An IT Platform Supporting Rectal Cancer Tumor Board Activities: Implementation Process and Impact Analysis.

Colorectal cancer (RC) is the third most common cancer, with an increasing incidence in recent years. Digital health solutions supporting multidisciplinary tumor boards (MTBs) could improve positive outcomes for RC patients. This paper describes the implementation process of a digital solution within the RC-MTB and its impact analysis in the context of the Fondazione Policlinico 'A. Gemelli' in Italy. Adopting a two-phase methodological approach, the first phase qualitatively describes each phase of the implementation of the IT platform, while the second phase quantitatively describes the analysis of the impact of the IT platform. Descriptive and inferential analyses were performed for all variables, with a p-value < 0.05 being considered statistically significant. The implementation of the platform allowed more healthcare professionals to attend meetings and resulted in a decrease in patients sent to the RC-MTB for re-staging and further diagnostic investigations and an increase in patients sent to the RC-MTB for treatment strategies. The results could be attributed to the facilitated access to the platform remotely for specialists, partly compensating for the restrictions imposed by the COVID-19 pandemic, as well as to the integration of the platform into the hospital's IT system. Furthermore, the early involvement of healthcare professionals in the process of customizing the platform to the specific needs of the RC-MTB may have facilitated its use and contributed to the encouraging quantitative results.

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

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

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

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.

Evidence-based of conjunctival COVID-19 positivity: An Italian experience: Gemelli Against COVID Group.

BACKGROUND: The possible transmission of severe acute respiratory coronavirus 2 (SARS-CoV-2) by tears and conjunctiva is still debated. METHODS: Main outcome was to investigate the agreement between nasopharyngeal swab (NPs) and conjunctival swabs (Cs) in patients with SARS-CoV-2 infection. We divided patients into four groups: (1) NPs and Cs both negative (C-NF-), (2) NPs positive and Cs negative (NFs+Cs-), (3) NPs negative and Cs positive (NFs-Cs+), and (4) NPs and Cs both positive (NFs-Cs+). The secondary outcomes were to correlate Cs results with systemic clinical parameters such as: oxygen saturation (SpO2), dyspnea degree (DP), radiologic pulmonary impairment based on chest radiography (XR) or computed tomography (CT), blood chemistry as D-Dimer (D-Dimer), fibrinogen, ferritin, lactate dehydrogenase (LDH), and C-reactive protein (C-RP). RESULTS: A total of 100 conjunctival swabs in 50 patients with SARS-CoV-2 have been enrolled in this interventional clinical trials. Ocular signs (conjunctivitis) were present in five patients (10%). NPs and Cs highlighted a poor level of agreement (0.025; p = 0.404). Median SpO2 levels are the highest in the NF-C- group (98%) and the lowest (90%) in the group NF+C+ (p = 0.001). Pulmonary impairment was statistically significantly different between NFs and Cs groups (p = 0.019). Pulmonary impairment score increased from NFs-Cs- group (3.8 ± 3.9), to NFs+Cs+ group (6.7 ± 4.1). Intensive care unit patients showed higher COVID-19 Cs positivity in conjunctiva (12.5%) against hospitalized ones (5.8%). CONCLUSIONS: In patients hospitalized for SARS-CoV-2 the virus can be detected in conjunctival swab. Intensive care unit patients may reveal a higher COVID-19 presence in the conjunctiva. The most severe pulmonary impairment can be observed in NFs and Cs positivity. TRIAL REGISTRATION: Clinicaltrials.gov registration. ETHICAL COMMITTEE AUTHORIZATION: ID number: 0013008/20.

Liability of Health Care Professionals and Institutions During COVID-19 Pandemic in Italy: Symposium Proceedings and Position Statement.

BACKGROUND: On May 12, 2020, a symposium titled "Liability of healthcare professionals and institutions during COVID-19 pandemic" was held in Italy with the participation of national experts in malpractice law, hospital management, legal medicine, and clinical risk management. The symposium's rationale was the highly likely inflation of criminal and civil proceedings concerning alleged errors committed by health care professionals and decision makers during the COVID-19 pandemic. Its aim was to identify and discuss the main issues of legal and medicolegal interest and thus to find solid solutions in the spirit of preparedness planning. METHODS: There were 5 main points of discussion: (A) how to judge errors committed during the pandemic because of the application of protocols and therapies based on no or weak evidence of efficacy, (B) whether hospital managers can be considered liable for infected health care professionals who were not given adequate personal protective equipment, (C) whether health care professionals and institutions can be considered liable for cases of infected inpatients who claim that the infection was transmitted in a hospital setting, (D) whether health care institutions and hospital managers can be considered liable for the hotspots in long-term care facilities/care homes, and (E) whether health care institutions and hospital managers can be considered liable for the worsening of chronic diseases. RESULTS AND CONCLUSION: Limitation of the liability to the cases of gross negligence (with an explicit definition of this term), a no-fault system with statal indemnities for infected cases, and a rigorous methodology for the expert witnesses were proposed as key interventions for successfully facing future proceedings.