This Viewpoint examines the increase in “mega-journals” (prolific publishers of medical articles) and both the opportunities and threats to scientific research they present.

Introduction Several countries are either planning or implementing national strategies for the development and integration of Personalized Medicine (PM) into their healthcare systems. Personalized Medicine is an undisputed priority of the European Commission (EC), which has funded the project “Integrating China into the International Consortium for Personalized Medicine” (IC2PerMed), in order to ensure a common basis for Sino-European collaborations. By mapping the current PM landscape in the European Union (EU) and in China, IC2PerMed aims to provide key solutions toward a synergistic and coordinated approach in the field of PM. Methods An extensive desk research was conducted, aimed at identifying documents on PM-related policies, programs, and action plans in the EU and in China, published up to November 2020. The search was conducted by exploring scientific and gray literature, and official institutional repositories. A descriptive summary condensed the information retrieved for both. Results Since 2013, the year of publication of the first PM policy by the EC “Use of omics technologies in PM development,” several documents have been published. PM is a key element of the policy agenda also in China, which in 2016 integrated PM into the 13th National Five-Year Plan, followed by the publication of several policies on technology infrastructure and big data. Both in the EU and China, especially in recent years, these policies addressed in detail the issues of big data, data interoperability and exchange, while defining the standards of information and communication infrastructures. Conclusions In order to allow optimal collaboration, it is essential to understand similarities and differences between the respective policy strategies, with particular attention to data management and adopted infrastructures. The results of this project may enable the development of joint Sino-European research and innovation initiatives, promoting developments in the field of PM.

Objective:Spinal muscular atrophy (SMA) is a neurodegenerative disorder caused by mutations in the SMN1 gene. The aim was to assess the prevalence of SMA and treatment prescription in Italy.Methods:An online survey was distributed to 36 centers identified by the Italian government as referral centers for SMA. Data on number of SMA patients subdivided according to age, type,SMN2copy number and treatment were collected.Results:1255 SMA patients are currently followed in the Italian centers with an estimated prevalence of 2.12/100000. Of the 1255, 284 were type I, 470 type II, 467 type III and 15 type IV with estimated prevalence of 0.48, 0.79, 0.79 and 0.02/100000 respectively. Three SMA 0 and 16 presymptomatic patients were also included.Around 85% were receiving one of the available treatments. The percentage of treated patients decreased with decreasing severity (SMA I: 95.77%, SMA II: 85.11%,SMA III: 79.01%).Discussion:The results provide for the first time an estimate of the prevalence of SMA at the national level and the current distribution of patients treated with the available therapeutical options. These data provide a baseline to assess future changes in relation to the evolving therapeutical scenario.

BackgroundThe COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters, potentially available at home, to help identifying patients with COVID-19 who are at higher risk of death.MethodsThe training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020 to November 5, 2020. Afterwards, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020 to February 5 2021. The primary outcome was in-hospital mortality.The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of 5-fold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 hours after the baseline measurement was plotted against its baseline value.ResultsAmong the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the 5-fold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n=1463) in which the mortality rate was 22.6 %. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the mortality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 hours after admission (adjusted R-squared= 0.48).ConclusionsWe developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients at home, in the Emergency Department, or during hospitalization.