ABSTRACT
Background Covid 19 has recently been defined as a syndemia, because it combines the epidemic emergency of Sars Cov 2 with that of non-communicable diseases. This aspect is particularly known in internal medicine wards (IM), which have had to split up to support these new criticalities. Methods Delphi method has been used to make the Ishikawa's diagram to analyze the reasons of the death in COVID ward. Each item was attributed a score according to a pros/cons and opportunities/threats system, derived from evidence in the literature. Scores were presented in a Cartesian graph showing the positioning according to the potential value and the perceived risk associated with the items. In the end, the performances of MC (Covid ward, split in I wave, II wave and overall) and MCF (internal medicine Covid-free ward) have been compared through the Barber's nomogram. Results MCF hospitalized 790 patients (-23,90% compared to 2019), Overall-MC patients: 50% of the 2019 MCF. Main cause of mortality-risk: patients originating from local facilities (+7%) and comorbidities (58% Chronic renal failure, 41% cancer, 90% age, 70% COVID 19 WHO stage 3. >3 comorbidities: 100%, ≥5: 24,7%). Length of stay: 8-60 days (mean value, MV: 17) for MC, 2-12 (MV 8) for MCF. Turnover Index: 10 day for MC II, 8 MC I, 1 for MCF. 25% of patients in MC have been treated with Non Invasive Ventilation (NIV), with high cost hospitalization-related. Conclusions Internal-Medicine ward model is a ward strictly tied to the community both before and after hospitalization. COVID 19 proposes a new model of IM, nearly subintensive ward, with NIV and continuous monitoring of vital signs, long length of stay and low turnover index. Is this the internal medicine ward for the future? Key messages COVID 19 proposes a new model of Internal Medicine ward, nearly subintensive ward, with Non Invasive Ventilation and continuous monitoring of vital signs, long length of stay and low turnover index. Covid 19 mortality is strictly connected with the origin from territorial health-assisted residences.
ABSTRACT
Background: Covid-19 has an impact on lung function and, consequently, on voice emission. By registering an adequate numberof patients with Covid-19, we can 'train' artificial intelligence algorithms in order to highlight the disease status of any personwhose voice is registered. Impact on voice increases with diseaseprogression, allowing staging. Materials and Methods: Prospective pilot study to evaluate thecondition of Covid-19 affection of critically ill patients hospitalizedand monitored by evaluating their speech capacity through measurement and recording of the voice. Primary End Point: remotelylocate people infected with Covid-19. Secondary end-points: establish the presence of any geographic areas with 'outbreaks', by'crossing' the geo-location data, staging the disease. Results: 85 patients evaluated and 18 (10F and 8M) recruited,average age 62, subjected to intubation 3/18. WHO stage 2: 50%;comorbidity>3: 61%;only 2 with P/F<200. Conclusions: Recruited patients have features of lower functionalimpairment than other patients, however a high incidence of previous intubation. Preliminary audio signal analysis of the patient's voice recordings are underway and will be treated with ArtificialIntelligence algorithms in order to select voice parameters thatcan identify the presence of the disease. By training appropriatemachine learning and data classification systems it will be possible to determine whether the recorded voice belongs to a healthysubject or affected by Covid-19 and use these skills to screen suspect patients by telephone triage.