Time-dynamic profiling with application to hospital readmission among patients on dialysis

Jason P. Estes, Danh V. Nguyen, Yanjun Chen, Lorien Dalrymple, Connie M. Rhee, Kamyar Kalantar-Zadeh, Damla Şentürk

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Standard profiling analysis aims to evaluate medical providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. The outcome, for instance, may be mortality, medical complications, or 30-day (unplanned) hospital readmission. Profiling analysis involves regression modeling of a patient outcome, adjusting for patient health status at baseline, and comparing each provider's outcome rate (e.g., 30-day readmission rate) to a normative standard (e.g., national “average”). Profiling methods exist mostly for non time-varying patient outcomes. However, for patients on dialysis, a unique population which requires continuous medical care, methodologies to monitor patient outcomes continuously over time are particularly relevant. Thus, we introduce a novel time-dynamic profiling (TDP) approach to assess the time-varying 30-day readmission rate. TDP is used to estimate, for the first time, the risk-standardized time-dynamic 30-day hospital readmission rate, throughout the time period that patients are on dialysis. We develop the framework for TDP by introducing the standardized dynamic readmission ratio as a function of time and a multilevel varying coefficient model with facility-specific time-varying effects. We propose estimation and inference procedures tailored to the problem of TDP and to overcome the challenge of high-dimensional parameters when examining thousands of dialysis facilities.

Original languageEnglish (US)
Pages (from-to)1383-1394
Number of pages12
JournalBiometrics
Volume74
Issue number4
DOIs
StatePublished - Dec 1 2018

Keywords

  • End-stage renal disease
  • Hospital readmission
  • Multilevel varying coefficient models
  • Profiling of medical care providers
  • United States Renal Data System

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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  • Cite this

    Estes, J. P., Nguyen, D. V., Chen, Y., Dalrymple, L., Rhee, C. M., Kalantar-Zadeh, K., & Şentürk, D. (2018). Time-dynamic profiling with application to hospital readmission among patients on dialysis. Biometrics, 74(4), 1383-1394. https://doi.org/10.1111/biom.12908