Clinical annotations for prostate cancer research: Defining data elements, creating a reproducible analytical pipeline, and assessing data quality

Abstract

Background: Routine clinical data from clinical charts are indispensable for retrospective and prospective observational studies and clinical trials. Their reproducibility is often not assessed. We developed a prostate cancer-specific database for clinical annotations and evaluated data reproducibility. Methods: For men with prostate cancer who had clinical-grade paired tumor-normal sequencing at a comprehensive cancer center, we performed team-based retrospective data collection from the electronic medical record using a defined source hierarchy. We developed an open-source R package for data processing. With blinded repeat annotation by a reference medical oncologist, we assessed data completeness, reproducibility of team-based annotations, and impact of measurement error on bias in survival analyses. Results: Data elements on demographics, diagnosis and staging, disease state at the time of procuring a genomically characterized sample, and clinical outcomes were piloted and then abstracted for 2261 patients (with 2631 samples). Completeness of data elements was generally high. Comparing to the repeat annotation by a medical oncologist blinded to the database (100 patients/samples), reproducibility of annotations was high; T stage, metastasis date, and presence and date of castration resistance had lower reproducibility. Impact of measurement error on estimates for strong prognostic factors was modest.Conclusions: With a prostate cancer-specific data dictionary and quality control measures, manual clinical annotations by a multidisciplinary team can be scalable and reproducible. The data dictionary and the R package for reproducible data processing are freely available to increase data quality and efficiency in clinical prostate cancer research.

Identification of Key Elements in Prostate Cancer for Ontology Building via a Multidisciplinary Consensus Agreement

“…This ensures that while treatments and technology evolve, comprehensive capture of core data elements using structured and routinely updated fields remains standardized across institutions. Overall, our work is synergistic with other ongoing efforts to define data elements in prostate cancer care for the construction of analytical pipelines and improved health information sharing [ 57 ].…”