ARTIFICIAL INTELLIGENCE TOOLS FOR EFFECTIVE MONITORING OF POPULATION AT DISTANCE DURING COVID-19 PANDEMIC. RESULTS FROM AN ITALIAN PILOT FEASIBILITY STUDY (RICOVAI-19 STUDY). (preprint)

In order to reduce the burden on healthcare systems and in particular to support an appropriate way to the Emergency Department (ED) access, home tele-monitoring patients was strongly recommended during the COVID-19 pandemic. Furthermore, paper from numerous groups has shown the potential of using data from wearable devices to characterize each individual's unique baseline, identify deviations from that baseline suggestive of a viral infection, and to aggregate that data to better inform population surveillance trends. However, no evidence about usage of Artificial Intelligence (AI) applicatives on digitally data collected from patients and doctors exists. With a growing global population of connected wearable users, this could potentially help to improve the earlier diagnosis and management of infectious individuals and improving timeliness and precision of tracking infectious disease outbreaks. During the study RICOVAI-19 (RICOVero ospedaliero con strumenti di Artificial Intelligence nei pazienti con COVid-19) performed in a Marche Region, Italy, we evaluated N129 subjects monitored at home in a six-months period between March 22, 2021 and October 22, 2021. During the monitoring, personal on demand health technologies were used to collect clinical and vital data in order to feed the database and the machine learning engine. The AI output resulted in a clinical stability index (CSI) which enables the system to deliver suggestions to the population and doctors about how intervene . Results showed the beneficial influence of CSI for predicting clinical classes of subjects and identifying who of them need to be admitted at ED. The same pattern of results was confirming the alert included in the decision support system in order to request further testing or clinical information in some cases. In conclusion, our study does support an high impact of AI tools on COVID outcomes to fight this pandemic by driving new approaches to public awareness.

Tocilizumab in Covid-19 Interstitial Pneumonia: A Phase II Pilot Study (preprint)

Background: Multiple studies have been conducted to investigate Tocilizumab in patients with cOVID-19 pneumonitis. However, published reports show conflicting results, largely due to weak retrospective designs and heterogeneity in critical methodological issues. Methods: : This open-label trial was structured according to the Simon’s optimal two-stage design in order to clarify which patients could really benefit from anti-IL6 strategies and how a future randomized trial should be designed to provide reliable and unequivocal results. 46 patients received a single infusion of Tocilizumab. Inclusion criteria were: SARS-CoV2 infection diagnosed by rt-PCR, multifocal interstitial pneumonia, need of oxygen therapy (FiO2 50%) to maintain SO2 >93%, recent (within the last 24 hours) worsening of lung function. Clinical outcomes were established a priori to assess whether a patient responded to treatment. A low number of carefully chosen clinical and biological markers was measured in order to test their predictive values. Primary end point was early and sustained clinical response. Results: : Twenty-one (46%) patients fulfilled pre-defined response criteria. Lower levels of IL-6 at 24 hours after tocilizumab infusion (p=0.049) and higher baseline values of PaO2/FiO2 (p=0.008) predicted a favorable clinical response. Patients not improving at 72 hours were also non-responder at day 7. 11/25 of non-responder patients were intubated and 7 died. High levels of vWF were detected in all sera, with a tendency towards higher concentrations in the non-responder group. Conclusions: : Objective clinical response rate overcame the pre-defined threshold of 30%. Efficacy of tocilizumab to improve respiratory function in selected patients with severe COVID-19 pneumonitis warrants investigations in randomized trials. Trial registration: NCT 04315480