Injury Traumatic clinical trials at UCLA
2 in progress, 0 open to eligible people
Trauma Follow-Up Prediction (Project 2: Aim 2)
Sorry, not yet accepting patients
Approximately 9% of the world's deaths, more than 5 million deaths annually, are due to injury. In high-income countries, where the epidemiology and outcomes of traumatic injury are well characterized, trauma primarily affects young, productive members of the population and is associated with significant long-term disability. In sub-Saharan Africa (SSA) countries like Cameroon, injured people face multiple obstacles to trauma care, including potentially lifesaving follow-up care after hospital discharge. The Investigators' community-based survey of 8,065 patients in South west Cameroon found that 34.6% of injured respondents did not seek immediate formal care after injury, and another 9.9% only sought formal care after alternative means, such as consultation with traditional medicine practitioners. In Cameroon, for the 65.4% of injured people who seek formal care after injury,5 therapeutic itineraries can be complex, often involving poorly supported referrals to other facilities or transitions away from formal care. As a result, formal systems of care fail to retain trauma patients for follow-up care, a missed opportunity as these patients have already overcome significant financial and personal challenges to seek initial care for their injuries. Consequently, discharged trauma patients who may benefit from follow-up care often delay care until advanced complications develop. The objective of this study is to evaluate a machine learning optimized phone-based screening tool that predicts which trauma patients are most likely to benefit from follow-up care. A Cluster randomized trial controlled trail will be carried out in 10 hospitals in Cameroon involving 852 trauma patients. The control group shall use the existing standard mHealth screening tool while the intervention shall use the optimized version of the mHealth screening tool (intervention) using the machine learning approach. Patients shall be followed up over a 6 months period to determine the proportion of trauma post discharge patients that need follow up care using mobile phone.
Trauma Follow-Up Prediction (Project 2: Aim 1)
Sorry, not yet accepting patients
Traumatic injury and inadequate follow-up care are a significant cause of morbidity and 10% of all deaths in sub-Saharan Africa (SSA). In Cameroon, ~50% of all emergency department (ED) visits are due to traumatic injury, which is likely only ~60% of all traumatic injuries. In the subset of patients who seek care, follow-up after discharge can save lives, yet is uncommon due to both supply-side (e.g., under-resourced health systems, poor data) and demand-side (e.g., poverty) barriers, resulting in preventable complications after discharge (e.g., sepsis, osteomyelitis). Consequently, better follow-up care of trauma patients is a neglected, but high-yield opportunity to improve injury outcomes, especially when coupled with mobile health technologies (mHealth) to better predict and implement post-discharge care, preventing disability and death. Thus, in this study, the investigators will scale up an existing trauma registry and expand use of a mHealth screening tool (triage tool). At 10 hospitals, the investigators will implement a trauma registry and mHealth tool and evaluate success in a mixed-methods study; a quantitative prospective cohort of all eligible injured patients will be followed for 6 months after discharge and an inductive qualitative study.
Our lead scientists for Injury Traumatic research studies include Catherine Juillard, MD, MPH.
Last updated: