Combining various severity of illness scoring systems to improve outcome prediction: pilot experience in the critically ill obstetric population
© BioMed Central Ltd. 2007
Published: 22 March 2007
No perfect severity score exists to predict ICU mortality, thus the search for new systems is still a preoccupation.
Use of many severity of illness scores simultaneously improves mortality prediction.
Patients and methods
An open prospective observational study as part of the APRiMo project . The study period was January 1996–September 2004. Inclusion criteria were critically ill obstetric patients and ICU length of stay >24 hours. Exclusion criteria were those of the used scores. The main outcome of interest was the survival status at ICU discharge. The database was divided into two samples: development and validation datasets. Development database patients were chosen randomly (n = 414) and the remaining patients composed the validation dataset (n = 229). A multivariable logistic regression model was developed to predict mortality associating the Acute Physiology and Chronic Health Evaluation II score , Simplified Acute Physiology Score II , Admission Mortality Prediction Model (MPM-H0) and Day 1 Mortality Prediction Model (MPM-H24) . Discrimination and calibration were assessed by goodness-of-fit C-hat statistics and area under the ROC curve. The developed model was then tested in the validation dataset. Good discrimination was retained if C-hat statistics P > 0.1 and good calibration if area under the ROC curve > 0.8.
Six hundred and forty-three patients enrolled. The overall mortality rate was 11.51%. The new model predicted accurately 99% of survivors and more than 60% of nonsurvivors.
Hosmer–Lemeshow C-hat statistics test
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