IPM.ai In the Orphanet Journal of Rare Diseases: Understanding Healthcare Resource Utilization for US X‑linked Myotubular Myopathy (XLMTM) Patients using Real World Data

By: Dan Fisher, Principal, IPM.ai

X‑linked myotubular myopathy (XLMTM) is a rare, life-threatening congenital myopathy caused by mutations in the MTM1 gene, which leads to myotubularin deficiency affecting skeletal muscles throughout the body. The condition has an estimated incidence of 1 in 40-50,000 newborn males, who require intensive medical intervention to survive. XLMTM is associated with numerous morbidities including respiratory failure, feeding and swallowing difficulties, delayed motor milestones, musculoskeletal issues, and hepatobiliary disease. As XLMTM is so rare, there is limited understanding of the patient journey, which further complicates diagnosis and thus treatment. To intercept this long standing pattern of misdiagnosis (or no diagnosis), IPM.ai partnered with the Boston Children’s Hospital through research sponsored by Astellas Gene Therapies, leveraging real world data and machine learning to analyze XLMTM patients. 

Recognizing that approximately half of XLMTM patients do not survive past 18 months, this devastating disease sparsely allows HCPs and caregivers time to diagnose and study this high burden condition. Common symptoms are expressed as chronic and acute respiratory events, feeding difficulties, and scoliosis, requiring medical interventions including ventilation management, tracheostomy, gastrostomy, and emergency care. 

To identify HCPs treating XLMTM patients faster, IPM.ai relied on an existing patient cohort contained within Boston Children Hospital’s registry, and supplemented ICD-10 code claims data. IPM.ai leveraged weekly-refreshed claims data from over 300 million US patients in identifying suspected patients using machine learning techniques. Finally, IPM.ai used data from genetic testing company Invitae on behalf of Astellas Gene Therapies. Advanced techniques such as tokenization were used to de-identify and link all datasets securely, ensuring patient privacy. By employing these methods, IPM.ai yielded 192 unique patients over the course of the program. Astellas Gene Therapies subsequently utilized medical science liaisons to engage with providers, accelerating the path to diagnosis for more than 10% of the suspected XLMTM patients. 

The study provided valuable insights into the disease burden of XLMTM and highlighted the extensive medical interventions and care required for managing XLMTM. The findings underscore the significant healthcare resource utilization and the need for specialized support in addressing the diverse clinical manifestations of the condition. By better understanding the disease burden of XLMTM, researchers and health care providers can strive for improved treatments and management strategies to enhance the quality of life for individuals living with XLMTM. Additionally, by intercepting in the diagnostic journey faster, clinical therapies can be administered at key intervals, improving outcomes.

About the Author

Dan Fisher

Principal, IPM.ai 

As Principal for IPM.ai, Dan leads a team that utilizes machine learning, artificial intelligence and advanced analytics to deliver valuable insights that guide and accelerate the clinical and commercial decisions of life sciences companies. With a focus on specialty markets, Dan’s deep expertise in rare disease and oncology disease states helps biopharma clients better understand and more effectively uncover ideal patients and their health care providers. Prior to joining IPM.ai, Dan led commercial operations and clinical analytics projects for ZS Associates. He holds a Master of Business Administration (MBA) from Vanderbilt University.