Heart failure management guided by remote multiparameter monitoring: A meta-analysis.

BACKGROUND: Several implant-based remote monitoring strategies are currently tested to optimize heart failure (HF) management by anticipating clinical decompensation and preventing hospitalization. Among these solutions, the modern implantable cardioverter-defibrillator and cardiac resynchronization therapy devices have been equipped with sensors allowing continuous monitoring of multiple preclinical markers of worsening HF, including factors of autonomic adaptation, patient activity, and intrathoracic impedance. OBJECTIVES: We aimed to assess whether implant-based multiparameter remote monitoring strategy for guided HF management improves clinical outcomes when compared to standard clinical care. METHODS: A systematic literature research for randomized controlled trials (RCTs) comparing multiparameter-guided HF management versus standard of care was performed on PubMed, Embase, and CENTRAL databases. Incidence rate ratios (IRRs) and associated 95% confidence intervals (CIs) were calculated using the Poisson regression model with random study effects. The primary outcome was a composite of all-cause death and HF hospitalization events, whereas secondary endpoints included the individual components of the primary outcome. RESULTS: Our meta-analysis included 6 RCTs, amounting to a total of 4869 patients with an average follow-up time of 18 months. Compared with standard clinical management, the multiparameter-guided strategy reduced the risk of the primary composite outcome (IRR 0.83, 95%CI 0.71-0.99), driven by statistically significant effect on both HF hospitalization events (IRR 0.75, 95%CI 0.61-0.93) and all-cause death (IRR 0.80, 95%CI 0.66-0.96). CONCLUSION: Implant-based multiparameter remote monitoring strategy for guided HF management is associated with significant benefit on clinical outcomes compared to standard clinical care, providing a benefit on both hospitalization events and all-cause death.

KIT 1 (Keep in Touch) Project-Televisits for Cancer Patients during Italian Lockdown for COVID-19 Pandemic: The Real-World Experience of Establishing a Telemedicine System.

To evaluate the adoption of an integrated eHealth platform for televisit/monitoring/consultation during the COVID-19 pandemic. METHODS: During the lockdown imposed by the Italian government during the COVID19 pandemic spread, a dedicated multi-professional working group was set up in the Radiation Oncology Department with the primary aim of reducing patients' exposure to COVID-19 by adopting de-centralized/remote consultation methodologies. Each patient's clinical history was screened before the visit to assess if a traditional clinical visit would be recommended or if a remote evaluation was to be preferred. Real world data (RWD) in the form of patient-reported outcomes (PROMs) and patient reported experiences (PREMs) were collected from patients who underwent televisit/teleconsultation through the eHealth platform. RESULTS: During the lockdown period (from 8 March to 4 May 2020) a total of 1956 visits were managed. A total of 983 (50.26%) of these visits were performed via email (to apply for and to upload of documents) and phone call management; 31 visits (1.58%) were performed using the eHealth system. Substantially, all patients found the eHealth platform useful and user-friendly, consistently indicating that this type of service would also be useful after the pandemic. CONCLUSIONS: The rapid implementation of an eHealth system was feasible and well-accepted by the patients during the pandemic. However, we believe that further evidence is to be generated to further support large-scale adoption.

Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves.

Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy). Artificial intelligence and big data processing were used to retrieve data. Patients and clinical characteristics of patients with available FIB-4 data derived from the Gemelli Generator Real World Data (G2 RWD) were used to develop predictive mortality models during the four waves of the COVID-19 pandemic. A logistic regression model was applied to the training and test set (75%:25%). The model's performance was assessed by receiver operating characteristic (ROC) curves. A total of 4936 patients were included. Hypertension (38.4%), cancer (12.15%) and diabetes (16.3%) were the most common comorbidities. 23.9% of patients were admitted to ICU, and 12.6% had mechanical ventilation. During the study period, 762 patients (15.4%) died. We developed a multivariable logistic regression model on patient data from all waves, which showed that the FIB-4 score > 2.53 was associated with increased mortality risk (OR = 4.53, 95% CI 2.83-7.25; p ≤ 0.001). These data may be useful in the risk stratification at the admission of hospitalized patients with COVID-19.

Eligibility for the 4 Pharmacological Pillars in Heart Failure With Reduced Ejection Fraction at Discharge.

Background Guidelines recommend using multiple drugs in patients with heart failure (HF) with reduced ejection fraction, but there is a paucity of real-world data on the simultaneous initiation of the 4 pharmacological pillars at discharge after a decompensation event. Methods and Results A retrospective data mart, including patients diagnosed with HF, was implemented. Consecutively admitted patients with HF with reduced ejection fraction were selected through an automated approach and categorized according to the number/type of treatments prescribed at discharge. The prevalence of contraindications and cautions for HF with reduced ejection fraction treatments was systematically assessed. Logistic regression models were fitted to assess predictors of the number of treatments (≥2 versus <2 drugs) prescribed and the risk of rehospitalization. A population of 305 patients with a first episode of HF hospitalization and a diagnosis of HF with reduced ejection fraction (ejection fraction, <40%) was selected. At discharge, 49.2% received 2 current recommended drugs, ß-blockers were prescribed in 93.4%, while a renin-angiotensin system inhibitor or an angiotensin receptor-neprilysin inhibitor was prescribed in 68.2%. A mineralocorticoid receptor antagonist was prescribed in 32.5%, although none of the patients showed contraindications to mineralocorticoid receptor antagonist prescription. A sodium-glucose cotransporter 2 inhibitor could be prescribed in 71.1% of patients. On the basis of current recommendations, 46.2% could receive the 4 foundational drugs at discharge. Renal dysfunction was associated with <2 foundational drugs prescribed. After adjusting for age and renal function, use of ≥2 drugs was associated with lower risk of rehospitalization during the 30 days after discharge. Conclusions A quadruple therapy could be directly implementable at discharge, potentially providing prognostic advantages. Renal dysfunction was the main prevalent condition limiting this approach.

Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study.

BACKGROUND: The current management of lung cancer patients has reached a high level of complexity. Indeed, besides the traditional clinical variables (e.g., age, sex, TNM stage), new omics data have recently been introduced in clinical practice, thereby making more complex the decision-making process. With the advent of Artificial intelligence (AI) techniques, various omics datasets may be used to create more accurate predictive models paving the way for a better care in lung cancer patients. METHODS: The LANTERN study is a multi-center observational clinical trial involving a multidisciplinary consortium of five institutions from different European countries. The aim of this trial is to develop accurate several predictive models for lung cancer patients, through the creation of Digital Human Avatars (DHA), defined as digital representations of patients using various omics-based variables and integrating well-established clinical factors with genomic data, quantitative imaging data etc. A total of 600 lung cancer patients will be prospectively enrolled by the recruiting centers and multi-omics data will be collected. Data will then be modelled and parameterized in an experimental context of cutting-edge big data analysis. All data variables will be recorded according to a shared common ontology based on variable-specific domains in order to enhance their direct actionability. An exploratory analysis will then initiate the biomarker identification process. The second phase of the project will focus on creating multiple multivariate models trained though advanced machine learning (ML) and AI techniques for the specific areas of interest. Finally, the developed models will be validated in order to test their robustness, transferability and generalizability, leading to the development of the DHA. All the potential clinical and scientific stakeholders will be involved in the DHA development process. The main goals aim of LANTERN project are: i) To develop predictive models for lung cancer diagnosis and histological characterization; (ii) to set up personalized predictive models for individual-specific treatments; iii) to enable feedback data loops for preventive healthcare strategies and quality of life management. DISCUSSION: The LANTERN project will develop a predictive platform based on integration of multi-omics data. This will enhance the generation of important and valuable information assets, in order to identify new biomarkers that can be used for early detection, improved tumor diagnosis and personalization of treatment protocols. ETHICS COMMITTEE APPROVAL NUMBER: 5420 - 0002485/23 from Fondazione Policlinico Universitario Agostino Gemelli IRCCS - Università Cattolica del Sacro Cuore Ethics Committee. TRIAL REGISTRATION: clinicaltrial.gov - NCT05802771.

GENERATOR HEART FAILURE DataMart: An integrated framework for heart failure research.

Background: Heart failure (HF) is a multifaceted clinical syndrome characterized by different etiologies, risk factors, comorbidities, and a heterogeneous clinical course. The current model, based on data from clinical trials, is limited by the biases related to a highly-selected sample in a protected environment, constraining the applicability of evidence in the real-world scenario. If properly leveraged, the enormous amount of data from real-world may have a groundbreaking impact on clinical care pathways. We present, here, the development of an HF DataMart framework for the management of clinical and research processes. Methods: Within our institution, Fondazione Policlinico Universitario A. Gemelli in Rome (Italy), a digital platform dedicated to HF patients has been envisioned (GENERATOR HF DataMart), based on two building blocks: 1. All retrospective information has been integrated into a multimodal, longitudinal data repository, providing in one single place the description of individual patients with drill-down functionalities in multiple dimensions. This functionality might allow investigators to dynamically filter subsets of patient populations characterized by demographic characteristics, biomarkers, comorbidities, and clinical events (e.g., re-hospitalization), enabling agile analyses of the outcomes by subsets of patients. 2. With respect to expected long-term health status and response to treatments, the use of the disease trajectory toolset and predictive models for the evolution of HF has been implemented. The methodological scaffolding has been constructed in respect of a set of the preferred standards recommended by the CODE-EHR framework. Results: Several examples of GENERATOR HF DataMart utilization are presented as follows: to select a specific retrospective cohort of HF patients within a particular period, along with their clinical and laboratory data, to explore multiple associations between clinical and laboratory data, as well as to identify a potential cohort for enrollment in future studies; to create a multi-parametric predictive models of early re-hospitalization after discharge; to cluster patients according to their ejection fraction (EF) variation, investigating its potential impact on hospital admissions. Conclusion: The GENERATOR HF DataMart has been developed to exploit a large amount of data from patients with HF from our institution and generate evidence from real-world data. The two components of the HF platform might provide the infrastructural basis for a combined patient support program dedicated to continuous monitoring and remote care, assisting patients, caregivers, and healthcare professionals.

Association of the Extent of Resection with Survival in Multiple Primary Lung Cancer: A Systematic Review.

BACKGROUND: The incidence of synchronous multiple primary lung cancer (SMPLC) has progressively increased, due to recent advances in imaging. To date, no guidelines defining recommendations for patients' selection and no standard treatment of cases with SMPLC have been defined.The primary aim of this systematic review was to assess survival among patients treated with lobectomy or sublobar resection MPLC. METHODS: Comprehensive literature search of Medline, the Cochrane Library, reference lists, and ongoing studies was performed according to a prospectively registered design (PROSPERO: CRD42019115487). All studies published between 1998 and December 2020 that examined treatments with lobectomy compared to sublobar resection were included. Two double-blind investigators independently selected articles.Primary outcomes were to assess the 5-year overall survival (OS) rate among patients treated with lobectomy or sublobar resection and the impact of lymph node status on 5-year OS and 5-year disease-free survival in patients with MPLC. RESULTS: The search yielded 424 articles; 4 observational studies met the inclusion criteria and collectively evaluated 298 patients with a mean age ranging from 61.5 to 67 years. A total of 112 patients were treated for bilateral synchronous tumors and 186 patients for unilateral multiple synchronous tumors. All included studies showed that the type of resection, lobectomy or limited resection, had no significant impact on survival. CONCLUSION: Limited resection is a valuable treatment option for MPLC. However, the clinical level of evidence of the studies found is low and randomized studies are needed to clarify the extent of resection in MPLC.

Art and digital technologies to support resilience during the oncological journey: The Art4ART project.

Introduction: New digital technologies can become a tool for welcoming the patient through the artistic dimension. Cancer patients, in particular, need support that accompanies and supports them throughout their treatment. Materials and methods: The Art4ART project consist in the structural proposal to cancer patients of a web-based digital platform containing several forms of art as video-entertainments; a multimedia immersive room; an art-based welcoming of the patients with several original paintings; an environment with a peacefulness vertical garden; a reconceptualization of the chemotherapy-infusion seats. Data regarding patients' preference and choices will be stored and analysed also using artificial intelligence (AI) algorithm to measure and predict impact indicators regarding clinical outcomes (survival and toxicity), psychological indicators. Moreover, the same digital platform will contribute to a better organization of the activities. Discussion: Through the systematic acquisition of patient preferences and through integration with other clinical parameters, it will be possible to measure the clinical, psychological, organisational, and social impact of the newly implemented Art4ART project. The use of digital technology leads us to apply the reversal of viewpoint from therapeutic acts to patient-centred care.

Device-based remote monitoring strategies for congestion-guided management of patients with heart failure: a systematic review and meta-analysis.

AIMS: Pre-clinical congestion markers of worsening heart failure (HF) can be monitored by devices and may support the management of patients with HF. We aimed to assess whether congestion-guided HF management according to device-based remote monitoring strategies is more effective than standard therapy. METHODS AND RESULTS: A comprehensive literature research for randomized controlled trials (RCTs) comparing device-based remote monitoring strategies for congestion-guided HF management versus standard therapy was performed on PubMed, Embase, and CENTRAL databases. Incidence rate ratios (IRRs) and associated 95% confidence intervals (CIs) were calculated using the Poisson regression model with random study effects. The primary outcome was a composite of all-cause death and HF hospitalizations. Secondary endpoints included the individual components of the primary outcome. A total of 4347 patients from eight RCTs were included. Findings varied according to the type of parameters monitored. Compared with standard therapy, haemodynamic-guided strategy (4 trials, 2224 patients, 12-month follow-up) reduced the risk of the primary composite outcome (IRR 0.79, 95% CI 0.70-0.89) and HF hospitalizations (IRR 0.76, 95% CI 0.67-0.86), without a significant impact on all-cause death (IRR 0.93, 95% CI 0.72-1.21). In contrast, impedance-guided strategy (4 trials, 2123 patients, 19-month follow-up) did not provide significant benefits. CONCLUSION: Haemodynamic-guided HF management is associated with better clinical outcomes as compared to standard clinical care.

Open Innovation as the Catalyst in the Personalized Medicine to Personalized Digital Medicine Transition.

Personalized medicine (PM) bridges several disciplines for understanding and addressing prevalent, complex, or rare situations in human health (e.g., complex phenotyping, risk stratification, etc.); therefore, digital and technological solutions have been integrated in the field to boost innovation and new knowledge generation. The open innovation (OI) paradigm proposes a method by which to respectfully manage disruptive change in biomedical organizations, as experienced by many organizations during digital transformation and the COVID-19 pandemic. In this article, we focus on how this paradigm has catalyzed the transition from PM to personalized digital medicine in a large-volume research hospital. Methods, challenges, and results are discussed. This case study is an endeavor to confirm that OI strategies could help manage urgent needs from the healthcare environment, while achieving sustainability-oriented, accountable innovation.

Role of Peripheral Blood Markers for Detecting Response and Predicting Prognosis in Patients with Non-small-cell Lung Cancer Undergoing Neoadjuvant Therapy and Surgery.

INTRODUCTION: To date, no validated predictors of response before neoadjuvant therapy (NAD) are currently available in locally advanced non-small-cell lung cancer (NSCLC). In this study, different peripheral blood markers were investigated before NAD (pre-NAD) and after NAD/before surgery (post-NAD) to evaluate their influence on the treatment outcomes. METHODS: Patients affected by locally advanced NSCLC (cT1-T4/N0-2/M0) who underwent NAD followed by surgery from January 1996 to December 2019 were considered for this retrospective analysis. The impact of peripheral blood markers on downstaging post-NAD and on overall survival (OS) was evaluated using multivariate logistic and Cox regression models. Time to event analysis was performed by means of Kaplan-Meier survival curves and Log Rank tests at 5 years from surgery. RESULTS: Two hundred and seventy-two consecutive patients were included. Most of the patients had Stage III NSCLC (83.5%). N2 disease was reported in 188 (69.1%) patients. Surgical resection was performed in patients with stable disease or downstaging post-NAD. Nodal downstaging was observed in 80% of clinical N2 (cN2) patients. The median follow-up of the total series was 74 months (range 6-302). Five-year OS in the overall population and in N2 population was 74.6% and 73.5%, respectively. The pre-surgery platelets level (PLT) (p = 0.019) and the variation (pre-NAD/post-NAD) of the neutrophil/lymphocyte ratio (p = 0.024) were identified as independent prognostic factors of OS. The preoperative PLT value (p value = 0.031) was confirmed as the only predictor of NAD response. CONCLUSIONS: The clinical role of peripheral blood markers in locally advanced NSCLC needs to be further investigated. Based on these preliminary results, these factors may be used as auxiliary markers for the prediction of response to neoadjuvant treatment and as prognostic factors for stratification in multimodal approaches.

Predictive models in SMA II natural history trajectories using machine learning: A proof of concept study.

It is known from previous literature that type II Spinal Muscular Atrophy (SMA) patients generally, after the age of 5 years, presents a steep deterioration until puberty followed by a relative stability, as most abilities have been lost. Although it is possible to identify points of slope indicating early improvement, steep decline and relative stabilizations, there is still a lot of variability within each age group and it's not always possible to predict individual trajectories of progression from age only. The aim of the study was to develop a predictive model based on machine learning using an XGBoost algorithm for regression and report, explore and quantify, in a single centre longitudinal natural history study, the influence of clinical variables on the 6/12-months Hammersmith Motor Functional Scale Expanded score prediction (HFMSE). This study represents the first approach to artificial intelligence and trained models for the prediction of individualized trajectories of HFMSE disease progression using individual characteristics of the patient. The application of this method to larger cohorts may allow to identify different classes of progression, a crucial information at the time of the new commercially available therapies.

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.

Personalized Approach for Obese Patients Undergoing Endoscopic Sleeve Gastroplasty.

Obesity is a chronic, relapsing disease representing a major global health problem in the 21st century. Several etiologic factors are involved in its pathogenesis, including a Western hypercaloric diet, sedentariness, metabolic imbalances, genetics, and gut microbiota modification. Lifestyle modifications and drugs often fail to obtain an adequate and sustained weight loss. To date, bariatric surgery (BS) is the most effective treatment, but only about 1% of eligible patients undergo BS, partly because of its negligible morbidity and mortality. Endoscopic sleeve gastroplasty (ESG) is a minimally invasive, endoscopic, bariatric procedure, which proved to be safe and effective. In this review, we aim to examine evidence supporting the role of a personalized and multidisciplinary approach, guided by a multidisciplinary team (MDT), for obese patients undergoing ESG, from patient selection to long-term follow-up. The cooperation of different health professionals, including an endocrinologist and/or obesity medicine physician, a bariatric surgeon, an endoscopist experienced in bariatrics, a registered dietitian, an exercise specialist, a behaviour coach, a psychologist, and a nurse or physician extender, aims to induce radical and sustained lifestyle changes. We also discussed the relationship between gut microbiota and outcomes after bariatric procedures, speculating that the characterization of gut microbiota before and after ESG may help develop new tools, including probiotics, to optimize weight loss outcomes.

Patients' Satisfaction by SmileInTM Totems in Radiotherapy: A Two-Year Mono-Institutional Experience.

BACKGROUND: Patient's satisfaction is recognized as an indicator to monitor quality in healthcare services. Patient-reported experience measures (PREMs) may contribute to create a benchmark of hospital performance by assessing quality and safety in cancer care. METHODS: The areas of interest assessed were: patient-centric welcome perception (PCWP), punctuality, professionalism and comfort using the Lean Six Sigma (LSS) methodology. The RAMSI (Radioterapia Amica Mia SmileINTM (SI) My Friend RadiotherapySI), project provided for the placement of SI totems with four push buttons using HappyOrNot technology in a high-volume radiation oncology (RO) department. The SI technology was implemented in the RO department of the Fondazione Policlinico Universitario A. Gemelli IRCCS. SI totems were installed in different areas of the department. The SI Experience Index was collected, analyzed and compared. Weekly and monthly reports were created showing hourly, daily and overall trends. RESULTS: From October 2017 to November 2019, a total of 42,755 votes were recorded: 8687, 10,431, 18,628 and 5009 feedback items were obtained for PCWP, professionalism, punctuality, and comfort, respectively. All areas obtained a SI-approved rate ≥ 8.0. CONCLUSIONS: The implementation of the RAMSI system proved to be doable according to the large amount of feedback items collected in a high-volume clinical department. The application of the LSS methodology led to specific corrective actions such as modification of the call-in-clinic system during operations planning. In order to provide healthcare optimization, a multicentric and multispecialty network should be defined in order to set up a benchmark.

Discovering Composite Lifestyle Biomarkers With Artificial Intelligence From Clinical Studies to Enable Smart eHealth and Digital Therapeutic Services.

Discovery of biomarkers is a continuous activity of the research community in the clinical domain that recently shifted its focus toward digital, non-traditional biomarkers that often use physiological, psychological, social, and environmental data to derive an intermediate biomarker. Such biomarkers, by triggering smart services, can be used in a clinical trial framework and eHealth or digital therapeutic services. In this work, we discuss the APACHE trial for determining the quality of life (QoL) of cervical cancer patients and demonstrate how we are discovering a biomarker for this therapeutic area that predicts significant QoL variations. To this extent, we present how real-world data can unfold a big potential for detecting the cervical cancer QoL biomarker and how it can be used for novel treatments. The presented methodology, derived in APACHE, is introduced by Healthentia eClinical solution, and it is beginning to be used in several clinical studies.

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.

COVID-19, adaptative immune response and metabolic-associated liver disease.

Metabolic diseases are associated with a higher risk of a severer coronavirus disease 2019 (COVID-19) course, since fatty liver is commonly associated with metabolic disorders, fatty liver itself is considered as a major contributor to low-grade inflammation in obesity and diabetes. Recently a comprehensive term, metabolic (dysfunction) associated fatty liver disease (MAFLD), has been proposed. The hepatic inflammatory status observed in MAFLD patients is amplified in presence of severe acute respiratory syndrome coronavirus 2 infection. Intestinal dysbiosis is a powerful activator of inflammatory mediator production of liver macrophages. The intestinal microbiome plays a key role in MAFLD progression, which results in non-alcoholic steatohepatitis and liver fibrosis. Therefore, patients with metabolic disorders and COVID-19 can have a worse outcome of COVID-19. This literature review attempts to disentangle the mechanistic link of MAFLD from COVID-19 complexity and to improve knowledge on its pathophysiology.

Development of a Digital Research Assistant for the Management of Patients' Enrollment in Oncology Clinical Trials within a Research Hospital.

Clinical trials in cancer treatment are imperative in enhancing patients' survival and quality of life outcomes. The lack of communication among professionals may produce a non-optimization of patients' accrual in clinical trials. We developed a specific platform, called "Digital Research Assistant" (DRA), to report real-time every available clinical trial and support clinician. Healthcare professionals involved in breast cancer working group agreed nine minimal fields of interest to preliminarily classify the characteristics of patients' records (including omic data, such as genomic mutations). A progressive web app (PWA) was developed to implement a cross-platform software that was scalable on several electronic devices to share the patients' records and clinical trials. A specialist is able to use and populate the platform. An AI algorithm helps in the matchmaking between patient's data and clinical trial's inclusion criteria to personalize patient enrollment. At the same time, an easy configuration allows the application of the DRA in different oncology working groups (from breast cancer to lung cancer). The DRA might represent a valid research tool supporting clinicians and scientists, in order to optimize the enrollment of patients in clinical trials. User Experience and Technology The acceptance of participants using the DRA is topic of a future analysis.