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

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,...

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Bibliographic Details
Published in:International journal of environmental research and public health 2022-08, Vol.19 (16), p.10021
Main Authors: Scala, Arianna, Loperto, Ilaria, Triassi, Maria, Improta, Giovanni
Format: Article
Language:English
Subjects:
Algorithms
Antibiotics
Artificial intelligence
Bacterial infections
Contamination
Costs
Decision making
Disease control
Disease prevention
Health care
Health services
Hospitals
Infections
Intervention
Length of stay
Morbidity
Nosocomial infections
Patients
Prophylaxis
Risk analysis
Risk factors
Staphylococcus infections
Statistical analysis
Statistical tests
Surgery
Surgical site infections
Surveillance
Variables
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Summary: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.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph191610021