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Time-dynamic profiling with application to hospital readmission among patients on dialysis.
Estes, Jason P; Nguyen, Danh V; Chen, Yanjun; Dalrymple, Lorien S; Rhee, Connie M; Kalantar-Zadeh, Kamyar; Sentürk, Damla.
Affiliation
  • Estes JP; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
  • Nguyen DV; Department of Medicine, University of California, Irvine, Orange, California 92868, U.S.A.
  • Chen Y; Institute for Clinical and Translational Science, Irvine, California 92697, U.S.A.
  • Dalrymple LS; Department of Medicine, University of California Davis, Sacramento, California 95817, U.S.A.
  • Rhee CM; Department of Medicine, University of California, Irvine, Orange, California 92868, U.S.A.
  • Kalantar-Zadeh K; Department of Medicine, University of California, Irvine, Orange, California 92868, U.S.A.
  • Sentürk D; Department of Biostatistics, University of California, Los Angeles, California 90095, U.S.A.
Biometrics ; 74(4): 1383-1394, 2018 12.
Article in En | MEDLINE | ID: mdl-29870064
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.
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Full text: 1 Database: MEDLINE Main subject: Patient Readmission / Retinal Perforations / Biometry Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Year: 2018 Type: Article

Full text: 1 Database: MEDLINE Main subject: Patient Readmission / Retinal Perforations / Biometry Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Year: 2018 Type: Article