The Effect of Fever Control on Mortality in ICU Patients: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Transcription

1 Protocol The Effect of Fever Control on Mortality in ICU Patients: A Systematic Review and Meta-Analysis of Randomised Controlled Trials Protocol No. 1 Registration: This systematic review and meta-analysis protocol was registered with The International Prospective Register of Systematic Reviews: PROSPERO on 30/03/2016 and was last updated on 30/03/2016. The registration number is [to insert]. Investigators: 1, 2* Paul Young Frederique Schortgen 3 Gordon Bernard 4 Manoj Saxena 5 Richard Beasley 1, 2 Stefan Ebmeier 1, 2 Darmiga Thayabaran 1, 2 Mark Weatherall 2, 6 Rinaldo Bellomo 7 *Corresponding Author: Dr Paul Young, Intensive Care Unit, Wellington Hospital, Capital and Coast District Health Board. Paul.Young@ccdhb.org.nz No March 2016 Page 1 of 12

2 Authors Affiliations 1 Capital & Coast District Health Board, Wellington, New Zealand 2 Medical Research Institute of New Zealand, Wellington, New Zealand 3 Hôpital Henri Mondor-APHP, Créteil, France 4 Vanderbilt University School of Medicine, USA 5 The George Institute for Global Health, Australia 6 University of Otago, Wellington, New Zealand 7 Austin Hospital, Heidelberg, Victoria, Australia Paul Young- Paul.Young@ccdhb.org.nz, Frederique Schortgen frederique.schortgen@aphp.fr, Gordon Bernard- gordon.bernard@vanderbilt.edu, Manoj Saxenamsaxena@georgeinstitute.org.au, Richard Beasley- Richard.beasley@mrinz.ac.nz, Stefan Ebmeier- Stefan.ebmeier@mrinz.ac.nz, Darmiga Thayabaran- Darmiga.thayabaran@mrinz.ac.nz, Mark Weatherall- mark.weatherall@otago.ac.nz, Rinaldo Bellomo- Rinaldo.bellomo@austin.org.au Support: The sponsor of this study is the Medical Research Institute of New Zealand (MRINZ). The MRINZ is supported by an independent research organisations grant from the Health Research Council of New Zealand. No March 2016 Page 2 of 12

3 Background More than 20 million people are admitted to an intensive care unit (ICU) annually 1. Fundamentally, ICU treatment supports patients beyond the limits of normal physiological homeostasis. In essence, if an illness is reversible, patients survive if they can be supported long enough to recover. However, there are limits to supportive care and when these limits are exceeded, progressive multiorgan failure develops, and patients often die. Patients with a range of critical illnesses including trauma, infection, major surgery, and pancreatitis develop fever and an associated increased metabolic rate 2-4. Treating fever reduces metabolic demands 5. Reduction of fever may be a way to extend the limits of homeostasis offered by supportive ICU therapy, so that the limits of supportive care are not exceeded. Two recent randomized controlled trials in ICU patients with sepsis; one which evaluated physical cooling 6 and another, the use of paracetamol to treat fever 7, reported that the intervention that reduced body temperature was associated with some mortality benefit. In a retrospective cohort study we reported that higher fever is associated with lower mortality risk in the presence of infection and with higher mortality risk in the absence of infection 8,9. If treating fever improves outcomes in patients with likely infection, then it is possible that treatment of fever will also improve outcomes for febrile patients without fever. We wish to test the hypothesis that in critically ill adults fever control with antipyretic drugs and/or physical cooling methods reduces mortality. Objective To evaluate the effect of fever-control therapy (i.e. physical cooling, NSAIDs, and paracetamol) on all-cause mortality in ICU patients. No March 2016 Page 3 of 12

4 Methods This study is a systematic review and meta-analysis. Eligibility criteria Studies considered for inclusion for this systematic review and meta-analysis, will meet the following eligibility criteria: Study characteristics 1. Study design must be a randomised controlled trial. 2. Participants must be adult patients admitted to ICU. 3. Body temperature must be reported as a baseline variable. 4. Study setting must be an ICU. We will exclude trials conducted solely in a Paediatric ICU, Coronary Care Unit, Respiratory Care Unit, or Burns Unit. 5. Interventions may be any treatment commonly administered to febrile patients in order to reduce body temperature. Our pre-specified list is: physical cooling, NSAIDs, paracetamol, or any combination of these. Any method of external cooling will be included, as will any dose of NSAID or paracetamol, delivered by any route. We will exclude trials of therapeutic hypothermia (intention to cool patients below 36 degrees). 6. Comparators must be the treatment of fever compared to no treatment or different thresholds for treatment of fever. 7. Outcomes must include all-cause mortality 8. There will be no specification for length of follow up. Report characteristics 1. The study must be published in a peer-reviewed journal. 2. The study must be written in English 3. There will be no specification for year of publication. No March 2016 Page 4 of 12

5 Information sources We will search the following electronic databases in April 2016: MEDLINE (OVID interface, 1946 onwards) EMBASE (OVID interface, 1947 onwards) PubMed (1946 onwards) CINAHL (EBSCO interface, 1937 onwards) The Cochrane Central Register for Controlled Trials (Wiley interface, current issue) We will also contact study authors as required, to identify further eligible studies and to request additional information. We will not include abstracts from conferences, theses, or other grey literature sources. Search strategy Our search strategy includes search filters for the themes of intensive care and antipyretic therapy and cooling, using a combination of exploded Medical Subject Heading (MeSH) terms and text words, which will be combined using the Boolean operator OR. Our precise search strategy for MEDLINE is shown in the appendix. Data management Throughout the review, we will manage data and records using reference management software with pre-designed data collection forms. No March 2016 Page 5 of 12

6 Selection process In the screening phase, two authors (SE, DT) will independently review the title and abstract of the full list of articles identified by the search strategy. After the initial screening, any articles that meet the inclusion criteria will be reviewed in full, alongside any articles where there is doubt regarding eligibility. A shortlist of articles for study inclusion will then be agreed between the two reviewing authors; disagreements will be resolved by consensus or arbitration by a third author. Data collection process Two authors will extract data independently and in duplicate from each eligible study. Data will be collected that describe the study, the patients, the antipyretic therapies and /or physical cooling used and the reported outcomes. Reviewers will resolve disagreements by discussion and consensus or arbitration by a third author. We will contact study authors to address any uncertainties in the data and as specified below, to attempt to retrieve appropriate data summaries if these are not reported explicitly. Data items We will collect the following data from the selected studies: 1. Study characteristics a. Year of publication b. Study size c. Nature of setting (single/ multi centre) d. Source of funding 2. Patient characteristics a. Age b. Sex c. Number of patients on invasive mechanical ventilation d. Number of patients on vasopressors e. Diagnosis category e.g. percentage with infection 3. Intervention details No March 2016 Page 6 of 12

7 a. Number of patients in each arm b. Antipyretic therapy and/or physical cooling technique used c. Duration of intervention d. Control used 4. Reported outcomes a. All-cause mortality (at the longest time point recorded) b. In hospital mortality c. Survival time d. ICU length of stay e. Hospital length of stay f. Temperature at 12 hours post randomisation Outcomes and Prioritisation The primary outcome variable for meta-analysis will be all-cause mortality at the longest time point after randomisation reported in each study. Additional outcomes will include all-cause early mortality (up to day 28 or before) and in-hospital mortality, ICU length of stay, hospital length of stay, and temperature at 12 hours after randomisation. Risk of bias in individual studies Studies meeting inclusion criteria will be assessed by two independent review authors for risk of bias according to the Cochrane Collaboration tool for assessing the risk of bias. Reviewers will resolve disagreements by discussion and consensus or arbitration by a third author. Publication bias will be examined through funnel plots together with a rankcorrelation coefficient formal test of publication bias for each outcome variable. Data extraction and synthesis relevant to meta-analysis For dichotomous variables; all-cause mortality, all-cause early mortality (up to day 28 or before) and in-hospital mortality, we anticipate and plan to extract and report appropriate numerators and denominators, by treatment group, from which point estimates and No March 2016 Page 7 of 12

8 summary estimates of the risk of death can be derived. Should papers report only summary estimates of relative risk (such as risk differences, odds ratios, or relative risks either adjusted or unadjusted for co-variates), these will be extracted, but then our strategy will be to firstly contact authors to obtain the actual numbers involved, and failing this to derive these numbers in a way that reproduces the confidence interval for the reported analysis e.g. by iteration on the risks in relation to the reported randomized participants, to reproduce a particular study confidence interval. The individual study and the pooled estimates will be presented as relative risks with 95% confidence intervals. For continuous variables; hospital length of stay and temperature; the means, standard deviations, and number of participants to which these summary statistics apply, will be extracted. Should papers only report summary estimates of differences (such as a mean difference and a confidence interval) these will be extracted, but then our strategy will be to firstly contact authors to obtain the actual summary statistics involved, and failing this to derive a pooled standard deviation from the confidence interval. The individual study and pooled estimates will be presented as mean differences (on the scale of measurement) with 95% confidence intervals. Time-related variables are likely to be presented as hazard ratios with confidence intervals from which the standard error of the log hazard ratio can be derived. The individual study and pooled estimate of the common hazard ratios will be presented with 95% confidence intervals. The pooled estimate will be first calculated on the logarithm hazard scale with exponentiation to the hazard ratio scale. For all three types of variables estimates of a common effect and its confidence interval will be by the inverse variance weighting method. If the statistical test for heterogeneity is not statistically significant at P=0.1 we will report the fixed effects estimate. If P <0.1 for heterogeneity we will report both a fixed and random effects estimates. This threshold has been chosen as formal statistical tests for heterogeneity typically have low statistical power for detecting this. The method of DerSimonian and Laird will be used to estimate the random effects pooled estimates. No March 2016 Page 8 of 12

9 Assessment of heterogeneity For each analysis, studies meeting inclusion criteria will be reviewed by authors for clinical and/or methodological homogeneity for pooled analysis. Groups of studies that are judged to be sufficiently similar on a clinical and methodological basis, will then be evaluated for statistical heterogeneity using the Cochran s Q test and the I 2 statistic. A threshold of P<0.1 will be used to indicate at least some evidence of statistical heterogeneity. Where there is evidence of statistical heterogeneity, and a sufficient number of studies (at least two per subgroup), we will perform the following subgroup analyses by metaregression for the following subgroup pairs: 1. Diagnosis of infection compared to no such diagnosis 2. Diagnosis of acute brain pathology compared to no such diagnosis 3. Invasively mechanically ventilated compared to not receiving this intervention 4. Infection and treatment with vasopressors compared to not receiving this intervention The meta-regression will be by the method of stratifying the subgroups by their contribution to the heterogeneity Chi-square statistic. Meta-bias (es) In order to identify outcome reporting bias, we will establish whether study protocols were published prior to participant recruitment. In particular, we will assess whether reported outcomes differ from pre-specified outcomes. Confidence in cumulative evidence We will determine the quality of evidence for our reported outcomes using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. No March 2016 Page 9 of 12

10 Appendix Precise Search Strategy Databases 1. MEDLINE 2. EMBASE 3. The Cochrane Central Register of Controlled Trials 4. PubMed 5. CINAHL Search Terms 1. Theme of intensive care Exploded Medical Subject Headings terms critical care (includes intensive care on medline) or intensive care units OR Keywords - critical care or intensive care or ICU or high dependency or HDU or level three care or level 3 care AND 2. Theme of antipyretic therapy and cooling Exploded Medical Subject Headings terms antipyretics or anti-inflammatory agents, non-steroidal or acetaminophen or ibuprofen or aspirin OR Keywords cooling or fever control or fever reduction or temperature control or temperature reduction or normotherm* or anti-pyre* or paracetamol or acetaminophen or non-steroidal anti-inflammatory or NSAID or cyclooxygenase inhibitor* or COX inhibitor* or salicyl* or aspirin or ibuprofen or diclofenac or naproxen or indomethacin or acemetacin or aceclofenac or fenoprofen or fenbufen or dexibuprofen or dexketoprofen or ketoprofen or flurbiprofen or oxaprozin or sulindac or etodolac or ketorolac or nabumetone or azapropazone or phenylbutazone or piroxicam or meloxicam or tenoxicam or droxicam or lornoxicam or isoxicam or mefenamic acid or No March 2016 Page 10 of 12

11 meclofenamic acid or flufenamic acid or tolfenamic acid or tiaprofenic acid or valdecoxib or parecoxib or metamizole or nimesulide or etoricoxib or lumiracoxib or firocoxib or celecoxib or rofecoxib No March 2016 Page 11 of 12

12 References 1. Vincent JL, Marshall JC, Namendys-Silva SA, et al. Assessment of the worldwide burden of critical illness: the intensive care over nations (ICON) audit. The Lancet. Respiratory medicine. 2014;2(5): Laupland KB, Shahpori R, Kirkpatrick AW, Ross T, Gregson DB, Stelfox HT. Occurrence and outcome of fever in critically ill adults. Critical care medicine. 2008;36(5): Laupland KB, Zahar JR, Adrie C, et al. Determinants of temperature abnormalities and influence on outcome of critical illness. Critical care medicine. 2012;40(1): Niven DJ, Stelfox HT, Shahpori R, Laupland KB. Fever in Adult ICUs: An Interrupted Time Series Analysis*. Critical care medicine. 2013;41(8): Manthous CA, Hall JB, Olson D, et al. Effect of cooling on oxygen consumption in febrile critically ill patients. American journal of respiratory and critical care medicine. 1995;151(1): Schortgen F, Clabault K, Katsahian S, et al. Fever control using external cooling in septic shock: a randomized controlled trial. American journal of respiratory and critical care medicine. 2012;185(10): Young P, Saxena M, Bellomo R, et al. Acetaminophen for Fever in Critically Ill Patients with Suspected Infection. The New England journal of medicine. 2015;373(23): Young PJ, Saxena M, Beasley R, et al. Early peak temperature and mortality in critically ill patients with or without infection. Intensive care medicine Saxena M, Young P, Pilcher D, et al. Early temperature and mortality in critically ill patients with acute neurological diseases: trauma and stroke differ from infection. Intensive care medicine. 2015;41(5): No March 2016 Page 12 of 12