A boosting inspired personalized threshold method for sepsis screening.

Journal: Journal of applied statistics
Published Date: Jan 23, 2020

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.

Authors

  • Chen Feng
    School of Industrial & Systems Engineering, Georgia Tech, Atlanta, GA, USA.
  • Paul Griffin
    Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA.
  • Shravan Kethireddy
    Critical Care Medicine, Northeast Georgia Medical Center, Gainesville, GA, USA.
  • Yajun Mei
    School of Industrial & Systems Engineering, Georgia Tech, Atlanta, GA, USA.

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