Predicting Kidney Transplant Recipient Cohorts’ 30-Day Rehospitalization Using Clinical Notes and Electronic Health Care Record Data

Michael Arenson, Julien Hogan, Liyan Xu, Raymond Lynch, Yi Ting Hana Lee, Jinho D. Choi, Jimeng Sun, Andrew Adams, Rachel E. Patzer

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Rehospitalization after kidney transplant is costly to patients and health care systems and is associated with poor outcomes. Few prediction model studies have examined whether inclusion of clinical notes data from the electronic medical record (EMR) enhances prediction of rehospitalization. Methods: In a retrospective, observational study of first-time, adult kidney transplant recipients at a large, urban hospital in southeastern United States (2005−2015), we examined 30-day rehospitalization (30DR) using structured EMR and unstructured (i.e., clinical notes) data. We used natural language processing (NLP) methods on 8 types of clinical notes and included terms in predictive models using unsupervised machine learning approaches. Both the area under the receiver operating curve and precision-recall curve (ROC and PRC, respectively) were used to determine and compare model accuracy, and 5-fold cross-validation tested model performance. Results: Among 2060 kidney transplant recipients, 30.7% were readmitted within 30 days. Predictive models using clinical notes did not meaningfully improve performance over previous models using structured data alone (ROC 0.6821; 95% confidence interval [CI]: 0.6644, 0.6998). Predictive models built using solely clinical notes performed worse than models using both clinical notes and structured data. The data that contributed to the top performing models were not identical but both included structured data and progress notes (ROC 0.6902; 95% CI: 0.6699, 0.7105). Conclusions: Including new features from clinical notes in risk prediction models did not substantially increase predictive accuracy for 30DR for kidney transplant recipients. Future research should consider pooling data from multiple institutions to increase sample size and avoid overfitting models.

Original languageEnglish (US)
Pages (from-to)489-498
Number of pages10
JournalKidney International Reports
Volume8
Issue number3
DOIs
StatePublished - Mar 2023

Bibliographical note

Funding Information:
We would like to acknowledge Bonggun Shin, PhD for his analysis during early stages of this project. This research was funded by the National Institutes on Minority Health and Health Disparities ( R01MD011682 ), and was also supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378 and TL1TR002382.

Publisher Copyright:
© 2022

Keywords

  • early readmission
  • kidney transplantation
  • machine learning
  • natural language processing
  • predicting readmission
  • risk prediction

PubMed: MeSH publication types

  • Journal Article

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