Faculty Bibliography

The 30-day hospital readmission rate has been used in provider profiling for evaluating inter-provider care coordination, medical cost effectiveness, and patient quality of life. Current profiling analyzes use logistic regression to model 30-day readmission as a binary outcome, but one disadvantage of this approach is that this outcome is strongly affected by competing risks (e.g., death). Thus, one, perhaps unintended, consequence is that if two facilities have the same rates of readmission, the one with the higher rate of competing risks will have the lower 30-day readmission rate. We propose a discrete time competing risk model wherein the cause-specific readmission hazard is used to assess provider-level effects. This approach takes account of the timing of events and focuses on the readmission rates which are of primary interest. The quality measure, then is a standardized readmission ratio, akin to a standardized mortality ratio. This measure is not systematically affected by the rate of competing risks. To facilitate the estimation and inference of a large number of provider effects, we develop an efficient Blockwise Inversion Newton algorithm, and a stabilized robust score test that overcomes the conservative nature of the classical robust score test. An application to dialysis patients demonstrates improved profiling, model fitting, and outlier detection over existing methods.

Provider profiling has been recognized as a useful tool in monitoring health care quality, facilitating inter-provider care coordination, and improving medical cost-effectiveness. Existing methods often use generalized linear models with fixed provider effects, especially when profiling dialysis facilities. As the number of providers under evaluation escalates, the computational burden becomes formidable even for specially designed workstations. To address this challenge, we introduce a serial blockwise inversion Newton algorithm exploiting the block structure of the information matrix. A shared-memory divide-and-conquer algorithm is proposed to further boost computational efficiency. In addition to the computational challenge, the current literature lacks an appropriate inferential approach to detecting providers with outlying performance especially when small providers with extreme outcomes are present. In this context, traditional score and Wald tests relying on large-sample distributions of the test statistics lead to inaccurate approximations of the small-sample properties. In light of the inferential issue, we develop an exact test of provider effects using exact finite-sample distributions, with the Poisson-binomial distribution as a special case when the outcome is binary. Simulation analyses demonstrate improved estimation and inference over existing methods. The proposed methods are applied to profiling dialysis facilities based on emergency department encounters using a dialysis patient database from the Centers for Medicare & Medicaid Services.

Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. When studying the cause-specific etiology of breast and prostate cancers using the large-scale data from the Surveillance, Epidemiology, and End Results (SEER) Program, we encountered two major challenges that existing methods for estimating time-varying coefficients cannot tackle. First, these methods, dependent on expanding the original data in a repeated measurement format, result in formidable time and memory consumption as the sample size escalates to over one million. In this case, even a well-configured workstation cannot accommodate their implementations. Second, when the large-scale data under analysis include binary predictors with near-zero variance (e.g., only 0.6% of patients in our SEER prostate cancer data had tumors regional to the lymph nodes), existing methods suffer from numerical instability due to ill-conditioned second-order information. The estimation accuracy deteriorates further with multiple competing risks. To address these issues, we propose a proximal Newton algorithm with a shared-memory parallelization scheme and tests of significance and nonproportionality for the time-varying effects. A simulation study shows that our scalable approach reduces the time and memory costs by orders of magnitude and enjoys improved estimation accuracy compared with alternative approaches. Applications to the SEER cancer data demonstrate the real-world performance of the proximal Newton algorithm.

BACKGROUND AND OBJECTIVES:About 30% of patients with AKI may require ongoing dialysis in the outpatient setting after hospital discharge. A 2017 Centers for Medicare & Medicaid Services policy change allows Medicare beneficiaries with AKI requiring dialysis to receive outpatient treatment in dialysis facilities. Outcomes for these patients have not been reported. We compare patient characteristics and mortality among patients with AKI requiring dialysis and patients without AKI requiring incident dialysis. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:We used a retrospective cohort design with 2017 Medicare claims to follow outpatients with AKI requiring dialysis and patients without AKI requiring incident dialysis up to 365 days. Outcomes are unadjusted and adjusted mortality using Kaplan-Meier estimation for unadjusted survival probability, Poisson regression for monthly mortality, and Cox proportional hazards modeling for adjusted mortality. RESULTS:<0.001), which persisted through month 7. Overall adjusted mortality risk was 22% higher for patients with AKI requiring dialysis (1.22; 95% confidence interval, 1.17 to 1.27). CONCLUSIONS:In fully adjusted analyses, patients with AKI requiring dialysis had higher early mortality compared with patients without AKI requiring incident dialysis, but these differences declined after several months. Differences were also observed by age, race, and ethnicity within both patient cohorts.

RATIONALE & OBJECTIVE:An arteriovenous fistula (AVF) is the preferred access for most patients receiving maintenance hemodialysis, but maturation failure remains a challenge. Surgeon characteristics have been proposed as contributors to AVF success. We examined variation in AVF placement and AVF outcomes by surgeon and surgeon characteristics. STUDY DESIGN:Retrospective cohort study. SETTING & PARTICIPANTS:National Medicare claims and web-based data submitted by dialysis facilities on maintenance hemodialysis patients from 2009 through 2015. EXPOSURES:Patient characteristics, including demographics and comorbid conditions; surgeon characteristics, including specialty, prior volume of AVF placements, and years since medical school graduation. OUTCOMES:Percent of access placements that were an AVF from 2009 to 2015 (designated AVF placement), and percent of AVFs with successful use within 6 months of placement (maturation) from 2013 to 2014. ANALYTICAL APPROACH:Multilevel logistic regression models examining the association of surgeon characteristics with the outcomes, adjusted for patient characteristics and dialysis facilities as random effects. RESULTS:Among 4,959 surgeons placing 467,827 accesses, median AVF placement was 71% (IQR, 59%-84%). More recent year of medical school graduation and general surgery specialty (vs vascular, cardiothoracic, or transplantation surgery) were associated with higher odds of AVF placement. Among 2,770 surgeons placing 49,826 AVFs, the median AVF maturation rate was 59% (IQR, 44%-71%). More recent year of medical school graduation, but not surgical specialty, was associated with higher odds of AVF maturation. Greater prior volume of AVF placement was associated with higher odds of AVF maturation: OR of 1.46 (95% CI, 1.37-1.57) for highest (>84 AVF placements in 2years) versus lowest (<14) volume quintile. LIMITATIONS:The study relied on administrative data, limiting capture of some factors affecting access outcomes. CONCLUSIONS:There is substantial surgeon-level variation in AVF placements and AVF maturation. Surgeons' prior volume of AVF placements is strongly associated with AVF maturation.

Repeat kidney transplantation (re-KT) is the preferred treatment for patients with graft failure. Changing allocation policies, widening the risk profile of recipients, and improving dialysis care may have altered the survival benefit of a re-KT. We characterized trends in re-KT survival benefit over 3 decades and tested whether it differed by age, race/ethnicity, sex, and panel reactive assay (PRA). By using the Scientific Registry of Transplant Recipient data, we identified 25 419 patients who underwent a re-KT from 1990 to 2019 and 25 419 waitlisted counterfactuals from the same year with the same waitlisted time following graft failure. In the adjusted analysis, a re-KT was associated with a lower risk of death (adjusted hazard ratio [aHR] = 0.63; 95% confidence interval [CI], 0.61-0.65). By using the 1990-1994 era as a reference (aHR = 0.77; 95% CI, 0.69-0.85), incremental improvements in the survival benefit were noted (1995-1999: aHR = 0.72; 95% CI, 0.67-0.78: 2000-2004: aHR = 0.59; 95% CI, 0.55-0.63: 2005-2009: aHR = 0.59; 95% CI, 0.56-0.63: 2010-2014: aHR = 0.57; 95% CI, 0.53-0.62: 2015-2019: aHR = 0.64; 95% CI, 0.57-0.73). The survival benefit of a re-KT was noted in both younger (age = 18-64 years: aHR = 0.63; 95% CI, 0.61-0.65) and older patients (age ≥65 years: aHR = 0.66; 95% CI, 0.58-0.74; P_interaction = .45). Patients of all races/ethnicities demonstrated similar benefits with a re-KT. However, it varied by the sex of the recipient (female patients: aHR = 0.60; 95% CI, 0.56-0.63: male patients: aHR = 0.66; 95% CI, 0.63-0.68; P_interaction = .004) and PRA (0-20: aHR = 0.69; 95% CI, 0.65-0.74: 21-80: aHR = 0.61; 95% CI, 0.57-0.66; P_interaction = .02; >80: aHR = 0.57; 95% CI, 0.53-0.61; P_interaction< .001). Our findings support the continued practice of a re-KT and efforts to overcome the medical, immunologic, and surgical challenges of a re-KT.

RATIONALE & OBJECTIVE/UNASSIGNED:The coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on hospitalizations in general and on dialysis patients in particular. This study modeled the impact of COVID-19 on hospitalizations of dialysis patients in 2020. STUDY DESIGN/UNASSIGNED:Retrospective cohort study. SETTING & PARTICIPANTS/UNASSIGNED:Medicare patients on dialysis in calendar year 2020. PREDICTORS/UNASSIGNED:COVID-19 status was divided into 4 stages: COVID1 (first 10 days after initial diagnosis), COVID2 (extends until the Post-COVID stage), Post-COVID (after 21 days with no COVID-19 diagnosis), and Late-COVID (begins after a hospitalization with a COVID-19 diagnosis); demographic and clinical characteristics; and dialysis facilities. OUTCOME/UNASSIGNED:The sequence of hospitalization events. ANALYTICAL APPROACH/UNASSIGNED:A proportional rate model with a nonparametric baseline rate function of calendar time on the study population. RESULTS/UNASSIGNED:A total of 509,609 patients were included in the study, 63,521 were observed to have a SARS-CoV-2 infection, 34,375 became Post-COVID, and 1,900 became Late-COVID. Compared with No-COVID, all 4 stages had significantly greater adjusted risks of hospitalizations with relative rates of 18.50 (95% CI, 18.19-18.81) for COVID1, 2.03 (95% CI, 1.99-2.08) for COVID2, 1.37 (95% CI, 1.35-1.40) for Post-COVID, and 2.00 (95% CI, 1.89-2.11) for Late-COVID. LIMITATIONS/UNASSIGNED:For Medicare Advantage patients, we only had inpatient claim information. The analysis was based on data from the year 2020, and the effects may have changed due to vaccinations, new treatments, and new variants. The COVID-19 effects may be somewhat overestimated due to missing information on patients with few or no symptoms and possible delay in COVID-19 diagnosis. CONCLUSIONS/UNASSIGNED:We discovered a marked time dependence in the effect of COVID-19 on hospitalization of dialysis patients, beginning with an extremely high risk for a relatively short period, with more moderate but continuing elevated risks later, and never returning to the No-COVID level.

INTRODUCTION/BACKGROUND:Extended venous thromboembolism prophylaxis (eVTEp) is recommended for select patients who have undergone major abdominopelvic surgery to prevent postdischarge venous thromboembolism (pdVTE). Criteria for selection of these patients are untested for this purpose and may be ineffective. To address this gap, we investigated the effectiveness of eVTEp on pdVTE rates. METHODS:A retrospective cohort study of patients undergoing abdominopelvic surgery from January 2016 to February 2020 was performed using data from the Michigan Surgical Quality Collaborative. pdVTE was the main outcome. Our exposure variable, eVTEp, was compared dichotomously. Length of stay (LOS) was compared categorically using clinically relevant groups. Age, race, cancer occurrence, inflammatory bowel disease, surgical approach, and surgical time were covariates among other variables. Descriptive statistics, propensity score matching, and multivariable logistic regression were performed to compare pdVTE rates. RESULTS:A total of 45,637 patients underwent abdominopelvic surgery. Of which, 3063 (6.71%) were prescribed eVTEp. Two hundred eighty-five (0.62%) had pdVTE. Of the 285, 59 (21%) patients received eVTEp, while 226 (79%) patients did not. After propensity score matching, multivariable logistic regression analysis showed pdVTE was associated with eVTEp and LOS of 5 d or more (P < 0.001). eVTEp was not associated with LOS. Further analysis showed increased risk of pdVTE with increasing LOS independent of prescription of eVTEp based on known risk factors. CONCLUSIONS:pdVTE was associated with increasing LOS but not with other VTE risk factors after propensity score matching. Current guidelines for eVTEp do not include LOS. Our findings suggest that LOS >5 d should be added to the criteria for eVTEp.