Abstract
ObjectivePredictive models for preterm infant mortality have been developed internationally, albeit not valid for all populations. This study aimed to develop and validate different mortality predictive models, using Spanish data, to be applicable to centers with similar morbidity and mortality.MethodsInfants born alive, admitted to NICU (BW<1500 g or GA<30 w), and registered in the SEN1500 database, were included. There were two time periods; development of the predictive models (2009–2012) and validation (2013–2015). Three models were produced; prenatal (1), first 24 hours of life (2), and whilst admitted (3). For the statistical analysis, hospital mortality was the dependent variable. Significant variables were used in multivariable regression models. Specificity, sensitivity, accuracy, and area under the curve (AUC), for all models, were calculated.ResultsOut of 14953 included newborns, 2015 died; 373 (18.5%) in their first 24 hours, 1315 (65.3%) during the first month, and 327 (16.2%) thereafter, before discharge. In the development stage, mortality prediction AUC was 0.834 (95% CI: 0.822–0.846) (p<0.001) in model 1 and 0.872 (95% CI: 0.860–0.884) (p<0.001) in model 2. Model 3’s AUC was 0.989 (95% CI: 0.983–0.996) (p<0.001) and 0.942 (95% CI: 0.929–0.956) (p<0.001) during the 0–30 and >30 days of life, respectively. During validation, models 1 and 2 showed moderate concordance, whilst that of model 3 was good.ConclusionUsing dynamic models to predict individual mortality can improve outcome estimations. Development of models in the prenatal period, first 24 hours, and during hospital admission, cover key stages of mortality prediction in preterm infants.
Highlights
Preterm birth is the main cause of perinatal mortality and accounts for more than 50% of child disability [1]
Mortality prediction area under the curve (AUC) was 0.834 (p
Preterm infants (gestational age (GA) under 28 weeks’ gestation), and especially those infants born at the limits of viability (GA between 22 and 25 weeks’ gestation), are the most vulnerable infants in neonatal intensive care units (NICUs), with high morbidity and mortality rates
Summary
Preterm birth is the main cause of perinatal mortality and accounts for more than 50% of child disability [1]. Predictive models estimate the probability, or risk, of a certain condition occurring in an individual after the combination of different prognostic factors [2]. They are of utmost importance for complex medical situations with high mortality rates. As reviewed by Medlock et al [3], at least fifty predictive models for preterm infant mortality have been developed. Some, such as the CRIB score [4] or the NICHD model [5], are widely used in neonatal units throughout the world. About ten models were developed in the pre-surfactant era [3,4,6] and very few have been externally validated in more than one study [4,5,7]
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