9 Clinical Trials for Various Conditions
This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict patient deterioration throughout a patient's admission. This algorithm was then validated in a validation cohort.
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.
Currently, all patients in the hospital are woken up throughout the night to check for vital signs, no matter how sick they are. The investigators are doing this study to determine whether skipping routine vital sign checks at night improves participant sleep quality and satisfaction without increasing the risk of adverse events.
The COVA clinical study is a global multicentric, double-blind, placebo-controlled, group sequential and adaptive 2 parts phase 2-3 study targeting in patients with SARS-CoV-2 pneumonia. Part 1 is a Phase 2 exploratory Proof of Concept (PoC) study to provide preliminary data on the activity, safety and tolerability of BIO101 in the target population. Part 2 is a phase 3 pivotal randomized study to provide further evidence of safety and efficacy of BIO101 after 28 days of double-blind dosing. BIO101 is the investigational new drug that activates the Mas receptor (MasR) through the protective arm of the Renin Angiotensin System (RAS).
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.
Life-threatening mass effect (LTME) arises when brain swelling displaces or compresses crucial midline structures subsequent to acute brain injuries (ABIs) like traumatic brain injury (TBI), ischemic stroke (IS), and intraparenchymal hemorrhage (IPH), which can manifest rapidly within hours or more gradually over days. Despite advancements in surgical management, significant gaps in understanding persist regarding optimal monitoring and therapeutic approaches. The current standard for identifying LTME involves neurologic decline in conjunction with radiographic evidence or increased intracranial pressure (ICP) indicating space-occupying mass effect. However, in critically ill patients, reliance on subjective physical exam findings, such as decreased arousal, often leads to delayed recognition, occurring only after catastrophic shifts have already occurred. The goal of this study is to determine the association of non-invasive biomarkers with neurologic deterioration, and to determine whether non-invasive biomarker inclusion improves detection of outcome and decline. The investigators propose to use various non-invasive methods to monitor ICP as adjuncts in detecting deteriorating mass effect. These methods include quantitative pupillometry, radiographic data, laboratory data, and other bedside diagnostic tests available including electroencephalography (EEG), skull vibrations detected via brain4care device, optic nerve sheath diameter assessment (ONSD), and ultrasound-guided eyeball compression. Some of these methods will be measured \*only\* for the purposes of the research study (such as skull vibrations via brain4care). Other measurements, such as quantitative pupillometry, will represent additional measurements beyond those already being collected for clinical care. This research study is necessary to understand the association of these non-invasive biomarkers with neurological decline and outcomes while considering potential confounding factors.
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.
This study will evaluate the tolerability, feasibility, and efficacy of the AKST1210 column in subjects with end-stage renal disease with cognitive impairment (ESRD-CI) undergoing hemodialysis 3 times per week.
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.