View clinical trials related to Deterioration, Clinical.
Filter by:This is a confirmatory study without any intervention. It is an uncontrolled, non-randomized and open-label study with measurements made with comparators, and it has a preset hypothesis for the primary endpoint. There are no similar devices to VitalThings Guardian M10 / M10 mobile on the market, consequently one or more different types of devices must be used as comparators.
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.
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.
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.
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.
Why? The investigators are trying to find out if participants that suddenly deteriorate on the ward can be identified sooner by wearing a wearable sensor. This is an important study to see if the sensor works correctly in recording continuous vital observations of heart rate, respiratory rate and temperature. This information can help doctors and nurses identify un-well participants. What? The investigators will ask the participants to wear a light wearable sensor on the chest that can be worn for 5 days. If the participants are still in hospital after this time the sensor can be changed. All sensors are disposable. The participants would not have to actively do anything to the sensor. We will also participants to complete a short questionnaire about the sensor. Who? All participants on the ward that are admitted with a new medical or surgical problem can take part in the study. Participants undergoing a surgical procedure that require at least one overnight stay are eligible to take part in this study. Where? This study is being conducted at West Middlesex University Hospital and St Marys Hospital Paddington. Only certain wards are being included at both sites, if the participant moves wards the sensor will be removed. How? The study will last around 5 years and we aim to recruit 1000 participants.