Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction

Lars Palmowski, Hartmuth Nowak, Andrea Witowski, Björn Koos, Alexander Wolf, Maike Weber, Daniel Kleefisch, Matthias Unterberg, Helge Haberl, Alexander von Busch, Christian Ertmer, Alexander Zarbock, Christian Bode, Christian Putensen, Ulrich Limper, Frank Wappler, Thomas Köhler, Dietrich Henzler, Daniel Oswald, Björn Ellger, Stefan F Ehrentraut, Lars Bergmann, Katharina Rump, Dominik Ziehe, Nina Babel, Barbara Sitek, Katrin Marcus, Ulrich H Frey, Patrick J Thoral, Michael Adamzik, Martin Eisenacher, Tim Rahmel and on behalf of the SepsisDataNet.NRW research Group

PLOS ONE, 2024, vol. 19, issue 3, 1-18

Abstract: Introduction: An increasing amount of longitudinal health data is available on critically ill septic patients in the age of digital medicine, including daily sequential organ failure assessment (SOFA) score measurements. Thus, the assessment in sepsis focuses increasingly on the evaluation of the individual disease’s trajectory. Machine learning (ML) algorithms may provide a promising approach here to improve the evaluation of daily SOFA score dynamics. We tested whether ML algorithms can outperform the conventional ΔSOFA score regarding the accuracy of 30-day mortality prediction. Methods: We used the multicentric SepsisDataNet.NRW study cohort that prospectively enrolled 252 sepsis patients between 03/2018 and 09/2019 for training ML algorithms, i.e. support vector machine (SVM) with polynomial kernel and artificial neural network (aNN). We used the Amsterdam UMC database covering 1,790 sepsis patients for external and independent validation. Results: Both SVM (AUC 0.84; 95% CI: 0.71–0.96) and aNN (AUC 0.82; 95% CI: 0.69–0.95) assessing the SOFA scores of the first seven days led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score between day 1 and 7 (AUC 0.73; 95% CI: 0.65–0.80; p = 0.02 and p = 0.05, respectively). These differences were even more prominent the shorter the time interval considered. Using the SOFA scores of day 1 to 3 SVM (AUC 0.82; 95% CI: 0.68 0.95) and aNN (AUC 0.80; 95% CI: 0.660.93) led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score (AUC 0.66; 95% CI: 0.58–0.74; p

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0300739

DOI: 10.1371/journal.pone.0300739

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