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Establishment of a Predictive Model Related to Pathogen Invasion for Infectious Diseases and Its Diagnostic Value in

Zhu-Hua Wu1,2, Ming-You Xing1, Sheng Wei3

  • 1Department of Infectious Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.

Current Medical Science
|December 12, 2018
PubMed

Insights

This study developed a predictive model to identify infectious causes of fever of unknown origin (FUO). The model uses 9 key factors to reliably distinguish between infectious and non-infectious etiologies in FUO patients.

Area of Science:

  • Infectious Diseases
  • Internal Medicine
  • Clinical Epidemiology

Background:

  • Fever of unknown origin (FUO) presents a diagnostic challenge, often requiring extensive investigation to determine its cause.
  • Distinguishing between infectious and non-infectious etiologies is crucial for appropriate patient management and treatment.
  • Existing diagnostic approaches may lack efficiency in rapidly identifying the underlying cause of FUO.

Purpose of the Study:

  • To identify key parameters indicative of pathogen invasion in patients with FUO.
  • To develop and validate a predictive model for differentiating infectious from non-infectious causes of FUO.
  • To create a reliable scoring system to aid in the etiological diagnosis of FUO.

Main Methods:

  • Prospective enrollment of 431 patients with FUO between January 2014 and September 2017.
  • Identification of 26 potential variables across host, epidemiological, behavioral, and iatrogenic factors.
  • Development of a multivariate logistic regression model using confirmed diagnoses, identifying 9 significant predictive variables.

Main Results:

  • A predictive model incorporating 9 identified factors was developed.
  • The model demonstrated an Area Under the Curve (AUC) of 0.72 for discriminating between infectious and non-infectious FUO causes.
  • The model achieved a sensitivity of 0.71 and a specificity of 0.63 in distinguishing etiologies.

Conclusions:

  • The developed predictive model and scoring system, based on pathogen invasion factors, serve as effective screening tools for FUO.
  • These tools can reliably assist clinicians in differentiating infectious from non-infectious causes of fever of unknown origin.
  • The study provides a valuable framework for improving the diagnostic efficiency in FUO cases.
Keywords:
empiric therapyetiologyfever of unknown originpredictive model

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