Donald K. K. Lee

Alloimmunization to red blood cell antigens affects clinical outcomes in liver transplant patients

Transfusion therapy of liver transplant patients remains a challenge. High volumes of intraoperative blood transfusion have been shown to increase the risk of poor graft or patient survival. We conducted a retrospective study of 209 consecutive liver transplant cases at our institution. Only patients receiving their first liver transplant, with no other simultaneous organ transplants, were included. Cox proportional hazard modeling was used to identify clinical variables correlated with postoperative patient mortality. Statistically significant variables for poor patient survival were the number of red blood cell and plasma units transfused, a history of red blood cell alloantibodies, and the immunosuppressive regimen used. History of pregnancy also approached statistical significance but was less robust than the other 3 variables. Our findings suggest that blood transfusion and immune modulation greatly affect the survival of patients after liver transplantation. Liver Transpl 13:1654–1661, 2007.

Reexploring Differences among For-Profit and Nonprofit Dialysis Providers

OBJECTIVE To determine whether profit status is associated with differences in hospital days per patient, an outcome that may also be influenced by provider financial goals. DATA SOURCES United States Renal Data System Standard Analysis Files and Centers for Medicare and Medicaid Services cost reports. DESIGN We compared the number of hospital days per patient per year across for-profit and nonprofit dialysis facilities during 2003. To address possible referral bias in the assignment of patients to dialysis facilities, we used an instrumental variable regression method and adjusted for selected patient-specific factors, facility characteristics such as size and chain affiliation, as well as metrics of market competition. DATA EXTRACTION METHODS All patients who received in-center hemodialysis at any time in 2003 and for whom Medicare was the primary payer were included (N=170,130; roughly two-thirds of the U.S. hemodialysis population). Patients dialyzed at hospital-based facilities and patients with no dialysis facilities within 30 miles of their residence were excluded. RESULTS Overall, adjusted hospital days per patient were 17+/-5 percent lower in nonprofit facilities. The difference between nonprofit and for-profit facilities persisted with the correction for referral bias. There was no association between hospital days per patient per year and chain affiliation, but larger facilities had inferior outcomes (facilities with 73 or more patients had a 14+/-1.7 percent increase in hospital days relative to facilities with 35 or fewer patients). Differences in outcomes among for-profit and nonprofit facilities translated to 1,600 patient-years in hospital that could be averted each year if the hospital utilization rates in for-profit facilities were to decrease to the level of their nonprofit counterparts. CONCLUSIONS Hospital days per patient-year were statistically and clinically significantly lower among nonprofit dialysis providers. These findings suggest that the indirect incentives in Medicares current payment system may provide insufficient incentive for for-profit providers to achieve optimal patient outcomes.

Sharp bounds on the variance in randomized experiments

We propose a consistent estimator of sharp bounds on the variance of the difference-in-means estimator in completely randomized experiments. Generalizing Robins [Stat. Med. 7 (1988) 773-785], our results resolve a well-known identification problem in causal inference posed by Neyman [Statist. Sci. 5 (1990) 465-472. Reprint of the original 1923 paper]. A practical implication of our results is that the upper bound estimator facilitates the asymptotically narrowest conservative Wald-type confidence intervals, with applications in randomized controlled and clinical trials.

An Evidence-Based Incentive System for Medicare's End-Stage Renal Disease Program

Recent legislations directed Medicare to revamp its decades-old system for reimbursing dialysis treatments, with focus on the risk adjustment of payments and on the transition toward a pay-for-compliance system. To design an optimal payment system that incorporates these features, we develop an empirical method to estimate the structural parameters of the principal--agent model underlying Medicares dialysis payment system. We use the model and parameter estimates to answer the following questions: Can a pay-for-compliance system based only on the intermediate performance measures currently identified by Medicare achieve first-best? How should patient outcomes be risk adjusted, and what welfare gains can be achieved by doing so? Our main findings are as follows: (1) the current set of intermediate measures identified by Medicare are not comprehensive enough for use alone in a pay-for-compliance system; (2) paying for risk-adjusted downstream outcomes instead of raw downstream outcomes can lengthen the hospital-free life of admitted patients by two weeks per patient per year without increasing Medicare expenditures. This paper was accepted by Christian Terwiesch, operations management.

Optimal Capacity Overbooking for the Regular Treatment of Chronic Conditions

Patients suffering from a chronic condition often require periodic treatment. For example, patients with End-Stage Renal Disease (ESRD) require dialysis three times a week. These patients are also frequently hospitalized for complications from their treatment, resulting in idle capacity at the clinic. These temporary patient absences make overbooking at the clinic attractive. This paper develops a semiclosed migration network to capture patient flow into the clinic and between the clinic and hospital. We consider a simple class of stationary control policies for patient admissions and provide algorithms for selecting one that maximizes long-run average earnings. Local diffusion approximations were constructed to provide square-root loading formulas for the optimal capacity level and patient overbooking level: as the total patient arrival rate increases, the deviation between the optimal and fluid-limit capacity and overbooking levels scale up with the square root of the total arrival rate. We find that high hospitalization rates and long inpatient stays allow for more overbooking. Numerical examples based on the typical dialysis clinic in the United States suggest an increase in earnings of 11%--14% over policies derived from traditional M/M/N models that do not account for hospitalizations and do not allow overbooking, while keeping the probability of capacity shortage arbitrarily small.

A K-nearest neighbors survival probability prediction method.

We introduce a nonparametric survival prediction method for right-censored data. The method generates a survival curve prediction by constructing a (weighted) Kaplan-Meier estimator using the outcomes of the K most similar training observations. Each observation has an associated set of covariates, and a metric on the covariate space is used to measure similarity between observations. We apply our method to a kidney transplantation data set to generate patient-specific distributions of graft survival and to a simulated data set in which the proportional hazards assumption is explicitly violated. We compare the performance of our method with the standard Cox model and the random survival forests method.

Demographic and clinical features of inclusion body myositis in North America

Introduction: Few studies of the demographics, natural history, and clinical management of inclusion body myositis (IBM) have been performed in a large patient population. To more accurately define these characteristics, we developed and distributed a questionnaire to patients with IBM. Methods: A cross‐sectional, self‐reporting survey was conducted. Results: The mean age of the 916 participants was 70.4 years, the male‐to‐female ratio was 2:1, and the majority reported difficulty with ambulation and activities of daily living. The earliest symptoms included impaired use and weakness of arms and legs. The mean time from first symptoms to diagnosis was 4.7 years. Half reported that IBM was their initial diagnosis. A composite functional index negatively associated with age and disease duration, and positively associated with participation in exercise. Conclusions: These data are valuable for informing patients how IBM manifestations are expected to impair daily living and indicate that self‐reporting could be used to establish outcome measures in clinical trials. Muscle Nerve 52: 527–533, 2015

Boosting Hazard Regression with Time-Varying Covariates

Consider a left-truncated right-censored survival process whose evolution depends on time-varying covariates. Given functional data samples from the process, we propose a practical boosting procedure for estimating its log-intensity function. Our method does not require any separability assumptions like Cox proportional- or Aalen additive-hazards, thus it can flexibly capture time-covariate interactions. The estimator is consistent if the model is correctly specified; alternatively an oracle inequality can be demonstrated for tree-based models. We use the procedure to shed new light on a question from the operations literature concerning the effect of workload on service rates in an emergency department.

Super-resolution Estimation of Cyclic Arrival Rates

Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a non-homogeneous Poisson process. The estimator is the super-resolution analogue to Shao 2010 and Shao & Lii 2011, which is a sum of p sinusoids where p and the frequency, amplitude, and phase of each wave are not known and need to be estimated. This results in an interpretable yet flexible specification that is suitable for use in modelling as well as in high resolution simulations. Our estimation procedure sits in between classic periodogram methods and atomic/total variation norm thresholding. Through a novel use of window functions in the point process domain, our approach attains super-resolution without semidefinite programming. Under suitable conditions, finite sample guarantees can be derived for our procedure. These resolve some open questions and expand existing results in spectral estimation literature.