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Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study

Research Medical/Healthcare AI

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TL;DR - This two-site retrospective study learns a continuous, hourly sepsis severity score from patient trajectories using mortality as a treatment-level ranking signal rather than requiring per-hour labels. The resulting index tracks outcomes and clinically relevant changes, suggesting potential as a decision-support complement to clinician judgment.

  • The model uses 43 routinely charted variables over a 72-hour treatment window across cohorts of 29,116 and 7,691 adults meeting Sepsis-3 criteria.
  • Within every baseline SOFA-2 stratum, non-survivors scored 1.19–1.64 points higher than survivors on the 0–10 scale; stratification by lactate, mean arterial pressure, and creatinine yielded similar separation.
  • Within-patient score changes correlated most strongly with lactate changes (Spearman ρ = 0.39; n = 1,854), with weaker correlations for mean arterial pressure and creatinine.
  • Models trained at different institutions achieved 70–77% of same-site cohort-level correlation, while external within-patient correlations reached 0.54 and 0.59 against estimated ceilings of 0.92 and 0.90.

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Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study

arXiv cs.AI Kevin Zhu, Ryan Zhang, Baraa Abed, Tilendra Choudhary, Malvern Madondo, Mehak Arora, Yixuan Yang, Alasdair Gent, Aditya Nagori, Omer T. Inan, Krista L. Haines, Patrick Georgoff, Suresh M. Agarwal, Vijay Krishnamoorthy, Tetsu Ohnuma, Mihai V. Podgoreanu, Michael R. Pinsky, Gilles Clermont, Craig M. Coopersmith, Craig S. Jabaley, Rishikesan Kamaleswaran 2026-08-27 arXiv:2608.27421
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-21 14:30:15.610027 UTC

TL;DR - This two-site retrospective study learns a continuous, hourly sepsis severity score from patient trajectories using mortality as a treatment-level ranking signal rather than requiring per-hour labels. The resulting index tracks outcomes and clinically relevant changes, suggesting potential as a decision-support complement to clinician judgment.

  • The model uses 43 routinely charted variables over a 72-hour treatment window across cohorts of 29,116 and 7,691 adults meeting Sepsis-3 criteria.
  • Within every baseline SOFA-2 stratum, non-survivors scored 1.19–1.64 points higher than survivors on the 0–10 scale; stratification by lactate, mean arterial pressure, and creatinine yielded similar separation.
  • Within-patient score changes correlated most strongly with lactate changes (Spearman ρ = 0.39; n = 1,854), with weaker correlations for mean arterial pressure and creatinine.
  • Models trained at different institutions achieved 70–77% of same-site cohort-level correlation, while external within-patient correlations reached 0.54 and 0.59 against estimated ceilings of 0.92 and 0.90.
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