Publications

Depression after liver transplantation has been associated with decreased survival, but the effects of pre-transplant depression on early and late post-transplant outcomes remain incompletely evaluated. We assessed all patients who had undergone single-organ liver transplantation at a single center over the prior 10 years. A diagnosis of pre-transplant depression, covariates, and the outcomes of interest were extracted from the electronic medical record. Potential covariates included demographics, etiology and severity of liver disease, comorbidities, donor age, graft type, immunosuppression, and ischemic times. In multivariable models adjusting for these factors, we evaluated the effect of pre-transplant depression on transplant length of stay (LOS), discharge disposition (home vs. facility) and long-term survival. Among 1115 transplant recipients with a median follow-up time of 5 years, the average age was 56±11 and MELD was 12±9. Nineteen percent of the study population had a history of pre-transplant depression. Pre-transplant depression was associated with longer LOS (median = 19 vs. 14 days, IRR = 1.25, CI = 1.13,1.39), discharge to a facility (36% vs. 25%, OR 1.70,CI = 1.18,2.45), and decreased survival (HR = 1.54,CI = 1.14,2.08) in this cohort, accounting for other potential confounders. In conclusion, pre-transplant depression was significantly associated with longer transplant length of stay, discharge to a facility, and mortality in this cohort.

BACKGROUND: Drugs and electrolyte imbalances are widely recognized as common triggers of a prolonged QT interval. We conducted a chart review to assess provider response to prolonged QT reported on a standard 12-lead electrocardiogram (EKG).

METHODS: We identified all Veterans Affairs Pittsburgh Healthcare System patients in a 6-month period with an EKG reporting a corrected QT (QTc) >500 ms. We excluded confounding or uninterpretable EKGs. Charts were reviewed to assess medications and electrolytes at the time of the EKG as well as the setting (inpatient vs outpatient) in which the EKG was obtained. Provider documentation of QTc and any corrective measures were sought.

RESULTS: After exclusions, 106 patients were included in this analysis (87 [82%] inpatient and 19 [18%] outpatient). Most were male (101, 95%) with a mean age of 63.5 ± 10.6 years. At the time of index EKG, most patients were receiving at least one (72, 68%), and frequently two or more (35, 33%), QTc prolonging medications. Providers documented QTc prolongation in 20 inpatients (19%). Drugs were adjusted or discontinued in only two inpatients (2%). There were 14 patients (14%) with potassium level <3.6 mmol/L and 10 of 69 (14%) patients had a magnesium level <1.7 mg/dL.

CONCLUSION: Patients with prolonged QTc on EKG were more likely to be inpatients than outpatients. Inpatients were more likely to be receiving multiple types and classes of QTc prolonging medications. In the vast majority of cases, providers did not address the prolonged QTc and only rarely initiated remedial actions.

BACKGROUND: Intensive care unit (ICU) telemedicine is an increasingly common strategy for improving the outcome of critical care, but its overall impact is uncertain.

OBJECTIVES: To determine the effectiveness of ICU telemedicine in a national sample of hospitals and quantify variation in effectiveness across hospitals.

RESEARCH DESIGN: We performed a multicenter retrospective case-control study using 2001-2010 Medicare claims data linked to a national survey identifying US hospitals adopting ICU telemedicine. We matched each adopting hospital (cases) to up to 3 nonadopting hospitals (controls) based on size, case-mix, and geographic proximity during the year of adoption. Using ICU admissions from 2 years before and after the adoption date, we compared outcomes between case and control hospitals using a difference-in-differences approach.

RESULTS: A total of 132 adopting case hospitals were matched to 389 similar nonadopting control hospitals. The preadoption and postadoption unadjusted 90-day mortality was similar in both case hospitals (24.0% vs. 24.3%, P=0.07) and control hospitals (23.5% vs. 23.7%, P<0.01). In the difference-in-differences analysis, ICU telemedicine adoption was associated with a small relative reduction in 90-day mortality (ratio of odds ratios=0.96; 95% CI, 0.95-0.98; P<0.001). However, there was wide variation in the ICU telemedicine effect across individual hospitals (median ratio of odds ratios=1.01; interquartile range, 0.85-1.12; range, 0.45-2.54). Only 16 case hospitals (12.2%) experienced statistically significant mortality reductions postadoption. Hospitals with a significant mortality reduction were more likely to have large annual admission volumes (P<0.001) and be located in urban areas (P=0.04) compared with other hospitals.

CONCLUSIONS: Although ICU telemedicine adoption resulted in a small relative overall mortality reduction, there was heterogeneity in effect across adopting hospitals, with large-volume urban hospitals experiencing the greatest mortality reductions.

BACKGROUND: Many Veterans treated within the VA Healthcare System (VA) are also enrolled in fee-for-service (FFS) Medicare and receive treatment outside the VA. Prior research has not accounted for the multiple ways that Veterans receive services across healthcare systems.

OBJECTIVE: We aimed to establish a typology of VA and Medicare utilization among dually enrolled Veterans with type 2 diabetes.

DESIGN: This was a retrospective cohort.

PARTICIPANTS: 316,775 community-dwelling Veterans age ≥ 65 years with type 2 diabetes who were dually enrolled in the VA and FFS Medicare in 2008-2009.

METHODS: Using latent class analysis, we identified classes of Veterans based upon their probability of using VA and Medicare diabetes care services, including patient visits, laboratory tests, glucose test strips, and medications. We compared the amount of healthcare use between classes and identified factors associated with class membership using multinomial regression.

KEY RESULTS: We identified four distinct latent classes: class 1 (53.9%) had high probabilities of VA use and low probabilities of Medicare use; classes 2 (17.2%), 3 (21.8%), and 4 (7.0%) had high probabilities of VA and Medicare use, but differed in their Medicare services used. For example, Veterans in class 3 received test strips exclusively through Medicare, while Veterans in class 4 were reliant on Medicare for medications. Living ≥ 40 miles from a VA predicted membership in classes 3 (OR 1.1, CI 1.06-1.15) and 4 (OR 1.11, CI 1.04-1.18), while Medicaid eligibility predicted membership in class 4 (OR 4.30, CI 4.10-4.51).

CONCLUSIONS: Veterans with diabetes can be grouped into four distinct classes of dual health system use, representing a novel way to characterize how patients use multiple services across healthcare systems. This classification has applications for identifying patients facing differential risk from care fragmentation.

BACKGROUND: All Department of Veterans Affairs Medical Centers (VAMCs) operate under a single national drug formulary, yet substantial variation in prescribing and spending exists across facilities. Local management of the national formulary may differ across VAMCs and may be one cause of this variation.

OBJECTIVE: To characterize variation in the management of nonformulary medication requests and pharmacy and therapeutics (P&T) committee member perceptions of the formulary environment at VAMCs nationwide.

METHODS: We performed an online survey of the chief of pharmacy and an additional staff pharmacist and physician on the P&T committee at all VAMCs. Respondents were asked questions regarding criteria for use for nonformulary medications, specific procedures for ordering nonformulary medications in general and specific lipid-lowering and diabetes agents, the appeals process, and the formulary environment at their VAMCs. We compared responses across facilities and between chiefs of pharmacy, pharmacists, and physicians.

RESULTS: A total of 212 chief pharmacists (n = 80), staff pharmacists (n = 78), and physicians (n = 54) responded, for an overall response rate of 49%. In total, 107/143 (75%) different VAMCs were represented. The majority of VAMCs reported adhering to national criteria for use, with 38 (36%) being very adherent and 69 (65%) being mostly adherent. There was substantial variation between VAMCs regarding how nonformulary drugs were ordered, evaluated, and appealed. The nonformulary lipid-lowering drugs ezetimibe, rosuvastatin, and atorvastatin were viewable to providers in the order entry screen at 67 (63%), 67 (63%), and 64 (60%) VAMCs, respectively. The nonformulary diabetes medication pioglitazone was only viewable at 58 (55%) VAMCs. In the remaining VAMCs, providers could not order these nonformulary drugs through the normal order-entry process. For questions about the formulary environment, physician respondent perceptions differed from those of staff pharmacists and chief pharmacists. Compared with pharmacy chiefs and staff pharmacists, physicians were less likely to agree that providers at their VAMC prescribed too many nonformulary medications (47% and 44% vs. 12%, P < 0.001), more likely to agree that providers must jump through too many hoops to prescribe nonformulary medication (5% and 3% vs. 25%, P < 0.001), and more likely to agree that providers make an effort to convert new patients from nonformulary to formulary lipid-lowering (65% and 73% vs. 94%, P <0.02) and diabetic medications (49% and 50% vs. 88%, P < 0.001).

CONCLUSIONS: Although the Department of Veterans Affairs (VA) operates under a single national formulary, we found significant differences among VAMCs regarding their management of nonformulary medication requests. We also found differences among formulary leaders regarding their perception of the environment in which their VAMC's formulary is managed. These findings have important implications not just for VA, but for any organization that develops, implements, and manages drug formularies across multiple facilities.

BACKGROUND AND AIMS: Uncertainty about optimal treatment duration for buprenorphine opioid agonist therapy may lead to substantial variation in provider and payer decision-making regarding treatment course. We aimed to identify distinct trajectories of buprenorphine use and examine outcomes associated with these trajectories to guide health system interventions regarding treatment length.

DESIGN: Retrospective cohort study.

SETTING: US Pennsylvania Medicaid.

PATIENTS: A total of 10 945 enrollees aged 18-64 years initiating buprenorphine treatment between 2007 and 2012.

MEASUREMENTS: Group-based trajectory models were used to identify trajectories based on monthly proportion of days covered with buprenorphine in the 12 months post-treatment initiation. We used separate multivariable Cox proportional hazard models to examine associations between trajectories and time to first all-cause hospitalization and emergency department (ED) visit within 12 months after the first-year treatment.

FINDINGS: Six trajectories [Bayesian information criterion (BIC) = -86 246.70] were identified: 24.9% discontinued buprenorphine < 3 months, 18.7% discontinued between 3 and 5 months, 12.4% discontinued between 5 and 8 months, 13.3% discontinued > 8 months, 9.5% refilled intermittently and 21.2% refilled persistently for 12 months. Persistent refill trajectories were associated with an 18% lower risk of all-cause hospitalizations [hazard ratio (HR) = 0.82, 95% confidence interval (CI) = 0.70-0.95] and 14% lower risk of ED visits (HR = 0.86, 95% CI = 0.78-0.95) in the subsequent year, compared with those discontinuing between 3 and 5 months.

CONCLUSIONS: Six distinct buprenorphine treatment trajectories were identified in this population-based low-income Medicaid cohort in Pennsylvania, USA. There appears to be an association between persistent use of buprenorphine for 12 months and lower risk of all-cause hospitalizations/emergency department visits.