9 Clinical Trials for Various Conditions
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 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.
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.
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 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.
An observational study will be conducted in approximately 14 participants to evaluate the ability of a wearable, wireless acoustic Respiratory Monitoring System (RMS) to accurately measure a participant's respiratory rate, tidal volume, minute ventilation, and duration of apnea in a noisy environment. Sensor accuracy will be measured with adaptive filtering and active noise cancellation turned on versus turned off.
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.
The purpose of the study is to compare the ability of physicians and a statistical index (the Rothman score) to predict clinical deterioration over the next 24 hours. Clinical deterioration is defined as concern in change in vital signs or patient status requiring a call to the rapid response team, cardiopulmonary arrest, or transfer to the ICU.
This study is designed to test two new risk scores - one designed to predict a patient's four-hour risk of developing sepsis and one designed to predict a patient's four-hour risk of deterioration (cardiac arrest, death, unplanned ICU transfer, or rapid response team call). The goal of this study is to improve provider awareness of a patient's risk of these two negative outcomes by providing them with new risk scores. The primary outcome will be the time from when the risk score becomes elevated to when vital signs such as heart rate or blood pressure are measured, suggesting an increased awareness.