View clinical trials related to Risk Stratification.
Filter by:Over the past decades, several ECG-based parameters have been identified as independent predictors of worsened prognosis in affected patients. In addition to visual assessment of morphology, methods of computer-based machine ECG analysis have gained importance in recent years. These methods allow the detection of systemic abnormalities in ECGs that are not visible to the naked eye. An example of this is provided by the so-called "QRS microfragmentations". The aim of this evaluation is to retrospectively collect all established as well as new quantitative and qualitative ECG parameters (such as QRS microfragmentation) in a large patient collective. Subsequently, after characterization of the patients, an independent multivariate risk prediction model should be developed based on computer-based ECG analysis using maschnine learning algorithms.
Retrospective observational study on the effects of altering components of RCRI to improve the predictive capacity.
Early Warning Score (EWS) is a clinical scoring system used in hospitals in Denmark and internationally to systematically observe admitted patients using a standardised response algorithm. Consisting of a score based on the patients' vital signs, it only leaves limited space for individual assessment. Patient safety but also resource utilisation is a key issue in health systems today. We have developed a new individual EWS system (I-EWS) that reintroduces the individual clinical assessment for a more personalised observation. Our hypothesis is that I-EWS will not increase the mortality among hospitalised patients compared to EWS but will improve workflow by reducing unnecessary observations and freeing staff resources, potentially leading to improved patient care. The impact of I-EWS on mortality, the occurrence of critical illness, and usage of staff resources will be evaluated in a prospective, cluster randomised, non-inferiority study conducted at eight hospitals in Denmark.
Main objective: To design a precision risk stratification system that predicts individual risk of rejection
Predict-VT is an investigator-initiated, prospective, observational clinical trial. Four hundred patients with ST elevation acute myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) will be included. The primary end point is a composite of ventricular tachyarrhythmia (VTA) and sudden cardiac death (SCD). VTAs will be recorded using continuous electrocardiographic (ECG) monitoring in the coronary unit for the first 72 hours, standard ECG and ECG holter monitoring. For the analysis of myocardial function, conventional 2D echocardiography and tissue doppler will be used. For the evaluation of myocardial mechanics, 2D speckle tracking, strain, strain rate and mechanical dispersion will be obtained. Important clinical, laboratory and angiographic variables will also be examined. Patients will be followed-up at 40 days and 1 year. The optimal VTA prediction model will be constructed using logistic regression and bootstrap models. Patients who experience primary end point should undergo secondary SCD prevention using implantable cardioverter defibrillator (ICD). Patients with left ventricular ejection fraction (LVEF) < 35%, 40 days post acute myocardial infarction (AMI), will be candidates for primary SCD prevention.
Acute myeloid leukemia with t(8;21) /AML1-ETO-positive (AE AML) is a heterogeneous disease entailing different prognoses. There were significant differences in the therapeutic effect between different subgroups of AE AML. Therefore, risk stratification-directed therapy is very necessary for AE AML.