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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
M. Stocker, I. Daunhawer, W. van Herk, S. El Helou, S. Dutta, FABA. Schuerman, RK. van den Tooren-de Groot, JW. Wieringa, J. Janota, LH. van der Meer-Kappelle, R. Moonen, SD. Sie, E. de Vries, AE. Donker, U. Zimmerman, LJ. Schlapbach, AC. de Mol,...
Language English Country United States
Document type Journal Article
- MeSH
- Anti-Bacterial Agents therapeutic use MeSH
- Biomarkers blood MeSH
- C-Reactive Protein analysis MeSH
- Infant MeSH
- Humans MeSH
- Infant, Newborn MeSH
- Neonatal Sepsis diagnosis drug therapy MeSH
- Prospective Studies MeSH
- Risk Factors MeSH
- ROC Curve MeSH
- Machine Learning * MeSH
- Check Tag
- Infant MeSH
- Humans MeSH
- Male MeSH
- Infant, Newborn MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
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.
Department of Computer Science ETH Zurich Switzerland
Department of Jeroen Bosch Academy Research Jeroen Bosch Hospital 's Hertogenbosch
Department of Neonatal Intensive Care Unit Isala Women and Children's Hospital Zwolle
Department of Neonatology Albert Schweitzer Hospital Dordrecht
Department of Neonatology Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam
Department of Neonatology Reinier de Graaf Gasthuis Delft
Department of Neonatology Sint Franciscus Gasthuis Rotterdam The Netherlands
Department of Neonatology St Josephs Healthcare Hamilton Health Sciences Hamilton ON Canada
Department of Neonatology Zuyderland Medical Centre Heerlen
Department of Paediatrics Haaglanden Medical Centre 's Gravenhage The Netherlands
Department of Paediatrics Kantonsspital Winterthur Winterthur
Department of Paediatrics Maxima Medical Centre Veldhoven The Netherlands
Department of Paediatrics Stadtspital Triemli Zürich Switzerland
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- $a Stocker, Martin $u From the Department of Paediatrics, Neonatal and Paediatric Intensive Care Unit, Children's Hospital Lucerne, Lucerne
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- $a 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 / $c M. Stocker, I. Daunhawer, W. van Herk, S. El Helou, S. Dutta, FABA. Schuerman, RK. van den Tooren-de Groot, JW. Wieringa, J. Janota, LH. van der Meer-Kappelle, R. Moonen, SD. Sie, E. de Vries, AE. Donker, U. Zimmerman, LJ. Schlapbach, AC. de Mol, A. Hoffmann-Haringsma, M. Roy, M. Tomaske, RF. Kornelisse, J. van Gijsel, FB. Plötz, S. Wellmann, NB. Achten, D. Lehnick, AMC. van Rossum, JE. Vogt
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- $a 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.
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