Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction.
Lars PalmowskiHartmuth NowakAndrea WitowskiBjörn KoosAlexander WolfMaike WeberDaniel KleefischMatthias UnterbergHelge HaberlAlexander von BuschChristian ErtmerAlexander ZarbockChristian BodeChristian PutensenUlrich LimperFrank WapplerThomas KöhlerDietrich HenzlerDaniel OswaldBjörn EllgerStefan F EhrentrautLars BergmannKatharina RumpDominik ZieheNina BabelBarbara SitekKatrin MarcusUlrich H FreyPatrick J ThoralMichael AdamzikMartin EisenacherTim Rahmelnull nullPublished in: PloS one (2024)
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.