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

Authors Li K, Banerjee I, Magnani CJ, Blayney DW, Brooks JD, Hernandez-Boussard T

Received 10 October 2019

Accepted for publication 11 December 2019

Published 23 January 2020 Volume 2020:12 Pages 7—14


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Jan Colli

Kevin Li,1 Imon Banerjee,2 Christopher J Magnani,1 Douglas W Blayney,3 James D Brooks,4 Tina Hernandez-Boussard5

1Stanford University School of Medicine, Stanford, CA, USA; 2Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA; 3Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA, USA; 4Department of Urology (Urologic Oncology), Stanford University School of Medicine, Stanford, CA, USA; 5Department of Medicine (Biomedical Informatics), Biomedical Data Sciences, and Surgery, Stanford University School of Medicine, Stanford, CA, USA

Correspondence: Tina Hernandez-Boussard
Department of Medicine (Biomedical Informatics), Biomedical Data Sciences, and Surgery, Stanford University School of Medicine, 1265 Welch Road, #245, Stanford, CA 94305-5479, USA
Tel +1650-725-5507

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.

Keywords: natural language processing, patient-centered outcomes, prostate cancer, urinary incontinence, soft labels

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