Real World Data of FLT3 Mutation Testing Rates Among Indian Patients with Acute Myeloid Leukemia: An AI-Enabled Intervention to Improve Guideline-Compliant Care
Authors: Nayak Lingaraj , Gokarn Anant , Ganesan Prasanth , Menon Hari , Tandon Nidhi , Ramarajan Naresh , Vashistha Vishal 
Published: December 2024

Background: Navya-Al is an online cancer informatics solution that leverages artificial intelligence (AI) and asynchronous expert review to provide individualized treatment plans for cancer patients. By analyzing NCCN Guidelines and expert recommendations, Navya-Al generates opinions for patients with high-confidence. Validated through prior research, Navya-Al demonstrates a remarkable 97% concordance with expert opinions from leading academic medical centers in India and the US. Furthermore, 80% of patients receive treatment concordant with Navya-Al recommendations, resulting in a significant reduction of patient waiting times for expert opinions by an average of 3.5 days. By streamlining the treatment planning process, Navya-Al improves patient outcomes and healthcare efficiency, setting a new standard for cancer care.

Acute Myeloid Leukemia (AML) is a heterogeneous disease with varying clinical outcomes, and FLT3 mutations serve as both prognostic and predictive biomarkers. Despite guidelines recommending FLT3 mutation testing, real-world data suggests that sequencing rates are suboptimal, leading to delayed or inadequate treatment. This study leverages Artificial Intelligence (AI) to analyze real-world data and develop an intervention to improve guideline-compliant FLT3 testing in AML patients.

Methods: A prospective analysis from June 2019 to July 2024 all patients diagnosed with AML who received a Navya-AI enabled review of their treatment plan were prospectively analyzed for concordance on genomic testing and precision care. Intervention with Navya AI was used to close any identified care gaps.

Results: 400 Indian patients who received a Navya-AI review were analyzed. Patients were diverse with respect to age, gender, diagnosis: Age &lt; 35: 41%, 35-50: 33%, 51-65: 18%, &gt;65: 8%, males: 57%, females: 43%, de novo: 90%, Relapsed/Refractory: 10%. Our analysis revealed that only 31% (124/400) of patients had FLT3 mutation testing results available at the time of expert review, leaving 69% (276/400) without any FLT3 mutation status. Moreover, 55% (151/276) of these patients had initiated frontline systemic treatment without FLT3 testing prior to initial Navya-AI evaluation, highlighting a critical gap in guideline adherence. To address this issue, we implemented a digital intervention using Navya-AI, providing patients and oncologists with information on the risks and benefits of genetic testing, the potential use of FLT3 inhibitors and other targeted therapies, and the importance of genetic testing in treatment planning. Additionally, the intervention enabled patient navigation to their treating oncologists, facilitating guideline compliance.

Conclusion: Two third of the patients in India seeking expert opinions through a national digital health platform did not have critical genomic testing at time of AI evaluation, highlighting the need for innovative solutions to address these disparities. This harnesses AI-driven technologies to identify and close care gaps globally, ensuring equitable access to high-quality care for patients. By leveraging digital health expertise and advanced analytics, this initiative aims to bridge the care disparity divide, ultimately improving patient outcomes and survival rates.