Active clinical trials for "Clinical Deterioration"

This is phase IIb, Randomized, Double-blinded, Placebo-controlled Study to Evaluate the Safety and Efficacy of Exosomes Overexpressing CD24 of one dose 10^10 exosome particles, to Prevent Clinical Deterioration in Patients with Mild-Moderate ARDS

In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients. The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.

Sometimes in hospital, it is not noticed that patients are becoming unwell quickly enough. This may mean that they are less likely to survive than if the worsening of their illness had been picked up sooner. One reason for this may be that hospital staff are unable to check patients' vital signs (such as breathing rate, heart rate and level of oxygen in their blood) frequently enough to help them decide if a patient is becoming more unwell. Currently, for nurses to watch these vital signs closely, patients are either attached to a static machine by the patient's bedside using wires, or staff visit the patient every few hours to measure these vital signs using a portable wired machine. It is now possible to closely monitor patients using small devices which attach to the wrist, finger or chest. These devices allow nursing staff to continually watch vital signs data from these patients when they are away from their bedside. These machines are also wireless and portable, so they do not stop patients moving around, which is important for recovery, and are comfortable to wear. In past years, the investigators have tested these devices and developed a system to allow the clinical staff to see the continuous vital signs. In this final stage of the project, the investigators will test this system (with the selected devices) on patients in hospital. The investigators will start by doing a small trial on one surgical ward, and asking for staff and patient feedback of how the system worked, how useful it was, and how easy to use. If the feedback from this first small trial is positive, the investigators will conduct a future trial in several hospitals, to test how useful the system is in improving patient recovery.

The primary aim of the current study is to assess the effect of continuous wireless vital signs monitoring with generation of real-time alerts compared to blinded monitoring without alerts on the cumulative duration of any severely deviating vital signs in patients admitted to general hospital wards with acute medical conditions. Patients admitted with medical conditions represents a large and heterogenous group occupying a substantial part of the total in-patient capacity in the Danish hospitals today. The hypothesize is that continuous vital signs monitoring, and real-time alerts will reduce the cumulative duration of severely deviating vital signs.

Hospitals aim to hospitalize patients when necessary and discharge patients when possible. However, the triage process and discharge management of patients in e.g. the Acute Admission Ward, is not a trivial task. The upcoming technology of wearable monitoring devices, whereby patients can be continuously monitored with an unobtrusive vital signs device, might help getting more insight into patients' health condition and thus help facilitate efficient and effective triaging. Therefore, the primary objective is to assess the effects of continuous monitoring of patients in the acute admission ward (AAW) on the percentage of patients who can be discharged home. Secondary objectives are to assess the length of stay in the acute admission ward and in the in-hospital wards, as well as the effect on admission to the intensive care unit, rapid response team calls and hospital readmission. The predictive value of algorithms applied to the monitoring data combined with other parameters to detect timely deterioration and predict discharge will be assessed. Facilitators and barriers for implementing such a system will be investigated.

Hypothesis: display of predictive analytics monitoring on acute care cardiology wards improves patient outcomes and is cost-effective to the health system. The investigators have developed and validated computational models for predicting key outcomes in adults, and a useful display has been developed, implemented and iteratively optimized. These models estimate risk of imminent patient deterioration using trends in vital signs, labs and cardiorespiratory dynamics derived from readily available continuous bedside monitoring. They are presented on LCD monitors using software called CoMET (Continuous Monitoring of Event Trajectories; AMP3D, Advanced Medical Predictive Devices, Diagnostics, and Displays, Charlottesville, VA) To test the impact on patient outcomes, the investigators propose a 22-month cluster-randomized control trial on the 4th floor of UVa Hospital, a medical-surgical floor for cardiology and cardiovascular surgery patients. Clinicians will receive standard CoMET device training. Three- to five-bed clusters will be randomized to intervention (predictive display plus standard monitoring) or control (standard monitoring alone) for two months at a time. In addition, risk scores for patients in the intervention clusters will be presented daily during rounds to members of the care team of physicians, residents, nurses, and other clinicians. Data on outcomes will be statistically compared between intervention and control clusters.

The study aims to investigate the use of wireless, continuous monitoring in patients at home including the frequency of alarms triggered by abnormal vital parameters and their significance for (re)hospitalisation/Serious Adverse Events(SAE) and/or death within 30 days.

The investigators aim to build a predictive tool for Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography.

This prospective observational research project aims to investigate how vital sign deterioration and complications within the (PACU) relate to early deterioration and complications in the surgical wards 72 hours post-PACU discharge. The participants studied will be high-risk surgical patients who will follow a normal postoperative course from the PACU to the surgical ward. The investigators seek to evaluate the association between deterioration and complications within the PACU with vital signs deterioration and complications in the surgical wards. Second, the investigators will explore how deterioration and complications affect PACU length of stay, morbidity, mortality, rapid response Teams call-outs (RRT) (Early warning score >7), extra medical patient supervision, and unplanned intensive care unit (ICU) admissions. The investigators will also examine the nurses' assessment of the patient's risk of deterioration and complications upon discharge from the PACU and admission to the surgical department.

Recent small subcortical infarction (RSSI) is defined as a small deep infarction in the territory of a perforating artery with maximum axial diameters (MAD) of less than 20 mm. Although RSSI is generally considered to be of a relatively favorable prognosis, about 13.5% to 43% of RSSI patients experience early neurological deterioration (END) in the acute phase, which often bring adverse effects on long-term outcomes. Although a number of risk factors for END have been identified previously, however, the risk factors of END and the underlying etiological mechanism are still ambiguous, and also the relevant intervention measures lack sufficient evidences, which is a thorny problem that clinicians have to face. In this multicenter, large-sample prospective registry study, we ought to investigate the natural course of END in patients with RSSI. Exploring the risk factors and potential mechanism of its occurrence and development, and trying to establish a comprehensive predictive model for END that integrates multi-dimensional information including clinical symptom, demographic data, biochemical biomarker and image data, and so as to provide a valuable tool for clinical evaluation and early management. Simultaneously, our study will provide information for the design of therapeutic randomized controlled trials in the future.