Clinical trial NCT06588452: Manual Versus AI-Assisted Clinical Trial Screening Using Large-Language Models
Official title: Manual Versus AI-Assisted Clinical Trial Screening Using Large-Language Models
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Comparing Manual and AI Patient Screening in Heart Failure
Lead sponsor: Brigham and Women's Hospital
Sponsor class: OTHER
First posted: 2024-09-19
Enrollment: 4500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Potentially eligible patients identified through a structured query in the Mass General Brigham Enterprise Datawarehouse (EDW).

Brief summary: A prospective randomized controlled trial comparing manual review and AI screening for patient eligibility determination and enrollments. A structured query will identify potentially eligible patients from the Mass General Brigham Electronic Data Warehouse (EDW), who will then be randomized into either the manual review arm or the AI-assisted review arm.

Detailed description: Screening participants for clinical trials is a critical yet challenging process that requires significant time and resources. Traditionally, patient screening has been manual, relying on the diligence and judgment of study staff. However, manual screening is prone to human error and inefficiencies, contributing to high costs and prolonged trial durations.

Recent advancements in natural language processing (NLP) and large language models (LLMs), such as GPT-4, offer potential solutions to improve the accuracy, efficiency, and reliability of the screening process. Retrieval-Augmented Generation (RAG)-enabled systems, like RECTIFIER, have shown promise in enhancing clinical trial screening by automating the extraction and analysis of relevant data from electronic health records (EHRs).

In the investigators' previous study, RECTIFIER demonstrated high accuracy in screening patients for clinical trials, aligning closely with expert clinician reviews and outperforming manual study staff in several criteria. It underscored the potential for LLMs to transform clinical trial screening, making it more efficient and cost-effective while maintaining high standards of accuracy and reliability. However, before RECTIFIER is scaled to be used across many domains of clinical trials, it should be validated prospectively in the real-world setting to enroll patients.

In the Co-Operative Program for Implementation of Optimal Therapy in Heart Failure (COPILOT-HF) trial (NCT05734690), the investigators will identify potential participants through EHR queries followed by manual review, which provides an opportunity for RECTIFIER to improve the screening process. By leveraging RECTIFIER, this study aims to evaluate the effectiveness of automated AI screening compared to traditional manual methods for enrollments of patients into an ongoing clinical trial.

Eligibility criteria: Inclusion Criteria:

* Documented diagnosis of heart failure (e.g., ICD-9 codes 428 ICD-10 codes I50 or Problem list in the electronic health record)
* Most recent left ventricular ejection fraction (LVEF) assessed within the past 24 months
* Seen Mass General Brigham provider within the last 24 months

Exclusion Criteria:

* LVEF \<50% currently prescribed or intolerant to an evidence-based beta-blocker, ARNI, MRA, and SGLT2i at least 50% goal dose
* LVEF\>50% currently prescribed or intolerant to SGLT2i
* Systolic blood pressure (SBP) \<90 mmHg at last measure

Primary outcomes:
measure: Determine study eligibility, analyzed using a survival analysis framework, specifically the Fine-Gray subdistribution hazards model, to account for competing risks.
timeFrame: Through study completion, an average of 6 months
Secondary outcomes:
measure: Likelihood of achieving successful enrollment or eligibility, assessed using the hierarchical win ratio.
timeFrame: Through study completion, an average of 6 months
Other outcomes:
Locations:
city: Boston
country: United States
facility: Brigham and Women's Hospital
state: Massachusetts
Study officials:
affiliation: Brigham and Women's Hospital
name: Benjamin M Scirica, MD, MSc
role: STUDY_CHAIR
References: