Impact of hourly serial SOFA score on signaling emerging sepsis

Bin Shenq Ho, Yan Hwa Wu Lee, Yi Bing Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Under suspicion of infection, sepsis can be identified as an acute increase in Sequential Organ Failure Assessment (SOFA) score of 2 points or more. Professional critical care societies have called for early detection and treatment of sepsis; however, the fundamental tool to address the need remains unmet. Objectives: The present study aims at exploring the possibility of a solution to bridging clinical information system with AI medicine and supporting decision-based medical tasks through integrated data-intensive intelligence. Patients and methods: We extracted data from a well-recognized database and explored the feasibility of a real-time solution to sepsis identification for adult ICU patients under suspicion of infection. To analogize the requirement of a randomized controlled trial, we adopted propensity score matching to tackle the imbalance of baseline covariates between study groups frequently encountered in observational studies. Results: Our study indicates that the hourly assessment protocol outperforms the 24-h assessment counterpart in terms of the timing of sepsis identification by a median of 14.5 h earlier and the change of total SOFA score by a median of 1.0 point lower. Conclusions: We conclude that real-time SOFA score to signal emerging sepsis becomes feasible through the introduction of data-intensive intelligence and data processing technologies.

Original languageEnglish
Article number100999
JournalInformatics in Medicine Unlocked
Volume31
DOIs
StatePublished - Jun 2022

Keywords

  • Artificial intelligence (AI)
  • Clinical information system
  • Data intelligence
  • Sepsis
  • Sequential organ failure assessment (SOFA)

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