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Background The previously published “Dose Response Multicentre International Collaborative Initiative (DoReMi)” study concluded that the high mortality of critically ill patients with acute kidney injury (AKI) was unlikely to be related to an inadequate dose of renal replacement therapy (RRT) and other factors were contributing. This follow-up study aimed to investigate the impact of daily fluid balance and fluid accumulation on mortality of critically ill patients without AKI (N-AKI), with AKI (AKI) and with AKI on RRT (AKI-RRT) receiving an adequate dose of RRT. Methods We prospectively enrolled all consecutive patients admitted to 21 intensive care units (ICUs) from nine countries and collected baseline characteristics, comorbidities, severity of illness, presence of sepsis, daily physiologic parameters and fluid intake-output, AKI stage, need for RRT and survival status. Daily fluid balance was computed and fluid overload (FO) was defined as percentage of admission body weight (BW). Maximum fluid overload (MFO) was the peak value of FO. Results We analysed 1734 patients. A total of 991 (57 %) had N-AKI, 560 (32 %) had AKI but did not have RRT and 183 (11 %) had AKI-RRT. ICU mortality was 22.3 % in AKI patients and 5.6 % in those without AKI ( p  < 0.0001). Progressive fluid accumulation was seen in all three groups. Maximum fluid accumulation occurred on day 2 in N-AKI patients (2.8 % of BW), on day 3 in AKI patients not receiving RRT (4.3 % of BW) and on day 5 in AKI-RRT patients (7.9 % of BW). The main findings were: (1) the odds ratio (OR) for hospital mortality increased by 1.075 (95 % confidence interval 1.055–1.095) with every 1 % increase of MFO. When adjusting for severity of illness and AKI status, the OR changed to 1.044. This phenomenon was a continuum and independent of thresholds as previously reported. (2) Multivariate analysis confirmed that the speed of fluid accumulation was independently associated with ICU mortality. (3) Fluid accumulation increased significantly in the 3-day period prior to the diagnosis of AKI and peaked 3 days later. Conclusions In critically ill patients, the severity and speed of fluid accumulation are independent risk factors for ICU mortality. Fluid balance abnormality precedes and follows the diagnosis of AKI.

While frameworks to systematically assess bias in systematic reviews and meta-analyses (SRMAs) and frameworks on causal inference are well established, they are less frequently integrated beyond the data analysis stages. This paper proposes the use of Directed Acyclic Graphs (DAGs) in the design stage of SRMAs. We hypothesize that DAGs created and registered a priori can offer a useful approach to more effective and efficient evidence synthesis. DAGs provide a visual representation of the complex assumed relationships between variables within and beyond individual studies prior to data analysis, facilitating discussion among researchers, guiding data analysis, and may lead to more targeted inclusion criteria or set of data extraction items. We illustrate this argument through both experimental and observational case examples.

Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having an unmeasured confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one unmeasured confounder. Without imposing any assumptions on the unmeasured confounder or confounders, we derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous sensitivity analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder.

Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence

For researchers using observational data, a useful way to answer a causal question is to design the target trial that would answer it and then emulate its protocol. The example of the HIV-treatment-as-prevention strategy illustrates the benefits of this approach.

In Denying to the Grave, authors Sara and Jack Gorman explore the psychology of health science denial. Using several examples of such denial as test cases, they propose seven key principles that may lead individuals to reject \"accepted\" health-related wisdom.

Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.

Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questions in clinical and epidemiologic research and inform study design and statistical analysis. DAGs are constructed to depict prior knowledge about biological and behavioral systems related to specific causal research questions. DAG components portray who receives treatment or experiences exposures; mechanisms by which treatments and exposures operate; and other factors that influence the outcome of interest or which persons are included in an analysis. Once assembled, DAGs — via a few simple rules — guide the researcher in identifying whether the causal effect of interest can be identified without bias and, if so, what must be done either in study design or data analysis to achieve this. Specifically, DAGs can identify variables that, if controlled for in the design or analysis phase, are sufficient to eliminate confounding and some forms of selection bias. DAGs also help recognize variables that, if controlled for, bias the analysis (e.g., mediators or factors influenced by both exposure and outcome). Finally, DAGs help researchers recognize insidious sources of bias introduced by selection of individuals into studies or failure to completely observe all individuals until study outcomes are reached. DAGs, however, are not infallible, largely owing to limitations in prior knowledge about the system in question. In such instances, several alternative DAGs are plausible, and researchers should assess whether results differ meaningfully across analyses guided by different DAGs and be forthright about uncertainty. DAGs are powerful tools to guide the conduct of clinical research.

Selecting an appropriate set of confounders for which to control is critical for reliable causal inference. Recent theoretical and methodological developments have helped clarify a number of principles of confounder selection. When complete knowledge of a causal diagram relating all covariates to each other is available, graphical rules can be used to make decisions about covariate control. Unfortunately, such complete knowledge is often unavailable. This paper puts forward a practical approach to confounder selection decisions when the somewhat less stringent assumption is made that knowledge is available for each covariate whether it is a cause of the exposure, and whether it is a cause of the outcome. Based on recent theoretically justified developments in the causal inference literature, the following proposal is made for covariate control decisions: control for each covariate that is a cause of the exposure, or of the outcome, or of both; exclude from this set any variable known to be an instrumental variable; and include as a covariate any proxy for an unmeasured variable that is a common cause of both the exposure and the outcome. Various principles of confounder selection are then further related to statistical covariate selection methods.