Injury TraumaticNCT05464017
Trauma Follow-Up Prediction (Project 2: Aim 2)
A study testing Optimized version of the mHealth screening tool (intervention) using the machine learning approach in people with Injury Traumatic and Injuries, run by University of Buea.
Trial 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… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Injury Traumatic, Injuries
- What is being tested?
- Optimized version of the mHealth screening tool (intervention) using the machine learning approach
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 852 participants
- Where
- No sites listed yet
- Study dates
- Jun 2024to Dec 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Trauma Registry (CTR): Patients satisfying the following inclusion criteria will be included in the registry:
- 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
Show all 9Show fewer
- 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.
Who cannot join
- Trauma Registry Exclusion criteria: Patients will not be excluded based on age, gender, race, or nationality. If patients or their surrogate decision-maker do not give consent to participation, those patients will be excluded.
- 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.
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Using Data-Adaptive Methods to Optimize Follow Up Of Injured Patients After Hospital Discharge in Cameroon (Aim 2)
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.
Read the detailed description
The technological convergence of mHealth and machine learning provides an unprecedented opportunity to transform injury care in SSA, particularly for disadvantaged populations. The ubiquity of mobile phones and the advent of mHealth provides a novel opportunity to improve injury care in SSA. Given high levels of mobile phone penetration in Cameroon (85% to 95%) and elsewhere in SSA, the investigators designed and piloted an mHealth, phone-based 7-item screening tool for trauma patients to predict the need for in-person follow-up care after discharge. If effective, this approach could efficiently identify the subset of patients most likely to benefit from follow-up care, which is more feasible, scalable, and cost-effective than blanket advice for post-discharge care. The investigators found that phone follow-up is feasible and acceptable and a validation study revealed good correlation of the screening tool with an independent, in-person exam.
Investigators will build upon their prior research and use data science to improve, implement and evaluate the mHealth screening tool, with the ultimate objective of reducing the crippling burden of injury. This will be achieved by leveraging on machine learning, which has demonstrated promise in optimizing trauma care and trauma systems.The novel combination of mHealth and machine learning provides a powerful opportunity to transform access to health care for those least likely to receive it. Building on existing knowledge, the investigators hypothesize that a data-adaptive, machine-learning approach to outcomes prediction could radically improve survival and reduce morbidity after injury in SSA.
Investigators will apply a machine learning approach to adaptively optimize the mHealth triage tool, improving the phone call timing and algorithm that predicts the need for follow-up care via a cluster randomized controlled trial. This will be achieved using SuperLearner for prediction and cross-validated targeted maximum likelihood estimation (CV-TMLE) for variable importance, using the trauma registry, contact attempt, and screening survey data collected in Aim 1. The overall goal is to improve the mHealth tool's prediction of vulnerable patients needing follow-up care after discharge. This study shall be conducted over an 18-months period; enrollment in 6 months and follow-up participants for 12 months. Investigators will evaluate the impact of the optimized approach in a randomized study in 10 hospitals with 852 injury patients with the primary outcome of the Glasgow Outcomes Scale-Extended (GOSE)24,25 score at 3 months.
- Conditions
- Injury Traumatic, Injuries
- Being tested
- Optimized version of the mHealth screening tool (intervention) using the machine learning approach
- Keywords
- Cameroon, Sub-Sahara Africa, mHealth, Machine learning, Injury, Trauma, Mobile phone, SuperLearner
- Medical subject headings
- Wounds and Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Optimized version of the mHealth screening tool (intervention) using the machine learning approachDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: Optimized version of the mHealth screening tool (intervention) using the machine learning approach |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: At 3 months
Secondary outcomes (3)
GOSE score
This outcome will measure recovery after traumatic injury and ranges from 1 (death), 2 (vegetative state), through 8 (good upper recovery).
Measured: At 6 and 12 months
Proportion reached by mobile phones
Number and proportion of hospitalized trauma patients who are reached by mobile phone after discharge in both arms
Measured: At 0.5, 1, 3, and 6 months
Proportion needing follow-up care
Number and proportion of hospitalized trauma patients who are reached by mobile phone post-discharge and who are identified by the screening tool as needing follow-up care.
Measured: At 0.5, 1, 3, and 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Jul 2022
- Study startJun 2024 (estimated)
- Primary completionMar 2025 (estimated)
- Study completionDec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Injury TraumaticStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 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
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 852 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Buea
- Sponsor type
- University, hospital or other
- Collaborators
- Fogarty International Center of the National Institute of Health, University of California, Los Angeles, University of California, Berkeley
- Responsible party
- Principal investigator, Mefire Alain Chichom, University of Buea
- Investigators
- Alain Chichom-Mefire, MD, Principal investigator, University of BueaCatherine Juillard, MD, MPH, Principal investigator, University of California, Los Angeles
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05464017
- Sponsor's protocol ID
- GRANT13254336 - Aim 2
- Other IDs
- U54TW012087
- Status last verified
- Jul 2022
- First submitted
- 14 Jul 2022
- Last updated
- 21 Jul 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: De-identified individual participant data for all primary and secondary outcome measures will be made available upon reasonable request to Harnessing Data Science for Health Discovery and Innovation in Africa (DSI Africa) consortium and other researchers.
Study at a glance
From the registry record, last updated by the sponsor on 21 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Data-Adaptive Methods to Optimize Follow Up Of Injured Patients After Hospital Discharge in Cameroon (Aim 2)
- ClinicalTrials.gov ID
- NCT05464017
- Acronym / sponsor ID
- GRANT13254336 - Aim 2
- Conditions
- Injury Traumatic, Injuries
- Keywords
- Cameroon, Sub-Sahara Africa, mHealth, Machine learning, Injury, Trauma, Mobile phone, SuperLearner
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 852 participants (estimated)
- Lead sponsor
- University of BueaUniversity, hospital or other
- Collaborators
- Fogarty International Center of the National Institute of Health, University of California, Los Angeles, University of California, Berkeley
- Study start
- Jun 2024 (estimated)
- Primary completion
- Mar 2025 (estimated)
- Study completion
- Dec 2025 (estimated)
- First posted
- 19 Jul 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
The technological convergence of mHealth and machine learning provides an unprecedented opportunity to transform injury care in SSA, particularly for disadvantaged populations. The ubiquity of mobile phones and the advent of mHealth provides a novel opportunity to improve injury care in SSA. Given high levels of mobile phone penetration in Cameroon (85% to 95%) and elsewhere in SSA, the investigators designed and piloted an mHealth, phone-based 7-item screening tool for trauma patients to predict the need for in-person follow-up care after discharge. If effective, this approach could efficiently identify the subset of patients most likely to benefit from follow-up care, which is more feasible, scalable, and cost-effective than blanket advice for post-discharge care. The investigators found that phone follow-up is feasible and acceptable and a validation study revealed good correlation of the screening tool with an independent, in-person exam.
Investigators will build upon their prior research and use data science to improve, implement and evaluate the mHealth screening tool, with the ultimate objective of reducing the crippling burden of injury. This will be achieved by leveraging on machine learning, which has demonstrated promise in optimizing trauma care and trauma systems.The novel combination of mHealth and machine learning provides a powerful opportunity to transform access to health care for those least likely to receive it. Building on existing knowledge, the investigators hypothesize that a data-adaptive, machine-learning approach to outcomes prediction could radically improve survival and reduce morbidity after injury in SSA.
Investigators will apply a machine learning approach to adaptively optimize the mHealth triage tool, improving the phone call timing and algorithm that predicts the need for follow-up care via a cluster randomized controlled trial. This will be achieved using SuperLearner for prediction and cross-validated targeted maximum likelihood estimation (CV-TMLE) for variable importance, using the trauma registry, contact attempt, and screening survey data collected in Aim 1. The overall goal is to improve the mHealth tool's prediction of vulnerable patients needing follow-up care after discharge. This study shall be conducted over an 18-months period; enrollment in 6 months and follow-up participants for 12 months. Investigators will evaluate the impact of the optimized approach in a randomized study in 10 hospitals with 852 injury patients with the primary outcome of the Glasgow Outcomes Scale-Extended (GOSE)24,25 score at 3 months.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- 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
| Arm | Type | Receives |
|---|---|---|
| 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 | Device: Optimized version of the mHealth screening tool (intervention) using the machine learning approachThis 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (9 listed)
- Trauma Registry (CTR): Patients satisfying the following inclusion criteria will be included in the registry:
- 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.
Key exclusion criteria (3 listed)
- Trauma Registry Exclusion criteria: Patients will not be excluded based on age, gender, race, or nationality. If patients or their surrogate decision-maker do not give consent to participation, those patients will be excluded.
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Optimized version of the mHealth screening tool (intervention) using the machine learning approachAn 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. | Device | Optimized version of the mHealth screening tool (intervention) using the machine learning approach |
Primary outcome
Assess Glasgow Outcomes Scale-Extended (GOSE) score
Time frame: At 3 months
This outcome will measure recovery after traumatic injury and ranges from 1 (death), 2 (vegetative state), through 8 (good upper recovery).
3 secondary outcomes
GOSE score
Time frame: At 6 and 12 months
Proportion reached by mobile phones
Time frame: At 0.5, 1, 3, and 6 months
Proportion needing follow-up care
Time frame: At 0.5, 1, 3, and 6 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Jul 2022.
- Individual participant dataSharing planned: No. De-identified individual participant data for all primary and secondary outcome measures will be made available upon reasonable request to Harnessing Data Science for Health Discovery and Innovation in Africa (DSI Africa) consortium and other researchers.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Alain Chichom-Mefire, MD principal investigator
University of Buea
Catherine Juillard, MD, MPH principal investigator
University of California, Los Angeles
Questions about this trial
What is this clinical trial studying?
A study testing Optimized version of the mHealth screening tool (intervention) using the machine learning approach in people with Injury Traumatic and Injuries, run by University of Buea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 21 Jul 2022. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Alain Chichom-Mefire, MD (+237677530532, chichom.mefire@ubuea.cm), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Assess Glasgow Outcomes Scale-Extended (GOSE) score (time frame: At 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Jul 2022.