ArticlePloS one2024
Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 18 citations in OpenAlex.
- Artificial Intelligence Models for Mortality and Outcome Prediction in Intensive Care Unit Sepsis: A Systematic Review.Journal of personalized medicine · 2026Review
- Association between red cell distribution width-to-albumin ratio and sepsis-associated acute kidney injury.BMC nephrology · 2026Article
- Evaluation of 6-hour urine creatinine clearance as the kidney component in the SOFA score: An observational cohort study.Journal of intensive medicine · 2026Article
- The vasoactive-age adjusted sepsis-induced coagulopathy score predicts 28-day new-onset multiple organ dysfunction syndrome in patients with sepsis: a single-centre retrospective cohort study.Frontiers in medicine · 2026Article
- The international, prospective CytOSorbJournal of intensive medicine · 2025Article
- Evaluation of the C-reactive protein-albumin-lymphocyte (CALLY) index as a prognostic marker in patients with sepsis.BMC emergency medicine · 2025Article
- Long non-coding RNA EPB41L4A-AS1 as a biomarker of sepsis alleviates inflammatory response by targeting miR-146a-5p.European journal of medical research · 2025Article
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- Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.BMC medical informatics and decision making · 2024Article
- High expression of L-GILZ transcript variant 1 (GILZ TV 1) is associated with increased 30-day sepsis mortality, and a high expression ratio possibly contraindicates hydrocortisone administration.Critical care (London, England) · 2024Article
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Authors and funding
33 authors at 9 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
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 < 0.01 and p < 0.01, respectively). Strikingly, all these findings could be confirmed in the independent external validation cohort.
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.
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