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Stocker M, Daunhawer I, van Herk W, et al. 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. Pediatr Infect Dis J. 2021;doi: 10.1097/INF.0000000000003344.
Stocker, M., Daunhawer, I., van Herk, W., El Helou, S., Dutta, S., Schuerman, F. A. B. A., van den Tooren-de Groot, R. K., Wieringa, J. W., Janota, J., van der Meer-Kappelle, L. H., Moonen, R., Sie, S. D., de Vries, E., Donker, A. E., Zimmerman, U., Schlapbach, L. J., de Mol, A. C., Hoffmann-Haringsma, A., Roy, M., Tomaske, M., Kornelisse, R. F., van Gijsel, J., Plötz, F. B., Wellmann, S., Achten, N. B., Lehnick, D., van Rossum, A. M. C., & Vogt, J. E. (2021). 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. The Pediatric infectious disease journal, . https://doi.org/10.1097/INF.0000000000003344
Stocker, Martin, et al. "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." The Pediatric infectious disease journal vol. (2021). doi: https://doi.org/10.1097/INF.0000000000003344
Stocker M, Daunhawer I, van Herk W, El Helou S, Dutta S, Schuerman FABA, van den Tooren-de Groot RK, Wieringa JW, Janota J, van der Meer-Kappelle LH, Moonen R, Sie SD, de Vries E, Donker AE, Zimmerman U, Schlapbach LJ, de Mol AC, Hoffmann-Haringsma A, Roy M, Tomaske M, Kornelisse RF, van Gijsel J, Plötz FB, Wellmann S, Achten NB, Lehnick D, van Rossum AMC, Vogt JE. 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. Pediatr Infect Dis J. 2021 Sep 09; doi: 10.1097/INF.0000000000003344. Epub 2021 Sep 09. PMID: 34508027.
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Pediatr Infect Dis J. 2021 Sep 09; doi: 10.1097/INF.0000000000003344. Epub 2021 Sep 09.

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.

The Pediatric infectious disease journal

Martin Stocker, Imant Daunhawer, Wendy van Herk, Salhab El Helou, Sourabh Dutta, Frank A B A Schuerman, Rita K van den Tooren-de Groot, Jantien W Wieringa, Jan Janota, Laura H van der Meer-Kappelle, Rob Moonen, Sintha D Sie, Esther de Vries, Albertine E Donker, Urs Zimmerman, Luregn J Schlapbach, Amerik C de Mol, Angelique Hoffmann-Haringsma, Madan Roy, Maren Tomaske, René F Kornelisse, Juliette van Gijsel, Frans B Plötz, Sven Wellmann, Niek B Achten, Dirk Lehnick, Annemarie M C van Rossum, Julia E Vogt

Affiliations

  1. From the Department of Paediatrics, Neonatal and Paediatric Intensive Care Unit, Children's Hospital Lucerne, Lucerne; Department of Computer Science, ETH Zurich, Switzerland; Department of Paediatrics, Division of Paediatric Infectious Diseases and Immunology, Erasmus MC University Medical Centre-Sophia Children's Hospital, Rotterdam, The Netherlands; Division of Neonatology, McMaster University Children's Hospital, Hamilton Health Sciences, Hamilton, ON, Canada; Department of Neonatal Intensive Care Unit, Isala Women and Children's Hospital, Zwolle; Department of Paediatrics, Haaglanden Medical Centre, 's Gravenhage, The Netherlands; Department of Obstetrics and Gynecology, Motol University Hospital, Second Medical Faculty, Prague, Czech Republic; Department of Neonatology, Reinier de Graaf Gasthuis, Delft; Department of Neonatology, Zuyderland Medical Centre, Heerlen; Department of Neonatology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam; Department of Jeroen Bosch Academy Research, Jeroen Bosch Hospital, 's-Hertogenbosch; Department of Tranzo, Tilburg University, Tilburg; Department of Paediatrics, Maxima Medical Centre, Veldhoven, The Netherlands; Department of Paediatrics, Kantonsspital Winterthur, Winterthur; Neonatal and Pediatric Intensive Care Unit, Children`s Research Center, University Children's Hospital Zurich, Zurich, Switzerland; Department of Neonatology, Albert Schweitzer Hospital, Dordrecht; Department of Neonatology, Sint Franciscus Gasthuis, Rotterdam, The Netherlands; Department of Neonatology, St. Josephs Healthcare, Hamilton Health Sciences, Hamilton, ON, Canada; Department of Paediatrics, Stadtspital Triemli, Zürich, Switzerland; Department of Paediatrics, Division of Neonatology, Erasmus MC University Medical Centre-Sophia Children's Hospital, Rotterdam; Therapeuticum Utrecht, Utrecht, Department of Pediatrics, Tergooi Hospital, Blaricum, the Netherlands and Amsterdam University Medical Center, Department of Pediatrics, Amsterdam, The Netherlands; Department of Neonatology, University Children's Hospital Regensburg (KUNO), University of Regensburg, Regensburg, Germany; and Department of Health Sciences and Medicine, Head Biostatistics and Methodology, University of Lucerne, Lucerne, Switzerland.

PMID: 34508027 DOI: 10.1097/INF.0000000000003344

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.

Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

Conflict of interest statement

The authors have no conflicts of interest to disclose.

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