Learned sepsis score tracks hourly severity without hourly outcome labels

Across two retrospective hospital cohorts, the model produced a continuous score that separated survivors from non-survivors and tracked changes in clinical markers.

Independent
Kevin Zhu · Ryan Zhang · Baraa Abed · Tilendra Choudhary · Malvern Madondo · Mehak Arora · +15 more
Research Digest··2 min read

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A sepsis severity score learned from EHR data using mortality as a ranking signal provides continuous hourly prognostic information.

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Developed a continuous sepsis severity index using 43 routinely charted variables over a 72-hour treatment window from two hospital systems (total 36,807 patients). Unlike previous work, they used mortality as a treatment-level ranking signal rather than a per-state target, allowing non-uniform credit redistribution across timesteps.

Non-survivors scored 1.19-1.64 points higher on a 0-10 scale within all SOFA-2 strata. Within-patient changes correlated with lactate (Spearman rho=0.39) and more weakly with MAP and creatinine. Cross-institutional agreement was 70-77% of same-site correlation.

The index provides hourly prognostic information that separates patient outcomes and aligns with clinical expectations, suggesting potential as a decision support tool complementing clinical judgment.

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