12. Fisher M
    13. Gendelman HE
    14. Golub RM
    15. Goudreau JL
    16. Gross RA
    17. Gubitz AK
    18. Hesterlee SE
    19. Howells DW
    20. Huguenard J
    21. Kelner K
    22. Koroshetz W
    23. Krainc D
    24. Lazic SE
    25. Levine MS
    26. Macleod MR
    27. McCall JM
    28. Moxley RT
    29. Narasimhan K
    30. Noble LJ
    31. Perrin S
    32. Porter JD
    33. Steward O
    34. Unger E
    35. Utz U
    36. Silberberg SD

    (2012) A call for transparent reporting to optimize the predictive value of preclinical research

    Nature 490:187–191.

    https://doi.org/10.1038/nature11556

    • PubMed
    • Google Scholar
36. 1. Lee DS
    2. Nguyen QT
    3. Lapointe N
    4. Austin PC
    5. Ohlsson A
    6. Tu JV
    7. Stewart DJ
    8. Rouleau JL

    (2003) Meta-analysis of the effects of endothelin receptor blockade on survival in experimental heart failure

    Journal of Cardiac Failure 9:.

    https://doi.org/10.1054/S1071-9164(03)00125-8

    • PubMed
    • Google Scholar
37. 1. Li J
    2. Li D
    3. Liu X
    4. Tang S
    5. Wei F

    (2012) Human umbilical cord mesenchymal stem cells reduce systemic inflammation and attenuate LPS-induced acute lung injury in rats

    Journal of Inflammation 9:33.

    https://doi.org/10.1186/1476-9255-9-33

    • Google Scholar
38. 1. Liang ZX
    2. Sun JP
    3. Wang P
    4. Tian Q
    5. Yang Z
    6. Chen LA

    (2011)

    Bone marrow-derived mesenchymal stem cells protect rats from endotoxin-induced acute lung injury

    Chinese Medical Journal 124:2715–2722.

    • PubMed
    • Google Scholar
39. 1. Llovera G
    2. Hofmann K
    3. Roth S
    4. Salas-Pérdomo A
    5. Ferrer-Ferrer M
    6. Perego C
    7. Zanier ER
    8. Mamrak U
    9. Rex A
    10. Party H
    11. Agin V
    12. Fauchon C
    13. Orset C
    14. Haelewyn B
    15. De Simoni MG
    16. Dirnagl U
    17. Grittner U
    18. Planas AM
    19. Plesnila N
    20. Vivien D
    21. Liesz A

    (2015) Results of a preclinical randomized controlled multicenter trial (pRCT): Anti-CD49d treatment for acute brain ischemia

    Science Translational Medicine 7:299ra121.

    https://doi.org/10.1126/scitranslmed.aaa9853

    • PubMed
    • Google Scholar
40. 1. Luo CJ
    2. Zhang FJ
    3. Zhang L
    4. Geng YQ
    5. Li QG
    6. Hong Q
    7. Fu B
    8. Zhu F
    9. Cui SY
    10. Feng Z
    11. Sun XF
    12. Chen XM

    (2014) Mesenchymal stem cells ameliorate sepsis-associated acute kidney injury in mice

    Shock 41:123–129.

    https://doi.org/10.1097/SHK.0000000000000080

    • PubMed
    • Google Scholar
41. 1. Macleod MR
    2. Lawson McLean A
    3. Kyriakopoulou A
    4. Serghiou S
    5. de Wilde A
    6. Sherratt N
    7. Hirst T
    8. Hemblade R
    9. Bahor Z
    10. Nunes-Fonseca C
    11. Potluru A
    12. Thomson A
    13. Baginskaite J
    14. Egan K
    15. Vesterinen H
    16. Currie GL
    17. Churilov L
    18. Howells DW
    19. Sena ES

    (2015) Risk of bias in reports of in vivo research: a focus for improvement

    PLoS Biology 13:e1002273.

    https://doi.org/10.1371/journal.pbio.1002273

    • PubMed
    • Google Scholar
42. 1. Macleod MR
    2. van der Worp HB
    3. Sena ES
    4. Howells DW
    5. Dirnagl U
    6. Donnan GA

    (2008) Evidence for the efficacy of NXY-059 in experimental focal cerebral ischaemia is confounded by study quality

    Stroke 39:2824–2829.

    https://doi.org/10.1161/STROKEAHA.108.515957

    • PubMed
    • Google Scholar
43. 1. McCann SK
    2. Irvine C
    3. Mead GE
    4. Sena ES
    5. Currie GL
    6. Egan KE
    7. Macleod MR
    8. Howells DW

    (2014) Efficacy of antidepressants in animal models of ischemic stroke: a systematic review and meta-analysis

    Stroke 45:3055–3063.

    https://doi.org/10.1161/STROKEAHA.114.006304

    • PubMed
    • Google Scholar
44. 1. Mei SH
    2. Haitsma JJ
    3. Dos Santos CC
    4. Deng Y
    5. Lai PF
    6. Slutsky AS
    7. Liles WC
    8. Stewart DJ
    9. Stewart DJ

    (2010) Mesenchymal stem cells reduce inflammation while enhancing bacterial clearance and improving survival in sepsis

    American Journal of Respiratory and Critical Care Medicine 182:1047.

    https://doi.org/10.1164/rccm.201001-0010OC

    • PubMed
    • Google Scholar
45. 1. Moher D
    2. Liberati A
    3. Tetzlaff J
    4. Altman DG
    5. The PRISMA Group

    (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

    PLoS Medicine 6:e1000097.

    https://doi.org/10.1371/journal.pmed.1000097

    • Google Scholar
46. 1. Moonesinghe R
    2. Khoury MJ
    3. Janssens AC

    (2007) Most published research findings are false-but a little replication goes a long way

    PLoS Medicine 4:e28.

    https://doi.org/10.1371/journal.pmed.0040028

    • PubMed
    • Google Scholar
47. 1. Mouncey PR
    2. Osborn TM
    3. Power GS
    4. Harrison DA
    5. Sadique MZ
    6. Grieve RD
    7. Jahan R
    8. Harvey SE
    9. Bell D
    10. Bion JF
    11. Coats TJ
    12. Singer M
    13. Young JD
    14. Rowan KM
    15. ProMISe Trial Investigators

    (2015) Trial of early, goal-directed resuscitation for septic shock

    New England Journal of Medicine 372:1301–1311.

    https://doi.org/10.1056/NEJMoa1500896

    • PubMed
    • Google Scholar
48. Website

    1. National Institutes of Health

    (2015) Principles and Guidelines for Reporting Preclinical Research

    http://www.nih.gov/research-training/rigor-reproducibility/principles-guidelines-reporting-preclinical-research
49. 1. Németh K
    2. Leelahavanichkul A
    3. Yuen PS
    4. Mayer B
    5. Parmelee A
    6. Doi K
    7. Robey PG
    8. Leelahavanichkul K
    9. Koller BH
    10. Brown JM
    11. Hu X
    12. Jelinek I
    13. Star RA
    14. Mezey E

    (2009) Bone marrow stromal cells attenuate sepsis via prostaglandin E(2)-dependent reprogramming of host macrophages to increase their interleukin-10 production

    Nature Medicine 15:42–49.

    https://doi.org/10.1038/nm.1905

    • PubMed
    • Google Scholar
50. 1. Osuchowski MF
    2. Remick DG
    3. Lederer JA
    4. Lang CH
    5. Aasen AO
    6. Aibiki M
    7. Azevedo LC
    8. Bahrami S
    9. Boros M
    10. Cooney R
    11. Cuzzocrea S
    12. Jiang Y
    13. Junger WG
    14. Hirasawa H
    15. Hotchkiss RS
    16. Li XA
    17. Radermacher P
    18. Redl H
    19. Salomao R
    20. Soebandrio A
    21. Thiemermann C
    22. Vincent JL
    23. Ward P
    24. Yao YM
    25. Yu HP
    26. Zingarelli B
    27. Chaudry IH

    (2014) Abandon the mouse research ship? Not just yet!

    Shock 41:463–475.

    https://doi.org/10.1097/SHK.0000000000000153

    • PubMed
    • Google Scholar
51. 1. Pan Z
    2. Trikalinos TA
    3. Kavvoura FK
    4. Lau J
    5. Ioannidis JP

    (2005) Local literature bias in genetic epidemiology: an empirical evaluation of the Chinese literature

    PLoS Medicine 2:e124.

    https://doi.org/10.1371/journal.pmed.0020334

    • PubMed
    • Google Scholar
52. 1. Peake SL
    2. Delaney A
    3. Bailey M
    4. Bellomo R
    5. Cameron PA
    6. Cooper DJ
    7. Higgins AM
    8. Holdgate A
    9. Howe BD
    10. Webb SA
    11. Williams P
    12. ARISE Investigators
    13. ANZICS Clinical Trials Group

    (2014) Goal-directed resuscitation for patients with early septic shock

    The New England Journal of Medicine 371:1496–1506.

    https://doi.org/10.1056/NEJMoa1404380

    • PubMed
    • Google Scholar
53. 1. Pedrazza L
    2. Lunardelli A
    3. Luft C
    4. Cruz CU
    5. de Mesquita FC
    6. Bitencourt S
    7. Nunes FB
    8. de Oliveira JR

    (2014) Mesenchymal stem cells decrease splenocytes apoptosis in a sepsis experimental model

    Inflammation Research 63:719–728.

    https://doi.org/10.1007/s00011-014-0745-1

    • PubMed
    • Google Scholar
54. 1. Reagan-Shaw S
    2. Nihal M
    3. Ahmad N

    (2008) Dose translation from animal to human studies revisited

    The FASEB Journal 22:659–661.

    https://doi.org/10.1096/fj.07-9574LSF

    • PubMed
    • Google Scholar
55. 1. Rochefort GY
    2. Delorme B
    3. Lopez A
    4. Hérault O
    5. Bonnet P
    6. Charbord P
    7. Eder V
    8. Domenech J

    (2006) Multipotential mesenchymal stem cells are mobilized into peripheral blood by hypoxia

    Stem Cells 24:2202–2208.

    https://doi.org/10.1634/stemcells.2006-0164

    • PubMed
    • Google Scholar
56. 1. Rooke EDM
    2. Vesterinen HM
    3. Sena ES
    4. Egan KJ
    5. Macleod MR

    (2011) Dopamine agonists in animal models of Parkinson’s disease: a systematic review and meta-analysis

    Parkinsonism & Related Disorders 17:313–320.

    https://doi.org/10.1016/j.parkreldis.2011.02.010

    • Google Scholar
57. 1. Scott S
    2. Kranz JE
    3. Cole J
    4. Lincecum JM
    5. Thompson K
    6. Kelly N
    7. Bostrom A
    8. Theodoss J
    9. Al-Nakhala BM
    10. Vieira FG
    11. Ramasubbu J
    12. Heywood JA

    (2008) Design, power, and interpretation of studies in the standard murine model of ALS

    Amyotrophic Lateral Sclerosis 9:4–15.

    https://doi.org/10.1080/17482960701856300

    • PubMed
    • Google Scholar
58. 1. Sena ES
    2. Currie GL
    3. McCann SK
    4. Macleod MR
    5. Howells DW

    (2014) Systematic reviews and meta-analysis of preclinical studies: why perform them and how to appraise them critically

    Journal of Cerebral Blood Flow & Metabolism 34:737–742.

    https://doi.org/10.1038/jcbfm.2014.28

    • PubMed
    • Google Scholar
59. 1. Sena ES
    2. van der Worp HB
    3. Bath PM
    4. Howells DW
    5. Macleod MR

    (2010) Publication bias in reports of animal stroke studies leads to major overstatement of efficacy

    PLoS Biology 8:e1000344.

    https://doi.org/10.1371/journal.pbio.1000344

    • PubMed
    • Google Scholar
60. 1. Seok J
    2. Warren HS
    3. Cuenca AG
    4. Mindrinos MN
    5. Baker HV
    6. Xu W
    7. Richards DR
    8. McDonald-Smith GP
    9. Gao H
    10. Hennessy L
    11. Finnerty CC
    12. López CM
    13. Honari S
    14. Moore EE
    15. Minei JP
    16. Cuschieri J
    17. Bankey PE
    18. Johnson JL
    19. Sperry J
    20. Nathens AB
    21. Billiar TR
    22. West MA
    23. Jeschke MG
    24. Klein MB
    25. Gamelli RL
    26. Gibran NS
    27. Brownstein BH
    28. Miller-Graziano C
    29. Calvano SE
    30. Mason PH
    31. Cobb JP
    32. Rahme LG
    33. Lowry SF
    34. Maier RV
    35. Moldawer LL
    36. Herndon DN
    37. Davis RW
    38. Xiao W
    39. Tompkins RG
    40. Inflammation and Host Response to Injury, Large Scale Collaborative Research Program

    (2013) Genomic responses in mouse models poorly mimic human inflammatory diseases

    PNAS 110:3507–3512.

    https://doi.org/10.1073/pnas.1222878110

    • PubMed
    • Google Scholar
61. 1. Sepúlveda JC
    2. Tomé M
    3. Fernández ME
    4. Delgado M
    5. Campisi J
    6. Bernad A
    7. González MA

    (2014) Cell senescence abrogates the therapeutic potential of human mesenchymal stem cells in the lethal endotoxemia model

    Stem Cells 32:1865 .

    https://doi.org/10.1002/stem.1654

    • PubMed
    • Google Scholar
62. 1. Shuaib A
    2. Lees KR
    3. Lyden P
    4. Grotta J
    5. Davalos A
    6. Davis SM
    7. Diener HC
    8. Ashwood T
    9. Wasiewski WW
    10. Emeribe U
    11. SAINT II Trial Investigators

    (2007) NXY-059 for the treatment of acute ischemic stroke

    New England Journal of Medicine 357:562–571.

    https://doi.org/10.1056/NEJMoa070240

    • PubMed
    • Google Scholar
63. 1. Spees JL
    2. Lee RH
    3. Gregory CA

    (2016) Mechanisms of mesenchymal stem/stromal cell function

    Stem Cell Research & Therapy 7:125.

    https://doi.org/10.1186/s13287-016-0363-7

    • PubMed
    • Google Scholar
64. 1. Sterne JA
    2. Sutton AJ
    3. Ioannidis JP
    4. Terrin N
    5. Jones DR
    6. Lau J
    7. Carpenter J
    8. Rücker G
    9. Harbord RM
    10. Schmid CH
    11. Tetzlaff J
    12. Deeks JJ
    13. Peters J
    14. Macaskill P
    15. Schwarzer G
    16. Duval S
    17. Altman DG
    18. Moher D
    19. Higgins JP

    (2011) Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials

    BMJ 343:d4002.

    https://doi.org/10.1136/bmj.d4002

    • PubMed
    • Google Scholar
65. 1. Stevenson EK
    2. Rubenstein AR
    3. Radin GT
    4. Wiener RS
    5. Walkey AJ

    (2014) Two decades of mortality trends among patients with severe sepsis: a comparative meta-analysis*

    Critical Care Medicine 42:625–631.

    https://doi.org/10.1097/CCM.0000000000000026

    • PubMed
    • Google Scholar
66. 1. Steward O
    2. Popovich PG
    3. Dietrich WD
    4. Kleitman N

    (2012) Replication and reproducibility in spinal cord injury research

    Experimental Neurology 233:597–605.

    https://doi.org/10.1016/j.expneurol.2011.06.017

    • PubMed
    • Google Scholar
67. 1. U.S. Department of Health and Human Services, Food and Drug Administration

    (2013) Guidance for Industry: Preclinical Assessment of Investigational Cellular and Gene Therapy Products

    Guidance for Industry: Preclinical Assessment of Investigational Cellular and Gene Therapy Products, http://www.fda.gov/downloads/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guidances/CellularandGeneTherapy/UCM376521.pdf.

    http://www.fda.gov/downloads/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guidances/CellularandGeneTherapy/UCM376521.pdf

    • Google Scholar
68. 1. van der Worp HB
    2. Sena ES
    3. Donnan GA
    4. Howells DW
    5. Macleod MR

    (2007) Hypothermia in animal models of acute ischaemic stroke: a systematic review and meta-analysis

    Brain 130:3063–3074.

    https://doi.org/10.1093/brain/awm083

    • PubMed
    • Google Scholar
69. 1. Walter J
    2. Ware LB
    3. Matthay MA

    (2014) Mesenchymal stem cells: mechanisms of potential therapeutic benefit in ARDS and sepsis

    The Lancet Respiratory Medicine 2:1016–1026.

    https://doi.org/10.1016/S2213-2600(14)70217-6

    • PubMed
    • Google Scholar
70. 1. Yang JF
    2. Cao HC
    3. Pan QL
    4. Yu J
    5. Li J
    6. Li LJ

    (2015) Mesenchymal stem cells from the human umbilical cord ameliorate fulminant hepatic failure and increase survival in mice

    Hepatobiliary & Pancreatic Diseases International 14:186–193.

    https://doi.org/10.1016/S1499-3872(15)60354-X

    • PubMed
    • Google Scholar
71. 1. Zhao X
    2. Liu D
    3. Gong W
    4. Zhao G
    5. Liu L
    6. Yang L
    7. Hou Y

    (2014) The toll-like receptor 3 ligand, poly(I:C), improves immunosuppressive function and therapeutic effect of mesenchymal stem cells on sepsis via inhibiting MiR-143

    Stem Cells 32:521–533.

    https://doi.org/10.1002/stem.1543

    • PubMed
    • Google Scholar
72. 1. Zhao Y
    2. Yang C
    3. Wang H
    4. Li H
    5. Du J
    6. Gu W
    7. Jiang J

    (2013) Therapeutic effects of bone marrow-derived mesenchymal stem cells on pulmonary impact injury complicated with endotoxemia in rats

    International Immunopharmacology 15:246–253.

    https://doi.org/10.1016/j.intimp.2012.12.008

    • PubMed
    • Google Scholar
73. 1. Zhou R
    2. Li Z
    3. He C
    4. Li R
    5. Xia H
    6. Li C
    7. Xiao J
    8. Chen ZY

    (2014) Human umbilical cord mesenchymal stem cells and derived hepatocyte-like cells exhibit similar therapeutic effects on an acute liver failure mouse model

    PLoS One 9:e104392.

    https://doi.org/10.1371/journal.pone.0104392

    • PubMed
    • Google Scholar

Author details

1. Manoj M Lalu

   1. Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, Canada
   2. Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
   3. Regenerative Medicine Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   MML, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-0322-382X

2. Katrina J Sullivan

   Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   KJS, Acquisition of data, Analysis and interpretation of data, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

3. Shirley HJ Mei

   Regenerative Medicine Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   SHJM, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

4. David Moher

   1. Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
   2. School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Canada

   Contribution

   DM, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

5. Alexander Straus

   Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   AS, Acquisition of data, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

6. Dean A Fergusson

   Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

   Contribution

   DAF, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

7. Duncan J Stewart

   1. Regenerative Medicine Program, The Ottawa Hospital Research Institute, Ottawa, Canada
   2. Department of Cell and Molecular Medicine, University of Ottawa, Ottawa, Canada

   Contribution

   DJS, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-9113-8691

8. Mazen Jazi

   1. Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
   2. Faculty of Medicine, University of Ottawa, Ottawa, Canada

   Contribution

   MJ, Acquisition of data, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

9. Malcolm MacLeod

   Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   MM, Conception and design, Drafting and revision of article

   Competing interests

   The authors declare that no competing interests exist.

10. Brent Winston

    Department of Critical Care Medicine, University of Calgary, Calgary, Canada

    Contribution

    BW, Conception and design, Drafting and revision of article

    Competing interests

    The authors declare that no competing interests exist.

11. John Marshall

    Departments of Surgery and Critical Care Medicine, Keenan Research Centre of the Li KaShing Knowledge Institute, St. Michaels Hospital, The University of Toronto, Toronto, Canada

    Contribution

    JM, Conception and design, Drafting and revision of article

    Competing interests

    The authors declare that no competing interests exist.

12. Brian Hutton

    Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada

    Contribution

    BH, Analysis and interpretation of data, Drafting and revision of article

    Competing interests

    The authors declare that no competing interests exist.

13. Keith R Walley

    Department of Medicine, Centre for Heart Lung Innovation, University of British Columbia, Vancouver, Canada

    Contribution

    KRW, Conception and design, Drafting and revision of article

    Competing interests

    The authors declare that no competing interests exist.

14. Lauralyn McIntyre

    1. Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
    2. Department of Medicine, University of Ottawa, Ottawa, Canada

    Contribution

    LM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    lmcintyre@ohri.ca

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0001-7421-1407

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