PLoS ONE (Jan 2024)

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,
  • SepsisDataNet.NRW research group

DOI
https://doi.org/10.1371/journal.pone.0300739
Journal volume & issue
Vol. 19, no. 3
p. e0300739

Abstract

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IntroductionAn 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.MethodsWe 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.ResultsBoth 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 ConclusionsThe ML-based algorithms using daily SOFA scores markedly improved the accuracy of mortality compared to the conventional ΔSOFA score. Therefore, this approach could provide a promising and automated approach to assess the individual disease trajectory in sepsis. These findings reflect the potential of incorporating ML algorithms as robust and generalizable support tools on intensive care units.