Alexandroupoli, Greece

A Study to Investigate Leramistat in Patients With IPF

This will be a Phase 2, double-blind, placebo-controlled, 2-arm, parallel-group, multi-centre study to investigate leramistat treatment of patients aged 40 years or older with IPF. The study is planned to consist of the following parts: Screening period: 1 to 28 days (Weeks -4 to -1). Treatment period: a 12-week blinded, placebo-controlled treatment period (Weeks 1 to 12). Follow up period: 56 days (Weeks 13 to 20). All participants will return for a follow-up visit 56 days after their final dose. Randomization will be stratified by concomitant use of an approved anti-fibrotic drug (nintedanib or pirfenidone) at randomization versus no concomitant use of an approved anti-fibrotic drug at randomization. Number of Participants: Approximately 150 participants will be enrolled and randomly assigned in a 2:1 ratio to receive either leramistat or matched placebo. If the participant is receiving nintedanib or pirfenidone treatment, it should be stable for at least 8 weeks prior to study entry and be predicted to remain stable during the course of the study. The maximum duration of participation (including screening period and follow-up) is 24 weeks. Data Monitoring/Other Committee: A DSMB has been appointed for this study.

Safety and Efficacy of Anakinra Treatment for Patients With Post Acute Covid Syndrome

People with COVID-19 might have sustained post-infection sequelae, known as Post-Acute Covid Syndrome (PACS). A recent consensus definition by an international panel of 265 patients, clinicians, researchers, and WHO staff suggests that post-COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset, with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include fatigue, shortness of breath, and cognitive dysfunction and generally have an impact on everyday functioning. The role of immune dysregulation in PACS is indirectly supported from the findings of the SAVE-MORE randomized clinical trial, in which patients with moderate and severe COVID-19, were 1:2 randomized to treatment with placebo or anakinra once daily for 10 days. The primary endpoint was the distribution of the frequencies of patients in the 11 points of the WHO clinical progression scale (CPS) by day 28. Patients' follow-up until day 90 showed significant reduction of the incidence of PACS; this was 24.4% among placebo-treated patients and 15.7% among patients treated with anakinra. After the end of the SAVE-MORE trial, the understanding of the immune activation of PACS and the development of tools for the evaluation of patients have become the main aims of the Hellenic Institute for the study of sepsis (HISS) group. More precisely, patients with medical history of COVID-19 pneumonia during three separate time periods and matched comparators for age, sex, comorbidities, and state of vaccination were followed up and evaluated for PACS. Main findings can be summarized as follows: 1. For at least one year after acute COVID-19 there is considerable immune dysregulation involving both the innate and the adaptive responses. 2. Patients with PACS may be classified into four main phenotype clusters: fatigue involving 70.8%, respiratory cluster involving 33.2%, systemic symptoms involving 17.7% and other symptoms involving 26.1%. 3. The risk for progression into PACS was significantly lower among patients treated with anakinra in the acute stage (odds ratio 0.59, p: 0.017) showing a role of IL-1 for the progression into PACS. 4. Patients with fatigue bring distinct immunotype compared to the respiratory cluster. 5. IP-10 (interferon-gamma-induced protein-10) at levels more than 250 pg/ml has sensitivity 99.3%, specificity 90.9%, positive predictive value (PPV) 97.9% and negative predictive value (NPV) 97.6% for the diagnosis of the post acute COVID immune dysregulation. PRECISION is a proof-of-concept, randomized clinical trial (RCT) aiming to evaluate the efficacy and safety of anakinra in patients with PACS in improving the clinical and immunological state over 4 to 8 weeks as measured by a composite endpoint, namely, the "Score of PACS progression reversal".

Cladribine Tablets Level of Response Predictors in Clinical Practice (CLODINA)

Artificial Intelligence for Automated Clinical Data Exploration from Electronic Medical Records (CardioMining-AI)

Despite the rapid development of medicine and computer science in recent years, the medical treatment in modern clinical practice is often empirical and based on retrospective data. With the growing number of patients and their concentration in large tertiary centers, it becomes attractive to systematically collect clinical data and apply them to risk stratification models. However, with the increasing volume of data, manual data collection and processing becomes a challenge, as this approach is time consuming and costly for the healthcare systems. In addition, unstructured information, such as clinical notes, are very often written as free text that is unsuitable for direct analysis. The use of artificial intelligence is very promising and is going to rapidly change the future of medicine in the upcoming years. Due to the automated processes it offers, it is possible to quickly and reliably extract data for further processing. The results from its use can be easily extended to different healthcare systems, amplifying the knowledge produced and improving diagnostic and therapeutic accuracy, and ultimately positively affecting health services. Collecting the vast amount of data from different sources without compromising patients' personal data is a major challenge in modern science. Electronically-registered clinical notes of patients who were hospitalized in the Cardiology ward of tertiary hospitals will be retrospectively collected, as well as additional files such as the laboratory and imaging examinations related to each hospitalization. Given the size of the participating clinics and the years during which the recording of electronic hospital records in electronic form was applied, it is estimated that the sample of patient records will be about 60.000. All information that could potentially be used to identify a person, such as name, ID number, postal code, place of residence, occupation, will be deleted from these electronic files. Only the age will be recorded, not the exact date of birth of each patient. Only the days of hospitalization will be recorded and not the exact dates of admission and discharge from the hospital. Thus, the data will not be able to be assigned to a specific subject, as no additional information or identifiers will be collected for the subjects. After the files are anonymized, each patient's clinical note will be linked with a specific key ("identifier"). The electronic file that contains the correlation of the "identifier" with the patient's clinical note will be stored in a secure hospital electronic location. The fully anonymized files will initially be manually analyzed to extract information into a database containing all of patients' clinical information, such as discharge diagnoses, medications, treatment protocols, laboratory and diagnostic tests. At the same time, a sample (1/3) of the clinical notes will be analyzed to identify the keywords or phrases associated with each diagnosis (for example, the atrial fibrillation diagnosis will probably be recorded as "atrial fibrillation", " AF ", etc.). By using this generated dictionary of keywords and by integrating artificial intelligence methods and text mining, such as natural language processing (NLP), an automated extraction of data and diagnoses from these electronic medical notes will be attempted. The reliability and accuracy of the computational methods will be evaluated internally, comparing the data extracted automatically with those recorded manually. In addition, the reliability and accuracy of these computational methods will be evaluated externally, applying these methods to 2/3 of the clinical notes in which no association between keywords and specific diagnoses was attempted. Regarding Greece, the present study aims to be the first to analyze the usefulness of artificial intelligence for automated extraction and processing of unstructured clinical data from patients' medical clinical notes. The results of this study will have a positive impact on: 1. the automation of large-scale data analysis and processing procedures 2. the rapid epidemiological recording and utilization of clinical data 3. the early diagnosis of diseases 4. the development of phenotypic patient profiles that could benefit from targeted therapies 5. the development of clinical decision support systems that will provide information about the possible clinical course of patients after hospital discharge and assist medical decisions 6. the development and validation of prognostic models for major cardiovascular diseases

A Study Evaluating the Efficacy and Safety of Oral Etrasimod in the Treatment of Adult Participants With Moderately to Severely Active Crohn's Disease

This study includes 5 substudies: Substudy A - Phase 2: A Phase 2, randomized, double-blind, substudy to assess the safety, tolerability, and efficacy of oral etrasimod therapy in participants with moderate to severe CD that supports the selection of an induction and maintenance dose(s) for Phase 3. Substudy 1 - Phase 2: A Phase 2b randomized, double-blind, placebo-controlled, dose-ranging induction substudy to evaluate etrasimod as induction therapy and select an induction and maintenance dose(s) for continued evaluation in Phase 3. Substudy 2 - Induction: A Phase 3 randomized, double-blind, placebo-controlled substudy to evaluate etrasimod as induction therapy. Substudy 3 - Maintenance: A Phase 3 randomized, double-blind, placebo-controlled substudy to evaluate etrasimod as maintenance therapy. Participants from Substudy 1 and Substudy 2 will be enrolled in Substudy 3. Substudy 4 - Long-Term Extension: A long-term extension substudy for participants who complete at least 52 weeks of treatment. Participants from Substudy 3 and Substudy A are planned to be enrolled in Substudy 4.

ZEUS - A Research Study to Look at How Ziltivekimab Works Compared to Placebo in People With Cardiovascular Disease, Chronic Kidney Disease and Inflammation

EVOLVE Study: The Real-life Clinical Practice With Tezepelumab in Greece