Clinical Research Directory

Predicting ICU Transfers and Other Unforeseen Events (PICTURE)-Pediatric

The purpose of this study is to evaluate the effectiveness and user satisfaction of the study teams early warning system, called PICTURE, which utilizes artificial intelligence (AI) techniques and algorithms to identify patient deterioration on pediatric units within Mott Children's Hospital. In this pilot study the patient care team will review the PICTURE information and alerts. Morning rounds will be partially informed by the PICTURE scores and the scores will be included in the hand off notes for the patients with a red score. The primary purpose of this study is to test the hypothesis that the combination of the PICTURE-Pediatric model, the proposed workflow and the proposed interface results in at least 80% compliance. No participants will be consented as the Institutional review board has approved a waiver of consent for THE clinicians and the patients information being reviewed. The enrollment numbers will include only the clinicians.

Wireless Assessment of Respiratory and Circulatory Distress - Europe

Hospital patient monitoring in Europe is often intermittent, which can delay detection of clinical deterioration during hospitalization and after discharge. The WARD-EU study aims to describe current monitoring practices and to evaluate the feasibility, acceptability, and usability of the WARD-CSS (Wireless Assessment of Respiratory and Circulatory Distress - Clinical Support System), an algorithm-based continuous monitoring system. The study seeks to assess whether WARD-CSS can be integrated into routine hospital and post-discharge care to support earlier detection of patient deterioration and improved patient monitoring.

A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning

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.

Prevention of Functional and Cognitive Impairment Through a Multicomponent Exercise Program in Hospitalized Older Adults

The goal of this clinical trial is to determine whether a multicomponent exercise program improves functional capacity and cognitive status in hospitalized elderly patients aged 75 and older. Additionally, the study aims to assess the impact of this program on medication use, quality of life, and overall health outcomes. Key questions include whether the program enhances functional and cognitive capacities and which subgroups benefit most, such as frail patients or those with cognitive impairment. Participants will engage in a structured exercise program that includes strength training, balance exercises, and walking, all designed to improve mobility and reduce fall risk. They will attend supervised sessions several times a week, allowing for individualized attention and adjustments based on their abilities and health status. Throughout the study, changes in functional capacity will be monitored using standardized assessments that measure mobility, strength, and overall physical functioning. Cognitive assessments will evaluate any changes in cognitive status during the intervention and follow-up periods post-discharge. Participants will provide information on medication usage to analyze whether the exercise program can reduce the need for medications or help manage common polypharmacy issues. Surveys and interviews will assess participants' quality of life, including physical, emotional, and social well-being, before and after the intervention. Follow-up assessments will track progress and outcomes, ensuring long-term benefits of the exercise program are documented. The study will include a diverse sample from multiple centers, focusing on individuals aged over 75 who are hospitalized in Geriatrics and Internal Medicine services across participating centers (HUN, CHU-T, ULSBM, and HNSM). Exclusion criteria will ensure safety, excluding those with terminal illnesses or significant contraindications for exercise. This clinical trial aims to recruit 296 patients, providing valuable insights into the benefits of physical activity for elderly patients during hospitalization and informing future care practices.

Predictive Monitoring - IMPact in Acute Care Cardiology Trial

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.