The theory has implications for healthcare organisations, nursing education and individual nurses. Mentoring Up expounds on the interpersonal connections and reciprocal interactions vital for successful nurse-to-nurse mentoring.

BackgroundWe sought to determine the effects of alternative resuscitation strategies on microcirculatory perfusion and examine any association between microcirculatory perfusion and mortality in sepsis.MethodsThis was a prospective, formally designed substudy of participants in the Protocolized Care in Early Septic Shock (ProCESS) trial. We recruited from six sites with the equipment and training to perform these study procedures. All subjects were adults with septic shock, and each was assigned to alternative resuscitation strategies. The two main analyses assessed (1) the impact of resuscitation strategies on microcirculatory perfusion parameters and (2) the association of microcirculatory perfusion with 60-day in-hospital mortality. We measured sublingual microcirculatory perfusion using sidestream dark field in vivo video microscopy at the completion of the 6-h ProCESS resuscitation protocol and then again at 24 and 72 h.ResultsWe enrolled 207 subjects (demographics were similar to the overall ProCESS cohort) and observed 40 (19.3%) deaths. There were no differences in average perfusion characteristics between treatment arms. Analyzing the relationship between microcirculatory perfusion and mortality, we found an association between vascular density parameters and mortality. Total vascular density (beta = 0.006, p < 0.003), perfused vascular density (beta = 0.005, p < 0.04), and De Backer score (beta = 0.009, p < 0.01) were higher overall in survivors in a generalized estimating equation model, and this association was significant at the 72-h time point (p < 0.05 for each parameter).ConclusionsMicrocirculatory perfusion did not differ between three early septic shock treatment arms. We found an association between microcirculatory perfusion parameters of vascular density at 72 h and mortality.Trial registrationClinicalTrials.gov, NCT00510835. Registered on August 2, 2007.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2240-5) contains supplementary material, which is available to authorized users.

Introduction: About one-third of critically ill patients with acute kidney injury (AKI) develop persistently decreased kidney function, known as acute kidney disease (AKD), which may progress to chronic kidney disease (CKD). Although sepsis is the most common cause of AKI, little is known about sepsis-associated AKD.Methods: Using data from a large randomized trial including 1341 patients with septic shock, we studied patients with stage 2 or 3 AKI on day 1 of hospitalization. We defined AKD as a persistently reduced glomerular filtration rate for >7 days. In addition to clinical data, we measured several urinary biomarkers (tissue inhibitor of metalloproteinases-2 and insulin-like growth factor-binding protein 7 [TIMP-2*IGFBP7], neutrophil gelatinase-associated lipocalin [NGAL], kidney injury molecule-1 [KIM-1], liver-type fatty acid binding protein, and type 4 collagen) at 0, 6, and 24 hours, to predict AKD.Results: Of 598 patients, 119 (19.9%) died within 7 days, 318 (53.2%) had early reversal of AKI within the first 7 days, whereas 161 (26.9%) developed AKD. In patients with early reversal, 45 (14.2%) had relapsed AKI after early reversal, and only about one-third of these recovered. Among patients developing AKD, only 15 (9.3%) recovered renal function prior to discharge. Male sex, African American race, and underlying CKD were more predominant in patients developing AKD. None of the biomarkers tested performed well for prediction of AKD, although NGAL modestly increased the performance of a clinical model. Conclusions: AKD is common in patients with septic shock, especially among African American males and those with underlying CKD. Existing AKI biomarkers have limited utility for predicting AKD but might be useful together with clinical variables. Novel predictive biomarkers for renal recovery are needed.

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