Providers' compliance with practice guidelines of prenatal and neonatal care to reduce neonatal mortality: 2004 versus 2012

SNAPPE II: ANALYSIS OF ACCURACY AND DETERMINATION OF THE CUTOFF POINT AS A DEATH PREDICTOR IN A BRAZILIAN NEONATAL INTENSIVE CARE UNIT

OBJECTIVE

To analyze the accuracy of the Score for Neonatal Acute Physiology Perinatal Extension (SNAPPE II) as a death predictor, to determine the cutoff point for mortality, and to analyze the association of independent variables with death.

METHODS

Prospective, longitudinal, hospital-based study on newborns admitted to the Neonatal Intensive Care Unit (NICU) for the first time from November 1, 2016 to April 30, 2017. Newborns with less than 12 hours of length of stay at the NICU, out-of-hospital births, major congenital malformations, and inter-hospital transfer were excluded. Variables were grouped according to hierarchical framework, related to maternal characteristics (distal level), prenatal and childbirth care (intermediate level), and birth conditions (proximal level). Descriptive analyses of SNAPPE II score ranges, Receiver Operating Characteristics Curve (ROC curve) to define the cutoff point for mortality, and bivariate analysis by the Wald test and multiple logistic regression were conducted.

RESULTS

After selection, the sample consisted of 247 newborns. In this study, the SNAPPE II cutoff point for mortality was 27, with sensitivity of 84.1% and specificity of 82.4%. 61% of those with a score ≥27 died. Multiple logistic regression showed an association between death and proximal-level variables: sepsis (Odds Ratio [OR] 10.68; 95% confidence interval [95%CI] 2.82-40.48; p<0.001); SNAPPE II ≥27 (OR 5.85; 95%CI 1.90-18.05; p=0.002); birth weight 750-999 g (OR 4.15; 95%CI 1.06-16.14; p=0.040); and nonuse of surfactant (OR 0.159; 95%CI 0.04-0.53; p=0.003).

CONCLUSIONS

Neonatal mortality was directly proportional to increase in SNAPPE II. Score≥27 increased the odds of dying by six times compared with neonates with lower scores. The proximal variables related to health conditions and neonatal care were associated with death.

Cytokines in Preterm Delivery: Proposal of a New Diagnostic Algorithm

Predicting preterm delivery within 7 days is very important for the proper timing of glucocorticosteroid administration. If within 7 days after glucocorticosteroid administration, the delivery does not occur, it remains questionable if repeated glucocorticosteroid therapy results in improved infant respiratory function. Therefore, differentiation of preterm delivery from false preterm delivery is clinically significant. The aim of this study was to create a diagnostic algorithm to distinguish preterm delivery from false preterm delivery on the basis of concentrations of selected cytokines. The study group (n = 622) were patients hospitalized due to threatened preterm delivery. To assess the concentration of cytokines in the serum, we used a multiplex method, which allows simultaneous determination of 13 cytokines. The sets consist of the following cytokines: IGFBP-1, IGFBP-2, BDNF, L-Selectin, E-Selectin, ICAM-1, PECAM, VCAM-1, MIP-1d, MIP-3b, Eotaxin-1, Eotaxin-2, and BLC. In the study group, 67.8% patients had preterm delivery and 32.2% had false preterm delivery. Based on the analysis of cytokine concentrations, a classification tree to distinguish between preterm delivery and false preterm delivery was created. Our findings show the possibility of prediction of preterm delivery with the use of a classification and regression tree of selected cytokine concentration.