ArticleBMC emergency medicine2026
Serial assessment of serum HMGB1 and ΔSOFA for predicting 28-day mortality in sepsis patients: a prospective cohort study in the emergency department.
Article in BMC emergency medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundPrognostic assessment in sepsis involves evaluating the dynamic progression of organ dysfunction and the inflammatory response. This study compared the predictive value of serially measured serum HMGB1, a key late mediator of sepsis, with the ΔSOFA score for 28-day mortality.
methodsThis prospective cohort study enrolled 250 sepsis patients admitted to the emergency department of a tertiary hospital from January 2022 to August 2024. Serum HMGB1 levels and SOFA scores were dynamically assessed on Days 1, 4, and 7. Receiver operating characteristic (ROC) curve analysis and Kaplan-Meier survival analysis were utilized to compare their prognostic performance. The primary endpoint was 28-day all-cause mortality.
resultsA total of 232 patients (median age 71.5 years, 56.0% male) with a 28-day mortality rate of 13.8% (32/232) were included. Serum HMGB1 levels peaked on Day 4 and were significantly higher in non-survivors and septic shock patients (p < 0.05). The area under the ROC curve (AUC) for predicting 28-day mortality was 0.858 (95% CI: 0.789–0.925) for D4-HMGB1 and 0.893 (95% CI: 0.830–0.935) for D7-ΔSOFA. The AUC for D4-HMGB1 was not significantly different from that of D7-ΔSOFA (0.858 vs. 0.893, p = 0.29), but was significantly higher than that of D1-HMGB1 (0.858 vs. 0.699, p = 0.04) and D4-ΔSOFA (0.858 vs. 0.767, p = 0.01). The AUC was enhanced when HMGB1 was combined with SOFA scores, with the combination yielding higher AUC values (D7-HMGB1 + D7-SOFA: AUC = 0.898, 95% CI: 0.829–0.977, p = 0.09; D4-HMGB1 + D4-SOFA: AUC = 0.877, 95% CI: 0.792–0.928, p = 0.90) compared to the individual components at respective time points, but these increases were not statistically significant. The optimal cut-off value for D4-HMGB1 was 6.4 ng/mL. Kaplan-Meier analysis showed that patients with D4-HMGB1 ≥ 6.4 ng/mL had significantly higher mortality (log-rank test, p = 0.001). This prognostic performance remained consistent across key patient subgroups, including those with acute kidney injury, autoimmune diseases, or respiratory comorbidities.
conclusionDynamic monitoring of serum HMGB1 levels in sepsis patients for 28-day mortality provides potential prognostic information. Specifically, D4-HMGB1 levels showed similar predictive performance for 28-day mortality to the D7-ΔSOFA score, highlighting its potential as an earlier warning tool in the emergency department. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.