The incidence and outcomes of healthcare-associated respiratory tract infections in non-ventilated neurocritical care patients: Results of a 10-year cohort study.

The incidence of healthcare-associated respiratory tract infections in non-ventilated patients (NVA-HARTI) in neurosurgical intensive care units (ICUs) is unknown. The impact of NVA-HARTI on patient outcomes and differences between NVA-HARTI and ventilator-associated healthcare-associated respiratory tract infections (VA-HARTI) are poorly understood. Our objectives were to report the incidence, hospital length of stay (LOS), ICU LOS, and mortality in NVA-HARTI patients and compare these characteristics to VA-HARTI in neurocritical care patients. This cohort study was conducted in a neurosurgical ICU in Moscow. From 2011 to 2020, all patients with an ICU LOS > 48 h were included. A competing risk model was used for survival and risk analysis. A total of 3,937 ICU admissions were analyzed. NVA-HARTI vs VA-HARTI results were as follows: cumulative incidence 7.2 (95%CI: 6.4-8.0) vs 15.4 (95%CI: 14.2-16.5) per 100 ICU admissions; incidence rate 4.2 ± 2.0 vs 9.5 ± 3.0 per 1000 patient-days in the ICU; median LOS 32 [Q1Q3: 21, 48.5] vs 46 [Q1Q3: 28, 76.5] days; median ICU LOS 15 [Q1Q3: 10, 28.75] vs 26 [Q1Q3: 17, 43] days; mortality 12.3% (95%CI: 7.9-16.8) vs 16.7% (95%CI: 13.6-19.7). The incidence of VA-HARTI decreased over ten years while NVA-HARTI incidence did not change. VA-HARTI was an independent risk factor of death, OR 1.54 (1.11-2.14), while NVA-HARTI was not. Our findings suggest that NVA-HARTI in neurocritical care patients represents a significant healthcare burden with relatively high incidence and associated poor outcomes. Unlike VA-HARTI, the incidence of NVA-HARTI remained constant despite preventive measures. This suggests that extrapolating VA-HARTI research findings to NVA-HARTI should be avoided.

Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach.

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.