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Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Author

Listed:
  • Arianna Scala

    (Department of Public Health, University of Naples “Federico II”, 80100 Naples, Italy)

  • Ilaria Loperto

    (Department of Public Health, University of Naples “Federico II”, 80100 Naples, Italy)

  • Maria Triassi

    (Department of Public Health, University of Naples “Federico II”, 80100 Naples, Italy
    Interdepartmental Center for Research in Health Care Management and Innovation in Health Care (CIRMIS), University of Naples “Federico II”, 80100 Naples, Italy)

  • Giovanni Improta

    (Department of Public Health, University of Naples “Federico II”, 80100 Naples, Italy
    Interdepartmental Center for Research in Health Care Management and Innovation in Health Care (CIRMIS), University of Naples “Federico II”, 80100 Naples, Italy)

Abstract

Background: Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to increase the hospitalization duration, cost, and risk of death, in fact, the SSIs are a leading cause of morbidity and mortality in modern health care. Methods: A study based on statistical test and logistic regression for unveiling the association between SSIs and different risk factors was carried out. Successively, a predictive analysis of SSIs on the basis of risk factors was performed. Results: The obtained data demonstrated that the level of surgery contamination impacts significantly on the infection rate. In addition, data also reveals that the length of postoperative hospital stay increases the rate of surgical infections. Finally, the postoperative length of stay, surgery department and the antibiotic prophylaxis with 2 or more antibiotics are a significant predictor for the development of infection. Conclusions: The data report that the type of surgery department and antibiotic prophylaxis there are a statistically significant predictor of SSIs. Moreover, KNN model better handle the imbalanced dataset (48 infected and 3983 healthy), observing highest accuracy value.

Suggested Citation

  • Arianna Scala & Ilaria Loperto & Maria Triassi & Giovanni Improta, 2022. "Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study," IJERPH, MDPI, vol. 19(16), pages 1-10, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:16:p:10021-:d:887929
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    References listed on IDEAS

    as
    1. Emma Montella & Antonino Ferraro & Giancarlo Sperlì & Maria Triassi & Stefania Santini & Giovanni Improta, 2022. "Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study," IJERPH, MDPI, vol. 19(5), pages 1-9, February.
    2. Teresa Angela Trunfio & Anna Borrelli & Giovanni Improta, 2022. "Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?," IJERPH, MDPI, vol. 19(10), pages 1-16, May.
    3. Arianna Scala & Teresa Angela Trunfio & Lucia De Coppi & Giovanni Rossi & Anna Borrelli & Maria Triassi & Giovanni Improta, 2022. "Regression Models to Study the Total LOS Related to Valvuloplasty," IJERPH, MDPI, vol. 19(5), pages 1-13, March.
    4. Giovanni Improta & Giuseppe Converso & Teresa Murino & Mosè Gallo & Antonietta Perrone & Maria Romano, 2019. "Analytic Hierarchy Process (AHP) in Dynamic Configuration as a Tool for Health Technology Assessment (HTA): The Case of Biosensing Optoelectronics in Oncology," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1533-1550, September.
    5. Giovanni Improta & Anna Borrelli & Maria Triassi, 2022. "Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
    6. Ylenia Colella & Antonio Saverio Valente & Lucia Rossano & Teresa Angela Trunfio & Antonella Fiorillo & Giovanni Improta, 2022. "A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection," IJERPH, MDPI, vol. 19(6), pages 1-20, March.
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