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The discovery of nitric oxide [1] generated excitement among intensivists because, as a therapeutic gas, it improved perfusion to ventilated lung units and increased arterial oxygenation without obvious systemic effects. Subsequent randomised trials in patients with acute respiratory distress syndrome (ARDS) found shortterm improvements in oxygenation, with no effect on mortality and an unexpected increased risk of acute kidney injury (AKI) [2, 3]. In the absence of compelling biological mechanisms, one explanation could be that nitric oxide was used harmfully in trial protocols as opposed to clinician-directed practice. Although observational investigations can address this hypothesis, they are prone to bias and confounding that persist despite efforts at statistical ‘control’ during study design or analysis. However, empirical comparisons of treatment effects in randomised trials and observational studies have yielded mixed results [4, 5], and design alone does not determine the truth of study findings. Ruan and colleagues recently published a retrospective cohort study (n = 547; 2007–2015) evaluating the relationship between inhaled nitric oxide administered in the first 3 days of ARDS and subsequent need for renal replacement therapy (RRT) [6]. They found that nitric oxide was associated with a substantial increase in RRT (adjusted hazard ratio 1.59, 95% confidence interval 1.08–2.34), consistent with meta-analyses of trials [2, 3]. The authors used propensity score matching to adjust for confounders, a stratified analysis to evaluate clinically important subgroups, and appropriate models to handle the competing risk of mortality. In a sensitivity analysis, residual confounding was evaluated using the rule-out approach, which concluded that an unmeasured binary confounding variable would have to be highly—and implausibly—associated with AKI and present much more frequently in the nitric oxide group to account for its observed nephrotoxicity. Notwithstanding the rigour of these analyses, we must consider their limitations. The propensity score is the probability that a patient will receive a treatment, conditional on baseline characteristics entered into a logistic regression model with the treatment as the dependent variable. The score is used in several techniques to evaluate the effect of an intervention. One popular approach creates a group of patients who receive a treatment, each of whom is matched to at least one unexposed patient with the same propensity score. This method creates groups that appear to be well-balanced in a table of baseline characteristics. Unlike multivariable regression, propensity score-matched cohorts mimic the familiar structure of a randomised trial. Outcomes are compared directly between groups, preferably accounting for the matched nature of the data. However, propensity scoring does not overcome a fundamental limitation of observational studies: only measured confounders can be assessed. In addition, the ‘rule-out’ simulations to determine the necessary magnitude of a hypothetical confounder that would render the exposure harmless does not consider the more plausible scenario of several unmeasured * Correspondence: neill.adhikari@utoronto.ca Department of Critical Care Medicine and Interdepartmental Division of Critical Care, Sunnybrook Health Sciences Centre and University of Toronto, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada Full list of author information is available at the end of the article

Inhaled nitric oxide and acute kidney injury: new insights from observational data