Background and Objectives: Healthcare-associated infections (HAIs) are on the rise worldwide because the range of etiologic agents involved is very diverse and their antimicrobial resistance poses a threat to population health in the third millennium. Materials and Methods: We conducted a retrospective, longitudinal, descriptive clinical–epidemiological study in a hospital with a neurosurgical profile in northeastern Romania (“Prof. Dr. N. Oblu” Clinical Emergency Hospital in Iasi), during 2020–2024. The study is centered on the involvement of Acinetobacter spp. in the occurrence and evolution of HAIs. Results: The highest incidence of Acinetobacter spp. HAIs was recorded in the intensive care unit (ICU)—82.78% compared to neurosurgical wards (15.38%), with predominance in males (69.23%) and rural residence patients (55.67%). Most HAIs were represented by ventilator-associated pneumonia (42.13%) and lower respiratory tract infections (23.08%). Strains with high virulence and pathogenicity (CR-MDR, ESBL-MDR) were found, with the highest proportion of CR-MDR strains (88.27%). Conclusions: Our study provides useful data for surveillance of the antimicrobial resistance of pathogens involved in HAIs at the hospital level and for guiding antibiotic therapy in hospital-acquired infections.

Background: Pan-drug-resistant (PDR) Acinetobacterinfections are an escalating ICU threat, demanding both patient-level triage and facility-wide forecasting. Objective: The aim of this study was to build a dual-scale AI framework that (i) predicts PDR status at infection onset and (ii) forecasts hospital-level PDR burden through 2027. Methods: We retrospectively analyzed 270 Acinetobacter infection episodes (2020–2024) with 65 predictors spanning demographics, timelines, infection type, resistance-class flags, and a 25-drug antibiogram. TabTransformer and XGBoost were trained on 2020–2023 episodes (n = 210), evaluated by stratified 5-fold CV, and externally tested on 2024 episodes (n = 60). Metrics included AUROC, AUPRC, accuracy, and recall at 90% specificity; AUROC was optimism-corrected via 0.632 + bootstrap and DeLong-tested for drift. SHAP values quantified feature impact. Weekly PDR incidence was forecast with an attention–LSTM model retrained monthly (200 weekly origins, 4-week horizon) and benchmarked against seasonal-naïve, Prophet, and SARIMA models (MAPE and RMSE). Quarterly projections (TFT-lite) extended forecasts to 2027. Results: The CV AUROC was 0.924 (optimism-corrected 0.874); an ensemble of TabTransformer + XGBoost reached 0.958. The 2024 AUROC fell to 0.586 (p < 0.001), coinciding with a PDR prevalence drop (75→38%) and three covariates with PSIs > 1.0. Isotonic recalibration improved the Brier score from 0.326 to 0.207 and yielded a net benefit equivalent to 26 unnecessary isolation-days averted per 100 ICU admissions at a 0.20 threshold. SHAP highlighted Ampicillin/Sulbactam resistance, unknown acquisition mode, and device-related infection as dominant drivers. The attention–LSTM achieved a median weekly MAE of 0.10 (IQR: 0.028–0.985) vs. 1.00 for the seasonal-naïve rule, outperforming it on 48.5% of weeks and surpassing Prophet and SARIMA (MAPE = 6.2%, RMSE = 0.032). TFT-lite projected a ≥ 25% PDR tipping point in 2025 Q1 with a sustained rise in 2027. Conclusions: The proposed framework delivers explainable patient-level PDR risk scores and competitive 4-week and multi-year incidence forecasts despite temporal drift, supporting antimicrobial stewardship and ICU capacity planning. Shrinkage and bootstrap correction were applied to address the small sample size (EPV = 2.1), which poses an overfitting risk. Continuous recalibration and multi-center validation remain priorities.

Background and Objectives: Healthcare-associated infections (HAIs) are a major cause of morbidity and mortality and can lead to serious long-term consequences, increased hospital length stay, higher rates of antibiotic resistance, and additional financial costs. The study aim was to highlight important aspects related to the level of knowledge of HAIs, risk factors for HAI and methods of preventing HAIs among nurses from urban hospitals in the North-East of Romania. Materials and Methods: We conducted a cross-sectional study on the level of knowledge of HAI problems among medical personnel (nurses). The study group consisted of 288 nurses who responded online to a questionnaire. Results: Multivariate analysis suggested that 45.5% of the value of the knowledge score of prevention and limitation of HAIs could be determined by the answers to the questions regarding the importance of the nurse role, professional experience, and training. Moreover 84.6% of the knowledge score could be determined by the answers to the questions regarding the assessment of the importance of factors related to lack of medical personnel, professional burnout, insufficient knowledge and staff training, inefficient team, poor collaboration within departments, stressful work environment, staff health status, multiple tasks at work (p = 0.038). While 50% of the knowledge score could be explained by responses regarding personal problems related to daily activity; physical and mental health status; and physical, psychological, and social components (p = 0.038). Conclusions: Our study highlighted aspects of the level of knowledge regarding HAIs among nurses, an issue that plays a key role in hospital management. Overall, the knowledge score for the prevention and limitation of HAIs was higher in nurses/females aged over 40 and in nurses with more than 15 years of experience.

Background and Objectives: Healthcare-associated infections (HAIs) caused by Pseudomonas aeruginosa represent a challenge for healthcare systems worldwide, with significant incidence, high mortality, and increasing antibiotic resistance. This paper aims to describe and analyze the epidemiology of HAI cases with Pseudomonas aeruginosa in a tertiary neurosurgical hospital in the northeastern region of Romania during 2020–2024. Materials and Methods: Study inclusion criteria were patients of any age (as the hospital also admits pediatric patients from the region) and a diagnosis of P. aeruginosa HAI, with a signed informed consent form by the adult patient or relatives for pediatric patients. Results: During 2020–2024, 163 cases of P. aeruginosa HAIs were identified. The annual incidence decreased from 4.41‰ in 2020 to 2.37‰ in 2024 (p = 0.0038). Most cases occurred in the intensive care unit (ICU) (119/163; 73.0%), followed by neurosurgery wards (37/163; 22.7%). Overall, 110 cases (67.5%) occurred in men and 53 (32.5%) in women. The analysis also showed a predominance of late-onset cases and the presence of antimicrobial-resistant phenotypes among the identified P. aeruginosa isolates. Conclusions: This study provides an integrated epidemiological characterization of P. aeruginosa HAIs in a regional neurosurgical hospital, highlighting the predominance of ICU-associated cases, late-onset infections, and antimicrobial-resistant phenotypes. These findings support the need for continued epidemiological surveillance and antimicrobial resistance monitoring in high-risk neurosurgical settings.