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.
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.
Authors: Shane M Tibby; Joanna Correa-West; Andrew Durward; Lesley Ferguson; Ian A Murdoch Journal: Intensive Care Med Date: 2004-04-06 Impact factor: 17.440
Authors: Martin C J Kneyber; Mohammed I Hersi; Jos W R Twisk; Dick G Markhorst; Frans B Plötz Journal: Intensive Care Med Date: 2007-06-16 Impact factor: 17.440