Human Immunodeficiency VirusCASCADENCT06604663
Data Science and Qualitative Research for Decision Support in the HIV Care Cascade
A study testing Activation of the CDSS system in people with Human Immunodeficiency Virus and Treatment Adherence, run by Brown University at 1 site in Kenya.
Trial summary
The goal of this study is to determine whether clinical prediction algorithms derived using statistical machine learning methods can be used to improve patient outcomes in large HIV care programs in sub-Saharan Africa and elsewhere. There are two main questions to be answered. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Human Immunodeficiency Virus, Treatment Adherence, Treatment Compliance
- What is being tested?
- Activation of the CDSS system
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 80,000 participants
- Where
- 1 siteKenya
- Study dates
- 20 May 2024to 31 Oct 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 18 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- The study will include adult patients (age 18 and over) receiving HIV care through the AMPATH program in Eldoret, Kenya. There is not a patient-level enrollment process. The primary endpoints will be summarized at the clinic level (e.g., proportion of patients who keep an appointment within 7 days of the scheduled appointment).
Who cannot join
- None
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Data Science for Decision Support in the HIV Care Cascade
The goal of this study is to determine whether clinical prediction algorithms derived using statistical machine learning methods can be used to improve patient outcomes in large HIV care programs in sub-Saharan Africa and elsewhere.
There are two main questions to be answered. First, can the prediction algorithms accurately identify those who are at risk for (a) missing scheduled clinic visits and/or (b) treatment failure, evidenced by elevated HIV viral load? And second, can the risk predictions be used in a structured way to (a) improve retention in care and/or (b) reduce the number of patients having elevated viral load? Researchers will develop machine learning prediction algorithms, incorporate the risk prediction information into the electronic health record, provide guidance to clinical health workers on use of the point-of-care interface tools that display risk prediction information, and incorporate feedback from clinic staff to modify and co-develop the protocol for using risk predictions for improving patient outcomes.
They will then compare the proportion of patients having missed visits and longer-term loss to follow up, and the proportion with elevated viral load, between clinics that use the information from the risk prediction algorithms and those that do not.
Read the detailed description
Clinical decision support systems (CDSS) tailored to the requirements of low- and middle-income countries (LMIC) have been shown to improve compliance with guidelines and quality of care by a range of healthcare staff. To be most effective CDSS should be developed and tested with large clinical data sets from the local region. Use of machine learning algorithms allows the development of prediction models for clinical complications and outcomes, which can guide health care staff in early identification of problems and appropriate interventions. This requires well established electronic health record (EHR) systems acting as both data sources and as platforms for delivering feedback through CDSS (Learning Health System approach). The EHR at the Academic Model Providing Access to Healthcare (AMPATH), a large HIV care program in western Kenya funded in large part by President's Emergency Plan for AIDS Relief (PEPFAR), has used a version of the OpenMRS EHR for nearly two decades and provides a unique environment for this research.
The objective of this proposal is to develop and implement data-driven tools to aid health-related decision-making at patient, clinic and county levels, and evaluate the efficacy of using these methods. The hypothesis is that health facilities utilizing these data driven CDSS will show improvements in the processes and outcomes of care compared to health facilities not utilizing data driven CDSS within their EHRs.
The two primary endpoints for the study are retention in care and viral load suppression.
The motivation is driven by 95-95-95 HIV cascade of care benchmarks established by UNAIDS for eradicating HIV worldwide. In brief, the framework calls for diagnosis of 95% of individuals who have HIV, initiating antiretroviral (ART) treatment for 95% of those who have been diagnosed, and achieving suppression of viral load (VL) for 95% of those who are on treatment. Our project addresses the second and third phases.
Regarding the second 95, retention is a necessary condition for maintaining persons living with HIV (PLWH) on antiretroviral therapy (ART) because global care guidelines now specify that all PLWH initiate ART once engaged in care. Regarding the third 95, in Kenya and many other LMIC, viral load testing for most adult clients is done six months after treatment initiation and annually thereafter. Even after a measured VL indicating suppression, viral failure due to nonadherence or drug resistance can occur well before the next follow up one year later. Hence our models will generate predicted viral load values in the interim and use them to flag individuals who should have a VL measurement prior to the scheduled follow up.
The trial is part of a larger NIH-funded study. The aims related to the trial are as follows:
Aim 1: Develop and validate statistical machine learning models and algorithms for clinical and programmatic decision support.
1a: Develop and validate statistical machine learning algorithms to identify those at high risk for disengagement from care and viral failure, and to generate predicted values of current viral load.
- b: Develop representations of statistical uncertainty about the predictions to enable optimal decision making. Aim 2. Develop, implement and field test decision support and data visualization tools to enhance data driven decision making by physicians and program managers.
- a: Create the server architecture to support the prediction models in the OpenMRS user interface (UI).
2b: Develop and refine the specific protocol for using the risk predictions to reduce missed visits and reduce incidence of viral load failure.
Aim 3: Conduct evaluation of the impact and efficacy of the clinical decision support tools in the AMPATH Care Program
3a: Implement the CDSS at the point of care in all clinics using the AMPATH Medical Records System (AMRS) in Uzima and Dumisha catchment areas. These clinics have varying size and geographic location.
3b: Following a pilot phase, conduct a stepped wedge randomized longitudinal comparison of retention rates and viral load suppression rates in 30 clinics at AMPATH
The successful completion of the work will provide effective CDSS tools to improve HIV care in Kenya and other LMICs, as well as a set of tools for the development, updating and evaluation of CDSS for other clinical problems. Previous work by the investigators and colleagues in development and wide deployment OpenMRS in more than 44 countries provides a platform for broad dissemination of this work.
- Conditions
- Human Immunodeficiency Virus, Treatment Adherence, Treatment Compliance, Patient No Show, Patient Engagement, Patient Dropouts
- Being tested
- Activation of the CDSS system
- Keywords
- Electronic Medical Records, Clinical Decision Support System, Machine Learning, HIV Treatment Interruption, HIV Treatment Failure, Artificial Intelligence, Electronic Health Records, Bayesian statistics, Clinical prediction models
- Medical subject headings
- Acquired Immunodeficiency Syndrome, Treatment Adherence and Compliance, Patient Compliance, No-Show Patients, Patient Participation, Patient Dropouts
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Activation of the CDSS systemBehavioural
Activation of the CDSS system, whereby outreach workers and clinicians have access to and ability to act upon lists of patients who are at highest risk of missing their upcoming clinical appointment.
| Study group | Role | What they receive |
|---|---|---|
| Usual Care | No intervention | Usual Care at AMPATH involves telephoning clients or care supporters the day prior to their appointment (at some clinics) and/or telephoning or making a home visits after appointments are missed. This will be in place at usual care clinics until the date at which the clinic is randomized to receive the CDSS support. |
| Clinical decision support, CDSS | Experimental | When a clinic is assigned to receive the CDSS support intervention two components will be enacted to enable proactive outreach that prevents a missed visit. These patients are considered to be in the active, experimental arm. Please seem the section above on Detailed Description for background and details on how this intervention is implemented. Receives: Behavioral: Activation of the CDSS system |
What is being measured
The primary outcome is the main question the study is designed to answer.
The proportion of scheduled patient visits kept by the patient (1-day)
The proportion of scheduled patient visits where the patient returns on or before the scheduled visit date. Measured weekly at the clinic level.
Measured: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured weekly for the 4 weeks preceding the first wave of CDSS implementation, and then weekly until 8 weeks after the date of the final wave of CDSS implementation.
The proportion of scheduled patient visits kept by the patient (7-day)
The proportion of scheduled patient visits where the patient returns on or before the 7th day following the scheduled visit date. Measured weekly at the clinic level.
Measured: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured weekly for the 4 weeks preceding the first wave of CDSS implementation, and then weekly until 8 weeks after the date of the final wave of CDSS implementation.
The proportion of patients with suppressed VL among those with measured VL
The proportion of patients with suppressed VL among those with measured VL. This outcome reflects the fraction suppressed only among those who show up for their VL measurement and is the metric designed for tracking progress toward 95-95-95 goals. This endpoint will be measured monthly at the clinic level. The denominator will be number of patient-level VL measures, and the numerator will be number of occasions where the measured VL is undetectable.
Measured: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured for the month preceding the first wave of CDSS implementation, and then monthly until 2 months after the date of the final wave of CDSS implementation.
The proportion of patients with suppressed VL among those with scheduled VL measurement, whether or not that measure was taken.
The proportion of patients with suppressed VL among those with scheduled VL measurement, whether or not that measure was taken. This outcome is designed to reflect the fraction of the overall patient population having suppressed VL and is potentially more relevant as a population-level parameter. This endpoint will be measured monthly at the clinic level. The denominator will be number of VL measurements scheduled, and the numerator will be number of occasions where the VL is measured and is undetectable.
Measured: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured for the month preceding the first wave of CDSS implementation, and then monthly until 2 months after the date of the final wave of CDSS implementation.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study start20 May 2024
- Primary completion31 Jan 2026 (estimated)
- Study completion31 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Human Immunodeficiency VirusStudies 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
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- The investigators will evaluate the intervention using a stepped-wedge design whereby clinics are assigned the intervention in sequential order, and in batches. Specifically, the investigators will implement the decision support tool at 5 or 6 clinics per month over a period of 6 months. By the end of 6 months, all clinics will be receiving the decision support tool. There may be some variation in the allocation, owing to readiness of individual clinics to begin using the decision support tools. To ensure balance in allocation, randomization will be stratified by geographic location, clinic size and pre-intervention appointment default rate.
- Masking
- None (open label)
- Enrolment
- 80,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Brown University
- Sponsor type
- University, hospital or other
- Collaborators
- Moi University College of Health Sciences, Moi Teaching and Referral Hospital, National Institute of Allergy and Infectious Diseases (NIAID)
- Responsible party
- Sponsor
- Investigators
- Joseph W Hogan, ScD, Principal investigator, Brown University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06604663
- Sponsor's protocol ID
- 2022003414
- Other IDs
- R01AI167694
- Acronym
- CASCADE
- Status last verified
- Sep 2025
- First submitted
- 9 Sep 2024
- Last updated
- 12 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Data Science for Decision Support in the HIV Care Cascade
- ClinicalTrials.gov ID
- NCT06604663
- Acronym / sponsor ID
- CASCADE / 2022003414
- Conditions
- Human Immunodeficiency Virus, Treatment Adherence, Treatment Compliance, Patient No Show, Patient Engagement, Patient Dropouts
- Keywords
- Electronic Medical Records, Clinical Decision Support System, Machine Learning, HIV Treatment Interruption, HIV Treatment Failure, Artificial Intelligence, Electronic Health Records, Bayesian statistics
- Study type
- Interventional
- Recruitment status
- Enrolling by invitation
- Enrolment
- 80,000 participants (estimated)
- Lead sponsor
- Brown UniversityUniversity, hospital or other
- Collaborators
- Moi University College of Health Sciences, Moi Teaching and Referral Hospital, National Institute of Allergy and Infectious Diseases (NIAID)
- Study start
- 20 May 2024 (actual)
- Primary completion
- 31 Jan 2026 (estimated)
- Study completion
- 31 Oct 2026 (estimated)
- First posted
- 19 Sep 2024 (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.
The goal of this study is to determine whether clinical prediction algorithms derived using statistical machine learning methods can be used to improve patient outcomes in large HIV care programs in sub-Saharan Africa and elsewhere.
There are two main questions to be answered. First, can the prediction algorithms accurately identify those who are at risk for (a) missing scheduled clinic visits and/or (b) treatment failure, evidenced by elevated HIV viral load? And second, can the risk predictions be used in a structured way to (a) improve retention in care and/or (b) reduce the number of patients having elevated viral load? Researchers will develop machine learning prediction algorithms, incorporate the risk prediction information into the electronic health record, provide guidance to clinical health workers on use of the point-of-care interface tools that display risk prediction information, and incorporate feedback from clinic staff to modify and co-develop the protocol for using risk predictions for improving patient outcomes.
They will then compare the proportion of patients having missed visits and longer-term loss to follow up, and the proportion with elevated viral load, between clinics that use the information from the risk prediction algorithms and those that do not.
Detailed description
Clinical decision support systems (CDSS) tailored to the requirements of low- and middle-income countries (LMIC) have been shown to improve compliance with guidelines and quality of care by a range of healthcare staff. To be most effective CDSS should be developed and tested with large clinical data sets from the local region. Use of machine learning algorithms allows the development of prediction models for clinical complications and outcomes, which can guide health care staff in early identification of problems and appropriate interventions. This requires well established electronic health record (EHR) systems acting as both data sources and as platforms for delivering feedback through CDSS (Learning Health System approach). The EHR at the Academic Model Providing Access to Healthcare (AMPATH), a large HIV care program in western Kenya funded in large part by President's Emergency Plan for AIDS Relief (PEPFAR), has used a version of the OpenMRS EHR for nearly two decades and provides a unique environment for this research.
The objective of this proposal is to develop and implement data-driven tools to aid health-related decision-making at patient, clinic and county levels, and evaluate the efficacy of using these methods. The hypothesis is that health facilities utilizing these data driven CDSS will show improvements in the processes and outcomes of care compared to health facilities not utilizing data driven CDSS within their EHRs.
The two primary endpoints for the study are retention in care and viral load suppression.
The motivation is driven by 95-95-95 HIV cascade of care benchmarks established by UNAIDS for eradicating HIV worldwide. In brief, the framework calls for diagnosis of 95% of individuals who have HIV, initiating antiretroviral (ART) treatment for 95% of those who have been diagnosed, and achieving suppression of viral load (VL) for 95% of those who are on treatment. Our project addresses the second and third phases.
Regarding the second 95, retention is a necessary condition for maintaining persons living with HIV (PLWH) on antiretroviral therapy (ART) because global care guidelines now specify that all PLWH initiate ART once engaged in care. Regarding the third 95, in Kenya and many other LMIC, viral load testing for most adult clients is done six months after treatment initiation and annually thereafter. Even after a measured VL indicating suppression, viral failure due to nonadherence or drug resistance can occur well before the next follow up one year later. Hence our models will generate predicted viral load values in the interim and use them to flag individuals who should have a VL measurement prior to the scheduled follow up.
The trial is part of a larger NIH-funded study. The aims related to the trial are as follows:
Aim 1: Develop and validate statistical machine learning models and algorithms for clinical and programmatic decision support.
1a: Develop and validate statistical machine learning algorithms to identify those at high risk for disengagement from care and viral failure, and to generate predicted values of current viral load.
- b: Develop representations of statistical uncertainty about the predictions to enable optimal decision making. Aim 2. Develop, implement and field test decision support and data visualization tools to enhance data driven decision making by physicians and program managers.
- a: Create the server architecture to support the prediction models in the OpenMRS user interface (UI).
2b: Develop and refine the specific protocol for using the risk predictions to reduce missed visits and reduce incidence of viral load failure.
Aim 3: Conduct evaluation of the impact and efficacy of the clinical decision support tools in the AMPATH Care Program
3a: Implement the CDSS at the point of care in all clinics using the AMPATH Medical Records System (AMRS) in Uzima and Dumisha catchment areas. These clinics have varying size and geographic location.
3b: Following a pilot phase, conduct a stepped wedge randomized longitudinal comparison of retention rates and viral load suppression rates in 30 clinics at AMPATH
The successful completion of the work will provide effective CDSS tools to improve HIV care in Kenya and other LMICs, as well as a set of tools for the development, updating and evaluation of CDSS for other clinical problems. Previous work by the investigators and colleagues in development and wide deployment OpenMRS in more than 44 countries provides a platform for broad dissemination of this work.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- The investigators will evaluate the intervention using a stepped-wedge design whereby clinics are assigned the intervention in sequential order, and in batches. Specifically, the investigators will implement the decision support tool at 5 or 6 clinics per month over a period of 6 months. By the end of 6 months, all clinics will be receiving the decision support tool. There may be some variation in the allocation, owing to readiness of individual clinics to begin using the decision support tools. To ensure balance in allocation, randomization will be stratified by geographic location, clinic size and pre-intervention appointment default rate.
| Arm | Type | Receives |
|---|---|---|
| Usual Care | No intervention | –Usual Care at AMPATH involves telephoning clients or care supporters the day prior to their appointment (at some clinics) and/or telephoning or making a home visits after appointments are missed. This will be in place at usual care clinics until the date at which the clinic is randomized to receive the CDSS support. |
| Clinical decision support, CDSS | Experimental | Behavioral: Activation of the CDSS systemWhen a clinic is assigned to receive the CDSS support intervention two components will be enacted to enable proactive outreach that prevents a missed visit. These patients are considered to be in the active, experimental arm. Please seem the section above on Detailed Description for background and details on how this intervention is implemented. |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- The study will include adult patients (age 18 and over) receiving HIV care through the AMPATH program in Eldoret, Kenya. There is not a patient-level enrollment process. The primary endpoints will be summarized at the clinic level (e.g., proportion of patients who keep an appointment within 7 days of the scheduled appointment).
Key exclusion criteria (1 listed)
- None
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 |
|---|---|---|
| Activation of the CDSS systemActivation of the CDSS system, whereby outreach workers and clinicians have access to and ability to act upon lists of patients who are at highest risk of missing their upcoming clinical appointment. | Behavioural | Clinical decision support, CDSS |
Primary outcome
The proportion of scheduled patient visits kept by the patient (1-day)
Time frame: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured weekly for the 4 weeks preceding the first wave of CDSS implementation, and then weekly until 8 weeks after the date of the final wave of CDSS implementation.
The proportion of scheduled patient visits where the patient returns on or before the scheduled visit date. Measured weekly at the clinic level.
The proportion of scheduled patient visits kept by the patient (7-day)
Time frame: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured weekly for the 4 weeks preceding the first wave of CDSS implementation, and then weekly until 8 weeks after the date of the final wave of CDSS implementation.
The proportion of scheduled patient visits where the patient returns on or before the 7th day following the scheduled visit date. Measured weekly at the clinic level.
The proportion of patients with suppressed VL among those with measured VL
Time frame: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured for the month preceding the first wave of CDSS implementation, and then monthly until 2 months after the date of the final wave of CDSS implementation.
The proportion of patients with suppressed VL among those with measured VL. This outcome reflects the fraction suppressed only among those who show up for their VL measurement and is the metric designed for tracking progress toward 95-95-95 goals. This endpoint will be measured monthly at the clinic level. The denominator will be number of patient-level VL measures, and the numerator will be number of occasions where the measured VL is undetectable.
The proportion of patients with suppressed VL among those with scheduled VL measurement, whether or not that measure was taken.
Time frame: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured for the month preceding the first wave of CDSS implementation, and then monthly until 2 months after the date of the final wave of CDSS implementation.
The proportion of patients with suppressed VL among those with scheduled VL measurement, whether or not that measure was taken. This outcome is designed to reflect the fraction of the overall patient population having suppressed VL and is potentially more relevant as a population-level parameter. This endpoint will be measured monthly at the clinic level. The denominator will be number of VL measurements scheduled, and the numerator will be number of occasions where the VL is measured and is undetectable.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Jan 2026.
- Individual participant dataSharing planned: No
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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.
Joseph W Hogan, ScD principal investigator
Brown University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Activation of the CDSS system in people with Human Immunodeficiency Virus and Treatment Adherence, run by Brown University at 1 site in Kenya.
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 1 inclusion and 1 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: enrolling by invitation, as of the sponsor's last update on 12 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Kenya. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The proportion of scheduled patient visits kept by the patient (1-day) (time frame: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured weekly for the 4 weeks preceding the first wave of CDSS implementation, and then weekly until 8 weeks after the date of the final wave of CDSS implementation.). The study lists 4 primary outcomes in all. 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 12 Jan 2026.