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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
Stocker,Martin Daunhawer,Imant Van Herk,Wendy El Helou,Salhab Dutta,Sourabh Schuerman,Frank A. B. A. Van Den Tooren-de Groot,Rita K. Wieringa,Jantien W. Janota,Jan Van Der Meer-Kappelle,Laura H.
Stocker,Martin
Daunhawer,Imant
Van Herk,Wendy
El Helou,Salhab
Dutta,Sourabh
Schuerman,Frank A. B. A.
Van Den Tooren-de Groot,Rita K.
Wieringa,Jantien W.
Janota,Jan
Van Der Meer-Kappelle,Laura H.
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.
Description
This study was supported by The Thrasher Foundation (9143) to [M.S.]; The NutsOhra Foundation (1101-059) to [A.M.C.v.R.]; The Sophia Foundation for Scientific research (681) to [W.v.H.]; and the Swiss National Science Foundation (200021_188466) to [I.D.].
Date
2022
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Research Projects
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Keywords
BIRTH, DURATION, INFECTION, NEWBORNS, TERM, THERAPY, antibiotic therapy, biomarkers, clinical signs, early-onset sepsis, risk factors
Citation
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 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, vol. 41, no. 3, pp. 248-254. https://doi.org/10.1097/INF.0000000000003344
License
info:eu-repo/semantics/openAccess