Literature DB >> 29870064

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

Jason P Estes1, Danh V Nguyen2, Yanjun Chen3, Lorien S Dalrymple4, Connie M Rhee2, Kamyar Kalantar-Zadeh2, Damla Şentürk5.   

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.
© 2018, The International Biometric Society.

Entities:  

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

Mesh:

Year:  2018        PMID: 29870064      PMCID: PMC6296887          DOI: 10.1111/biom.12908

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  13 in total

1.  Generalized linear mixed models with varying coefficients for longitudinal data.

Authors:  Daowen Zhang
Journal:  Biometrics       Date:  2004-03       Impact factor: 2.571

2.  Quadratic inference functions for varying-coefficient models with longitudinal data.

Authors:  Annie Qu; Runze Li
Journal:  Biometrics       Date:  2006-06       Impact factor: 2.571

3.  Statistical Methods with Varying Coefficient Models.

Authors:  Jianqing Fan; Wenyang Zhang
Journal:  Stat Interface       Date:  2008       Impact factor: 0.582

4.  Evaluating hospital readmission rates in dialysis facilities; adjusting for hospital effects.

Authors:  Kevin He; Jack D Kalbfleisch; Yijiang Li; Yi Li
Journal:  Lifetime Data Anal       Date:  2013-05-26       Impact factor: 1.588

5.  Generalized Multilevel Functional Regression.

Authors:  Ciprian M Crainiceanu; Ana-Maria Staicu; Chong-Zhi Di
Journal:  J Am Stat Assoc       Date:  2009-12-01       Impact factor: 5.033

6.  A risk-adjusted O-E CUSUM with monitoring bands for monitoring medical outcomes.

Authors:  Rena Jie Sun; John D Kalbfleisch
Journal:  Biometrics       Date:  2013-02-05       Impact factor: 2.571

7.  Modeling time-varying effects with generalized and unsynchronized longitudinal data.

Authors:  Damla Şentürk; Lorien S Dalrymple; Sandra M Mohammed; George A Kaysen; Danh V Nguyen
Journal:  Stat Med       Date:  2013-01-18       Impact factor: 2.373

8.  Time-varying effect modeling with longitudinal data truncated by death: conditional models, interpretations, and inference.

Authors:  Jason P Estes; Danh V Nguyen; Lorien S Dalrymple; Yi Mu; Damla Şentürk
Journal:  Stat Med       Date:  2015-12-08       Impact factor: 2.373

9.  The relationship between virologic and immunologic responses in AIDS clinical research using mixed-effects varying-coefficient models with measurement error.

Authors:  Hua Liang; Hulin Wu; Raymond J Carroll
Journal:  Biostatistics       Date:  2003-04       Impact factor: 5.899

10.  Evaluating hospital performance based on excess cause-specific incidence.

Authors:  Bart Van Rompaye; Marie Eriksson; Els Goetghebeur
Journal:  Stat Med       Date:  2015-01-15       Impact factor: 2.373

View more
  6 in total

1.  Profiling dialysis facilities for adverse recurrent events.

Authors:  Jason P Estes; Yanjun Chen; Damla Şentürk; Connie M Rhee; Esra Kürüm; Amy S You; Elani Streja; Kamyar Kalantar-Zadeh; Danh V Nguyen
Journal:  Stat Med       Date:  2020-01-30       Impact factor: 2.373

2.  Association of US Dialysis Facility Staffing with Profiling of Hospital-Wide 30-Day Unplanned Readmission.

Authors:  Yanjun Chen; Connie Rhee; Damla Senturk; Esra Kurum; Luis Campos; Yihao Li; Kamyar Kalantar-Zadeh; Danh Nguyen
Journal:  Kidney Dis (Basel)       Date:  2019-02-05

3.  Stratified Cox models with time-varying effects for national kidney transplant patients: A new blockwise steepest ascent method.

Authors:  Kevin He; Ji Zhu; Jian Kang; Yi Li
Journal:  Biometrics       Date:  2021-05-04       Impact factor: 1.701

4.  The profile inter-unit reliability.

Authors:  Kevin He; Claudia Dahlerus; Lu Xia; Yanming Li; John D Kalbfleisch
Journal:  Biometrics       Date:  2019-11-10       Impact factor: 2.571

5.  Fixed Effects High-Dimensional Profiling Models in Low Information Context.

Authors:  Jason P Estes; Damla Şentürk; Esra Kürüm; Connie M Rhee; Danh V Nguyen
Journal:  Int J Stat Med Res       Date:  2021-09-27

6.  Improving large-scale estimation and inference for profiling health care providers.

Authors:  Wenbo Wu; Yuan Yang; Jian Kang; Kevin He
Journal:  Stat Med       Date:  2022-03-22       Impact factor: 2.497

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.