There are about 21062 clinical studies being (or have been) conducted in Italy. The country of the clinical trial is determined by the location of where the clinical research is being studied. Most studies are often held in multiple locations & countries.
This is a physician-initiated, observational, monocentric, retrospective and prospective Study. The study is intended to assess the feasibility of mechanical thrombectomy of caval and iliofemoral veins according to normal clinical practice in adult patients with symptomatic acute or subacute ileofemoral or caval deep vein thrombosis objectively diagnosed with CT scan imaging.
The goal of this randomized controlled trial is to measure the periprosthetic bone mineral density changes around a 3D-printed highly-porous titanium acetabular cup used in primary total hip arthroplasty compared to a standard hydroxyapatite/titanium plasma-sprayed acetabular cup up to 2-year follow-up.
The aim of this randomized controlled trial is to evaluate the tridimensional bone stability after horizontal one-stage GBR using collagene membrane (OssMem) with a mix of Bovine Bone Substitute (A-Oss) and autogenous bone (test group) versus A-Oss and LCR-A, a synthetic bone (control group).
The aim of this randomized controlled trial is to compare the clinical and radiographic of immediately loaded, immediate (post-extractive, test group) versus delayed (control group) implants with new SOI surface
The aim of this prospective cohort study is to compare clinical and radiographic data of Osstem implants with SOI surface placed in patients with or without diabetes.
The goal of this pilot interventional study is to assess the feasibility and acceptability of a supportive intervention for patients affected by heart failure. The main questions it aims to answer are: - Are implementation strategies effective in facilitating participant fidelity? - What factors contribute to patients' satisfaction with the designed intervention, and how can these be optimized for improved patient experience and adherence? - Are the methods and tools established the most appropriate to ensure the completeness of the data collection? Participants will follow a combined intervention consisting of: 1. pre-discharge educational meeting; 2. telephone nurse-led coaching sessions; 3. home telemonitoring of vital signs. In the main trial, researchers will compare data from the intervention group with a control group to assess whether it reduces hospitalization rates and improves self-care capabilities
Cancer patients are burdened by an increased risk of venous thromboembolism (VTE), which has a significant impact on morbidity and mortality. Existing Risk Prediction Models (RPMs), including the widely accepted Khorana Risk Score (KRS), have some limitations when used in certain tumor site populations, such as gynecological cancers. Notably, gynecological patients exhibit a variable risk of VTE based on their specific tumor sites, with ovarian cancer representing the highest risk. Moreover, currently available RPMs lack validation in a broad gynecological population and may fail to effectively stratify VTE risk. GynCAT is a prospective cohort study that will be conducted on female patients with gynecologic malignancies scheduled for systemic antineoplastic treatment. During the screening phase, symptomatic VTE will be excluded, and KRS will be assessed. Pharmacological thromboprophylaxis will be considered and prescribed at clinical judgement, for patients with a KRS score of 3 or higher. Clinical, hematological, biochemical, coagulation, and genetic variables will be collected. Follow-up will last for the entire duration of the antineoplastic treatment line, and VTE events, bleeding events, and mortality will be recorded. The primary objective is the development and validation of an RPM for VTE in gynecologic cancer patients undergoing systemic antineoplastic treatment. Secondary objectives are evaluation of the predictive value of the identified model, comparing it with existing general oncology RPMs; assessment of its performance in predicting mortality; evaluation of VTE incidence in patients with KRSā„3 receiving thromboprophylaxis; identification of risk factors for bleeding in this patient population. The sample size calculation is based on an estimated VTE incidence of 5% over a mean follow-up of 12 months. Hence, a sample size of at least 1,200 patients in the derivation cohort is considered sufficient for the determination of a risk prediction model incorporating up to six predictor variables. A split-sample method will be used, with two-thirds of the study participants randomly assigned to the model derivation cohort (n=1,200) and one-third (n=600) to an independent validation cohort. The total number of patients recruited in the study will thus be of 1,800. A competing risk survival analysis with Fine & Gray model will be used to study the association between prognostic variables and VTE occurrence, considering death as a competitive risk. The RPM will be identified through a bootstrap approach to reduce the risk of overfitting. Discrimination power of the RPM will be assessed using time-dependent Receiving Operating Characteristic curve, and model calibration will be evaluated graphically and with the calculation of relative calibration slopes. In conclusion, this prospective cohort study aims to overcome the limitations of current RPMs in gynecologic cancer patients, improving the accuracy of VTE risk stratification in this population.
To explore the impact of early transcatheter edge-to-edge repair of acute functional mitral regurgitation after myocardial infarction on the combined incidence of death and heart-failure associated hospitalisations at one-year follow-up, and quality of life and LV remodelling at two-year follow-up.
This is a retrospective observational cohort study, the primary objective is investigate the activity and efficacy of anti PD-1 antibodies in children, adolescents and young adult melanoma patients, with radically resected or metastatic disease
The study explores the utilization of artificial intelligence (AI) to predict disease progression trajectories in patients with diabetes. By analyzing historical data from a retrospective cohort, we aim to identify patterns and predictors of disease evolution. The approach seeks to enhance personalized treatment strategies and improve outcomes by foreseeing potential complications and disease milestones. The findings could pave the way for more targeted and effective management of diabetes through AI-driven insights.