ArticleEpidemiology and infection2023
A diagnostic model based on routine blood examination for serious bacterial infections in neonates-a cross-sectional study.
Article in Epidemiology and infection, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predictive role of systemic immune-inflammation index (SII) in tuberculosis infection morbidity and ICU tuberculosis patient mortality: an observational study.Journal of thoracic disease · 2026Article
- Recent advancements in pediatric purulent meningitis: diagnosis and treatment.Italian journal of pediatrics · 2026Review
- Machine learning models based on laboratory data: new insight into the differential diagnosis of tuberculous and viral meningitis.Frontiers in cellular and infection microbiology · 2026Article
- Accuracy of Systemic Inflammatory Indices in Identifying Neonatal Sepsis in Preterms in a Tertiary Care Hospital in Mangalore.F1000Research · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
Routine blood examination is an easy way to examine infectious diseases. This study is aimed to develop a model to diagnose serious bacterial infections (SBI) in ICU neonates based on routine blood parameters. This was a cross-sectional study, and data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III). SBI was defined as suffering from one of the following: pyelonephritis, bacteraemia, bacterial meningitis, sepsis, pneumonia, cellulitis, and osteomyelitis. Variables with statistical significance in the univariate logistic regression analysis and log systemic immune-inflammatory index (SII) were used to develop the model. The area under the curve (AUC) was calculated to assess the performance of the model. A total of 1,880 participants were finally included for analysis. Weight, haemoglobin, mean corpuscular volume, white blood cell, monocyte, premature delivery, and log SII were selected to develop the model. The developed model showed a good performance to diagnose SBI for ICU neonates, with an AUC of 0.812 (95% confidence interval (CI): 0.737-0.888). A nomogram was developed to make this model visualise. In conclusion, our model based on routine blood parameters performed well in the diagnosis of neonatal SBI, which may be helpful for clinicians to improve treatment recommendations.
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