0001) The multivariate Fine and Gray model revealed that R, I and

0001).The multivariate Fine and Gray model revealed that R, I and F classes of the RIFLE criteria were independent risk factors for in-hospital mortality (Table (Table4).4). Other variables independently associated with in-hospital mortality were nonrenal SOFA score, McCabe class 3 and respiratory failure occurring before AKI onset selleck chem (Table (Table44).Table 4Association of AKI with hospital mortality: results of the unadjusted and adjusted Fine and Gray modelsaImpact of AKI on lengths of stays and need for prolonged renal supportPatients with AKI had longer (median days (interquartile range)) ICU stays (no AKI: 4 (3 to 7), R class: 6 (3 to 11), I class: 7 (4 to 12) and F class: 8 (4 to 17), P < 0.001) and longer hospital stays (no AKI: 16 (9 to 30), R class: 22 (12 to 40), I class: 21 (10 to 37) and F class: 25 (12 to 44); P < 0.

001) than patients without AKI. Upon ICU discharge, 92 survivors (3.2%) among the 2,846 AKI patients still needed renal support.DiscussionThe association of AKI with critically ill patients’ outcomes has been widely investigated, but very few multiple-center evaluations using the RIFLE criteria have been published so far [10-13]. Our study, carried out in a large cohort of general ICU patients, supports the use of RIFLE as a classification tool and confirms previous evidence that AKI negatively influences patients’ outcomes.The originality of our results lies mainly in the original competing risks approach. This approach has many potential advantages over the commonly used logistic regression and Cox models.

Actually, logistic regression has been reported to cause loss of information because it yields a time-independent probability of dying and ignores the timing of events and their chronological order [27,28]. While the Cox model may partially alleviate these limits, it has been shown to overestimate the incidence of the event of interest, with most of the overestimation being related to the rate of the competing event [29]. By contrast, the Fine and Gray model adequately addresses time spent in the hospital as a risk factor for mortality by considering death hazard rates and takes into account the time-varying exposure status, thus avoiding a potential misjudgment in terms of time-dependent bias [30,31]. Moreover, it provides a more accurate estimation of mortality because death hazard rates are not confounded by the competing event “discharge alive.

“In keeping with the few similar multiple-center evaluations that have used the RIFLE criteria [10,11,13], we found that AKI was an overall predictor of poor outcomes (it must be noted, however, that Drug_discovery results regarding crude hospital mortality rates vary considerably from one study to another, indicating residual heterogeneity despite the use of consensual definition criteria) and that mortality differed according to the maximum RIFLE class reached during the ICU stay.

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