Risk Factors Analysis of Surgical Infection Using Artificial Intelligence
A Single Center Study
Bibliographic Data
| ID | 15499473 |
|---|---|
| Authors | Arianna Scala (0000-0001-9735-5227, University of Naples Federico II, corresponding author), Ilaria Loperto (0000-0003-4943-6780, University of Naples Federico II), Maria Triassi (0000-0002-9420-9571, University of Naples Federico II), Giovanni Improta (0000-0002-9485-1687, University of Naples Federico II) |
| Year | 2022 |
| Volume | 19 |
| Issue | 16 |
| Pages | 10021-10021 |
| Publication date | 2022-08-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph191610021 |
| PMID | 36011656 |
| OpenAlex | W4291465141 |
| Language | EN |
| Citations received | 1 |
| References cited | 41 |
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
Antibiotic prophylaxis · Antibiotics · Infection rate · Intensive care medicine · Logistic regression · Mortality rate · Risk factor · Surgical Infections · Surgical site infection · Cardiac, Anesthesia and Surgical Outcomes · Medicine · Pelvic and Acetabular Injuries · Surgical site infection prevention · Emergency Medicine · Internal Medicine · Surgery
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |