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Importance of a registered and structured protocol when conducting systematic reviews: Comments about nebulized antibiotics for ventilator-associated pneumonia

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We appreciate Gu's [1] interest in our study. We apologize and agree with his comment about attributing units to standardized mean difference (SMD). Nevertheless, similar to the SMD, results in mean difference (control – nebulized) were unaffected by neb-ulized antibiotics (2.67 days, 95 % confidence interval (CI) –2.89, 8.23 for ICU length of stay (LOS); and 0.70 days, 95 % CI −3.40, 4.80 for mechanical ventilation). However, we strongly disagree with other points raised by the letter. First, the study protocol was defined a priori [2]. We disagree that combining observational studies with intervention studies is reserved only for safety evaluation. This topic has been discussed in the literature and combining both types of studies was adequate for our aim [3]. Furthermore, we presented the main results separating interventional studies from observational studies, thereby allowing the reader to interpret both analyses independently.
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L E T T E R Open Access
Importance of a registered and structured
protocol when conducting systematic
reviews: comments about nebulized
antibiotics for ventilator-associated
pneumonia
Fernando G. Zampieri
1,2,3
, Antonio P. Nassar Jr
1,2,4
, Dimitri Gusmao-Flores
1,5
, Leandro U. Taniguchi
2,6
,AntoniTorres
7
and Otavio T. Ranzani
1,7,8,9*
See related Letter by Gu, http://www.ccforum.com/content/19/1/236, and related research by Zampieri et al., http://ccforum.com/content/19/1/150
We appreciate Gus [1] interest in our study. We
apologize and agree with his comment about attribut-
ing units to standardized mean difference (SMD).
Nevertheless, similar to the SMD, results in mean dif-
ference (control nebulized) were unaffected by neb-
ulized antibiotics (2.67 days, 95 % confidence interval
(CI) 2.89, 8.23 for ICU length of stay (LOS); and
0.70 days, 95 % CI 3.40, 4.80 for mechanical ventilation).
However, we strongly disagree with other points raised by
the letter.
First, the study protocol was defined a priori [2]. We
disagree that combining observational studies with inter-
vention studies is reserved only for safety evaluation.
This topic has been discussed in the literature and com-
bining both types of studies was adequate for our aim
[3]. Furthermore, we presented the main results separat-
ing interventional studies from observational studies,
thereby allowing the reader to interpret both analyses
independently.
Second, both of the studies cited as casecontrol
studies[1] received this denomination in their title
and abstract. However, by reading their methods it
becomes clear that they are actually matched cohort
studies [4, 5]. Indeed, they matched exposed patients
(nebulized group) to unexposed patients (no-nebu-
lized group). A casecontrol design starts with the
outcome (case = clinical success) and matches them
with controls (clinical failures). Therefore, our measure
of effect was correct [5]. For exploration, we report the
analysis for clinical cure using the odds ratio (OR) (Fig. 1).
The results are unchanged.
Third, Kalinsstudywasincludedbecauseitful-
filled our inclusion/exclusion criteria [2]. Gussug-
gestion to exclude this study based solely on its
effects in heterogeneity could be considered selective
reporting [1].
Our study provided data for further trials aiming to
evaluate the effect of nebulized antibiotics in ventilator-
associated pneumonia (VAP) [2].
* Correspondence: otavioranzani@yahoo.com.br
1
Cooperative Network for ResearchAMIB-Net, Associação de Medicina
Intensiva Brasileira, Rua Arminda, 93, 7 andar, São Paulo 04545-100, Brazil
7
Department of Pulmonology, Hospital Clinic of Barcelona, Institut
Dinvestigacions August Pi I Sunyer (IDIBAPS), University of Barcelona, Ciber
de Enfermedades Respiratorias (CIBERES), Carrer Villarroel, 170, Barcelona
08036, Spain
Full list of author information is available at the end of the article
© 2015 Zampieri et al.
Open Access
This article is distributed under the terms of the Creative Commons Attribution
4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Zampieri et al. Critical Care (2015) 19:298
DOI 10.1186/s13054-015-1020-8
Abbreviations
CI: Confidence interval; LOS: Length of stay; OR: Odds ratio;
SMD: Standardized mean difference; VAP: Ventilator-associated pneumonia.
Competing interests
The authors declare that they have no competing interests.
Author contributions
FGZ conceived the study, participated in data acquisition, data analysis,
and interpretation, and helped to revise the manuscript for important
intellectual content. APN participated in conception of the study, data
acquisition, data analysis, and interpretation, and helped to revise the
manuscript for important intellectual content. DG-F participated in conception
of the study and interpretation, and helped to revise the manuscript for
important intellectual content. LUT participated in conception and design of
the study and interpretation, and helped to revise the manuscript for important
intellectual content. AT participated in conception and design of the study and
interpretation, and helped to revise the manuscript for important intellectual
content. OTR conceived the design of the study, participated in data acquisition
and interpretation, and wrote the draft of the manuscript. All authors read and
approved the final manuscript.
Author details
1
Cooperative Network for ResearchAMIB-Net, Associação de Medicina
Intensiva Brasileira, Rua Arminda, 93, 7 andar, São Paulo 04545-100, Brazil.
2
Emergency Medicine Discipline, Faculty of Medicine, University of São Paulo,
Rua Dr. Enéas de Carvalho Aguiar, 255, 5th floor, room 5023, São Paulo
05403-010, Brazil.
3
Intensive Care Unit, Hospital Alemão Oswaldo Cruz, Rua
João Julião, 331, São Paulo 01323-903, Brazil.
4
Adult Intensive Care Unit, A.C.
Camargo Cancer Center, Rua Professor Antônio Prudente, 211, São Paulo
01509-010, Brazil.
5
Intensive Care Unit, University Hospital Prof. Edgar Santos,
Universidade Federal da Bahia, Rua Augusto Viana, Salvador 40110-910, Brazil.
6
Research and Education Institute (IEP), Hospital Sirio-Libanes, Rua Prof.
Daher Cutait, 69, São Paulo 01308-060, Brazil.
7
Department of Pulmonology,
Hospital Clinic of Barcelona, Institut Dinvestigacions August Pi I Sunyer
(IDIBAPS), University of Barcelona, Ciber de Enfermedades Respiratorias
(CIBERES), Carrer Villarroel, 170, Barcelona 08036, Spain.
8
Amil Critical Care
Group, Hospital Paulistano, Rua Martiniano de Carvalho, 741, São Paulo
01321-001, Brazil.
9
Respiratory Intensive Care Unit, Pulmonary Division, Heart
Institute, Hospital das Clínicas, University of São Paulo, Av. Dr. Arnaldo, 455
Laboratório de Pneumologia, andar, sala 2144, Cerqueira César, 01246903
Sao Paulo, Brazil.
References
1. Gu WJ. Nebulized antibiotics for ventilator-associated pneumonia:
misleading analysis and interpretation of the data. Crit Care.
2015;19:236.
2. Zampieri FG, Nassar AP, Gusmao-Flores D, Taniguchi LU, Torres A,
Ranzani OT. Nebulized antibiotics for ventilator-associated pneumonia: a
systematic review and meta-analysis. Crit Care. 2015;19:150.
3. Shrier I, Boivin JF, Steele RJ, Platt RW, Furlan A, Kakuma R, et al. Should
meta-analyses of interventions include observational studies in addition to
randomized controlled trials? A critical examination of underlying principles.
Am J Epidemiol. 2007;166:12039.
4. Cummings P, McKnight B, Greenland S. Matched cohort methods for injury
research. Epidemiol Rev. 2003;25:4350.
5. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed.
Philadelphia, PA: Lippincott Williams & Wilkins; 2008.
Study
Random effects model
Heterogeneity: I−squared=34%, tau−squared=0.145, p=0.1267
Type = Observational
Type = Randomized
Random effects model
Random effects model
Heterogeneity: I−squared=53.3%, tau−squared=0.2501, p=0.0576
Heterogeneity: I−squared=0%, tau−squared=0, p=0.5424
Doshi
Ghannam
Kalin
Kofteridis
Korbila
Tumbarello
Hallal
Le Conte
Lu
Niederman
Rattanaupawan
Events
24
13
4
23
62
72
5
7
14
27
26
Total
440
311
129
44
13
29
43
78
104
5
21
20
32
51
Nebulized Antibiotics
Events
20
5
6
14
26
57
3
3
11
14
26
Total
372
265
107
51
9
15
43
43
104
5
17
20
16
49
Control
0.01 0.1 1 10 100
Odds Ratio
Favours Control Favours Nebulized Antibiotics
OR
1.66
1.81
1.26
1.86
22.09
0.24
2.38
2.53
1.86
7.86
2.33
1.91
0.77
0.92
95%−CI
[1.10; 2.49]
[1.01; 3.22]
[0.71; 2.24]
[0.82; 4.21]
[1.01; 483.26]
[0.05; 1.05]
[0.99; 5.72]
[1.11; 5.76]
[1.05; 3.27]
[0.28; 217.11]
[0.50; 10.91]
[0.52; 7.01]
[0.13; 4.49]
[0.42; 2.02]
W(random)
100%
66.6%
33.4%
13.6%
1.7%
6.1%
12.6%
13.6%
19.0%
1.4%
5.7%
7.4%
4.6%
14.2%
Fig. 1 Forest plot for clinical cure using odds ratios (OR). Pfor overall effect = 0.015. CI confidence interval
Zampieri et al. Critical Care (2015) 19:298 Page 2 of 2
Article
Ventilator-associated pneumonia (VAP) represents a major clinical challenge for all physicians caring for critically ill patients. Important concerns regarding the optimum treatment regimen for the management of pulmonary infections-such as the choice of antibiotic, the dosing, the duration of therapy, and drug delivery to the infected tissue-play an even more prominent role in the management of VAP. Patients with VAP are usually severely ill and have already been exposed to prolonged periods of intensive care; therefore, the safety margin for error is narrowed. Nebulized antibiotics represent a promising way to deliver high doses to the lung tissue with fewer concerns regarding systemic toxicity. In the context of multidrug-resistant VAP, where therapeutic options may be reduced to more toxic drugs, nebulized antibiotics may be a reasonable and logical choice. While most studies focused on the adjunctive role (ie, complementary to intravenous infusion) of nebulized antibiotics, some reports have suggested that nebulization may be effective even as a single therapy. Nevertheless, important differences in nebulization techniques contribute to the difficulty in obtaining robust data regarding the efficacy of nebulized therapy. Two recent meta-analyses have evaluated the role of nebulized antibiotics for VAP, one of them focusing specifically on nebulized polymyxin and the other assessing the role of nebulization therapy regardless of the drug used. Although conceptually different, both studies concluded that nebulized therapy could indeed play a significant role in the management of VAP. In this review, we reassess the evidence for nebulized antibiotics and provide guidance for future studies in this field to fill the knowledge gaps.
Article
Full-text available
I read with interest the recent systematic review of nebulized antibiotics for ventilator-associated pneumonia [1]. I congratulate and applaud Zampieri and colleagues’ important work, but several important issues should be noted. First, there is one important methodological issue with the review. The data from randomized controlled trials and observational studies were inappropriately pooled together, which goes against the precept of pooling studies with similar design. In fact, the pooling of data from randomized controlled trials and observational studies is only recommended for the assessment of harm/adverse effects. Thus, results from randomized controlled trials and results from observational studies should be separately pooled. Second, two of the included observational studies were case–control studies [2, 3], and therefore using the relative risk as the summary statistic is improper. Instead, the odds ratio should be used as the summary statistic to pool data from observational studies. Third, the authors used the standardized mean difference as the summary statistic for the continuous variables. As we know, the standardized mean difference is unitless because it is a relative, rather than an absolute, measure of effect. Instead, the authors should have used the mean difference. Last, Kalin and colleagues’ study should be excluded from the review. This study does not state why patients received inhaled colistin and how many patients received a high, normal or low dose in the inhaled plus intravenous colistin group and the intravenous colistin group [4]. Inadequate allocation may thus exist, resulting in a significant potential for selection bias. Inclusion of this study leads to significant heterogeneity, as shown in the forest plot for clinical cure.
Article
Full-text available
Nebulized antibiotics are a promising new treatment option for ventilator-associated pneumonia (VAP). However, more evidence of the benefit of this therapy is desired. The Medline, Scopus, EMBASE, Biological Abstracts, CAB Abstracts, Food Science and Technology Abstracts, CENTRAL, Scielo and Lilacs databases were searched to identify randomized controlled trials or matched observational studies that compared nebulized antibiotics with or without intravenous antibiotics to intravenous antibiotics alone for VAP treatment. Two reviewers independently collected data and assessed outcomes and risk of bias. The primary outcome was clinical cure. Secondary outcomes were microbiological cure, ICU and hospital mortality, duration of mechanical ventilation, ICU length of stay and adverse events. A mixed-effect model meta-analysis was performed. Trial sequential analysis was used for the main outcome of interest. Twelve studies were analyzed, including six randomized controlled trials. For the main outcome analysis, 812 patients were included. Nebulized antibiotics were associated with higher rates of clinical cure (risk ratio (RR) = 1.23; 95% confidence interval (CI), 1.05-1.43; I(2) = 34%; D(2) = 45%). Nebulized antibiotics were not associated with microbiological cure (RR = 1.24; 95% CI, 0.95-1.62; I(2) = 62.5), mortality (RR = 0.90; CI 95%, 0.76-1.08; I(2) = 0%), duration of mechanical ventilation (standardized mean difference = -0.10 days; 95% CI, -1.22 to 1.00; I(2) = 96.5%), ICU length of stay (standardized mean difference (SMD) = 0.14 days; 95% CI, -0.46 to 0.73; I(2) = 89.2%) or renal toxicity (RR = 1.05; 95% CI, 0.70 to 1.57; I(2) = 15.6%). Regarding the primary outcome, the number of patients included was below the information size required for a definitive conclusion by trial sequential analysis; therefore, our results regarding this parameter are inconclusive. Nebulized antibiotics seem to be associated with higher rates of clinical cure in the treatment of VAP. However, the apparent benefit in the clinical cure rate observed by traditional meta-analysis does not persist after trial sequential analysis. Additional high-quality studies on this subject are highly warranted. CRD42014009116 . Registered 29 March 2014.
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Some authors argue that systematic reviews and meta-analyses of intervention studies should include only randomized controlled trials because the randomized controlled trial is a more valid study design for causal inference compared with the observational study design. However, a review of the principal elements underlying this claim (randomization removes the chance of confounding, and the double-blind process minimizes biases caused by the placebo effect) suggests that both classes of study designs have strengths and weaknesses, and including information from observational studies may improve the inference based on only randomized controlled trials. Furthermore, a review of empirical studies suggests that meta-analyses based on observational studies generally produce estimates of effect similar to those from meta-analyses based on randomized controlled trials. The authors found that the advantages of including both observational studies and randomized studies in a meta-analysis could outweigh the disadvantages in many situations and that observational studies should not be excluded a priori.
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This article reviews the design and analysis of matched cohort studies of injuries where exposed study subjects are matched to others not exposed. We focus on the situation in which data are available for the matched groups with at least one member who had the study outcome, but data are absent or incomplete for matched groups that have no members with the outcome. When matching is done in a case-control study, those with the outcome are matched to those without the outcome on certain confounder measures; this distorts the exposure status of the controls to be like that of the cases in regard to the matching variables (and perhaps other variables as well) (1). As a consequence, the selected controls may not represent the exposure experience of the entire population from which the cases were derived. Therefore, matching is a source of selection bias in a case-control study. The bias it produces can be removed in the analysis by accounting for the matching since, conditional on the values of the matching variables, controls will be representative of the source population. The consequence of one-to-one matching in a cohort study is different. A variable can be a confounder only if, in the study cohort, it is associated with but not affected by the exposure and is independently predictive of the outcome. If each exposed study subject is perfectly matched to an unexposed subject on the value of some variable, and if there is no subject loss or missing data, there will be no association of exposure with the matching variable in the data, and confounding by the matching variable will be eliminated (2). Confounding by the matching variable could still occur, however, if an imbalance arose between the exposed and unexposed study subjects; this might happen, for example, if follow-up were less complete for one group compared with the other, or if some records were omitted from the analysis because of missing data. Despite the potential of matching to prevent confounding in a cohort study and the potential of matching to sometimes increase study efficiency (2), it appears that this design is rarely used. For many cohort studies, matching exposed persons to one or several unexposed persons would be laborious. Furthermore, it might be wasteful in that matches might be unavailable for some potential cohort members. Today, cohort studies usually avoid matching, and the data are analyzed using regression methods that make it relatively easy to adjust for potential confounding factors that might otherwise be used for matching.
Article
This article reviews the design and analysis of matched cohort studies of injuries where exposed study subjects are matched to others not exposed. We focus on the situation in which data are available for the matched groups with at least one member who had the study outcome, but data are absent or incomplete for matched groups that have no members with the outcome. When matching is done in a case-control study, those with the outcome are matched to those without the outcome on certain confounder measures; this distorts the exposure status of the controls to be like that of the cases in regard to the matching variables (and perhaps other variables as well) (1). As a consequence, the selected controls may not repre- sent the exposure experience of the entire population from which the cases were derived. Therefore, matching is a source of selection bias in a case-control study. The bias it produces can be removed in the analysis by accounting for the matching since, conditional on the values of the matching variables, controls will be representative of the source popu- lation. The consequence of one-to-one matching in a cohort study is different. A variable can be a confounder only if, in the study cohort, it is associated with but not affected by the exposure and is independently predictive of the outcome. If each exposed study subject is perfectly matched to an unex- posed subject on the value of some variable, and if there is no subject loss or missing data, there will be no association of exposure with the matching variable in the data, and confounding by the matching variable will be eliminated (2). Confounding by the matching variable could still occur, however, if an imbalance arose between the exposed and unexposed study subjects; this might happen, for example, if follow-up were less complete for one group compared with the other, or if some records were omitted from the analysis because of missing data. Despite the potential of matching to prevent confounding in a cohort study and the potential of matching to sometimes increase study efficiency (2), it appears that this design is rarely used. For many cohort studies, matching exposed persons to one or several unexposed persons would be labo- rious. Furthermore, it might be wasteful in that matches might be unavailable for some potential cohort members. Today, cohort studies usually avoid matching, and the data are analyzed using regression methods that make it relatively easy to adjust for potential confounding factors that might otherwise be used for matching.
Modern epidemiology 95%−CI [1.10; 2.49] [1.01; 3.22] [0.71; 2.24] [0.82; 4.21] [1.01; 483
  • Kj Rothman
  • S Greenland
  • Tl Lash
Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008. 95%−CI [1.10; 2.49] [1.01; 3.22] [0.71; 2.24] [0.82; 4.21] [1.01; 483.26] [0.05; 1.05] [0.99; 5.72] [1.11; 5.76] [1.05; 3.27] [0.28; 217.11] [0.50; 10.91] [0.52; 7.01] [0.13; 4.49] [0.42; 2.02]
Forest plot for clinical cure using odds ratios (OR). P for overall effect = 0.015. CI confidence interval Zampieri et al
Fig. 1 Forest plot for clinical cure using odds ratios (OR). P for overall effect = 0.015. CI confidence interval Zampieri et al. Critical Care (2015) 19:298