A boosting inspired personalized threshold method for sepsis screening

Abstract

Sepsis is one of the biggest risks to patient safety, with a natural mortality rate between 25% and 50%. It is difficult to diagnose, and no validated standard for diagnosis currently exists. A commonly used scoring criteria is the quick sequential organ failure assessment (qSOFA). It demonstrates very low specificity in ICU populations, however. We develop a method to personalize thresholds in qSOFA that incorporates easily to measure patient baseline characteristics. We compare the personalized threshold method to qSOFA, five previously published methods that obtain an optimal constant threshold for a single biomarker, and to the machine learning algorithms based on logistic regression and AdaBoosting using patient data in the MIMIC-III database. The personalized threshold method achieves higher accuracy than qSOFA and the five published methods and has comparable performance to machine learning methods. Personalized thresholds, however, are much easier to adopt in real-life monitoring than machine learning methods as they are computed once for a patient and used in the same way as qSOFA, whereas the machine learning methods are hard to implement and interpret.

Keywords:

• Sepsis
• clinical risk score
• personalized thresholds
• screening
• boosting

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors are grateful to the Associate Editor and two anonymous reviewers for their constructive comments that greatly improved the quality and presentation of this article. This research was supported in part by NSF grants CMMI-1362876 and DMS-1830344 through Georgia Institute of Technology, and in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR000454.