Trauma Follow-Up Prediction (Project 2: Aim 2)

Summary

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.

Conditions

• Injury Traumatic
• Injuries

Interventions

DEVICE

Optimized version of the mHealth screening tool (intervention) using the machine learning approach

An improvement to the mHealth triage tool using a machine learning approach, optimizing the efficiency of call schedule and the prediction of which patients are most likely to benefit from follow-up care given data collected at the hospital through the Cameroon Trauma Registry, as well as post-discharge phone contact attempts and survey information. The backbone of the estimators is the ensemble machine learning algorithm the Superlearner, which has been applied to medical contexts, including injury and trauma. It is a theory-driven method based on cross-validation, which combines potentially many different learners (e.g., standard regression, tree regression, random forest, neural nets) such that the model chosen (a weighted average of the learners) is asymptotically equivalent to the so called "Oracle" - the learner that fits optimally for the data-generating distribution. Note, double-robust CV-TMLE versions of this estimator are available as the tmle3mopttx function in tlverse.

Sponsors & Collaborators

• Fogarty International Center of the National Institute of Health

  collaborator NIH

• University of California, Los Angeles

  collaborator OTHER

• University of California, Berkeley

  collaborator OTHER

• University of Buea

  lead OTHER

Principal Investigators

• Alain Chichom-Mefire, MD · University of Buea

• Catherine Juillard, MD, MPH · University of California, Los Angeles

Study Design

Allocation

RANDOMIZED

Purpose

PREVENTION

Masking

SINGLE

Model

PARALLEL

Eligibility

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2024-06-30

Primary Completion

2025-03-31

Completion

2025-12-31