Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Finding a method for optimizing risk adjustment when comparing surgical-site infection rates.

Christian Brandt1, Sonja Hansen, Dorit Sohr

  • 1Institut für Hygiene und Umweltmedizin, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Infection Control and Hospital Epidemiology
|April 28, 2004
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Consensus Guidelines for Therapeutic Drug Monitoring in Neuropsychopharmacology: Update 2026 - Pharmacokinetic, pharmacogenetic and clinical aspects.

Pharmacopsychiatry·2026
Same author

Microbial cell-free DNA for rapid pathogen identification in clinical diagnostics: a proof of concept study.

Genome medicine·2026
Same author

Subacute Bartonella quintana bacteremia following immunomodulatory therapy: a diagnostic challenge.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026
Same author

Validation of a German version of the Anxiety, Abuse, and Somatization Questionnaire (AASQ) for functional/dissociative seizures.

Epilepsy & behavior : E&B·2026
Same author

The burden of hospital-acquired legionellosis in German teaching hospitals.

Infection·2026
Same author

Lessons from Two Early COVID-19 Hospital Outbreaks in Germany to Inform Strategies for Controlling Emerging Nosocomial Outbreaks of Highly Transmissible Respiratory Viruses.

Journal of clinical medicine·2026

Logistic regression models do not significantly improve surgical-site infection (SSI) risk stratification compared to the NNIS risk index. Enhancing models requires procedure-specific variables for better hospital comparisons.

Area of Science:

  • Healthcare epidemiology
  • Infectious disease surveillance
  • Surgical outcomes research

Background:

  • Surgical-site infections (SSIs) pose a significant threat to patient safety and healthcare costs.
  • Accurate risk stratification is crucial for comparing infection rates across hospitals.
  • The modified National Nosocomial Infections Surveillance (NNIS) System risk index is a widely used tool.

Purpose of the Study:

  • To evaluate if logistic regression models, tailored to individual procedure categories, offer improved risk stratification for SSIs compared to the standard NNIS risk index.
  • To assess the predictive power of logistic regression models incorporating traditional NNIS variables plus age and gender.

Main Methods:

  • Utilized data from the German Nosocomial Infection Surveillance System, including 214,271 operations across 9 procedure categories.

Related Experiment Videos

  • Applied multiple logistic regression analyses to identify significant risk factors for SSIs within each procedure category.
  • Evaluated model predictive performance using the area under the receiver operating characteristic (ROC) curve.

    • Main Results:

    • For most procedures, key NNIS risk index variables (ASA score, wound class, operation duration) were confirmed as independent risk factors.
    • The predictive power of the logistic regression models was generally low (0.55-0.71 ROC AUC).
    • Models showed only marginal improvement over the NNIS risk index in predicting SSIs for most procedures.

    Conclusions:

    • Logistic regression models, without additional procedure-specific variables, do not substantially enhance the ability to compare SSI rates between hospitals.
    • Further research should focus on incorporating more granular, procedure-specific risk factors to improve predictive accuracy.