Differences Among Cardiologists in Rates of Positive Coronary Angiograms

Geographic Disparity in Deceased Donor Liver Transplant Rates Following Share 35

BACKGROUND

The Organ Procurement and Transplantation Network implemented Share 35 on June 18, 2013, to broaden deceased donor liver sharing within regional boundaries. We investigated whether increased sharing under Share 35 impacted geographic disparity in deceased donor liver transplantation (DDLT) across donation service areas (DSAs).

METHODS

Using Scientific Registry of Transplant Recipients June 2009 to June 2017, we identified 86 083 adult liver transplant candidates and retrospectively estimated Model for End-Stage Liver Disease (MELD)-adjusted DDLT rates using nested multilevel Poisson regression with random intercepts for DSA and transplant program. From the variance in DDLT rates across 49 DSAs and 102 programs, we derived the DSA-level median incidence rate ratio (MIRR) of DDLT rates. MIRR is a robust metric of heterogeneity across each hierarchical level; larger MIRR indicates greater disparity.

RESULTS

MIRR was 2.18 pre-Share 35 and 2.16 post-Share 35. Thus, 2 candidates with the same MELD in 2 different DSAs were expected to have a 2.2-fold difference in DDLT rate driven by geography alone. After accounting for program-level heterogeneity, MIRR was attenuated to 2.10 pre-Share 35 and 1.96 post-Share 35. For candidates with MELD 15-34, MIRR decreased from 2.51 pre- to 2.27 post-Share 35, and for candidates with MELD 35-40, MIRR increased from 1.46 pre- to 1.51 post-Share 35, independent of program-level heterogeneity in DDLT. DSA-level heterogeneity in DDLT rates was greater than program-level heterogeneity pre- and post-Share 35.

CONCLUSIONS

Geographic disparity substantially impacted DDLT rates before and after Share 35, independent of program-level heterogeneity and particularly for candidates with MELD 35-40. Despite broader sharing, geography remains a major determinant of access to DDLT.

Variation in the Echocardiographic Surveillance of Primary Mitral Regurgitation

BACKGROUND

Clinical outcomes after surgical treatment of mitral regurgitation are worse if intervention occurs after deterioration of left ventricular size and function. Transthoracic echocardiographic (TTE) surveillance of patients with mitral regurgitation is indicated to avoid adverse ventricular remodeling. Overly frequent TTEs can impair patient access and reduce value in care delivery. This balance between timely surveillance and overutilization of TTE in valvular disease provides a model to study variation in the delivery of healthcare services. We investigated patient and provider factors contributing to variation in TTE utilization and hypothesized that variation was attributable to provider practice even after adjustment for patient characteristics.

METHODS AND RESULTS

We obtained records of all TTEs from 2001 to 2016 completed at a large echocardiography laboratory. The outcome variable was time interval between TTEs. We constructed a mixed-effects linear regression model with the individual physician as the random effect in the model and used intraclass correlation coefficient to assess the proportion of outcome variation because of provider practice. Our study cohort was 55 773 TTEs corresponding to 37 843 intervals ordered by 635 providers. The mean interval between TTEs was 12.4 months, 17.0 months, 18.3 months, and 17.4 months for severe, moderate, mild, and trace mitral regurgitation, respectively, with 20% of providers deemed overutilizers of TTEs and 25% underutilizers.

CONCLUSIONS

We conclude that there is substantial variation in follow-up intervals for TTE assessment of mitral regurgitation, despite risk-adjustment for patient variables, likely because of provider factors.

Differences Among Cardiologists in Rates of Positive Coronary Angiograms

Background

Understanding the sources of variation for high‐cost services has the potential to improve both patient outcomes and value in health care delivery. Nationally, the overall diagnostic yield of coronary angiography is relatively low, suggesting overutilization. Understanding how individual cardiologists request catheterization may suggest opportunities for improving quality and value. We aimed to assess and explain variation in positive angiograms among referring cardiologists.

Methods and Results

We identified all cases of diagnostic coronary angiography at Massachusetts General Hospital from January 1, 2012, to June 30, 2013. We excluded angiograms for acute coronary syndrome. For each angiogram, we identified clinical features of the patients and characteristics of the requesting cardiologists. We also identified angiogram positivity, defined as at least 1 epicardial coronary stenosis ≥50% luminal narrowing. We then constructed a series of mixed‐effects logistic regression models to analyze predictors of positive coronary angiograms. We assessed variation by physician in the models with median odds ratios. Over this time period, 5015 angiograms were identified. We excluded angiograms ordered by cardiologists requesting <10 angiograms. Among the remaining 2925 angiograms, 1450 (49.6%) were positive. Significant predictors of positive angiograms included age, male patients, and peripheral arterial disease. After adjustment for clinical variables only, the median odds ratio was 1.23 (95% CI 1.0–1.36), consistent with only borderline clinical variation after adjustment. In the full clinical and nonclinical model, the median odds ratio was 1.07 (95% CI 1.07–1.20), also consistent with clinically insignificant variation.

Conclusions

Substantial variation exists among requesting cardiologists with respect to positive and negative coronary angiograms. After adjustment for clinical variables, there was only borderline clinically significant variation. These results emphasize the importance of risk adjustment in reporting related to quality and value.