View clinical trials related to Clinical Deterioration.
Filter by:The current study aims to investigate the feasibility of transmitting continuous and wireless vital sign data in real time from patients home to the hospital in patients discharged after an acute medical hospitalization
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 multi-center retrospective study collected clinical, laboratory, and CT pulmonary angiography parameters with acute pulmonary embolism patients from admission to predict adverse outcomes within 30 days after admission into hospital.
The current pilot study aims to investigate the feasibility of wireless, continuous monitoring of patients in the days around discharge after an acute medical hospitalization, as well as occurrence of deviating vital signs in this patient group.
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.
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.
Rapid response teams (RRTs) have been adopted by hospitals to provide urgent critical care to hospitalized patients who require quick intervention to prevent further deterioration. Early warning scores (EWS) serve as a method to identify patients requiring RRT assessment by analyzing routinely collected data such as vital signs and laboratory results. The Visensia Safety Index (VSI) is an EWS that uses continuous vital sign monitoring and machine learning to identify the likelihood of deterioration and can be integrated with existing hospital data infrastructure. Initial studies of the VSI have both validated the system and found that patients monitored using VSI had a shorter duration of any instability and fewer episodes of serious and persistent instability. The investigators' recent retrospective analysis at The Ottawa Hospital (TOH) identified that implementation of an EWS could have detected earlier deterioration in over half of the patients identified, potentially preventing subsequent ICU admission, severity of illness, and/or mortality. Thus, this study aims to determine the feasibility and potential impact of implementing a portable continuous monitoring system with a VSI trigger at TOH to identify patients at high risk of deterioration.
This study collected clinical, laboratory, and CT parameters of acute patients with acute pulmonary embolism from admission to predict adverse outcomes within 30 days after admission into hospital.
A Phase II Randomized, double-blind, Placebo-controlled Study to Evaluate the safety and efficacy of exosomes overexpressing CD24 to prevent clinical deterioration .The study population will include patients with moderate or severe COVID-19 infection and laboratory markers predictive of the cytokine storm from the Corona department of each site, who have provided an informed consent. 155 patients will be randomized in a 2:1 ratio to receive either 1010 exosome particles (103 patients) or placebo (52 patients).The exosomes will be diluted in 4ml normal saline for inhalation, administered once daily (QD) for 5 days. Placebo (saline) will be prepared for inhalation and administered in the same manner as the exosomes.
The aim of this study is to use artificial intelligence in the form of machine learning analysing vital signs as well as symptoms of patients suffering from Covid19 to identify predictors of disease progression and severe course of disease.