Observational studyPloS one2023
Performance of bedside tools for predicting infection-related mortality and administrative data for sepsis surveillance: An observational cohort study.
Observational study in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Digital innovation in healthcare: quantifying the impact of digital sepsis screening tools on patient outcomes-a multi-site natural experiment.BMJ health & care informatics · 2025Article
- A Practical Nomogram Based on RDW-CV for Predicting Clinical Outcome in Elderly Septic Patients.Infection and drug resistance · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMeasuring sepsis incidence and associated mortality at scale using administrative data is hampered by variation in diagnostic coding. This study aimed first to compare how well bedside severity scores predict 30-day mortality in hospitalised patients with infection, then to assess the ability of combinations of administrative data items to identify patients with sepsis.
methodsThis retrospective case note review examined 958 adult hospital admissions between October 2015 and March 2016. Admissions with blood culture sampling were matched 1:1 to admissions without a blood culture. Case note review data were linked to discharge coding and mortality. For patients with infection the performance characteristics of Sequential Organ Failure Assessment (SOFA), National Early Warning System (NEWS), quick SOFA (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS) were calculated for predicting 30-day mortality. Next, the performance characteristics of administrative data (blood cultures and discharge codes) for identifying patients with sepsis, defined as SOFA ≥2 because of infection, were calculated.
resultsInfection was documented in 630 (65.8%) admissions and 347 (55.1%) patients with infection had sepsis. NEWS (Area Under the Receiver Operating Characteristic, AUROC 0.78 95%CI 0.72-0.83) and SOFA (AUROC 0.77, 95%CI 0.72-0.83), performed similarly well for prediction of 30-day mortality. Having an infection and/or sepsis International Classification of Diseases, Tenth Revision (ICD-10) code (AUROC 0.68, 95%CI 0.64-0.71) performed as well in identifying patients with sepsis as having at least one of: an infection code; sepsis code, or; blood culture (AUROC 0.68, 95%CI 0.65-0.71), Sepsis codes (AUROC 0.53, 95%CI 0.49-0.57) and positive blood cultures (AUROC 0.52, 95%CI 0.49-0.56) performed least well.
conclusionsSOFA and NEWS best predicted 30-day mortality in patients with infection. Sepsis ICD-10 codes lack sensitivity. For health systems without suitable electronic health records, blood culture sampling has potential utility as a clinical component of a proxy marker for sepsis surveillance.
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