Artificial Intelligence

Sepsis Early-Warning Systems

How rule-based scores, machine-learning models, and bedside workflow combine to shorten time-to-antibiotic.

11 min readUpdated July 1, 20263 topics

Rule-based scores

ScoreInputsCut-offTypical use
SIRSTemp, HR, RR, WBC≥2Historic ED trigger
qSOFARR, SBP, mental status≥2Bedside, non-ICU
NEWS26 physiological params + O2 use≥5 or single param 3UK national standard
MEWS5 params≥5Common on general wards

Machine-learning models

Vendor and academic ML models (Epic Sepsis Model, TREWS, InSight) predict sepsis onset hours before overt deterioration by combining vitals trend features, lab velocity, medication signals, and nursing notes. External validation studies have been mixed — a 2021 JAMA Internal Medicine study of the Epic Sepsis Model reported sensitivity of 33% at the vendor-suggested threshold. Local calibration and prospective silent-mode evaluation are now the expected pre-deployment step.

The bundle that matters

The Surviving Sepsis Campaign Hour-1 Bundle — lactate, blood cultures, broad-spectrum antibiotics, fluid resuscitation, vasopressors as needed — is where outcomes are won. An alert that fires without a runbook to close does not change mortality.

Guidelines and evidence

  • Surviving Sepsis Campaign 2021 — international guidelines.
  • CMS SEP-1 — US inpatient quality measure.
  • NICE NG51 — UK sepsis guideline.
  • Wong et al., JAMA Intern Med 2021 — external validation of Epic Sepsis Model.

References & further reading

  1. 1Evans L et al. — Surviving Sepsis Campaign: International Guidelines 2021 (Crit Care Med, 2021)
  2. 2Wong A et al. — External Validation of a Widely Implemented Proprietary Sepsis Prediction Model (JAMA Intern Med, 2021)
  3. 3Royal College of Physicians — National Early Warning Score 2 (NEWS2)
  4. 4CMS SEP-1 Severe Sepsis and Septic Shock Early Management Bundle

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