Stocker, Martin and Daunhawer, Imant and van Herk, Wendy and el Helou, Salhab and Dutta, Sourabh and Schuerman, Frank A. B. A. and van den Tooren-de Groot, Rita K. and Wieringa, Jantien W. and Janota, Jan and van der Meer-Kappelle, Laura H. and Moonen, Rob and Sie, Sintha D. and de Vries, Esther and Donker, Albertine E. and Zimmerman, Urs and Schlapbach, Luregn J. and de Mol, Amerik C. and Hoffmann-Haringsma, Angelique and Roy, Madan and Tomaske, Maren and Kornelisse, Rene F. and van Gijsel, Juliette and Ploetz, Frans B. and Wellmann, Sven and Achten, Niek B. and Lehnick, Dirk and van Rossum, Annemarie M. C. and Vogt, Julia E. (2022) Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis. PEDIATRIC INFECTIOUS DISEASE JOURNAL, 41 (3). pp. 248-254. ISSN 0891-3668, 1532-0987
Full text not available from this repository. (Request a copy)Abstract
Background: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. Study Design: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. Results: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (+/- 8.8%) and an area-under-the-precision-recall-curve of 28.42% (+/- 11.5%). The predictive performance of the model with RFs alone was comparable with random. Conclusions: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | TERM; INFECTION; DURATION; NEWBORNS; THERAPY; BIRTH; early-onset sepsis; risk factors; clinical signs; biomarkers; antibiotic therapy |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Kinder- und Jugendmedizin |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 28 Feb 2024 13:52 |
| Last Modified: | 28 Feb 2024 13:52 |
| URI: | https://pred.uni-regensburg.de/id/eprint/58559 |
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