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

Author

Listed:
  • Jason P. Estes
  • Danh V. Nguyen
  • Yanjun Chen
  • Lorien S. Dalrymple
  • Connie M. Rhee
  • Kamyar Kalantar‐Zadeh
  • Damla Şentürk

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.

Suggested Citation

  • Jason P. Estes & Danh V. Nguyen & Yanjun Chen & Lorien S. Dalrymple & Connie M. Rhee & Kamyar Kalantar‐Zadeh & Damla Şentürk, 2018. "Time‐dynamic profiling with application to hospital readmission among patients on dialysis," Biometrics, The International Biometric Society, vol. 74(4), pages 1383-1394, December.
  • Handle: RePEc:bla:biomet:v:74:y:2018:i:4:p:1383-1394
    DOI: 10.1111/biom.12908
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    Citations

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    Cited by:

    1. Sebastien Haneuse & José Zubizarreta & Sharon‐Lise T. Normand, 2018. "Discussion on “Time‐dynamic profiling with application to hospital readmission among patients on dialysis,” by Jason P. Estes, Danh V. Nguyen, Yanjun Chen, Lorien S. Dalrymple, Connie M. Rhee, Kamyar ," Biometrics, The International Biometric Society, vol. 74(4), pages 1395-1397, December.
    2. Yihao Li & Danh V. Nguyen & Esra Kürüm & Connie M. Rhee & Yanjun Chen & Kamyar Kalantar‐Zadeh & Damla Şentürk, 2020. "A multilevel mixed effects varying coefficient model with multilevel predictors and random effects for modeling hospitalization risk in patients on dialysis," Biometrics, The International Biometric Society, vol. 76(3), pages 924-938, September.
    3. Kevin He & Claudia Dahlerus & Lu Xia & Yanming Li & John D. Kalbfleisch, 2020. "The profile inter‐unit reliability," Biometrics, The International Biometric Society, vol. 76(2), pages 654-663, June.
    4. Kevin He & Ji Zhu & Jian Kang & Yi Li, 2022. "Stratified Cox models with time‐varying effects for national kidney transplant patients: A new blockwise steepest ascent method," Biometrics, The International Biometric Society, vol. 78(3), pages 1221-1232, September.

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