The Paediatric Index of Mortality (PIM) has been revised to reflect improvements in paediatric intensive care outcomes. This study involved 12 specialist paediatric intensive care units and two combined adult and paediatric units in Australia, New Zealand, and the UK. A total of 20,787 patient admissions of children under 16 years were included after excluding 220 patients transferred to other ICUs and one patient still in ICU. A revised model was developed using forward and backward logistic regression, with three variables derived from the main reason for ICU admission improving the model's fit across diagnostic groups. Data from seven units were used to create a learning model, which was tested on data from seven other units. The model fitted the test data well and discriminated between death and survival effectively. The final PIM2 model, derived from the entire sample, also performed well. PIM2 estimates mortality risk from data available at ICU admission and is suitable for continuous monitoring of paediatric intensive care quality. Models predicting mortality risk are important for comparing patient groups in research trials and for assessing the standard of care between units. PIM2 has been recalibrated to reflect improvements in intensive care outcomes. Keywords: Paediatric; Intensive care; Mortality prediction models; Outcome assessment; Logistic regression.The Paediatric Index of Mortality (PIM) has been revised to reflect improvements in paediatric intensive care outcomes. This study involved 12 specialist paediatric intensive care units and two combined adult and paediatric units in Australia, New Zealand, and the UK. A total of 20,787 patient admissions of children under 16 years were included after excluding 220 patients transferred to other ICUs and one patient still in ICU. A revised model was developed using forward and backward logistic regression, with three variables derived from the main reason for ICU admission improving the model's fit across diagnostic groups. Data from seven units were used to create a learning model, which was tested on data from seven other units. The model fitted the test data well and discriminated between death and survival effectively. The final PIM2 model, derived from the entire sample, also performed well. PIM2 estimates mortality risk from data available at ICU admission and is suitable for continuous monitoring of paediatric intensive care quality. Models predicting mortality risk are important for comparing patient groups in research trials and for assessing the standard of care between units. PIM2 has been recalibrated to reflect improvements in intensive care outcomes. Keywords: Paediatric; Intensive care; Mortality prediction models; Outcome assessment; Logistic regression.