Discovering signature disease trajectories in pancreatic cancer and soft-tissue sarcoma from longitudinal patient records.

Abstract

Background: Most clinicians have limited experience with rare diseases, making diagnosis and treatment challenging. Large real-world data sources, such as electronic health records (EHRs), provide a massive amount of information that can potentially be leveraged to determine the patterns of diagnoses and treatments for rare tumors that can serve as clinical decision aids.

Objectives: We aimed to discover signature disease trajectories of 3 rare cancer types: pancreatic cancer, STS of the trunk and extremity (STS-TE), and STS of the abdomen and retroperitoneum (STS-AR).

Materials and methods: Leveraging IQVIA Oncology Electronic Medical Record, we identified significant diagnosis pairs across 3 years in patients with these cancers through matched cohort sampling, statistical computation, right-tailed binomial hypothesis test, and then visualized trajectories up to 3 progressions. We further conducted systematic validation for the discovered trajectories with the UTHealth Electronic Health Records (EHR).

Results: Results included 266 significant diagnosis pairs for pancreatic cancer, 130 for STS-TE, and 118 for STS-AR. We further found 44 2-hop (i.e., 2-progression) and 136 3-hop trajectories before pancreatic cancer, 36 2-hop and 37 3-hop trajectories before STS-TE, and 17 2-hop and 5 3-hop trajectories before STS-AR. Meanwhile, we found 54 2-hop and 129 3-hop trajectories following pancreatic cancer, 11 2-hop and 17 3-hop trajectories following STS-TE, 5 2-hop and 0 3-hop trajectories following STS-AR. For example, pain in joint and gastro-oesophageal reflux disease occurred before pancreatic cancer in 64 (0.5%) patients, pain in joint and "pain in limb, hand, foot, fingers and toes" occurred before STS-TE in 40 (0.9%) patients, agranulocytosis secondary to cancer chemotherapy and neoplasm related pain occurred after pancreatic cancer in 256 (1.9%) patients. Systematic validation using the UTHealth EHR confirmed the validity of the discovered trajectories.

Conclusion: We identified signature disease trajectories for the studied rare cancers by leveraging large-scale EHR data and trajectory mining approaches. These disease trajectories could serve as potential resources for clinicians to deepen their understanding of the temporal progression of conditions preceding and following these rare cancers, further informing patient-care decisions.