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.

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.

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.

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, β-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.

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.

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

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Digital technologies can be useful in welcoming patients using the beauty of art.

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Cancer patients typically need to be supported in their treatment pathway.

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Digital entertainment can become a resilience-enhancing strategy.

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Art4ART project offers an art-based digital supporting patients’ resilience.

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Art4ART offers a research platform about the role of humanities as cure.

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.

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.

Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept

Aim

The first prototype of the “Multidisciplinary Tumor Board Smart Virtual Assistant” is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician’s attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision making and standardized language and interpretation.

Patients and Method

Data from patients affected by Locally Advanced Cervical Cancer (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography–Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of eHR that capture the patient’s data before the diagnosis and then, through Natural Language Processing (NLP), analysis and categorization of all data to transform source information into structured data has been performed.

Results

In the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. The system has been able to classify all patients belonging to the training set and - through the NLP procedures - the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient’s staging (accuracy of 94%). Lastly, we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way.

Conclusion

This is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automated methods in the collaborative, crucial decision stages.

Building a Personalized Medicine Infrastructure for Gynecological Oncology Patients in a High-Volume Hospital

Gynecological cancers require complex intervention since patients have specific needs to be addressed. Centralization to high-volume centers improves the oncological outcomes of patients with gynecological cancers. Research in gynecological oncology is increasing thanks to modern technologies, from the comprehensive molecular characterization of tumors and individual pathophenotypes. Ongoing studies are focusing on personalizing therapies by integrating information across genomics, proteomics, and metabolomics with the genetic makeup and immune system of the patient. Hence, several challenges must be faced to provide holistic benefit to the patient. Personalized approaches should also recognize the unmet needs of each patient to successfully deliver the promise of personalized care, in a multidisciplinary effort. This may provide the greatest opportunity to improve patients' outcomes. Starting from a narrative review on gynecological oncology patients' needs, this article focuses on the experience of building a research and care infrastructure for personalized patient management.

Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator

The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.

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.

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.

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 SmileIN^{TM (SI)} My Friend Radiotherapy^SI), 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.

The personalized medicine discourse: archaeology and genealogy

Personalized Medicine (PM) is an evolving and often missinterpreted concept and no agreement of personalization exist. We examined the PM discourse towards foucauldian archeological and genealogical analysis to understand the meaning of "personalization" in medicine. In the archaeological analysis, the historical evolution is characterized by the coexistence of two epistemologies: the holistic vision and the omic sciences. The genealogical analysis shows how these epistemologies may affect the meaning of "person" and, consequently, the ontology of patients. Additionally, substitutions/confusions of the term PM are related to continuously evolving medical knowledge and new technologies; different etymological roots of "personalization" and "person"; and cultural differences. In conclusion, if the definition of "personalization" in medicine is not clear, patients might get wrong expectations about what is achievable for their health. Therefore, epistemological trends should not be separated as they drive same goals: providing accurate diagnosis and treatments based on large data to predict disease progression.

The Role of Artificial Intelligence in Managing Multimorbidity and Cancer

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in "Personalized Medicine" and "Innovative Biomedical Technologies") could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.

Which Is the Best Treatment in Recurrent Thymoma? A Systematic Review and Meta-Analysis

Simple Summary

Thymomas are rare tumors developing in the anterior mediastinum. Despite their usually indolent behavior, recurrence might occur in 5–15% of cases. Considering their rarity, the optimal recurrence treatment is still unclear even if surgical treatment seems to ensure a remarkable long-term survival compared to chemo- or radiotherapy. However, the major part of studies report low patient numbers, and it is difficult to plan prospective studies due to tumor characteristics, long follow-up and rarity of cases. For these reasons, we planned a systematic review and meta-analysis comparing surgical treatment with other therapies, in order to identify the best treatment for these patients. Our meta-analysis included more than 700 patients showing that surgical treatment seems to be associated with a better survival compared to other treatments and should be considered where feasible.

Abstract

Background: Optimal recurrent thymoma management remains arguable because of limited patient numbers, and relatively late and variable recurrence patterns. Given the absence of high-quality evidence and relatively small study cohorts, we performed a quantitative meta-analysis to determine the outcome of surgical and non-surgical approaches assessing the five-year overall survival (5y overall survival (OS)) in patients with recurrent thymoma. Methods: We performed a comprehensive literature search and analysis according to PRISMA guidelines of studies published from 1 January 1980 until 18 June 2020 from PubMed/MEDLINE, EMBASE, and Scopus. We included studies with the cohorts’ superior to 30 patients describing recurrent thymoma treatment, comparing surgical and non-surgical approaches reporting survival data. Results: Literature search revealed 3017 articles. Nine studies met all inclusion criteria and were selected for the meta-analysis. The recurrences were local/regional in 73–98% of cases and multiple in 49–72%. After treatment, 5y OS ranged from 48–77% and 10y OS from 37–51%. The quantitative meta-analysis showed a better outcome comparing surgical vs other treatments. Two studies showed statistically significant risk differences in the 5y OS favoring complete resection. After pooling results of seven studies using the random model, the combined 5y OS risk difference was 0.39, with lower and upper limits of 0.16 and 0.62, respectively (p = 0.001), and a moderate heterogeneity among studies (p = 0.098, I2 = 43.9%). Definitive conclusions could not be drawn regarding the prognostic impact of myasthenia gravis, histology, and patterns of relapse reported in literature. Conclusions: Surgical treatment after thymoma recurrence is associated with a significant better 5y OS; therefore, surgical resection should be preferred in all technically feasible cases.

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.

Thoracic Hyper-IgG4-Related Disease Mimicking Malignant Pleural Mesothelioma

We report a rare case of a IgG4-related disease presenting with recurrent pleural effusion, pleural thickness and multiple mediastinal lymphadenopathies and no involvement of other extrathoracic organs. A 65-year-old man with a previous asbestos exposure presented with cough and pain discomfort. A large right pleural effusion was detected and evacuated (siero-haematic liquid). With the suspicious of a pleural mesothelioma, a CT-scan before and a ^18F-FDG PET/CT-scan later were performed revealing multiple pleural thickenings and multiple mediastinal lymphadenopathies with radiotracer uptake. EBUS-TBNA EBUS-TBNA did not result in a formal pathological diagnosis; thus, multiple pleural biopsy were performed via right thoracoscopy. At pathology the pleura was markedly thickened by a chronic fibroinflammatory process with scattered lymphoid follicles and a large number of mature plasma cells. Immunohistochemistry shows a mixed B (CD20+) and T (CD3+) population of lymphocytes, without light chain restriction and an increased number of IgG4-positive plasma cells. A presumptive diagnosis of IgG4-related disease was formulated. Total body CT-scan excluded other organ involvement. Blood test showed elevated serum IgG4 concentrations (253 mg/dL) and mild elevation of acute-phase reactants (C-reactive protein 10.7 mg/L). Autoimmune profile was negative. A diagnosis of definite IgG4-related disease was made, and treatment with prednisone 50 mg/day was started.

Uniportal video-assisted thoracic surgery Roman experience-a report of the first 16-month Roman experience

Background

The acceptance of uniportal video-assisted thoracic surgery (U-VATS) for thoracic procedures has been growing worldwide. This study reports one of the widest Italian U-VATS experiences.

Methods

The prospectively collected data of 237 patients underwent a U-VATS procedure, between May 2016 and September 2017, were retrospectively reviewed. A wide range of procedures, like major and minor lung resections, esophageal surgery, pleural and mediastinal one, was performed. The main aim of the study was evaluating general outcomes in terms of safety and effectiveness, and analyzing short-term results of U-VATS approach.

Results

The mean age of population was 59.93±16.03 years. In 208 cases (85.3%) a U-VATS lung resection was performed, in 10 cases (4.1%) an esophagectomy or an esophageal diverticulectomy, in 15 (6.1%) a mediastinal procedure and in 11 (4.5%) a toilette for pleural empyema or removal of pleural lesions. The chest tube duration was 4.24±3.73 days and the postoperative hospital stay was 4.62±4.59 days. The intraoperative and thirty-day mortality were null. Mean level of pain in I postoperative day was 2.30±1.26 on VAS scale and the mean duration was of 1.54±1.21 days. In 93% of cases there was a resolution of pain after chest tube removal. Furthermore, the average level of cosmetic satisfaction was 2.73±0.49 (measured on a 0-3 scale).

Conclusions

According to our experience, U-VATS seems to be a safe and practicable mini-invasive technique, above all for surgeons who already have thoracoscopy experience or made proper training attending multilevel courses, hands-on conferences and wet-labs.

Learning curve of uniportal video-assisted lobectomy: analysis of 15-month experience in a single center

Background

Uniportal video-assisted thoracoscopic (U-VATS) lobectomy has been becoming the technique of choice in an increasing number of centers. The aim of our study was to review our experience, evaluating the learning curve of U-VATS for lung lobectomy and outcomes.

Methods

The prospectively collected clinical data of 43 consecutive patients, undergone U-VATS lobectomy from June 2016 to September 2017, were reviewed. The cumulative sum analysis was applied for defining the completion of learning curve (CLC), evaluating the relationship between operative time and the consecutive number of operations.

Results

The mean operative time of Uniportal VATS lobectomy was 179.93±43.41 min. According to the cumulative sum analysis, the CLC was reached after 25 patients. Using the cut-off of 25 patients, the whole populations was divided in group A (first 25 patients of the experience) and group B (the last 18 patients). The mean operative time in group B was significantly shorter than in group A (164.00±24.46 vs. 191.40±50.45 min, respectively, P=0.04). There were no differences in demographic characteristics, number of removed lymph nodes, chest tube duration, and hospital stay among the two groups. The number of conversions was higher in group A (4 vs. 0; P=0.07), as the number of major complications, like reoperations for bleeding (2 vs. 0; P=0.22). There was no postoperative 30-day-related death.

Conclusions

U-VATS lobectomy seems to be a quite safe and feasible procedure, with a steep learning curve and low complication rate, if performed by experienced surgeons after proper training.

Postoperative chest ultrasound findings and effectiveness after thoracic surgery: A pilot study

The aim of this study was to analyze the information from post-operative chest ultrasound (CU) to evaluate the possibility to use this method instead of chest X-ray (CXR) after thoracic surgery. Patients who underwent thoracic surgery were evaluated with CU blinded to CXR after surgery, deciding if it was useful or CU was exhaustive. Twenty-four patients were enrolled prospectively. The CU allowed a further discrimination of the lung abnormalities, discriminating between atelectasis, infections or hematoma. CXR was required in only 5 cases due to the presence of massive subcutaneous emphysema or absence of lung point. In the remaining 19 cases, CU was considered exhaustive and effective. In particular, CU was considered exhaustive in 67% of cases after open surgery and in 85% of cases after video-assisted thoracic surgery. In conclusion, CU appears to be effective in post-operative management after thoracic surgery and it can increase the diagnostic accuracy reducing any unnecessary X-ray exposure.

TP53 Codon 72 Polymorphism does not Affect Risk of Cervical Cancer in Patients from the Gambia

Aims

A case-control study was performed to investigate the relationship between cervical cancer and TP53 polymorphism at codon 72 in young black African women from The Gambia.

Materials and Methods

The TP53 polymorphism at codon 72 was examined by PCR amplification and SSCP analysis in 40 patients with primary cervical cancer and in 20 healthy women of the same age and from the same geographical area. The occurrence of TP53 polymorphism in combination with the HPV-16 E6 genotype (assayed by PCR) was evaluated.

Results

The distribution of TP53 genotypes in cervical cancer patients and in the control group was not statistically different (p=0.45) and homozygosity for argine at residue 72 was not associated with cervical cancer (odds ratio: 1.24; 95% confidence interval 0.21-9.16). Similarly, a different genotype distribution, cervical cancer and presence of HPV-16 E6 were not observed.

Conclusions

These results cannot rule out an association between TP53 polymorphism at codon 72, HPV infection and the etiology of cervical cancer in this population sample.

Osteopontin is not a Specific Marker in Malignant Pleural Mesothelioma

Background and aims: Osteopontin (OPN) is an integrin-binding protein recently shown to be related to tumorigenesis, progression and metastasis in different experimental models of malignancy. Malignant pleural mesothelioma (MPM) is a fatal disease in which the prognosis remains very poor and the knowledge of predictive factors for outcome is insufficient. The identification of new molecules involved in cancer initiation and development is a fundamental step for improving the curability of this kind of tumor. The purpose of this study is to define the role of OPN in the diagnosis of MPM by determining its prognostic and diagnostic value. Methods: a group of 24 surgically staged MPM subjects was compared with a group of 31 subjects with non-malignant pulmonary diseases, and with 37 healthy controls. Tumor tissue was analyzed for OPN by immunohistochemical tests, and plasma OPN levels were measured by an enzyme-linked immunosorbent assay. Results: Plasma OPN levels were not significantly higher in either of the patient groups compared with the control group. Immunohistochemical analysis revealed OPN staining of tumor cells in 21 of 24 MPMs. Receiver operating characteristic curve/area under the curve (ROC/AUC) analysis comparing the plasma OPN levels in the healthy group with those of MPM patients showed 40% sensitivity and 100% specificity at a cutoff value of 60.8 ng of OPN per milliliter (AUC 0.6). Conclusion: Plasma OPN levels do not discriminate between chronic inflammatory and malignant lung diseases and staining intensity in MPM specimens does not correlate with OPN plasma levels.

FORECAST - A cloud-based personalized intelligent virtual coaching platform for the well-being of cancer patients

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The challenges of cancer patients and the benefits of coaching are presented.

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Evidence of impact of coaching on the well being of cancer patients is presented.

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A cloud-based personalized intelligent virtual coaching platform for the well-being of cancer patients is proposed.

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Technology acceptance of the platform is presented, based on validation trials.

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Future work is discussed.

Well-being of cancer patients and survivors is a challenge worldwide, considering the often chronic nature of the disease. Today, a large number of initiatives, products and services are available that aim to provide strategies to face the challenge of well-being in cancer patients; nevertheless the proposed solutions are often non-sustainable, costly, unavailable to those in need, and less well-received by patients. These challenges were considered in designing FORECAST, a cloud-based personalized intelligent virtual coaching platform for improving the well-being of cancer patients. Personalized coaching for cancer patients focuses on physical, mental, and emotional concerns, which FORECAST is able to identify. Cancer patients can benefit from coaching that addresses their emotional problems, helps them focus on their goals, and supports them in coping with their disease-related stressors. Personalized coaching in FORECAST offers support, encouragement, motivation, confidence, and hope and is a valuable tool for the wellbeing of a patient.

SPRINTT and the involvement of stakeholders: strategy and structure

The current healthcare systems are built around the traditional paradigm of patients suffering from a single acute illness. They are, therefore, largely unprepared to face the increasing demands for health services arising from the expansion of an older population with specific medical needs related to multiple chronic disorders. As a consequence, the medical conditions of a large and growing segment of the older European population are not efficiently managed by the available healthcare services. In the context of an aging population, policy makers such as the European Commission and European Institutions, such as the European Medicines Agency (EMA), devote time and resources to study and accompany the need of the aging population. The EMA recognizes the importance of making sure that the needs of the Elderly are considered during development, approval, and use of new medicines, and, therefore, engages with healthcare professional organisations. The Sarcopenia and Physical Frailty in Older People: Multicomponent Intervention Strategies (SPRINTT) is the obvious result of these strategies. The present article describes the SPRINTT workpackage activities aimed at engaging the scientific discussion on the physical frailty and sarcopenia with the EMA as one of its interlocutor, acknowledging the need to collaborate on this topic to foster a productive dialogue.

Building Bridges for Innovation in Ageing: Synergies between Action Groups of the EIP on AHA

The Strategic Implementation Plan of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) proposed six Action Groups. After almost three years of activity, many achievements have been obtained through commitments or collaborative work of the Action Groups. However, they have often worked in silos and, consequently, synergies between Action Groups have been proposed to strengthen the triple win of the EIP on AHA. The paper presents the methodology and current status of the Task Force on EIP on AHA synergies. Synergies are in line with the Action Groups' new Renovated Action Plan (2016-2018) to ensure that their future objectives are coherent and fully connected. The outcomes and impact of synergies are using the Monitoring and Assessment Framework for the EIP on AHA (MAFEIP). Eight proposals for synergies have been approved by the Task Force: Five cross-cutting synergies which can be used for all current and future synergies as they consider overarching domains (appropriate polypharmacy, citizen empowerment, teaching and coaching on AHA, deployment of synergies to EU regions, Responsible Research and Innovation), and three cross-cutting synergies focussing on current Action Group activities (falls, frailty, integrated care and chronic respiratory diseases).

Pathologic Findings and Long-Term Results After Surgical Treatment for Pulmonary Sarcomatoid Tumors: A Multicenter Analysis

BACKGROUND

Pulmonary sarcomatoid carcinoma (PSC) is a very rare subtype of non-small cell lung cancer (NSCLC). The aim of this study was to clarify the pathologic characteristics and long-term survival after surgical treatment in patients with PSC.

METHODS

From January 2003 to December 2013, we retrospectively reviewed the clinical findings, surgical notes, and pathologic and follow-up data from 148 consecutive patients who underwent curative resection for PSC in 5 institutions. The Kaplan-Meier method, log-rank test, and Cox regression analysis were used.

RESULTS

Mean age and male to female ratio were 66.6 ± 9.9 years and 120:28, respectively. Surgical resection (pneumonectomy in 8 patients, bilobectomy in 132 patients, and sublobar resection in 8 patients) was complete in 142 cases (96%). At pathologic evaluation, 36 patients (24%) had stage I, 69 patients (47%) had stage II, 33 patients (22%) had stage III, and 10 patients (7%) had stage IV disease. A "biphasic tumor" (PSC with an NSCLC component) was observed in 77 patients (52%). We detected a high rate of vascular emboli in the surgical specimens (overall, 68%; 57% in pathologic stage I tumors), whereas lymphatic emboli were found in 30% of cases (5% of pathologic stage I tumors). Overall median and 5-year long-term survival (LTS) was 19 months and 12.6% (LTS, 16.3% in pathologic stage I), respectively. Distant recurrences frequently occurred after surgical treatment (81%), even in pathologic stage I tumors that underwent R0 resection (62%). Multivariable survival analysis identified R+ resection (hazard ratio [HR],12.3; 95% confidence interval [CI], 3.67-41.28; p < 0.0001), advanced pathologic stage (HR, 5.75; 95% CI, 2.55-12.98; p < 0.0001), and the presence of vascular emboli (HR, 1.67; 95% CI, 1.05-2.67; p = 0.0327) as independent negative prognostic factors.

CONCLUSIONS

PSCs have very aggressive behavior and high metastatic potential even in early stages. R+ resection, pathologic TNM status, and the presence of vascular emboli are independent prognostic factors.

Integrative systems medicine approaches to identify molecular targets in lymphoid malignancies

Although survival rates for lymphoproliferative disorders are steadily increasing both in the US and in Europe, there is need for optimizing front-line therapies and developing more effective salvage strategies. Recent advances in molecular genetics have highlighted the biological diversity of lymphoproliferative disorders. In particular, integrative approaches including whole genome sequencing, whole exome sequencing, and transcriptome or RNA sequencing have been instrumental to the identification of molecular targets for treatment. Herein, we will discuss how genomic, epigenomic and proteomic approaches in lymphoproliferative disorders have supported the discovery of molecular lesions and their therapeutic targeting in the clinic.

Soluble Epidermal Growth Factor Receptors (sEGFRs) in Cancer: Biological Aspects and Clinical Relevance

The identification of molecules that can reliably detect the presence of a tumor or predict its behavior is one of the biggest challenges of research in cancer biology. Biological fluids are intriguing mediums, containing many molecules that express the individual health status and, accordingly, may be useful in establishing the potential risk of cancer, defining differential diagnosis and prognosis, predicting the response to treatment, and monitoring the disease progression. The existence of circulating soluble growth factor receptors (sGFRs) deriving from their membrane counterparts has stimulated the interest of researchers to investigate the use of such molecules as potential cancer biomarkers. But what are the origins of circulating sGFRs? Are they naturally occurring molecules or tumor-derived products? Among these, the epidermal growth factor receptor (EGFR) is a cell-surface molecule significantly involved in cancer development and progression; it can be processed into biological active soluble isoforms (sEGFR). We have carried out an extensive review of the currently available literature on the sEGFRs and their mechanisms of regulation and biological function, with the intent to clarify the role of these molecules in cancer (and other pathological conditions) and, on the basis of the retrieved evidences, speculate about their potential use in the clinical setting.

A 79-Year-Old Man With Interstitial Lung Disease and Cryptic Area of High 18 Fluorodeoxyglucose Uptake in Left Upper Lobe

Seven years after left hemicolectomy and radical lymph nodal dissection followed by adjuvant chemotherapy for colorectal cancer (histotype, adenocarcinoma; stage, pT3N2M0; grading, G2), a slight increase in carcinoembryonic antigen levels (6.2 ng/mL; range, 0-5 ng/mL) was detected in a 79-year-old man. He was a heavy smoker with history of an interstitial fibrotic lung disease with associated areas of emphysema.

Operational Definition of Active and Healthy Ageing (AHA): A Conceptual Framework

Health is a multi-dimensional concept, capturing how people feel and function. The broad concept of Active and Healthy Ageing was proposed by the World Health Organisation (WHO) as the process of optimizing opportunities for health to enhance quality of life as people age. It applies to both individuals and population groups. A universal Active and Healthy Ageing definition is not available and it may differ depending on the purpose of the definition and/or the questions raised. While the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) has had a major impact, a definition of Active and Healthy Ageing is urgently needed. A meeting was organised in Montpellier, France, October 20-21, 2014 as the annual conference of the EIP on AHA Reference Site MACVIA-LR (Contre les Maladies Chroniques pour un Vieillissement Actif en Languedoc Roussillon) to propose an operational definition of Active and Healthy Ageing including tools that may be used for this. The current paper describes the rationale and the process by which the aims of the meeting will be reached.

Prognostic grading after complete resection for thymic malignancies

OBJECTIVE

Despite the World Health Organization (WHO) and Masaoka classifications have been widely accepted as the main describers of prognosis determinants in thymic malignancies, so far, these have been considered independently from one another. We have reviewed our single-centre 40-year results after surgical treatment of thymic malignancies evaluating the inter-relationships between the clinical, surgical and pathological variables and investigating their prognostic impact in completely resected patients.

PATIENTS AND METHODS

A surgical series of 347 patients was reviewed and, of these, 305 with complete resection enrolled. Long-term and disease-free survival (LTS, DFS) analyses were performed. Kaplan-Meir curves for WHO histotypes and Masaoka-stages were inspected and matched with the log-rank test; the Cox regression analysis was adopted in a multivariable approach.

RESULTS

Considered independently, the WHO-histotypes did not differentiate clearly from one to another in terms of LTS and DFS; however, types A-AB-B1-B2 and B3-C clustered in 2, statistically different, malignancy groups (LTS, DFS: Cox-p < 0.001). Masaoka staging was confirmed to be a relevant prognostic determinant, even if no evident difference between stages I vs II and stages III vs IV emerged when the Masaoka-classification was factored in. Thus, when investigating 13 surgical and pathological factors of invasiveness, these showed a clustering in 2 groups according to the presence/absence of pathological proven infiltration in the peri-thymic structures (LTS, DFS: Cox-p < 0.001). By matching the WHO-malignancy clusters and infiltration clusters, 4 classes may be identified, which proved to have a distinct prognostic significance: (LTS-Cox: stage-I vs stage-II, p = 0.003; III: p < 0.001, IV: p < 0.001; DFS-Cox: stage-I vs stage-II, p < 0.001; III: p < 0.001; IV: p < 0.001).

CONCLUSIONS

When analyzing the long-term outcome of patients underwent complete resection for thymic malignancies, the combination between pathological and surgical variables showed accurate prognosis predictability.