To talk better about vaccines, we should talk less about vaccines.

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Facial masks in children: the position statement of the Italian pediatric society

Facial masks may be one of the most cost-effective strategies to prevent the diffusion of COVID 19 infection. Nevertheless, fake news are spreading, alerting parents on dangerous side effects in children, such as hypercapnia, hypoxia, gut dysbiosis and immune system weakness. Aim of the Italian Pediatric Society statement is to face misconception towards the use of face masks and to spread scientific trustable information

Social media use to improve communication on children and adolescent’s health: the role of the Italian Paediatric Society influencers

Background Fake news on children’s and adolescent health are spreading. Internet availability and decreasing costs of media devices are contributing to an easy access to technology by families. Public health organizations are working to contrast misinformation and promote scientific communication. In this context, a new form of communication is emerging social media influencers. Aim of this study is to evaluate the role of paediatric influencers (PI) in communicating information about children and adolescents’ health. Materials and methods A group of PI was enrolled from December 2019 to January 2020 by a scientific commission nominated by the Italian Paediatric Society (SIP). PI were asked to share Facebook messages from the official page of the SIP to their own network. Social media tools have been evaluated across 12 months, from July 28, 2019, to July 11, 2020. For the purposes of clarity, we schematically divided the study period as follows: the period of PIs activity (January 6, 2020, to July 11, 2020) and the period when PIs were not yet active (July 28, 2019, to January 4, 2020). Information on Facebook page (lifetime total likes, daily new likes, daily page engaged, daily total reach) and on published post (lifetime post total reach, lifetime post organic reach, lifetime engaged users) were evaluated. Results A significant increase in Facebook daily new likes, page engagement and total reach, as well as in lifetime post total and organic reach was evidenced. As for PI, they reported a positive experience in most cases. Discussion In the digital era, communication strategies are becoming more important, so that the scientific community has to be actively involved in social media communication. Our pilot study demonstrated that the recruitment of paediatric influencers has increased communication and interaction of the SIP Facebook page. Conclusion Our study shows the potential role of influencers: spreading health messages via PI seems to be a successful strategy to promote correct communication about children’s and adolescents’ health

COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm

Background: Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters. Results: Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU. One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered model