• Idse H E Visser, Jan A Hazelzet, Marcel J I J Albers, Carin W M Verlaat, Karin Hogenbirk, Job B van Woensel, Marc van Heerde, Dick A van Waardenburg, Nicolaas J G Jansen, and Ewout W Steyerberg.
• Department of Pediatrics, Erasmus MC, Sophia Children's Hospital, Rotterdam, The Netherlands. i.visser@erasmusmc.nl
• Intensive Care Med. 2013 May 1; 39 (5): 942-50.

AimTo validate paediatric index of mortality (PIM) and pediatric risk of mortality (PRISM) models within the overall population as well as in specific subgroups in pediatric intensive care units (PICUs).MethodsVariants of PIM and PRISM prediction models were compared with respect to calibration (agreement between predicted risks and observed mortality) and discrimination (area under the receiver operating characteristic curve, AUC). We considered performance in the overall study population and in subgroups, defined by diagnoses, age and urgency at admission, and length of stay (LoS) at the PICU. We analyzed data from consecutive patients younger than 16 years admitted to the eight PICUs in the Netherlands between February 2006 and October 2009. Patients referred to another ICU or deceased within 2 h after admission were excluded.ResultsA total of 12,040 admissions were included, with 412 deaths. Variants of PIM2 were best calibrated. All models discriminated well, also in patients <28 days of age (neonates), with overall higher AUC for PRISM variants (PIM = 0.83, PIM2 = 0.85, PIM2-ANZ06 = 0.86, PIM2-ANZ08 = 0.85, PRISM = 0.88, PRISM3-24 = 0.90). Best discrimination for PRISM3-24 was confirmed in 13 out of 14 subgroup categories. After recalibration PRISM3-24 predicted accurately in most (12 out of 14) categories. Discrimination was poorer for all models (AUC < 0.73) after LoS of >6 days at the PICU.ConclusionAll models discriminated well, also in most subgroups including neonates, but had difficulties predicting mortality for patients >6 days at the PICU. In a western European setting both the PIM2(-ANZ06) or a recalibrated version of PRISM3-24 are suited for overall individualized risk prediction.

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