Threshold for Defining Fever Varies with Age Especially in Children and can be Further Improved Using Artificial Intelligence Techniques: A Multi-site Diagnostic Accuracy Study

Abstract

Background: Fever is an important prognosticator of illness. It is a symptom of mostly viral infections but on occasion bacterial infections that can be treated with antibiotics. The likelihood of an infection increases generally with the magnitude of the fever, but infection is the root cause of fever and is also the harmful element of illness. Therefore, when defining a fever threshold, the ideal fever threshold needs to be based on a comparison of the temperature measurement to ‘actual illness or infection based on diagnostic tests and a comprehensive patient examination by a physician or a health care professional’. Aim: The American Academy of Pediatrics and the European Centre for Pediatric and Adolescent Medicine guideline define fever as a temperature > 38.0°C for all ages. The fixed threshold of 100.4°F (38.0°C) provided as a guideline by the AAP and the ECPA, while reasonably safe, provides a poor prognostication to infections and illness. Herzog et al [1] took a different view and did an extensive review of the literature to study the different cutoff points and identify the lower limit of “fever” and “high fever” based on the patient’s ages. Design: A multi-site diagnostic accuracy study was conducted to compare an ‘age-based’ threshold model with a ‘fixed’ threshold over 38.0°C on a total of 894 patients of which 373 were ill. Methods: The ‘age-based’ and ‘fixed’ threshold fever determinations were then compared to a clinical categorization (“well” or ill”) conducted by a clinician through a comprehensive examination. Results: The sensitivity and accuracy for the age-based thresholds were found to be superior to the fixed thresholds in all ages. The sensitivity and accuracy were found to further improve through use of an ensemble decision tree based Artificial Intelligence algorithm using age and several other features. Conclusion: Our data clearly indicated that this empirical model for age-based fever thresholds suggested by Herzog et al [1] showed a closer agreement (in terms of sensitivity and accuracy) between fever due to elevated temperatures and illness as identified by a clinical impression from a Health Care Professional. Furthermore, the AI model using a decision tree ensemble algorithm improved upon this agreement. Clinical Significance: The framework provided by this study will allow parents and caregivers to make a better choice in seeking medical intervention [2]. It will also allow for initial management of fever at home, instead of unnecessary medical visits. In turn, this can help reduce medical costs for parents and reduce the waste of medical resources significantly. As an example, other authors [3] have demonstrated a significant cost saving due to accurate prognosis of infants having illness or infection.