Clinical Research Directory

Comparative Efficacy of 100-, 200-, & 400-mg Amiodarone in Patients With Paroxysmal AF Depending on Plasma Concentration

Amiodarone is a highly effective drug for the treatment and prevention of arrhythmias. In the 1970s and 1980s, numerous experimental and clinical studies comprehensively examined the mechanisms of amiodarone's antiarrhythmic action, as well as its effectiveness in various types of arrhythmias. This explains why drug therapy is so widespread today. According to the CAMIAT study, which included 1,202 patients with ventricular extrasystole who had suffered a myocardial infarction, amiodarone administration reduced the risk of arrhythmic death by 48.5%, and by 35% in the EMIAT study. In the GESICA study, amiodarone therapy was documented to reduce overall mortality by 45%, the incidence of sudden cardiac death by 54%, and mortality from progressive heart failure by 40%. In Russia, amiodarone is prescribed as a first-line drug for AF in 41% of cases with organic myocardial disease, in 52% as a second-line drug, and in 12.5% in the absence of organic heart damage. Moreover, the effectiveness of maintaining sinus rhythm in atrial fibrillation varies from 30 to 95.2%, which is a significant range when predicting the effectiveness of treatment. Attempts to assess amiodarone concentrations during therapy and to analyze the correlation with the achieved effects have been made repeatedly. While assessing amiodarone concentrations is generally considered inappropriate, based on the results of numerous studies, the use of monitoring is justified in certain clinical situations where the desired clinical effect or adverse events are absent. A detailed analysis of systemic reviews revealed significant shortcomings in the formation of such judgments. The groups were heterogeneous, small in number, and included, among other things, individual clinical situations and different routes of drug administration. A literature review did not reveal any data on the analysis of the concentration of amiodarone and desethylamiodarone as an anti-relapse therapy for atrial fibrillation when used consistently in doses acceptable by clinical guidelines with the possibility of titration under laboratory monitoring.

Linezolid Tolerance During the BPaL Regimen With Dosage Personalization Based on Therapeutic Drug Monitoring (TDM) During Multidrug-Resistant Tuberculosis Treatment

Multidrug-resistant tuberculosis (MDR-TB) poses a significant challenge to global public health. Globally, the World Health Organization (WHO) estimates the number at 400,000 patients with MDR-TB for 2023. Only 44% were diagnosed and put on treatment, the therapeutic success rate of the 2021 cohort is only 68%. In Guinea, the number of patients with MDR-TB is estimated at 450, and the treatment success rate is 74% for the 2021 cohort, primarily with the 9-months short oral regimen. Since 2022, the WHO has recommended the use of the 6-month short course of BPaL/BPaL-M for national tuberculosis control programs and Guinea began implementing this new regime within the programmatic framework starting in January 2025. Linezolid, a key component of new therapeutic regimens such as BPaL/BPaL-M, shows high bactericidal activity, although it is associated with serious adverse effects in a high percentage of patients, including myelosuppression, neuropathy and, in some cases, fatal lactic acidosis. In particular, peripheral neuropathy, an adverse event often irreversible that may lead to linezolid and the BPaL/BPaL-M regimen discontinuation, is reported in approximately 24% of patients receiving linezolid at 600 mg. A linezolid blood trough level of above 2 mg/l is associated with side effects, but its pharmacokinetics varies considerably between individuals and over time. There is little data on the role of therapeutic drug monitoring (TDM) in guiding its administration, some studies showing how the standard dose of 600 mg could exceed the toxicity target and the reduced dose of 300 mg might not achieve the target efficacy. The main objective of this study is to determine the role of TDM in the optimization of linezolid dosage in TB-MDR patients treated with the BPaL/BPaL-M regimen. The specific objectives aim to evaluate: * the variation in the occurrence of adverse events between patients who undergo a modification of the linezolid dose based on the TDM and patients taking linezolid standard dose * the treatment outcome in patients who undergo a modification of the linezolid dose based on TDM and patients taking linezolid standard dose * variations in the distribution of TDM throughout treatment in order to identify potential common trends.

Escitalopram Plasma Concentrations in Premenopausal and Postmenopausal Women

This observational cross-sectional case-control study aims to evaluate the effects of menopausal status on escitalopram metabolism in women. The study will include premenopausal and menopausal women aged 40-60 years who are currently receiving escitalopram treatment and have applied to the Psychiatry Clinic of Eskişehir Osmangazi University. The study aims to identify pharmacokinetic differences associated with hormonal status and to investigate their potential clinical implications. Findings from this research are expected to contribute to the development of evidence-based, individualized treatment strategies for women in menopausal transition.

Research on the Risk Warning Model and Prevention Strategies for Acute Kidney Injury Associated With Cyclosporine Based on Explainable Deep Neural Networks and Therapeutic Drug Monitoring

In this study, the investigators will focus on hospitalized patients using cyclosporine and develop an acute kidney injury risk prediction model through in-depth analysis of electronic medical record data, employing interpretable deep learning methods. This model aims to provide timely decision-making support for clinicians regarding prevention and treatment. Compared to traditional machine learning models, deep neural network models can extract deeper features from complex medical data and perform more precise pattern recognition, thereby improving the accuracy and reliability of predictions. By developing a prediction tool based on interpretable deep learning models, the investigators will be able to better assess the association between the use of CNI-class immunosuppressants and acute kidney injury, explore targeted prevention strategies, and offer more accurate prediction and intervention guidance for clinicians. Additionally, this study has significant socioeconomic benefits and promising prospects for application and promotion.