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
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...
2024: PloS One