High-Dimensional Fixed Effects Profiling Models and Applications in End-Stage Kidney Disease Patients: Current State and Future Directions.

Profiling analysis aims to evaluate health care providers, including hospitals, nursing homes, or dialysis facilities among others with respect to a patient outcome, such as 30-day unplanned hospital readmission or mortality. Fixed effects (FE) profiling models have been developed over the last decade, motivated by the overall need to (a) improve accurate identification or "flagging" of under-performing providers, (b) relax assumptions inherent in random effects (RE) profiling models, and (c) take into consideration the unique disease characteristics and care/treatment processes of end-stage kidney disease (ESKD) patients on dialysis. In this paper, we review the current state of FE methodologies and their rationale in the ESKD population and illustrate applications in four key areas: profiling dialysis facilities for (1) patient hospitalizations over time (longitudinally) using standardized dynamic readmission ratio (SDRR), (2) identification of dialysis facility characteristics (e.g., staffing level) that contribute to hospital readmission, and (3) adverse recurrent events using standardized event ratio (SER). Also, we examine the operating characteristics with a focus on FE profiling models. Throughout these areas of applications to the ESKD population, we identify challenges for future research in both methodology and clinical studies.

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

Profiling or evaluation of health care providers, including hospitals or dialysis facilities, involves the application of hierarchical regression models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission. This is achieved by comparing a specific provider's estimate of unplanned readmission rate, adjusted for patient case-mix, to a normative standard, typically defined as an "average" national readmission rate across all providers. Profiling is of national importance in the United States because the Centers for Medicare and Medicaid Services (CMS) policy for payment to providers is dependent on providers' performance, which is part of a national strategy to improve delivery and quality of patient care. Novel high dimensional fixed effects (FE) models have been proposed for profiling dialysis facilities and are more focused towards inference on the tail of the distribution of provider outcomes, which is well-suited for the objective of identifying sub-standard ("extreme") performance. However, the extent to which estimation and inference procedures for FE profiling models are effective when the outcome is sparse and/or when there are relatively few patients within a provider, referred to as the "low information" context, have not been examined. This scenario is common in practice when the patient outcome of interest is cause-specific 30-day readmissions, such as 30-day readmission due to infections in patients on dialysis, which is only about ~ 8% compared to the > 30% for all-cause 30-day readmission. Thus, we examine the feasibility and effectiveness of profiling models under the low information context in simulation studies and propose a novel correction method to FE profiling models to better handle sparse outcome data.

Profiling dialysis facilities for adverse recurrent events.

Profiling analysis aims to evaluate health care providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. Previous profiling methods have considered binary outcomes, such as 30-day hospital readmission or mortality. For the unique population of dialysis patients, regular blood works are required to evaluate effectiveness of treatment and avoid adverse events, including dialysis inadequacy, imbalance mineral levels, and anemia among others. For example, anemic events (when hemoglobin levels exceed normative range) are recurrent and common for patients on dialysis. Thus, we propose high-dimensional Poisson and negative binomial regression models for rate/count outcomes and introduce a standardized event ratio measure to compare the event rate at a specific facility relative to a chosen normative standard, typically defined as an "average" national rate across all facilities. Our proposed estimation and inference procedures overcome the challenge of high-dimensional parameters for thousands of dialysis facilities. Also, we investigate how overdispersion affects inference in the context of profiling analysis. The proposed methods are illustrated with profiling dialysis facilities for recurrent anemia events.