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Studying the prevention of acute kidney injury: lessons from an 18th-century mathematician.

Acute kidney injury (AKI) complicates approximately 5 to 15% of hospitalizations, depending on its definition (1–3), and is independently associated with a five-fold or more increase in in-hospital mortality rates (3–5). AKI also extends length of stay, obligates excess hospital expenditures, and may exert long-term adverse effects, including an increased risk for ESRD. Several known mechanisms contribute to the development of AKI, including ischemia, vasoconstriction, toxic injury related to selected endogenous substances (e.g., myoglobin), radiocontrast and drugs (e.g., amphotericin B), and microcirculatory changes, as observed with sepsis and other inflammatory states (6). Given the dire consequences that are associated with AKI, efforts at prevention seem desirable and worthy of intense investigation. Unfortunately, most episodes of AKI cannot be predicted readily, either from clinical criteria or from the timing of events. The vast majority of prevention trials in kidney disease have been conducted in the setting of radiocontrast exposure. Several recently published studies in AKI prevention have attracted significant attention and have changed practice considerably (7–11). In this report, we scrutinize three of these studies, focusing on issues related to effect estimates and statistical power, and incorporate principles of Bayes’ Theorem in our interpretation of study results. More general, we provide a cautionary note regarding the interpretation of “positive” studies with an insufficient sample size and a “significant” (P 0.05) result.