Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction
Author:
Palmowski LarsORCID, Nowak Hartmuth, Witowski Andrea, Koos Björn, Wolf Alexander, Weber Maike, Kleefisch Daniel, Unterberg Matthias, Haberl Helge, von Busch Alexander, Ertmer Christian, Zarbock Alexander, Bode Christian, Putensen Christian, Limper UlrichORCID, Wappler Frank, Köhler ThomasORCID, Henzler Dietrich, Oswald Daniel, Ellger Björn, Ehrentraut Stefan F., Bergmann Lars, Rump Katharina, Ziehe Dominik, Babel Nina, Sitek Barbara, Marcus Katrin, Frey Ulrich H., Thoral Patrick J., Adamzik Michael, Eisenacher Martin, Rahmel TimORCID,
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 < 0.01 and p < 0.01, respectively). Strikingly, all these findings could be confirmed in the independent external validation cohort.
Conclusions
The 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.
Funder
Faculty of Health at Witten/Herdecke University, Germany EFRE.NRW
Publisher
Public Library of Science (PLoS)
Cited by
2 articles.
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