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

NCT05464017 | Unknown

• 2 other identifiers: GRANT13254336 - Aim 2U54TW012087
• Study Type: interventional
• Target: 852
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

Cameroon; Sub-Sahara Africa; mHealth; Machine learning; Injury; Trauma; Mobile phone; SuperLearner

Outcome Measures

Primary Outcomes (1)

• Assess Glasgow Outcomes Scale-Extended (GOSE) score

  This outcome will measure recovery after traumatic injury and ranges from 1 (death), 2 (vegetative state), through 8 (good upper recovery).

  At 3 months

Secondary Outcomes (3)

• GOSE score

  At 6 and 12 months

• Proportion reached by mobile phones

  At 0.5, 1, 3, and 6 months

• Proportion needing follow-up care

  At 0.5, 1, 3, and 6 months

Study Arms (2)

Standard mHealth screening tool

NO INTERVENTION

This is a tested standard phone screening tool which determines the need for in-person follow-up after a patient has been discharge. Consenting trauma patients will be contacted via mobile phone at 0.5, 1, 3, and 6 months post-discharge by a research assistant to complete the screening which will guide whether or not the patient should seek follow-up care based on the number of flagged responses to ≥1 question on the 7-item screening survey.

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

EXPERIMENTAL

This arm will receive an improvement to the mHealth triage tool using a machine learning approach. Patients will be called using the optimized tool at outcome timepoints (3 months, 6months and 12months). At each call, research assistants will complete the GOSE survey and the mHealth triage tool, entering call outcomes and patient responses directly into the mHealth system. If follow-up care is indicated, the research assistant will share that information with the patient and offer to schedule an appointment.

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

Interventions

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

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.

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

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients with acute traumatic injury i.e. within 2 weeks of presentation for care.
• Trauma patients who are formally admitted to the hospital as in-patients.
• Trauma patients who die upon arriving to the Emergency Departments or while admitted in the hospital.
• Trauma patients who are transferred to other health facilities.
• Trauma patients with indications for hospital admission (based on physicians' assessments) but leave against medical advice
• Trauma patients who are kept under observation in the Emergency Department for over 24 hours
• Standard mHealth Triage Tool Eligibility: The mHealth triage tool will be administered to the subset of patients included in the trauma registry who are admitted then discharged home after treatment.
• Optimized version of the mHealth screening tool (intervention) Eligibility: The optimized version of mHealth screening tool will be administered to the subset of patients included in the trauma registry who are admitted then discharged home after treatment.

You may not qualify if:

• According to the World Health Organization (WHO) injury definition, the following will be excluded from the definition of "injury": "Whereas the above definition of an injury includes drowning (lack of oxygen), hypothermia (lack of heat), strangulation (lack of oxygen), decompression sickness or "the bends" (excess nitrogen compounds) and poisonings (by toxic substances), it does NOT include conditions that result from continual stress, such as carpal tunnel syndrome, chronic back pain and poisoning due to infections. Mental disorders and chronic disability, although these may be eventual consequences of physical injury, are also excluded by the above definition." Although included in the WHO definition, poisonings will be excluded from the CTR as these have been extremely rare events in the CTR to date and are not typically included in trauma registries in most other contexts.
• Patients who are not formally admitted and discharged within 24 hours from the Emergency Ward will be excluded.

Sponsors & Collaborators

• University of Buea: lead
• Fogarty International Center of the National Institute of Health: collaborator
• University of California, Los Angeles: collaborator
• University of California, Berkeley: collaborator

MeSH Terms

Conditions

Wounds and Injuries

Interventions

Methods

Intervention Hierarchy (Ancestors)

Investigative Techniques

Study Officials

• Alain Chichom-Mefire, MD

  University of Buea

  PRINCIPAL INVESTIGATOR

• Catherine Juillard, MD, MPH

  University of California, Los Angeles

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Alain Chichom-Mefire, MD

CONTACT

+237677530532; chichom.mefire@ubuea.cm

Fanny JN Dissak-Delon, MD, PhD

CONTACT

+237697582185; fannynadia@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Surgery

Model Details: Investigators will implement improvements to the mHealth triage tool using a machine learning approach, optimizing both the efficiency of the 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 CTR, as well as post-discharge phone contact attempts and survey information. The backbone of investigators' estimators is the ensemble machine learning algorithm the Superlearner

Study Record Dates

• First Submitted: July 14, 2022
• First Posted: July 19, 2022
• Study Start: June 1, 2024
• Primary Completion: March 1, 2025
• Study Completion: December 1, 2025
• Last Updated: July 21, 2022

Record last verified: 2022-07

Data Sharing

• IPD Sharing: Will not share