ABSTRACT
Background and Aims: A proportion of patients with coronavirus disease 2019 (COVID-19) need hospitalization due to severe respiratory symptoms. This study describes the characteristics of survivors of severe COVID-19 subsequently admitted to inpatient pulmonary rehabilitation (PR) and identifies their rehabilitation needs. Subjects and methods: From the COVID-19 Registry of the Fondazione Don Gnocchi we extracted 203 patients admitted for inpatient PR after severe COVID-19 from April 2020 till September 2021. Specific information on the acute-hospital stay, clinical and functional characteristics on admission to the rehabilitation units were collected. Result(s): During the acute phase of the disease 80% of patients needed ICU admission, receiving mechanical ventilation (MV) for 26 days. On admission to the rehabilitation units, 10% of patients were still on MV, 28% had tracheostomy, 70% were on O2 therapy, 24% were diagnosed critical illness neuropathy. Eighty % showed a modified Barthel Index <75 and only 25% were able to perform a six-minute walk test. Montreal Cognitive Assessment and Hospital Anxiety and Depression Scale were also performed, indicating a variable presence of neurocognitive impairment and symptoms of anxiety and/or depression. Moreover, 32% scored >=2 at the Malnutrition Universal Screening Tool and 47% showed dysphagia needing logopedic treatment Conclusion(s): Our analysis shows that patients admitted for inpatient PR after severe COVID-19 represent a multifaceted and clinically complex patient population who need customized, comprehensive rehabilitation programs, carried out by teams with different professional skills.
ABSTRACT
The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89-92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.