Active clinical trials for "Clinical Deterioration"

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 goal is to develop a two-tiered monitoring system to improve the care of patients at risk for clinical deterioration on general hospital wards (GHWs) at Barnes-Jewish Hospital (BJH). The investigators hypothesize that the use of an automated early warning system (EWS) that identifies patients at risk of clinical deterioration, with notification of nurses on the GHWs when patients are identified, will reduce the risk of ICU transfer or death within 24 hrs of an alert. As a substudy, the investigators will pilot the use of a wireless pulse oximeter to establish feasibility and to develop algorithms for a real-time event detection system (RDS) in these high-risk patients.

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.

This is a prospective, observational, multicenter cohort study to compare right ventricular dysfunction dependent and independent prognostic models for short-term serous adverse events in patients who are diagnosed with pulmonary embolism in the emergency department. Clinical endpoints are assessed at days 1-5. A thirty-day follow-up phone call is conducted to obtain further clinical endpoints and a quality of life assessment.

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.

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.

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.

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.

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 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.