Clinical trial NCT06604663: Data Science and Qualitative Research for Decision Support in the HIV Care Cascade
Official title: Data Science for Decision Support in the HIV Care Cascade
Study type: INTERVENTIONAL
Overall status: ENROLLING_BY_INVITATION
Phase: NA
Conditions: Human Immunodeficiency Virus
Treatment Adherence
Treatment Compliance
Patient No Show
Patient Engagement
Patient Dropouts
HIV Viremia
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
Lead sponsor: Brown University
Sponsor class: OTHER
Collaborators: name: Moi University College of Health Sciences
name: Moi Teaching and Referral Hospital
name: National Institute of Allergy and Infectious Diseases (NIAID)
First posted: 2024-09-19
Enrollment: 80000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention 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.
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief 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. 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.

1. 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.
2. 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.

Eligibility criteria: Inclusion Criteria:

* 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).

Exclusion Criteria:

* None

Primary outcomes:
measure: The proportion of scheduled patient visits kept by the patient (1-day)
timeFrame: 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.
measure: The proportion of scheduled patient visits kept by the patient (7-day)
timeFrame: 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.
measure: The proportion of patients with suppressed VL among those with measured VL
timeFrame: 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.
measure: The proportion of patients with suppressed VL among those with scheduled VL measurement, whether or not that measure was taken.
timeFrame: 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.
Secondary outcomes:
Other outcomes:
Locations:
city: Eldoret
country: Kenya
facility: AMPATH
Study officials:
affiliation: Brown University
name: Joseph W Hogan, ScD
role: PRINCIPAL_INVESTIGATOR
References: