COVID-19 Acute Respiratory Distress Syndrome (CARDS) is the most serious complication of COVID-19. The SARS-CoV-2 outbreaks rapidly saturated intensive care unit (ICU), forcing the application of non-invasive respiratory support (NIRS) in respiratory intermediate care unit (RICU). The primary aim of this study is to compare the patients’ clinical characteristics and outcomes (Helmet-Continuous Positive Airway Pressure (H-CPAP) success/failure and survival/death). The secondary aim is to evaluate and detect the main predictors of H-CPAP success and survival/death. A total of 515 patients were enrolled in our observational prospective study based on CARDS developed in RICU during the three Italian pandemic waves. All selected patients were treated with H-CPAP. The worst ratio of arterial partial pressure of oxygen (PaO₂) and fraction of inspired oxygen (FiO₂) PaO₂/FiO₂ during H-CPAP stratified the subjects into mild, moderate and severe CARDS. H-CPAP success has increased during the three waves (62%, 69% and 77%, respectively) and the mortality rate has decreased (28%, 21% and 13%). H-CPAP success/failure and survival/death were related to the PaO₂/FiO₂ (worst score) ratio in H-CPAP and to steroids’ administration. D-dimer at admission, FiO₂ and positive end expiratory pressure (PEEP) were also associated with H-CPAP success. Our study suggests good outcomes with H-CPAP in CARDS in RICU. A widespread use of steroids could play a role.

Helmet-Continuous Positive Airway Pressure (H-CPAP) is a non-invasive respiratory support that is used for the treatment of Acute Respiratory Distress Syndrome (ARDS), a severe medical condition diagnosed when symptoms like profound hypoxemia, pulmonary opacities on radiography, or unexplained respiratory failure are present. It can be classified as mild, moderate or severe. H-CPAP therapy is recommended as the initial treatment approach for mild ARDS. Even though the efficacy of H-CPAP in managing patients with moderate-to-severe hypoxemia remains unclear, its use has increased for these cases in response to the emergence of the COVID-19 Pandemic. Using the electronic medical records (EMR) from the Pulmonology Department of Vimercate Hospital, in this study we develop and evaluate a Machine Learning (ML) system able to predict the failure of H-CPAP therapy on ARDS patients. The Vimercate Hospital EMR provides demographic information, blood tests, and vital parameters of all hospitalizations of patients who are treated with H-CPAP and diagnosed with ARDS. This data is used to create a dataset of 622 records and 38 features, with 70%-30% split between training and test sets. Different ML models such as SVM, XGBoost, Neural Network, Random Forest, and Logistic Regression are iteratively trained in a cross-validation fashion. We also apply a feature selection algorithm to improve predictions quality and reduce the number of features. The SVM and Neural Network models proved to be the most effective, achieving final accuracies of 95.19% and 94.65%, respectively. In terms of F1-score, the models scored 88.61% and 87.18%, respectively. Additionally, the SVM and XGBoost models performed well with a reduced number of features (23 and 13, respectively). The PaO2/FiO2 Ratio, C-Reactive Protein, and O2 Saturation resulted as the most important features, followed by Heartbeats, White Blood Cells, and D-Dimer, in accordance with the clinical scientific literature.

Non-invasive respiratory support for acute hypoxemic respiratory failure (AHRF) remains controversial. Continuous positive airway pressure (CPAP) provides sustained positive end-expiratory pressure, while helmet interface (H-CPAP) may improve tolerance and re-duce air leaks. We aimed to evaluate clinical outcomes of H-CPAP delivered in a respira-tory intermediate care unit (RICU) by pulmonologists and identify early predictors of treatment failure. We conducted a prospective single-centre observational cohort study in-cluding consecutive adults with community-acquired pneumonia (CAP)-AHRF treated with H-CPAP in a RICU. H-CPAP success was defined as hospital discharge without in-vasive mechanical ventilation (IMV). Clinical, radiological, gas exchange, laboratory var-iables were collected at baseline and after 1 hour of H-CPAP. Among 1100 screened pa-tients, 198 met inclusion criteria. H-CPAP success occurred in 158/198 patients (79.8%), while overall in-hospital mortality was 9.1%. Success rates decreased with increasing AHRF severity. Lower haematocrit, bilateral pulmonary infiltrates, lower PaO₂/FiO₂ dur-ing H-CPAP, higher alveolar–arterial oxygen gradient, and higher APACHE II score were the main predictors of treatment failure. In selected patients with CAP-AHRF, H-CPAP delivered in a RICU was associated with high rates of avoidance of IMV and low in-hospital mortality. Early physiological and radiological variables identified patients at increased risk of H-CPAP failure with acceptable predictive performance.

Background/Objectives: Recent researches on Mortality rate (MR) Vs Bionomic functionality (BF) in the territory of the province of Monza and Milan led to the knowledge of the state of the environment of each municipality. This study verifies the need for an anamnesis capable of highlighting the presence of Cumulative Environmental Stress (ECS) and verifies the differences between smoking and non-smoking patients in their relationship with the living environment. Methods: Two questionnaires collected the patients' responses. Their evaluation is based on the cumulative impact of stress due to an incorrect Cumulative Wrong Relationships (cWR), detected through Physio-Environmental parameters (Wrong Relationships (WR-Phys)) increased by the stress due to the Wrong Relationships "Psychosocial" environment (WR-PSoc). These tables were tested in the Pneumology department of the Vimercate Hospital (Monza-Brianza) in 70 lung cancer patients. cWR receives a further cumulative impact from the bionomic conditions of the landscape unit where the patient lives, measurable by BF, leading to ECS. Results: All cases present a high ECS (above the norm), so a correlation emerges between the disease and the patient-environment relationship. An indication of morbidity is related to ECS levels. Smoker and ex-smoker patients differ from non-smoker in oncological histotypes independently from BF levels, and the ECS values of smoker are higher due to the heavy WR-PSoc factors. Conclusions: The examined patients present a high ECS, so, the distorted human-environment relationship is also accompanied by severe illnesses. Anamnesis’ extension to environmental parameters is to be suggested, also as a prevention factor.