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Paediatric index of mortality (PIM): a mortality prediction model for children in intensive care
1Intensive Care Unit, Royal Children's Hospital, Parkville, Victoria, Australia.
Insights
A new Pediatric Index of Mortality (PIM) model accurately predicts death risk in children under 16 admitted to intensive care. This simple, eight-variable model uses admission data for reliable risk assessment in pediatric intensive care units.
Area of Science:
- Pediatric Critical Care Medicine
- Biostatistics
- Health Services Research
Background:
- Accurate prediction of mortality risk in pediatric intensive care units (PICUs) is crucial for resource allocation and quality assessment.
- Existing models may not adequately capture the risk of death for critically ill children based on admission data.
Purpose of the Study:
- To develop and validate a logistic regression model to predict the risk of mortality in children under 16 years of age admitted to intensive care.
- To identify key variables, collected at admission, that are predictive of death in this population.
Main Methods:
- Development of a predictive model using data from three prospective cohort studies (1988-1995).
- Inclusion of ten potential variables for model refinement.
- Validation using a fourth prospective cohort study (1994-1996) involving 5695 children across multiple PICUs in Australia and Britain.
Main Results:
- A logistic regression model incorporating eight variables was developed and tested.
- The final Pediatric Index of Mortality (PIM) model, utilizing data from 5695 children, demonstrated excellent goodness-of-fit (p = 0.37) and discrimination (area under ROC curve = 0.90).
- The model accurately predicted mortality risk across different pediatric intensive care units.
Conclusions:
- The PIM model is a simple, accurate tool for assessing mortality risk in pediatric intensive care admissions.
- It is based on eight key variables readily available at the time of admission.
- The PIM model is suitable for describing mortality risk in groups of children and should not be used to compare individual unit performance based on raw scores.
Objective:
To develop a logistic regression model that predicts the risk of death for children less than 16 years of age in intensive care, using information collected at the time of admission to the unit.
Design:
Three prospective cohort studies, from 1988 to 1995, were used to determine the variables for the final model. A fourth cohort study, from 1994 to 1996, collected information from consecutive admissions to all seven dedicated paediatric intensive care units in Australia and one in Britain.
Results:
2904 patients were included in the first three parts of the study, which identified ten variables for further evaluation. 5695 children were in the fourth part of the study (including 1412 from the third part); a model that used eight variables was developed on data from four of the units and tested on data from the other four units. The model fitted the test data well (deciles of risk goodness-of-fit test p = 0.40) and discriminated well between death and survival (area under the receiver operating characteristic plot 0.90). The final PIM model used the data from all 5695 children and also fitted well (p = 0.37) and discriminated well (area 0.90).
Conclusions:
Scores that use the worst value of their predictor variables in the first 12-24 h should not be used to compare different units: patients mismanaged in a bad unit will have higher scores than similar patients managed in a good unit, and the bad unit's high mortality rate will be incorrectly attributed to its having sicker patients. PIM is a simple model that is based on only eight explanatory variables collected at the time of admission to intensive care. It is accurate enough to be used to describe the risk of mortality in groups of children.
