Related Experiment Video
Updated: Feb 14, 2026

In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Estimating attributable fraction of mortality from sepsis to inform clinical trials
Manu Shankar-Hari1, David A Harrison2, Kathryn M Rowan2
1Guy's and St Thomas' NHS Foundation Trust, ICU Support Offices, St Thomas' Hospital, 1st Floor, East Wing, SE1 7EH, UK; Division of Infection, Immunity and Inflammation, Kings College London, SE1 9RT, UK; Intensive Care National Audit & Research Centre, Napier House, 24 High Holborn, London, WC1V 6AZ, UK.
Purpose:
Nearly all sepsis trials report no statistically significant difference in mortality. The attributable fraction of deaths due to sepsis (AFsepsis) may be an important, yet overlooked consideration. We derived AFsepsis and explored the effect of incorporating AFsepsis into sample size calculations.
Materials And Methods:
We derived AFsepsis with a matched cohort study using consecutive admissions to adult general intensive care units (ICUs) in England (n = 614,509). Cases were ICU patients with sepsis and the two controls were ICU-non-sepsis controls, matched for propensity to have sepsis and age-sex-matched general population. The primary exposure was sepsis. The primary outcome was hospital mortality. We generated sample size graphs, by varying control group mortality (10%-60%), relative risk reduction (0-1), for 80% power and 5% alpha. We then compared AFsepsis derived sample sizes with sample size calculations from published sepsis trials.
Results:
AFsepsis was 15% (95% CI: 14%-16%) compared with propensity matched ICU-non-sepsis controls and 93% (95% CI: 92%-93%) compared with age-sex-matched general population controls. When comparing AFsepsis derived sample sizes with sample size calculations from 18 trials meeting our selection criteria, these calculations assumed very high AFsepsis and/or very effective treatments.
Conclusions:
Estimating trial specific AFsepsis to inform sample size calculations could be an additional step in sepsis trial design.
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