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This is a retrospective observational study including all COVID-19 patients admitted at our Institute throughout three successive pandemic waves, from January 2021 to June 2023. The main in-hospital outcomes (clinical progression [CP], defined as admission to Intensive Care Unit [ICU]/death, and death within 28 days) were compared among participants unvaccinated (NV), fully vaccinated (FV), with one (FV&B1) and two (FV&B2) booster doses. Vaccinated participants were stratified into recently and waned FV/FV&B1/FV&B2, depending on the time elapsed from last dose (≤ and >120 days, respectively). There were 4488 participants: 2224 NV, 674 FV, 1207 FV&B1, and 383 FV&B2. Within 28 days, there were 604 ICU admissions, 396 deaths, and 737 CP. After adjusting for the main confounders, the risk of both in-hospital outcomes was reduced in vaccinated individuals, especially in those who received the booster dose (approximately by 36% for FV and >50% for FV&B1 and FV&B2 compared to NV). Similarly, after restricting the analysis to vaccinated participants only, we observed a risk reduction of approximately 40% for FV&B1 and 50% for FV&B2, compared to FV, regardless of the distance since the last dose. Our data confirm the vaccine's effectiveness in preventing severe COVID-19 and support the efforts to increase the uptake of booster doses, mainly among older and frailer individuals, still at a greater risk of clinical progression. [ABSTRACT FROM AUTHOR]

We are in the era of Health 4.0 when novel technologies are providing tools capable of improving the quality and safety of the services provided. Our project involves the integration of different technologies (AI, big data, robotics, and telemedicine) to create a unique system for patients admitted to intensive care units suffering from infectious diseases capable of both increasing the personalization of care and ensuring a safer environment for caregivers. [ABSTRACT FROM AUTHOR]

Background: The 30-day hospital re-admission rate is a quality measure of hospital care to monitor the efficiency of the healthcare system. The hospital re-admission of acute stroke (AS) patients is often associated with higher mortality rates, greater levels of disability and increased healthcare costs. The aim of our study was to identify predictors of unplanned 30-day hospital re-admissions after discharge of AS patients and define an early re-admission risk score (RRS)., Methods: This observational, retrospective study was performed on AS patients who were discharged between 2014 and 2019. Early re-admission predictors were identified by machine learning models. The performances of these models were assessed by receiver operating characteristic curve analysis., Results: Of 7599 patients with AS, 3699 patients met the inclusion criteria, and 304 patients (8.22%) were re-admitted within 30 days from discharge. After identifying the predictors of early re-admission by logistic regression analysis, RRS was obtained and consisted of seven variables: hemoglobin level, atrial fibrillation, brain hemorrhage, discharge home, chronic obstructive pulmonary disease, one and more than one hospitalization in the previous year. The cohort of patients was then stratified into three risk categories: low (RRS = 0-1), medium (RRS = 2-3) and high (RRS >3) with re-admission rates of 5%, 8% and 14%, respectively., Conclusions: The identification of risk factors for early re-admission after AS and the elaboration of a score to stratify at discharge time the risk of re-admission can provide a tool for clinicians to plan a personalized follow-up and contain healthcare costs., (© 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)