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Introduction In Italy a Covid-19 pandemic pattern was observed, characterized by several waves, with an excess total mortality of 178000 deaths. Alessandria, Italy is the Piedmont province with the highest proportion of mortality from Covid-19 in the first 4 months of 2020, compared to the rest of the region. Objectives To analyze mortality in patients hospitalized for Covid-19 in the Alessandria Hospital (AO AL), considering the first 3 waves. Materials and methods Subjects aged ≥18 with a diagnosis of Covid-19 admitted to the AO AL in the first 50 days of the first 3 waves were included. The first wave started on 24 February 2020 (first day of available data by the Ministry of Health), the second wave on 14 September 2020 (first day of the 2020/21 school year), the third wave on 15 February 2021 (peak of cases detected by the Italian College of Health). The causes of death were obtained from the National Institute of Statistics death cards and codified according to the International Classification of Diseases, 9th revision, classification. Results We included 825 subjects (median age: 73 years;male prevalence: 60.7%). The subjects hospitalized in the first wave were 464, in the second wave 255, in the third wave 106. A total of 309 subjects died (37.5%), of which 218 in the first wave (70.6%), 69 in the second wave (22.3%), 22 in the third wave (7.1%). The most frequent causes of death were “Covid-19 pneumonia” (61.5%) and “respiratory distress syndrome” (19.4%). Death occurred after hospital discharge in 40% of cases. 6 months after admission, the survival rate was 53% among patients of the first wave, 73% and 78% for those of the second and third wave. Patients hospitalized in the first and second waves showed a greater risk of death compared to patients of the third wave (HR = 2.8;95% CI 1.8-4.4 and HR = 1.4;95% CI 0.8-2.2). Conclusions Data showed a difference in mortality between the 3 waves with a statistically significant variation between the first and third waves. Key messages • Data showed a difference in mortality between the 3 waves. • Data showed a statistically significant variation in mortality between the first and third waves.
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Background: Since February 2020, CoViD-19 spread in Italy. Acute respiratory failure (ARF) was the most relevant clinical presentation, often requiring invasive and non-invasive ventilation. We report the management of ARF in in-patients using Easy Vent Mask (EVM) system for C-PAP, a device registered for prehospital use. Methods: In this retrospective study, we included all patients admitted to Emergency Medicine Unit from March 2 to April 25, 2020 with ARF secondary to CoViD-19 pneumonia and treated with EVM system. Our aim was to evaluate the efficacy and tolerability of in-hospital use of EVM system. All demographic, clinical and treatment data were recorded. Results: Thirty patients affected by mild/moderate CoViD-19 pneumonia having PaO2/FiO2(P/F) ratio between 100 and 200 were treated with EVM system for C-PAP, of them 25(83%) were discharged and 5(17%) died in hospital. The system was well tolerated with a mean time of use of 13 consecutive days. Five patients were transferred to ICU due to failure of C-PAP treatment, of whom 3 died. Two patients died in our ward for worsening of clinical conditions. Pressure skin lesions and hypercapnia were recorded as adverse events in 24 and 2 patients respectively. Moreover, our finding showed a higher reduction of P/F value and a longer time from admission to C-PAP initiation in non-survivors, compared to survivor patients. Conclusions: EVM system for C-PAP seemed to be well tolerated and may represent an alternative to ventilators in in-hospital treatment of CoViD-19 mild/moderate pneumonia, but dedicated studies are needed.
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In 2020, severe coronavirus 2 respiratory syndrome (SARS-Cov-2) has quickly risen, becoming a worldwide pandemic that is still ongoing nowadays. Differently from other viruses the COVID-19, responsible for SARS-Cov-2, demonstrated an unmatched capability of transmission that led towards an unprecedented challenge for the global health system. All health facilities, ranging from Hospitals to local health surveillance units, have been severely tested due to the high number of infected people. In this scenario, the use of methodologies that can improve and optimize, at any level, the management of infected patients is highly advisable. One of the goals of Artificial Intelligence in medicine is to develop advanced tools and methodologies to support patient care and to help physicians and medical work in the decision-making process. More specifically, Machine Learning (ML) methods have been successfully used to build predictive models starting from clinical patient data. In our paper, we study whether ML can be used to build prognostic models capable of predicting the potential disease outcome. In our study, we evaluate different unsupervised and supervised ML approaches using SARS-Cov-2 data collected from the "Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo"Hospital in Alessandria area, Italy, from 24th February to 31st October 2020. Our preliminary goal is to develop a ML model able to promptly identify patients with a high risk of fatal outcome, to steer medical doctors and clinicians towards the best management strategies. © 2021 IEEE.
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Background: The long-term impact of COVID-19 in cancer patients (pts) is undefined. Methods: Among 2795 consecutive pts with COVID-19 and cancer registered to OnCovid between 01/2020 and 02/2021, we examined clinical outcomes of pts reassessed post COVID-19 recovery. Results: Among 1557 COVID-19 survivors, 234 (15%) reported sequelae including respiratory symptoms (49.6%), fatigue (41%) and cognitive/psychological dysfunction (4.3%). Persisting COVID-19 sequelae were more likely found in males (p=0.0407) aged ≥65 years (p=0.0489) with ≥2 comorbidities (p=0.0006) and positive smoking history (p=0.0004). Sequelae were associated with history of prior hospitalisation (p<0.0001), complicated disease (p<0.0001) and COVID-19 therapy (p=0.0002). With a median post-COVID-19 follow up of 128 days (95%CI 113-148), multivariable analysis of survival revealed COVID-19 sequelae to be associated with an increased risk of death (HR 1.76, 95%CI 1.16-2.66) after adjusting for sex, age, comorbidities, tumour characteristics, anticancer therapy and COVID-19 severity. Out of 473 patients who were on systemic anticancer therapy (SACT) at COVID-19 diagnosis;62 (13.1%) permanently discontinued therapy and 75 (15.8%) received SACT adjustments, respectively. Discontinuations were due to worsening performance status (45.1%), disease progression (16.1%) and residual organ disfunction (6.3%). SACT adjustments were pursued to avoid hospital attendance (40%), prevent immunosuppression (57.3%) or adverse events (20.3%). Multivariable analyses showed permanent discontinuation to be associated with an increased risk of death (HR 4.2, 95%CI: 1.62-10.7), whereas SACT adjustments did not adversely affect survival. Conclusions: Sequelae post-COVID-19 affect up to 15% of patients with cancer and adversely influence survival and oncological outcomes after recovery. SACT adjustments can be safely pursued to preserve oncological outcomes in patients who remain eligible to treatment. Clinical trial identification: NCT04393974. Legal entity responsible for the study: Imperial College London. Funding: Has not received any funding. Disclosure: A. Cortellini: Financial Interests, Personal, Advisory Board: MSD;Financial Interests, Personal, Advisory Board: BMS;Financial Interests, Personal, Advisory Board: Roche;Financial Interests, Personal, Invited Speaker: Novartis;Financial Interests, Personal, Invited Speaker: AstraZeneca;Financial Interests, Personal, Invited Speaker: Astellas;Financial Interests, Personal, Advisory Board: Sun Pharma. D.J. Pinato: Financial Interests, Personal, Advisory Board: ViiV Healthcare;Financial Interests, Personal, Invited Speaker: Bayer;Financial Interests, Personal, Advisory Board: Eisai;Financial Interests, Personal, Invited Speaker: Roche;Financial Interests, Personal, Invited Speaker: AstraZeneca. All other authors have declared no conflicts of interest.