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Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
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Correction: Coulter et al. OrgTRx: A Platform Developed in Queensland for the Extraction and Visualisation of Antimicrobial Susceptibility Data for the Surveillance of Resistance in Microorganisms. <i>Antibiotics</i> 2026, <i>15</i>, 63.

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A Risk Model Incorporating the Novel Inflammatory Biomarker CD64 for Predicting Bloodstream Infection in Suspected

Teng Xu1,2, Yu Zhou1,2, Bei Wang3

  • 1Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai 200040, China.

Antibiotics (Basel, Switzerland)
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

A new five-variable nomogram accurately predicts bloodstream infection (BSI) risk using common lab tests and inflammatory markers, aiding early treatment and reducing antibiotic overuse.

Keywords:
CD64bloodstream infectiondiagnostic performancenomogramserum inflammatory marker

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Area of Science:

  • Clinical Medicine
  • Infectious Diseases
  • Diagnostic Tools

Background:

  • Bloodstream infection (BSI) is a major cause of mortality.
  • Early prediction of BSI risk is crucial for improved patient outcomes and judicious antibiotic use.

Purpose of the Study:

  • To develop a convenient tool for early prediction of BSI risk.
  • To identify key predictors for BSI risk stratification.

Main Methods:

  • A multivariate prediction model was developed using LASSO and logistic regression.
  • Conventional laboratory tests and novel serum inflammatory markers were analyzed.
  • A cohort of 309 patients with suspected BSI was studied over one year.

Main Results:

  • BSI was confirmed in 32.0% of patients.
  • Five independent predictors were identified: local infection, platelet count, C-reactive protein, procalcitonin (PCT), and CD64.
  • The developed nomogram achieved an AUC of 0.85 for BSI risk prediction, outperforming PCT alone.

Conclusions:

  • A simple five-variable nomogram enables timely prediction of high-risk BSI patients.
  • This tool can guide antimicrobial treatment decisions and prevent unnecessary antibiotic prescriptions.