View clinical trials related to Clinical Deterioration.
Filter by:For patients admitted with COVID-19 infection, it is often difficult to predict if or when their clinical condition will deteriorate. However subtle changes in vital signs are usually present 8 to 24 hours before a life-threatening event such as respiratory failure leading to ICU admission, or unanticipated cardiac arrest. Such adverse trends in clinical observations can be missed, misinterpreted or not appreciated as urgent. New continuous and wearable 24/7 clinical vital parameter monitoring systems offer a unique possibility to identify clinical deterioration before patients condition progress beyond the point-of-no-return, where adverse events are inevitable. The primary aim of this study is to test the effect of continuous wireless vital signs monitoring with generation of real-time alerts through a purpose-built GUI, compared to standard EWS monitoring on the cumulative duration of any severely deviating vital signs
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.
This is a multicenter, randomized, double-blind, parallel group study to investigate the efficacy of pemziviptadil (PB1046) by improving the clinical outcomes in hospitalized COVID-19 patients at high risk for rapid clinical deterioration, acute respiratory distress syndrome (ARDS) and death. The study will enroll approximately 210 hospitalized COVID-19 patients who require urgent decision-making and treatment at approximately 20 centers in the United States.
This project investigates individual treatments using convalescent severe acute respiratory Syndrome Coronavirus 2 (SARS-CoV-2) plasma in SARS-CoV-2 infected patients at risk for disease progression. In addition to standard of care, SARS-CoV-2 infected patients for whom blood group compatible convalescent plasma is available and who are willing to sign the informed consent receive convalescent plasma. Only patients with moderate to severe disease at risk for transfer to intensive care unit or patients at the intensive care unit with limited treatment options will be treated.
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.
For patients admitted to the medical ward, it is often difficult to predict if their clinical condition will deteriorate, however subtle changes in vital signs are usually present 8 to 24 hours before a life-threatening event such as respiratory failure leading to ICU admission, or unanticipated cardiac arrest. Such adverse trends in clinical observations can be missed, misinterpreted or not appreciated as urgent. New continuous and wearable 24/7 clinical vital parameter monitoring systems offer a unique possibility to identify clinical deterioration before patients condition progress beyond the point-of-no-return, where adverse events are inevitable. The WARD project aims to determine the correlation between cardiopulmonary micro events and clinical adverse events during the first four days after hospital admission.
This study aims to provide myriad of physiological parameters in patients admitted in an internal medicine department, and which are defined as being in an increased risk of clinical deterioration within the first 72-hours after admission. The investigators will also conduct a retrospective comparison between physiological changes in patients who did deteriorate to those who did not. This will form the basis for the development of an algorithm for early prediction and warning of physiological and clinical deterioration during the first 72-hours of admission.
In 2018, continuous monitoring (CM) of 5 vital signs with a wearable device, including automated MEWS calculation within the EMR were introduced on the surgical and internal medicine ward of our hospital. Rather than taking the measurements manually, this enabled the nurses to periodically validate the continuously derived vital signs at the protocolled moments, and simultaneously get an automatically calculated MEWS reading,. Moreover, continuous vital sign monitoring provides single channel alarms and trends of the vital signs in between the regular measurement moments. Compared to periodic manual measurements and registration in the EMR, the continuous vital sign monitoring and automated MEWS calculations in the EMR may result in better identification of clinical deterioration, and may improve clinical outcome. The primary objective of this study is to evaluate changes in total hospital and ward stay, "Total Events" during admission (rapid response team (RRT) calls and unexpected intensive care unit (uICU) admissions and deaths) after implementation of CM on the regular surgical and internal medicine wards. Secondary objective is to evaluate changes in MEWS scores at the moment of the uICU admissions, length of hospital, ward and ICU stay and the proportion of RRT calls that results in a ICU admission.
The escalation of care for patients in a hospitalized setting between nurse practitioner managed services, teaching services, step-down units, and intensive care units is critical for appropriate care for any patient. Often such "triggers" for escalation are initiated based on the nursing evaluation of the patient, followed by physician history and physical exam, then augmented based on laboratory values. These "triggers" can enhance the care of patients without increasing the workload of responder teams. One of the goals in hospital medicine is the earlier identification of patients that require an escalation of care. The study team developed a model through a retrospective analysis of the historical data from the Mount Sinai Data Warehouse (MSDW), which can provide machine learning based triggers for escalation of care (Approved by: IRB-18-00581). This model is called "Medical Early Warning Score ++" (MEWS ++). This IRB seeks to prospectively validate the developed model through a pragmatic clinical trial of using these alerts to trigger an evaluation for appropriateness of escalation of care on two general inpatients wards, one medical and one surgical. These alerts will not change the standard of care. They will simply suggest to the care team that the patient should be further evaluated without specifying a subsequent specific course of action. In other words, these alerts in themselves does not designate any change to the care provider's clinical standard of care. The study team estimates that this study would require the evaluation of ~ 18380 bed movements and approximately 30 months to complete, based on the rate of escalation of care and rate of bed movements in the selected units.