PIM2: a revised version of the Paediatric Index of Mortality
- 2.1k Downloads
To revise the Paediatric Index of Mortality (PIM) to adjust for improvement in the outcome of paediatric intensive care.
International, multi-centre, prospective, observational study.
Twelve specialist paediatric intensive care units and two combined adult and paediatric units in Australia, New Zealand and the United Kingdom.
All children admitted during the study period. In the analysis, 20,787 patient admissions of children less than 16 years were included after 220 patients transferred to other ICUs and one patient still in ICU had been excluded.
Measurements and results
A revised model was developed by forward and backward logistic regression. Variable selection was based on the effect of including or dropping variables on discrimination and fit. The addition of three variables, all derived from the main reason for ICU admission, improved the fit across diagnostic groups. Data from seven units were used to derive a learning model that was tested using data from seven other units. The model fitted the test data well (deciles of risk goodness-of-fit χ2 8.14, p=0.42) and discriminated between death and survival well [area under the receiver operating characteristic (ROC) plot 0.90 (0.89–0.92)]. The final PIM2 model, derived from the entire sample of 19,638 survivors and 1,104 children who died, also fitted and discriminated well [χ2 11.56, p=0.17; area 0.90 (0.89–0.91)].
PIM2 has been re-calibrated to reflect the improvement that has occurred in intensive care outcome. PIM2 estimates mortality risk from data readily available at the time of ICU admission and is therefore suitable for continuous monitoring of the quality of paediatric intensive care.
KeywordsPaediatric Intensive care Mortality prediction models Outcome assessment Logistic regression
We thank the many nurses, doctors, research officers and secretaries who collected, entered and cleaned the data. We thank John Carlin for statistical advice and we gratefully acknowledge the support provided by the Australian and New Zealand Intensive Care Society.
- 9.Copas JB (1983) Plotting p against x. Appl Stat 32:25–31Google Scholar
- 11.Hosmer DW, Lemeshow S (2000) Applied logistic regression. John Wiley, New YorkGoogle Scholar
- 12.Collett D (1991) Modelling Binary Data. Chapman & Hall, LondonGoogle Scholar
- 13.Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i:307–310Google Scholar