Meeting the Need for a Discussion of Unmet Medical Need.

As Europe and the world continue to battle against COVID, the customary complacency of society over future threats is clearly on display. Just 30 months ago, such a massive disruption to global lives, livelihoods and quality of life seemed unimaginable. Some remedial European Union action is now emerging, and more is proposed, including in relation to tackling "unmet medical need" (UMN). This initiative-directing attention to the future of treating disease and contemplating incentives to stimulate research and development-is welcome in principle. But the current approach being considered by EU officials merits further discussion, because it may prove counter-productive, impeding rather than promoting innovation. This paper aims to feed into these ongoing policy discussions, and rather than presenting research in the classical sense, it discusses the key elements from a multistakeholder perspective. Its central concern is over the risk that the envisaged support will fail to generate valuable new treatments if the legislation is phrased in a rigidly linear manner that does not reflect the serpentine realities of the innovation process, or if the definition placed on unmet medical need is too restrictive. It cautions that such an approach presumes that "unmet need" can be precisely and comprehensively defined in advance on the basis of the past. It cautions that such an approach can reinforce the comfortable delusion that the future is totally predictable-the delusion that left the world as easy prey to COVID. Instead, the paper urges reflection on how the legislation that will shortly enter the pipeline can be phrased so as to allow for the flourishing of a culture capable of rapid adaptation to the unexpected.

A real-time integrated framework to support clinical decision making for covid-19 patients.

BACKGROUND: The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies. METHODS: The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented. RESULTS: The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level. INTERPRETATION: The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

The 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 to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, 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 case-fatality risk. 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 fivefold 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 h after the baseline measurement was plotted against its baseline value. Among 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 fivefold 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 case-fatality 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 case-fatality 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 h after admission (adjusted R-squared = 0.48). We 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 in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.

Repeated Exposure to Subinfectious Doses of SARS-CoV-2 May Promote T Cell Immunity and Protection against Severe COVID-19.

Europe is experiencing a third wave of COVID-19 due to the spread of highly transmissible SARS-CoV-2 variants. A number of positive and negative factors constantly shape the rates of COVID-19 infections, hospitalization, and mortality. Among these factors, the rise in increasingly transmissible variants on one side and the effect of vaccinations on the other side create a picture deeply different from that of the first pandemic wave. Starting from the observation that in several European countries the number of COVID-19 infections in the second and third pandemic wave increased without a proportional rise in disease severity and mortality, we hypothesize the existence of an additional factor influencing SARS-CoV-2 dynamics. This factor consists of an immune defence against severe COVID-19, provided by SARS-CoV-2-specific T cells progressively developing upon natural exposure to low virus doses present in populated environments. As suggested by recent studies, low-dose viral particles entering the respiratory and intestinal tracts may be able to induce T cell memory in the absence of inflammation, potentially resulting in different degrees of immunization. In this scenario, non-pharmaceutical interventions would play a double role, one in the short term by reducing the detrimental spreading of SARS-CoV-2 particles, and one in the long term by allowing the development of a widespread (although heterogeneous and uncontrollable) form of immune protection.

A moonshot approach toward the management of cancer patients in the COVID-19 time: what have we learned and what could the Italian network of cancer centers (Alliance Against Cancer, ACC) do after the pandemic wave?

If we focus our attention on seven main features of COVID-19 infection (heterogeneity, fragility, lack of effective treatments and vaccines, "miraculous cures", psychological suffering, deprivation, and globalization), we may establish parallelism with the challenges faced in the steep road to the understanding and treatment of neoplastic diseases. How the similarities between these two conditions can help us cope with the emergency effort represented by the management of cancer patients in the COVID-19 era, today and in the future? In a manner similar to the Cancer Moonshot initiative in the United States, we can hypothesize a multinational moonshot project towards the management of cancer patients during COVID-19 pandemic. In particular, we believe that the main road to elaborate meaningful scientific evidence is represented by the collection of all the data on COVID-19 and cancer comorbidity that are and will become available in cancer centers, coupled with the design of large clinical studies. To address this goal, it is essential to identify the entity that can produce this scientific evidences and the potentially most successful research strategy to undertake. The largest Italian organization for cancer research, Alliance Against Cancer (Alleanza Contro il Cancro, ACC), is called to play a scientific leadership in addressing these challenges, which requires the coordination of oncology teams at regional, national, and international levels. To fulfill this commitment, ACC will create a liaison with health government agencies in order to develop "dynamic" indications able to fight such an unpredictable pandemic.