• Carvalho Gloria Maria Custodio de GMC Medical Sciences Postgraduate Program, Department of Clinical Medicine, Universidade Federal do Ceará, Fortaleza, Ceara, Brazil., Tacyano Tavares Leite, and Alexandre Braga Libório.
• Medical Sciences Postgraduate Program, Department of Clinical Medicine, Universidade Federal do Ceará, Fortaleza, Ceara, Brazil.
• Shock. 2018 Aug 1; 50 (2): 156-161.

BackgroundA recent prognostic model, predicting 60-day case fatality in critically ill patients requiring renal replacement therapy (RRT), has been developed (Acute Renal Failure Trial Network [ATN] study). Because many prognostic models are suggested in literature, but just a few have found its way into clinical practice, we aimed to externally validate this prediction model in an independent cohort.MethodsA total of 1,053 critically ill patients requiring RRT from the MIMIC-III database were analyzed. The models' discrimination was evaluated using c-statistics. Calibration was evaluated by Hosmer-Lemeshow (H-L) test and GiViTi calibration belt.ResultsIn a case-mix population, including patients with normal or altered serum creatinine (sCr) at intensive care unit admission, discrimination was moderate, with a c-statistic of 0.71 in the nonintegerized risk model. In patients with altered baseline sCr, better discrimination was achieved with the integer risk model (0.76, 95% confidence interval, 0.71-0.81). As for the calibration, although the H-L test was good only in patients with normal/slightly altered sCr at admission, the calibration belt disclosed no significant deviations from the bisector line for any of the models in patients, regardless of admission sCr. Of note, a refitted model had a c-statistics of 0.85, similar to the derivation cohort.ConclusionsThe ATN prognostic model can be useful in a broad cohort of critically ill patients. Although it showed only moderate discrimination capacity when patients with elevated admission sCr were included, using a refitted model improved it, illustrating the need for continuous external validation and updating of prognostic models over time before their implementation in clinical practice.

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