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Predicting the short- and long-term outcomes of extremely preterm infants remains a challenge. Multivariable prognostic models might be valuable tools for clinicians, parents, and policymakers for providing accurate outcome estimates. In this perspective, we discuss the opportunities and challenges of using prognostic models in extremely preterm infants at population and individual levels. At a population level, these models could support the development of guidelines for decisions about treatment limits and may support policy processes such as benchmarking and resource allocation. At an individual level, these models may enhance prenatal counselling conversations by considering multiple variables and improving transparency about expected outcomes. Furthermore, they may improve consistency in projections shared with parents. For the development of prognostic models, we discuss important considerations such as predictor and outcome measure selection, clinical impact assessment, and generalizability. Lastly, future recommendations for developing and using prognostic models are suggested. Importantly, the purpose of a prognostic model should be clearly defined, and integrating these models into prenatal counselling requires thoughtful consideration.

A significant proportion of babies that are admitted to the neonatal intensive care unit (NICU) suffer from late onset sepsis (LOS). In order to prevent mortality and morbidity, the early detection of LOS is of the utmost importance. Recent works have found that the use of machine learning techniques might help detect LOS at an early stage. Some works have shown that linear methods (i.e., logistic regression) display a superior performance when predicting LOS. Nevertheless, as research on this topic is still in an early phase, it has not been ruled out that non-linear machine learning (ML) techniques can improve the predictive performance. Moreover, few studies have assessed the effect of parameters other than heart rate variability (HRV). Therefore, the current study investigates the effect of non-linear methods and assesses whether other vital parameters such as respiratory rate, perfusion index, and oxygen saturation could be of added value when predicting LOS. In contrast with the findings in the literature, it was found that non-linear methods showed a superior performance compared with linear models. In particular, it was found that random forest performed best (AUROC: 0.973), 24% better than logistic regression (AUROC: 0.782). Nevertheless, logistic regression was found to perform similarly to some non-linear models when trained with a short training window. Furthermore, when also taking training time into account, K-Nearest Neighbors was found to be the most beneficial (AUROC: 0.950). In line with the literature, we found that training the models on HRV features yielded the best results. Lastly, the results revealed that non-linear methods demonstrated a superior performance compared with linear methods when adding respiratory features to the HRV feature set, which ensured the greatest improvement in terms of AUROC score.

Background: Due to a lack of rapid, accurate diagnostic tools for early-onset neonatal sepsis (EOS) at the initial suspicion, infants are often unnecessarily given antibiotics directly after birth. We aimed to determine the diagnostic accuracy of presepsin for EOS before antibiotic initiation and to investigate whether presepsin can be used to guide clinicians’ decisions on whether to start antibiotics. Methods: In this multicenter prospective observational cohort study, all infants who started on antibiotics for EOS suspicion were consecutively included. Presepsin concentrations were determined in blood samples collected at the initial EOS suspicion (t = 0). In addition to this, samples were collected at 3, 6, 12 and 24 h after the initial EOS suspicion and from the umbilical cord directly after birth. The diagnostic accuracy of presepsin was calculated. Results: A total of 333 infants were included, of whom 169 were born preterm. We included 65 term and 15 preterm EOS cases. At the initial EOS suspicion, the area under the curve (AUC) was 0.60 (95% confidence interval (CI) 0.50–0.70) in the term-born infants compared to 0.84 (95% CI 0.73–0.95) in the preterm infants. A cut-off value of 645 pg/mL resulted in a sensitivity of 100% and a specificity of 54% in the preterm infants. The presepsin concentrations in cord blood and at other time points did not differ significantly from the concentrations at the initial EOS suspicion. Conclusions: Presepsin is a biomarker with an acceptable diagnostic accuracy for EOS (culture-proven and clinical EOS) in preterm infants and might be of value in reducing antibiotic exposure after birth when appended to current EOS guidelines. However, the small number of EOS cases prevents us from drawing firm conclusions. Further research should be performed to evaluate whether appending a presepsin-guided step to current EOS guidelines leads to a safe decrease in antibiotic overtreatment and antibiotic-related morbidity.