Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach
| Autor: | Vladimir Zelman, Ksenia Ershova, O.N. Ershova, Oleg Khomenko, Gleb Danilov, Michael A. Shifrin, Ivan A. Savin, N V Kurdyumova |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Adult
Male Adolescent medicine.medical_treatment Critical Care and Intensive Care Medicine Machine learning computer.software_genre law.invention Cerebral Ventriculitis Meningitis Bacterial Russia Machine Learning 03 medical and health sciences Young Adult 0302 clinical medicine Postoperative Complications law Risk Factors medicine Ventriculitis Humans Surgical Wound Infection Cumulative incidence 030212 general & internal medicine Prospective Studies Child Craniotomy Cross Infection business.industry Incidence (epidemiology) Incidence Middle Aged medicine.disease Intensive care unit Intensive Care Units Relative risk Child Preschool Female Artificial intelligence business Meningitis computer 030217 neurology & neurosurgery Cohort study |
| Zdroj: | Journal of critical care. 45 |
| ISSN: | 1557-8615 |
| Popis: | Purpose To define the incidence of healthcare-associated ventriculitis and meningitis (HAVM) in the neuro-ICU and to identify HAVM risk factors using tree-based machine learning (ML) algorithms. Methods An observational cohort study was conducted in Russia from 2010 to 2017, and included high-risk neuro-ICU patients. We utilized relative risk analysis, regressions, and ML to identify factors associated with HAVM development. Results 2286 patients of all ages were included, 216 of them had HAVM. The cumulative incidence of HAVM was 9.45% [95% CI 8.25–10.65]. The incidence of EVD-associated HAVM was 17.2 per 1000 EVD-days or 4.3% [95% CI 3.47–5.13] per 100 patients. Combining all three methods, we selected four important factors contributing to HAVM development: EVD, craniotomy, superficial surgical site infections after neurosurgery, and CSF leakage. The ML models performed better than regressions. Conclusion We first reported HAVM incidence in a neuro-ICU in Russia. We showed that tree-based ML is an effective approach to study risk factors because it enables the identification of nonlinear interaction across factors. We suggest that the number of found risk factors and the duration of their presence in patients should be reduced to prevent HAVM. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect