Clinical Documentation to Predict Factors Associated with Urinary Incontinence Following Prostatectomy for Prostate Cancer

• Type of Material: Article
• Authors: Kevin Li, Stanford University; Imon Banerjee, Emory University; Christopher J. Magnani, Stanford University; Douglas W. Blayney, Stanford University; James D. Brooks, Stanford University; Tina Hernandez-Boussard, Stanford University
• Language: English
• Date: 2020-01-23
• Publisher: Dove Medical Press
• Publication Version: Final Published Version
• Copyright Statement: © 2020 Li et al.
• License: Creative Commons Attribution-NonCommercial 3.0 United States
• Final Published Version (URL): https://doi.org/10.2147/RRU.S234178
• Title of Journal or Parent Work: Research and Reports in Urology
• Volume: 12
• Start Page: 7
• End Page: 14
• Grant/Funding Agency: National Institutes of Health
• Grant/Funding Information: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA183962.
• Keywords: natural language processing; patient-centered outcomes; prostate cancer; soft labels; urinary incontinence
• Subject - Topics: Clinical informatics; Urology; Natural language processing

Abstract

Background Advances in data collection provide opportunities to use population samples in identifying risk factors for urinary incontinence (UI), which occurs in up to 71% of men with prostate cancer following prostatectomy. Most studies on patient-centered outcomes use surveys or manual chart abstraction for data collection, which can be costly and difficult to scale. We sought to evaluate rates of and risk factors for UI following prostatectomy using natural language processing on electronic health record (EHR) data. Methods We conducted a retrospective analysis of patients undergoing prostatectomy for prostate cancer between January 2008 and August 2018 using EHR data from an academic medical center. UI incidence for each patient in the cohort was assessed using natural language processing from clinical notes generated pre- and postoperatively. Multivariable logistic regression was used to evaluate potential risk factors for postoperative UI at various time points within 2 years following surgery. Results We identified 3792 patients who underwent prostatectomy for prostate cancer. We found a significant association between preoperative UI and UI in the first (odds ratio [OR], 2.30; 95% confidence interval [CI], 1.24–4.28) and second (OR 2.24, 95% CI 1.04–4.83) years following surgery. Preoperative body mass index was also associated with UI in the second postoperative year (OR 1.11, 95% CI 1.02–1.21). Conclusion We show that a natural language processing approach using clinical narratives can be used to assess risk for UI in prostate cancer patients. Unstructured clinical narrative text can help advance future population-level research in patient-centered outcomes and quality of care.