Objectives: To develop and evaluate a model that predicts mortality risk based on admission data for infants weighing 501 to 1500 grams at birth, and to use the model to identify neonatal ICUs where the observed mortality rate differs significantly from the predicted rate.
Design: Validation cohort study.
Setting: University-based, tertiary care neonatal ICUs.
Patients: Sample of 3,603 infants with birth weights of 501 to 1500 grams who were born at seven National Institute of Child Health and Human Development (NICHHD) Neonatal Research Network Centers, over a 2-yr period of time.
Measurements and main results: Based on logistic regression analysis, admission factors associated with mortality risk for inborn infants were: decreasing birth weight, appropriate size for gestational age, male gender, non-black race, and 1-min Apgar score of < or = 3. The mortality prediction model based on these factors had a sensitivity of 0.50, a specificity of 0.92, a correct classification rate of 0.82, and an area under the receiver operating characteristic curve of 0.82 when applied to a validation sample. Goodness-of-fit testing showed that there was a marginal degree of fit between the observations and model predictions (chi 2 = 15.4, p = .06). The observed mortality rate for 3,603 infants at the seven centers was 24.7%, ranging from 21.8% to 27.7% at individual centers. There were no statistically significant differences between observed and predicted mortality rates at any of the centers. One center had an observed mortality rate that was 2.8% lower than predicted by the model (95% confidence interval -6.0% to 0.5%), and another center had an observed rate that was 3% higher than expected (95% confidence interval -0.3% to 6.2%).
Conclusions: Mortality risk for infants weighing 501 to 1500 grams can be predicted based on admission factors. However, until more accurate predictive models are developed and validated and the relationships between care practices and outcomes are better understood, such models should not be relied on for evaluating the quality of care provided in different neonatal ICUs.