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Updated: Oct 8, 2025
A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis
Published on: August 12, 2020
Using Classification Tree Analysis to Predict the Type of Infection in Preterm Neonates: Proof of Concept Study
Şerife Kurul1, Sinno H P Simons1, Christian R B Ramakers2
1Department of Pediatrics, Division Neonatology, Erasmus MC, University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands.
Background:
Late-onset neonatal sepsis is a major complication in preterm neonates. Early identification of the type of infection could help to improve therapy and outcome depending on the suspected microorganism by tailoring antibiotic treatment to the individual patient based on the predicted organism. Results of blood cultures may take up to 2 days or may remain negative in case of clinical sepsis. Chemical biomarkers may show different patterns in response to different type of microorganisms.
Objective:
The aim of this study was to develop, as a proof of concept, a simple classification tree algorithm using readily available information from biomarkers to show that biomarkers can potentially be used in discriminating in the type of infection in preterm neonates suspected of late-onset neonatal sepsis.
Derivation Cohort:
A total of 509 suspected late-onset neonatal sepsis episodes in neonates born before less than 32 weeks of gestation were analyzed. To examine model performance, 70% of the original dataset was randomly selected as a derivation cohort (n = 356; training dataset).
Validation Cohort:
The remaining 30% of the original dataset was used as a validation cohort (n = 153; test dataset).
Prediction Model:
A classification tree prediction algorithm was applied to predict type of infection (defined as no/Gram-positive/Gram-negative sepsis).
Results:
Suspected late-onset neonatal sepsis episodes were classified as no sepsis (80.8% [n = 411]), Gram-positive sepsis (13.9% [n = 71]), and Gram-negative sepsis (5.3% [n = 27]). When the derived classification tree was applied to the test cohort, the overall accuracy was 87.6% (95% CI, 81.3-92.4; p = 0.008). The classification tree demonstrates that interleukin-6 is the most important differentiating biomarker and C-reactive protein and procalcitonin help to further differentiate.
Conclusion:
We have developed and internally validated a simple, clinically relevant model to discriminate patients with different types of infection at moment of onset. Further research is needed to prospectively validate this in a larger population and assess whether adaptive antibiotic regimens are feasible.
