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Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning

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  • Chunyang Li

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA)

  • Vikas Patil

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA)

  • Kelli M. Rasmussen

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA)

  • Christina Yong

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA)

  • Hsu-Chih Chien

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA)

  • Debbie Morreall

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA)

  • Jeffrey Humpherys

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA)

  • Brian C. Sauer

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA)

  • Zachary Burningham

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA)

  • Ahmad S. Halwani

    (Veritas, Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
    George E. Wahlen Veterans Health Administration, Salt Lake City, UT 84148, USA
    Hematology & Hematologic Malignancies, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA)

Abstract

The most accurate prognostic approach for follicular lymphoma (FL), progression of disease at 24 months (POD24), requires two years’ observation after initiating first-line therapy (L1) to predict outcomes. We applied machine learning to structured electronic health record (EHR) data to predict individual survival at L1 initiation. We grouped 523 observations and 1933 variables from a nationwide cohort of FL patients diagnosed 2006–2014 in the Veterans Health Administration into traditionally used prognostic variables (“curated”), commonly measured labs (“labs”), and International Classification of Diseases diagnostic codes (“ICD”) sets. We compared performance of random survival forests (RSF) vs. traditional Cox model using four datasets: curated, curated + labs, curated + ICD, and curated + ICD + labs, also using Cox on curated + POD24. We evaluated variable importance and partial dependence plots with area under the receiver operating characteristic curve (AUC). RSF with curated + labs performed best, with mean AUC 0.73 (95% CI: 0.71–0.75). It approximated, but did not surpass, Cox with POD24 (mean AUC 0.74 [95% CI: 0.71–0.77]). RSF using EHR data achieved better performance than traditional prognostic variables, setting the foundation for the incorporation of our algorithm into the EHR. It also provides for possible future scenarios in which clinicians could be provided an EHR-based tool which approximates the predictive ability of the most accurate known indicator, using information available 24 months earlier.

Suggested Citation

  • Chunyang Li & Vikas Patil & Kelli M. Rasmussen & Christina Yong & Hsu-Chih Chien & Debbie Morreall & Jeffrey Humpherys & Brian C. Sauer & Zachary Burningham & Ahmad S. Halwani, 2021. "Predicting Survival in Veterans with Follicular Lymphoma Using Structured Electronic Health Record Information and Machine Learning," IJERPH, MDPI, vol. 18(5), pages 1-19, March.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2679-:d:512214
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    References listed on IDEAS

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    1. Monica A Konerman & Lauren A Beste & Tony Van & Boang Liu & Xuefei Zhang & Ji Zhu & Sameer D Saini & Grace L Su & Brahmajee K Nallamothu & George N Ioannou & Akbar K Waljee, 2019. "Machine learning models to predict disease progression among veterans with hepatitis C virus," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.
    2. Mogensen, Ulla B. & Ishwaran, Hemant & Gerds, Thomas A., 2012. "Evaluating Random Forests for Survival Analysis Using Prediction Error Curves," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i11).
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